| Q103265870 | Q103265870 |
| Q124129702 | Q124129702 |
| Q51710514 | Q51710514 |
| Q63717189 | Q63717189 |
| Q66456309 | Q66456309 |
| Q66465241 | Q66465241 |
| Q99311862 | Q99311862 |
| Q121994768 | Blázinec v Silicon Valley: rychlá auta, luxusní jachty, divoké večírky a... naše budoucnost |
| Q122004058 | Orbis tweetus: první twitter kniha na světě ilustrovaná fanoušky |
| Q122041114 | Social selling: nakopněte svůj B2B prodej pomocí sociálních sítí |
| Q121973056 | À propos des pratiques scripturales dans l'espace virtuel: entre Facebook et Twitter |
| Q115408257 | Mehr als Twitter. Werkstattbericht zur Wissenschaftskommunikation am Institut für Sächsische Geschichte und Volkskunde |
| Q92250931 | "24%" Figure Appropriately Qualified in Article, If Not on Twitter. Also, Have Faith in the National Violent Death Reporting System |
| Q46160065 | "5 mins of uncomfyness is better than dealing with cancer 4 a lifetime": an exploratory qualitative analysis of cervical and breast cancer screening dialogue on Twitter |
| Q101410528 | "A Toxic Trend"?: Generational Conflict and Connectivity in Twitter Discourse Under the #BoomerRemover Hashtag |
| Q28602795 | "Anyone Know What Species This Is?" - Twitter Conversations as Embryonic Citizen Science Communities |
| Q92365238 | "Can't Wait to Blackout Tonight": An Analysis of the Motives to Drink to Blackout Expressed on Twitter |
| Q100488491 | "Capturing your audience": analysis of Twitter engagements between tweets linked with an educational infographic or a peer-reviewed journal article |
| Q37116203 | "Get drunk. Smoke weed. Have fun.": A Content Analysis of Tweets About Marijuana and Alcohol |
| Q35828977 | "Hey Everyone, I'm Drunk." An Evaluation of Drinking-Related Twitter Chatter |
| Q57181346 | "I Was Grabbed by My Pussy and Its #NotOkay": A Twitter Backlash Against Donald Trump's Degrading Commentary |
| Q90580604 | "I just might kill myself": Suicide expressions on Twitter |
| Q61815700 | "I just want to be skinny.": A content analysis of tweets expressing eating disorder symptoms |
| Q122703470 | "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data |
| Q97424404 | "I'm using cigarettes to quit JUUL": An analysis of Twitter posts about JUUL cessation |
| Q92561244 | "It Makes People Uneasy, but It's Necessary. #BTSM": Using Twitter to Explore Advance Care Planning among Brain Tumor Stakeholders |
| Q104566214 | "Mental Health" as Defined by Twitter: Frames, Emotions, Stigma |
| Q45948822 | "No High Like a Brownie High": A Content Analysis of Edible Marijuana Tweets. |
| Q41085804 | "Obesity is the New Major Cause of Cancer": Connections Between Obesity and Cancer on Facebook and Twitter. |
| Q89446172 | "Okay, We Get It. You Vape": An Analysis of Geocoded Content, Context, and Sentiment regarding E-Cigarettes on Twitter |
| Q47395517 | "Retweet to Pass the Blunt": Analyzing Geographic and Content Features of Cannabis-Related Tweeting Across the United States |
| Q36435684 | "Right time, right place" health communication on Twitter: value and accuracy of location information |
| Q101237741 | "Show this thread": policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic |
| Q89262773 | "The power of Twitter": Using social media at a conference with nursing students |
| Q31096493 | "Those edibles hit hard": Exploration of Twitter data on cannabis edibles in the U.S. |
| Q30991496 | "Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S. |
| Q51416064 | "To sleep, perchance to tweet": in-bed electronic social media use and its associations with insomnia, daytime sleepiness, mood, and sleep duration in adults. |
| Q102324700 | "Towards Re-Inventing Psychohistory": Predicting the Popularity of Tomorrow's News from Yesterday's Twitter and News Feeds |
| Q52632953 | "Tweet"-Format Writing Is an Effective Tool for Medical Student Reflection. |
| Q27334675 | "We definitely need an audience": experiences of Twitter, Twitter networks and tweet content in adults with severe communication disabilities who use augmentative and alternative communication (AAC) |
| Q51471063 | "What would you do if asked for health advice on Twitter?". |
| Q37399527 | "When 'Bad' is 'Good'": Identifying Personal Communication and Sentiment in Drug-Related Tweets. |
| Q47196325 | "You got to love rosin: Solventless dabs, pure, clean, natural medicine." Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates |
| Q57888018 | "i read my Twitter the next morning and was astonished" |
| Q92684045 | #4645Boricuas: Twitter reactions to the estimates of deaths by Hurricane María in Puerto Rico |
| Q90115685 | #ASEchoJC Twitter Journal Club To CME: A Paradigm Shift in Cardiology Education |
| Q38097099 | #BlueJC: BJOG and Katherine Twining Network collaborate to facilitate post-publication peer review and enhance research literacy via a Twitter journal club |
| Q51460589 | #BlueJC: BJOG and Katherine Twining Network collaborate to facilitate post-publication peer review and enhance research literacy via a Twitter journal club. |
| Q60194666 | #CDCGrandRounds and #VitalSigns: A Twitter Analysis |
| Q55417015 | #CGS2015: An Evaluation of Twitter Use at the Canadian Geriatrics Society Annual Scientific Meeting. |
| Q95288992 | #COVID-19: A Public Coronavirus Twitter Dataset Tracking Social Media Discourse about the Pandemic |
| Q99406113 | #CardioOncology: Twitter chat as a mechanism for increasing awareness of heart health for cancer patients |
| Q98781506 | #Coronavirus: Monitoring the Belgian Twitter Discourse on the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic |
| Q100388098 | #Covid4Rheum: an analytical twitter study in the time of the COVID-19 pandemic |
| Q47555957 | #Deathbedlive: the end-of-life trajectory, reflected in a cancer patient's tweets |
| Q90892343 | #Diversity: Conversations on Twitter about Women and Black Men in Medicine |
| Q38603961 | #DocsOnTwitter: How Physicians use Social Media to Build Social Capital. |
| Q50218899 | #EMConf: utilising Twitter to increase dissemination of conference content. |
| Q91694625 | #EULAR2018: The Annual European Congress of Rheumatology-a Twitter hashtag analysis |
| Q93117439 | #EUROmicroMOOC: using Twitter to share trends in Microbiology worldwide |
| Q56773338 | #Earthquake: Twitter as a Distributed Sensor System |
| Q58079699 | #Engagement: use of Twitter chats to construct nominal participatory spaces during health crises |
| Q94652553 | #FNAFriday: How cytopathologists learn, teach, and share knowledge on Twitter |
| Q91467701 | #Fake news: a systematic review of mechanical thrombectomy results among neurointerventional stroke surgeons on Twitter |
| Q104739956 | #FlavorsSaveLives: An analysis of Twitter posts opposing flavored e-cigarette bans |
| Q88586191 | #Frailty: A snapshot Twitter report on frailty knowledge translation |
| Q94183498 | #GERIATRICS: A 7-YEAR TWITTER ANALYSIS |
| Q94169789 | #GERIATRICS: AN ANALYSIS OF THE IMPACT OF THE GERIATRICS TWITTER NETWORK |
| Q47901821 | #GeriMedJC: The Twitter Complement to the Traditional-Format Geriatric Medicine Journal Club |
| Q45943129 | #Globalhealth Twitter Conversations on #Malaria, #HIV, #TB, #NCDS, and #NTDS: a Cross-Sectional Analysis. |
| Q39501728 | #IHMayDay: tweeting for empowerment and social and emotional wellbeing. |
| Q91968397 | #ILookLikeAUrologist: Using Twitter to Discuss Diversity and Inclusion in Urology |
| Q47927853 | #InSituPathologists: how the #USCAP2015 meeting went viral on Twitter and founded the social media movement for the United States and Canadian Academy of Pathology. |
| Q55076870 | #LancerHealth: Using Twitter and Instagram as a tool in a campus wide health promotion initiative. |
| Q57724676 | #London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data |
| Q92223514 | #MeToo: Disclosure and Response to Sexual Victimization on Twitter |
| Q47125274 | #MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter. |
| Q101344086 | #Participating #Contesting: Studying Counterpublics' Discourses on Twitter About the Social Acceptability of Medical Assistance in Dying Legislation in Canada |
| Q47624591 | #Proana: Pro-Eating Disorder Socialization on Twitter |
| Q50587336 | #Schizophrenia: Use and misuse on Twitter |
| Q60191660 | #SocialJournalism: Local News Media on Twitter |
| Q50822639 | #TrumpenMexico. Transnational connective action on Twitter and the border wall dispute |
| Q46264609 | #TwittIR: Understanding and Establishing a Twitter Ecosystem for Interventional Radiologists and Their Practices |
| Q90980889 | #WhyID: Crowdsourcing the Top Reasons to Choose Infectious Diseases in the Age of Twitter |
| Q33586903 | #WhyWeTweetMH: Understanding Why People Use Twitter to Discuss Mental Health Problems |
| Q87818332 | #Wikipedia on Twitter |
| Q47553926 | #dermpathJC: The First Online Dermatopathology Twitter Journal Club |
| Q57818127 | #drunktwitter: Examining the relations between alcohol-related Twitter content and alcohol willingness and use among underage young adults |
| Q55279640 | #ec: Findings and implications from a quantitative content analysis of tweets about emergency contraception. |
| Q98242350 | #herniasurgery on the #SoMe platform Twitter: the importance of communication in science |
| Q94955515 | #pandemicpedagogy: using twitter for knowledge exchange |
| Q46293289 | #wheezing: A Content Analysis of Asthma-Related Tweets |
| Q57457450 | 'Not your daughter's Facebook': Twitter use at the European Society of Cardiology Conference 2018 |
| Q38847780 | 'Schizophrenia' on Twitter: Content Analysis of Greek Language Tweets |
| Q37398935 | 'Sweeter Than a Swisher': amount and themes of little cigar and cigarillo content on Twitter |
| Q57827189 | 'Twitter has changed my life' |
| Q89494467 | 'Twitter is an amazing communication tool' |
| Q56888411 | 'We have a rich heritage and, we believe, a bright future': how transnational tobacco companies are using Twitter to oppose policy and shape their public identity |
| Q42694310 | 'What's happening?' A content analysis of concussion-related traffic on Twitter |
| Q59256797 | (NOT) THE TWITTER ELECTION |
| Q57403559 | 140 characters to victory?: Using Twitter to predict the UK 2015 General Election |
| Q94119011 | 1948. Impact of @WuidQ, a Free Open-access Medical Education Twitter Resource, on Infectious Disease Learning and Teaching |
| Q94123140 | 2063. Using Twitter Data and Machine Learning to Identify Outpatient Antibiotic Misuse: A Proof-of-Concept Study |
| Q100794528 | 28 Days Later: Twitter Hashtags as “Just in Time” Teacher Professional Development |
| Q56422200 | 8. Twitter |
| Q47597185 | @AACAnatomy twitter account goes live: A sustainable social media model for professional societies |
| Q105592863 | @Houstonpolice: an exploratory case of Twitter during Hurricane Harvey |
| Q58560583 | A Biased Review of Biases in Twitter Studies on Political Collective Action |
| Q95627863 | A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter |
| Q105963101 | A Comparative Study on Twitter Sentiment Analysis: Which Features are Good? |
| Q92391296 | A Content Analysis of Gambling Operators' Twitter Accounts at the Start of the English Premier League Football Season |
| Q37299996 | A Content Analysis of Vaping Advertisements on Twitter, November 2014. |
| Q51420093 | A Cross-Sectional Review of Cervical Cancer Messages on Twitter During Cervical Cancer Awareness Month. |
| Q55335162 | A Data-Driven Method of Discovering Misspellings of Medication Names on Twitter. |
| Q51417564 | A Descriptive Analysis of the Use of Twitter by Emergency Medicine Residency Programs. |
| Q36199458 | A Digital Ethnography of Medical Students who Use Twitter for Professional Development |
| Q37737722 | A Distributed Approach for the Analysis of Discussions in Twitter |
| Q57693555 | A Framework for Evaluating Anti Spammer Systems for Twitter |
| Q58306990 | A Hands-On Guide to Conducting Psychological Research on Twitter |
| Q58188250 | A How-to for Using Netlytic to collect and Analyze Social Media Data: A Case Study of the Use of Twitter During the 2014 Euromaidan Revolution in Ukraine |
| Q59224250 | A Hybrid Approach for Big Data Analysis of Cricket Fan Sentiments in Twitter |
| Q57740893 | A Language-Centric Study of Twitter Connectivity |
| Q30773719 | A Lexicon approach to multidimensional analysis of tweets opinion |
| Q38947806 | A Linguistic Analysis of Suicide-Related Twitter Posts. |
| Q57656336 | A Macroscopic Analysis of News Content in Twitter |
| Q27230311 | A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs |
| Q92366965 | A New Tool for Public Health Opinion to Give Insight Into Telemedicine: Twitter Poll Analysis |
| Q112938889 | A New Understanding of Brand Development through Social-Actor Engagement |
| Q104514777 | A Perspective of Electronic Cigarette Users on the COVID-19 Pandemic in Twitter: Observational Study |
| Q101565281 | A Portrait of Current Radiation Oncology Twitter Influencers |
| Q61918756 | A Probabilistic Author-Centered Model for Twitter Discussions |
| Q58583080 | A Review of “Face2Face: Using Facebook, Twitter, and Other Social Media Tools to Create Great Customer Connections” |
| Q28643295 | A Scalable Framework to Detect Personal Health Mentions on Twitter |
| Q30095862 | A Semantic Graph-Based Approach for Radicalisation Detection on Social Media |
| Q29120465 | A Simple Approach to Multilingual Polarity Classification in Twitter |
| Q57721617 | A Smart System for Twitter Corpus Collection, Management and Visualization |
| Q38875874 | A Smoking Cessation Campaign on Twitter: Understanding the Use of Twitter and Identifying Major Players in a Health Campaign |
| Q92750689 | A Social Networks Approach to Understanding Vaccine Conversations on Twitter: Network Clusters, Sentiment, and Certainty in HPV Social Networks |
| Q28038727 | A Study on Efficient Market Hypothesis to Predict Exchange Rate Trends Using Sentiment Analysis of Twitter Data |
| Q57756702 | A Survey of Location Prediction on Twitter |
| Q56411036 | A Text Mining Analysis of Academic Libraries' Tweets |
| Q98939151 | A Thematic Analysis of Weibo Topics (Chinese Twitter Hashtags) regarding Older Adults During the COVID-19 Outbreak |
| Q38606280 | A Twitter Education: Why Psychiatrists Should Tweet |
| Q92555718 | A Twitter campaign against pseudoscience: The sceptical discourse on complementary therapies in Spain |
| Q48292249 | A Twitter primer: Dos and don'ts for cytopathologists. |
| Q57663526 | A Twitter-based survey on marijuana concentrate use |
| Q51471630 | A breath of Twitter. |
| Q30863045 | A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives |
| Q57558086 | A combination of active learning and self-learning for named entity recognition on Twitter using conditional random fields |
| Q104288443 | A communication strategy based on Twitter improves article citation rate and impact factor of medical journals |
| Q57676054 | A community role approach to assess social capitalists visibility in the Twitter network |
| Q90751225 | A comparison of information sharing behaviours across 379 health conditions on Twitter |
| Q89526839 | A comprehensive analysis of #Enuresis conversation on Twitter |
| Q51471093 | A content analysis of chronic diseases social groups on Facebook and Twitter. |
| Q36071377 | A content analysis of depression-related Tweets |
| Q91160163 | A content analysis of the promotional strategies employed by e-cigarette brands on Twitter |
| Q38842889 | A content analysis of tweets about high-potency marijuana |
| Q37484790 | A corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities. |
| Q90349539 | A critical discourse analysis of gendered cardiovascular disease meanings of the #MoreMoments campaign on Twitter |
| Q36354997 | A cross-hazard analysis of terse message retransmission on Twitter |
| Q33834418 | A cross-sectional examination of marketing of electronic cigarettes on Twitter |
| Q104470480 | A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter |
| Q61918754 | A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions |
| Q96576637 | A first look at COVID-19 information and misinformation sharing on Twitter |
| Q98727061 | A first public dataset from Brazilian twitter and news on COVID-19 in Portuguese |
| Q96416851 | A focus on students’ use of Twitter – their interactions with each other, content and interface |
| Q66710966 | A framework for detecting public health trends with Twitter |
| Q51443925 | A hashtag recommendation system for twitter data streams. |
| Q96576643 | A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration |
| Q112923318 | A little birdie told me : Twitter and the 2014 New Zealand general election |
| Q59043893 | A member recognition approach for specific organizations based on relationships among users in social networking Twitter |
| Q28657902 | A multi-level geographical study of Italian political elections from Twitter data |
| Q92717550 | A natural language processing framework to analyse the opinions on HPV vaccination reflected in twitter over 10 years (2008 - 2017) |
| Q89609307 | A neighborhood analysis of Twitter communication in emergency medicine |
| Q58169490 | A network analysis of financial conversations on Twitter |
| Q58169499 | A network analysis of financial conversations on Twitter |
| Q44572067 | A novel use of Twitter to provide feedback and evaluations |
| Q47401319 | A pattern-matched Twitter analysis of US cancer-patient sentiments |
| Q59816339 | A picture tells a thousand words: What Facebook and Twitter images convey about our personality |
| Q50226962 | A pilot lifestyle intervention study: effects of an intervention using an activity monitor and Twitter on physical activity and body composition |
| Q59444067 | A pipeline of tweets: environmental movements’ use of Twitter in response to the Keystone XL pipeline |
| Q62628448 | A polarity analysis framework for Twitter messages |
| Q36959261 | A practical approach for content mining of Tweets |
| Q49989309 | A practical approach to incorporating Twitter in a college course |
| Q112909403 | A predictive model to detect online cyberbullying |
| Q45746857 | A qualitative analysis of orthodontic-related posts on Twitter |
| Q57676053 | A review of features for the discrimination of twitter users: application to the prediction of offline influence |
| Q95261944 | A scoping review of the use of Twitter for public health research |
| Q58511360 | A simplistic model for identifying prominent web users in directed multiplex social networks: a case study using Twitter networks |
| Q29011636 | A social network analysis of Twitter: Mapping the digital humanities community |
| Q60164073 | A social network as information: The effect of system generated reports of connectedness on credibility on Twitter |
| Q50765623 | A social way to experience a scientific event: Twitter use at the 7th European Public Health Conference. |
| Q100763980 | A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19 |
| Q58037739 | A step forward for Topic Detection in Twitter: An FCA-based approach |
| Q41326396 | A study on real-time low-quality content detection on Twitter from the users' perspective |
| Q58169252 | A survey on real-time event detection from the Twitter data stream |
| Q57708580 | A system for twitter user list curation |
| Q33553966 | A systematic identification and analysis of scientists on Twitter |
| Q61865817 | A systematic review investigating the use of Twitter and Facebook in university-based healthcare education |
| Q101239929 | A text-mining analysis of public perceptions and topic modeling during the COVID-19 pandemic using Twitter data |
| Q58899778 | A topology of Twitter research: disciplines, methods, and ethics |
| Q60914468 | A viral video and pet lemurs on Twitter |
| Q59887555 | A world without Down’s syndrome? Online resistance on Twitter: #worldwithoutdowns and #justaboutcoping |
| Q98897704 | A-twitter over Twitter |
| Q36140648 | AAHSL Twitter Use From 2007 to 2014: An Exploratory Analysis |
| Q63565787 | AI Deep Learning with Multiple Labels for Sentiment Classification of Tweets |
| Q58290709 | ALSUntangled (ALSU): A Scientific Approach to Off-Label Treatment Options for People with ALS Using Tweets and Twitters |
| Q61763982 | ANEW for Spanish Twitter Sentiment Analysis Using Instance-Based Multi-label Learning Algorithms |
| Q101239935 | Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter |
| Q55040218 | Academic information on Twitter: A user survey. |
| Q52325795 | Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing. |
| Q47349567 | Accounts of bullying on Twitter in relation to dentofacial features and orthodontic treatment. |
| Q111966380 | Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis |
| Q36902049 | Action Tweets Linked to Reduced County-Level HIV Prevalence in the United States: Online Messages and Structural Determinants |
| Q38449144 | Activity, content, contributors, and influencers of the twitter discussion on urologic oncology |
| Q59522776 | Activity-based Twitter sampling for content-based and user-centric prediction models |
| Q100406492 | Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter |
| Q57942835 | Adjustable Time-Window-Based Event Detection on Twitter |
| Q62029191 | Adolescents Texting and Twittering: The Flash Mob Phenomena |
| Q62039214 | Adverse Drug Extraction in Twitter Data Using Convolutional Neural Network |
| Q93043946 | Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR |
| Q55389212 | Adverse event detection by integrating twitter data and VAERS. |
| Q63035435 | Affect in Tweets Using Experts Model |
| Q64097403 | Affective responses to uncertain real-world outcomes: Sentiment change on Twitter |
| Q49981705 | After Aylan Kurdi: How Tweeting About Death, Threat, and Harm Predict Increased Expressions of Solidarity With Refugees Over Time |
| Q57708583 | Aggregating content and network information to curate twitter user lists |
| Q34904370 | Aging 2.0: health information about dementia on Twitter. |
| Q56593129 | Al-Jazeera Sport’s US Twitter followers: sport-politics nexus? |
| Q58267867 | Alcohol Advertising on Twitter—A Topic Model |
| Q64103189 | Alcohol Industry CSR Organisations: What Can Their Twitter Activity Tell Us about Their Independence and Their Priorities? A Comparative Analysis |
| Q40285183 | Alcohol Marketing on Twitter and Instagram: Evidence of Directly Advertising to Youth/Adolescents. |
| Q120984709 | Aligning Tweets With Events: Automation Via Semantics |
| Q84462403 | All a-Twitter about chemistry |
| Q51472884 | All a-Twitter. |
| Q47750668 | Allegation of scientific misconduct increases Twitter attention |
| Q110779918 | Almans and Kanaks: Inter-cultural Stereotypes in a German Twitter Corpus |
| Q56422325 | Ambient affiliation: A linguistic perspective on Twitter |
| Q57745529 | Ambient intimacy on Twitter |
| Q101053984 | An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak |
| Q90377323 | An "Infodemic": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak |
| Q39667276 | An Academic Healthcare Twitter Account: The Mayo Clinic Experience |
| Q114598542 | An Analysis of Trends and Connections in Google, Twitter, and Wikipedia |
| Q91053378 | An Analysis of Twitter Discourse Regarding Identifying Language for People on the Autism Spectrum |
| Q57017094 | An Analysis of Twitter Messages in the 2011 Tohoku Earthquake |
| Q64946345 | An Analysis of a Twitter Corpus for Training a Medication Intake Classifier. |
| Q104619820 | An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian |
| Q46026655 | An Ensemble Based Method for Predicting Emotion Intensity of Tweets |
| Q92778427 | An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere |
| Q58216353 | An Evaluation of Performance and Competition in Customer Services on Twitter |
| Q61847394 | An Examination of Personal Mobility Patterns in Space and Time Using Twitter |
| Q64108284 | An Examination of Twitter Data to Identify Risky Sexual Practices Among Youth and Young Adults in Botswana |
| Q93182879 | An Infodemiological Study on Novel Coronavirus (COVID-19) in South Korea: Conversations and Medical News Frames on Twitter |
| Q62677357 | An Innovative Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Project Finalist at HHS Opioid Code-a-Thon |
| Q112957365 | An Investigation Into Twitter as an Echo Chamber: A Case Study Using Hashtags During CAA Protests in India |
| Q107399428 | An Open-Source Strategy for Documenting Events: The Case Study of the 42nd Canadian Federal Election on Twitter |
| Q98507851 | An analysis of Twitter for an Academic Emergency Medicine Ultrasound Division |
| Q99566191 | An annotated data set for identifying women reporting adverse pregnancy outcomes on Twitter |
| Q33099914 | An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships |
| Q97439876 | An empirical evaluation of electronic annotation tools for Twitter data |
| Q37204691 | An exploration of social circles and prescription drug abuse through Twitter |
| Q56408408 | An exploratory case study examining undergraduate accounting students' perceptions of using Twitter as a learning support tool |
| Q100794527 | An investigation of State Educational Twitter Hashtags (SETHs) as affinity spaces |
| Q35055838 | An observational study of social and emotional support in smoking cessation Twitter accounts: content analysis of tweets |
| Q115907634 | An open dataset of scholars on Twitter |
| Q57887119 | An overview of the use of Twitter in National Weather Services |
| Q57292552 | Anal sex is more common than having a Twitter account in the United States |
| Q96635896 | Analysing Sentiment and Topics Related to Multiple Sclerosis on Twitter |
| Q33941897 | Analysing Twitter and web queries for flu trend prediction |
| Q59439378 | Analysing successful public spaces in an urban street network using data from the social networks Foursquare and Twitter |
| Q87833322 | Analysing the Usage of Wikipedia on Twitter: Understanding Inter-Language Links |
| Q36669068 | Analysing the connectivity and communication of suicidal users on twitter |
| Q66710402 | Analysing the duration of trending topics in Twitter using wikipedia |
| Q46058919 | Analysis of First-Year Twitter Metrics of a Rare Disease Community for Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN) on Social Media: #BPDCN. |
| Q58051492 | Analysis of Security Messages Posted on Twitter |
| Q97893897 | Analysis of Twitter data with the Bayesian fused graphical lasso |
| Q89586045 | Analysis of Twitter to Identify Topics Related to Eating Disorder Symptoms |
| Q45258964 | Analysis of emergency physicians' Twitter accounts |
| Q55266037 | Analysis of named entity recognition and linking for tweets |
| Q44438618 | Analysis of pharmacists' use of Twitter |
| Q62809631 | Analysis of product Twitter data though opinion mining |
| Q101559301 | Analysis of psoriasis-related posts on Twitter: an abundance of patient-driven advocacy versus a scarcity of dermatologists |
| Q99414679 | Analysis of the American College of Surgeons Clinical Congress Twitter Hashtags and Its Impact on Online Engagement and Attendance Rates : The Era of Health Care Social Media |
| Q89171326 | Analysis of the Twitter "Don't Fry Day" Campaign |
| Q38780706 | Analysis of the Use and Impact of Twitter During American Society of Clinical Oncology Annual Meetings From 2011 to 2016: Focus on Advanced Metrics and User Trends |
| Q37168572 | Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts |
| Q37344934 | Analysis of the purpose of state health departments' tweets: information sharing, engagement, and action |
| Q33855348 | Analysis of twitter users' sharing of official new york storm response messages |
| Q98172745 | Analyzing Spanish News Frames on Twitter during COVID-19-A Network Study of El País and El Mundo |
| Q102066633 | Analyzing Twitter Conversation on Genome-Edited Foods and Their Labeling in Japan |
| Q90094186 | Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets |
| Q45015229 | Analyzing health organizations' use of Twitter for promoting health literacy |
| Q101476754 | Analyzing the Quality of Twitter Data Streams |
| Q57782226 | Analyzing the climate change debate on Twitter |
| Q73168161 | Analyzing the effect of earthquakes on OpenStreetMap contribution patterns and tweeting activities |
| Q89794126 | Anna Batchelor: Twitter trooper, ICU ace |
| Q28806750 | Antecedents of Retweeting in a (Political) Marketing Context |
| Q89668174 | Application of Twitter and web news mining in infectious disease surveillance systems and prospects for public health |
| Q31117312 | Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. |
| Q28354093 | Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter |
| Q37037847 | Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A Sequential Mixed-Methods Study. |
| Q97568975 | Applying a Solution-Focused Approach to Life With Diabetes: Insights Gleaned via Twitter |
| Q30584292 | Applying linguistic methods to understanding smoking-related conversations on Twitter |
| Q74737125 | Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support |
| Q26777932 | Are Health-Related Tweets Evidence Based? Review and Analysis of Health-Related Tweets on Twitter |
| Q49919937 | Are Twitter and Blogs Important Tools for the Modern Psychological Scientist? |
| Q44127625 | Are You Using Twitter for Your Next Survey? |
| Q60516801 | Are men funnier than women on Facebook and Twitter? |
| Q102215557 | Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors |
| Q37653403 | Are public health organizations tweeting to the choir? Understanding local health department Twitter followership |
| Q50557876 | Are they talking to me? Cognitive and affective effects of interactivity in politicians' twitter communication |
| Q34561742 | Are you Scared Yet?: Evaluating Fear Appeal Messages in Tweets about the Tips Campaign |
| Q92366905 | Areas of Interest and Stigmatic Attitudes of the General Public in Five Relevant Medical Conditions: Thematic and Quantitative Analysis Using Twitter |
| Q30251371 | As mudanças climáticas no Twitter: a ascendência da mídia e da política |
| Q51470099 | As parents invade Facebook, teens tweet more. |
| Q47316362 | Ask an anatomist: Identifying global trends, topics and themes of academic anatomists using twitter |
| Q36273149 | Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning |
| Q92922444 | Assessing Mental Health Signals among Sexual and Gender Minorities using Twitter Data |
| Q92997196 | Assessing mental health signals among sexual and gender minorities using Twitter data |
| Q51415273 | Assessing mobile health applications with twitter analytics. |
| Q98658002 | Assessing perceptions about medications for opioid use disorder and Naloxone on Twitter |
| Q57516391 | Assessing the Carrying Capacity of Twitter and Online News |
| Q91450901 | Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017 |
| Q36193564 | Assessing the Viability of Social Media for Disseminating Evidence-Based Nutrition Practice Guideline Through Content Analysis of Twitter Messages and Health Professional Interviews: An Observational Study |
| Q57104914 | Assessing the effect of weather on human outdoor perception using Twitter |
| Q29957787 | Assessing the effectiveness of e-petitioning through Twitter conversations |
| Q51345650 | Assessing the reTweet proneness of tweets: predictive models for retweeting |
| Q96692210 | Assessment of Beliefs and Attitudes About Statins Posted on Twitter: A Qualitative Study |
| Q46479309 | Assessment of provider attitudes toward #naloxone on Twitter |
| Q90006017 | Assessment of public perceptions and concerns of celiac disease: A Twitter-based sentiment analysis study |
| Q92135697 | Association Between Twitter Reception at a National Urology Conference and Future Publication Status |
| Q57712151 | Association between Selective Exposure and Attitude on Twitter |
| Q93372642 | Association between social media use (Twitter, Instagram, Facebook) and depressive symptoms: Are Twitter users at higher risk? |
| Q56797786 | Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States |
| Q56797788 | Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States |
| Q50050473 | Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States |
| Q57782227 | Astrophysicists on Twitter |
| Q34084040 | Astrophysicists' conversational connections on Twitter |
| Q92093243 | Asymmetric participation of defenders and critics of vaccines to debates on French-speaking Twitter |
| Q90541522 | At the speed of Juul: measuring the Twitter conversation related to ENDS and Juul across space and time (2017-2018) |
| Q58464274 | Atmospheric Environment and Quality of Life Information Extraction from Twitter with the Use of Self-Organizing Maps |
| Q38631789 | Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts |
| Q63384363 | Augmenting Flexnerism Via Twitterism: Need for Integrating Social Media Application in Blueprinting Pedagogical Strategies for Undergraduate Medical Education |
| Q63384364 | Augmenting Flexnerism Via Twitterism: Need for Integrating Social Media Application in Blueprinting Pedagogical Strategies for Undergraduate Medical Education (Preprint) |
| Q75033565 | Authentic chatter |
| Q59282381 | Author Correction: Frequent discussion of insomnia and weight gain with glucocorticoid therapy: an analysis of Twitter posts |
| Q48026587 | Authors' Reply to Jouanjus and Colleagues' Comment on "Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter". |
| Q43864620 | Authors' reply: #BlueJC: BJOG and Katherine Twining Network collaborate to facilitate post-publication peer review and enhance research literacy via a Twitter journal club |
| Q57731928 | Automatic Classification and PLS-PM Modeling for Profiling Reputation of Corporate Entities on Twitter |
| Q62043893 | Automatic Creation of a Large and Polished Training Set for Sentiment Analysis on Twitter |
| Q60584316 | Automatic detection of satire in Twitter: A psycholinguistic-based approach |
| Q35247444 | Automatic detection of tweets reporting cases of influenza like illnesses in Australia |
| Q35250979 | Automatic mood classification of Indonesian tweets using linguistic approach |
| Q91088940 | Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study |
| Q96024685 | Automatically Identifying Comparator Groups on Twitter for Digital Epidemiology of Pregnancy Outcomes |
| Q91390947 | BACCN Twitter Chat |
| Q88350239 | BACCN Twitter chats |
| Q89371446 | BACCN Twitter chats |
| Q51428674 | BACCN Twitter chats. |
| Q38840788 | Bacterial tweets and podcasts #signaling#eavesdropping#microbialfightclub |
| Q50240515 | Bahraini doctor detained for Tweets needs legal and medical support, says BMA. |
| Q98281626 | Barriers to blood donation on social media: An analysis of Facebook and Twitter posts |
| Q112923476 | Beauty and the Bump: Celebrities, Social Media and the Consumption of Pregnancy |
| Q38797244 | Behavioral Effects of Framing on Social Media Users: How Conflict, Economic, Human Interest, and Morality Frames Drive News Sharing |
| Q56415186 | Being knowledge, power and profession subordinates: Students' perceptions of Twitter for learning |
| Q47289109 | Better safe than sorry: An observational study of #ESALondon tweets |
| Q62041873 | Beyond Binary Labels: Political Ideology Prediction of Twitter Users |
| Q57724671 | Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data |
| Q46281696 | Beyond likes and tweets: an in-depth look at the physician social media landscape |
| Q28590850 | Bibliographic Analysis of Nature Based on Twitter and Facebook Altmetrics Data |
| Q123229574 | Bibliotheks- und InformationswissenschaftlerInnen in der Twittersphäre: Gekürzte und überarbeitete Fassung einer Bachelorarbeit |
| Q98199695 | Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: An Infoveillance Study on Twitter and Instagram |
| Q58925535 | Bigfoot sighted on Twitter |
| Q30765277 | Bigger data for big data: from Twitter to brain-computer interfaces. |
| Q122905288 | Bill Ackman Tweets a Lot of Big Ideas. His Biggest Might Be Combining With Twitter Itself. |
| Q22976271 | Biodiversity data mining from Argus-eyed citizens: the first illegal introduction record of Lepomis macrochirus macrochirus Rafinesque, 1819 in Japan based on Twitter information |
| Q47528901 | Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data |
| Q58594797 | Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN) on Social Media: #BPDCN-Increasing Exposure over Two Years Since Inception of a Disease-Specific Twitter Community |
| Q37978367 | Blogs and Twitter in medical publications: too unreliable to quote, or a change waiting to happen? |
| Q98853762 | Blurred Lines: An Examination of High School Football Recruits’ Self-Presentation on Twitter |
| Q57395111 | Bot-based collective blocklists in Twitter: the counterpublic moderation of harassment in a networked public space |
| Q100496405 | Brain Tumor Discussions on Twitter (#BTSM): Social Network Analysis |
| Q95465664 | Brand Innovation and Value-Creating Practices of the City of Johannesburg’s Twitter Community |
| Q58513063 | Breaking the Limits of Time and Space: How Twitter is Helping #medlibs Collaborate and Communicate. A Descriptive Study |
| Q39015595 | Breast Cancer Screening and Social Media: a Content Analysis of Evidence Use and Guideline Opinions on Twitter. |
| Q97429777 | Breastfeeding promotion on Twitter: A social network and content analysis approach |
| Q65966720 | Broad Twitter Corpus: A Diverse Named Entity Recognition Resource |
| Q38902637 | Building Community Through a #pulmcc Twitter Chat to Advocate for Pulmonary, Critical Care, and Sleep |
| Q27440922 | Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity |
| Q93014533 | Building a Twitter Journal Club |
| Q58560153 | Building a Twitter opinion lexicon from automatically-annotated tweets |
| Q121322373 | Bypassing Interlibrary Loan Via Twitter: An Exploration of #icanhazpdf Requests |
| Q58088928 | Bystander Prevention for Sexual Violence: #HowIWillChange and Gaps in Twitter Discourse |
| Q94160719 | COMPUTATIONAL EXPLORATION OF GERONTOLOGY-RELATED TOPICS SHARED ON SOCIAL MEDIA PLATFORM TWITTER |
| Q99554632 | COVID-19 and the "Film Your Hospital" Conspiracy Theory: Social Network Analysis of Twitter Data |
| Q94466537 | COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data |
| Q100537178 | COVID-19 and the gendered use of emojis on Twitter |
| Q101565473 | COVID-19 pandemic and information diffusion analysis on Twitter |
| Q97431753 | COVID-19 pandemic lockdown: An emotional health perspective of Indians on Twitter |
| Q104748734 | COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning |
| Q38897886 | Campaigns and counter campaigns: reactions on Twitter to e-cigarette education |
| Q28546681 | Can Twitter Be a Source of Information on Allergy? Correlation of Pollen Counts with Tweets Reporting Symptoms of Allergic Rhinoconjunctivitis and Names of Antihistamine Drugs |
| Q38374435 | Can Twitter Save Lives? A Broad-scale Study on Visual Social Media Analytics for Public Safety |
| Q55005223 | Can Twitter be used to predict county excessive alcohol consumption rates? |
| Q35963970 | Can Twitter improve your health? An analysis of alcohol consumption guidelines on Twitter |
| Q43171888 | Can Twitter predict disease outbreaks? |
| Q58208615 | Can Twitter social media be an effective tool for breast cancer survivor support and education? |
| Q24632853 | Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact |
| Q47563349 | Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram |
| Q96112087 | Cancer on Twitter |
| Q34523818 | Cancer patients on Twitter: a novel patient community on social media |
| Q43419814 | Cancer talk on twitter: community structure and information sources in breast and prostate cancer social networks |
| Q89451856 | Cancer, aging and Twitter: A mixed methods evaluation of tweets about Geriatric Oncology (#gerionc) and Geriatric Hematology (#geriheme) |
| Q92119588 | Cannabis Surveillance With Twitter Data: Emerging Topics and Social Bots |
| Q40301041 | Capsule Commentary on Chretien et al., A Digital Ethnography of Medical Students who use Twitter for Professional Development |
| Q51431071 | Carbohydrates and sports practice: a Twitter virtual ethnography |
| Q93178482 | Case Reports in the Age of Twitter |
| Q57748714 | Catching Earworms on Twitter: Using Big Data to Study Involuntary Musical Imagery |
| Q95603413 | Causal Modeling of Twitter Activity During COVID-19 |
| Q58926874 | Celebration of scientific art kicks off Twitter storm |
| Q63246406 | Celebrity Cancer on Twitter: Mapping a Novel Opportunity for Cancer Prevention |
| Q91280912 | Celebrity suicide on Twitter: Activity, content and network analysis related to the death of Swedish DJ Tim Bergling alias Avicii |
| Q56827904 | Celebrity's self-disclosure on Twitter and parasocial relationships: A mediating role of social presence |
| Q104501747 | Challenges for plastic surgeons and estheticians during COVID-19: A Twitter study |
| Q107751207 | Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study |
| Q57005766 | Characterising Emergent Semantics in Twitter Lists |
| Q100545748 | Characteristics of General Surgery Social Media Influencers on Twitter |
| Q90687398 | Characteristics of Gun Advertisements on Social Media: Systematic Search and Content Analysis of Twitter and YouTube Posts |
| Q98506433 | Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach |
| Q58682885 | Characterization of Football Supporters from Twitter Conversations |
| Q57909035 | Characterization of natural events and epidemics from Twitter: El Niño, Zika and Chikungunya |
| Q47949387 | Characterizing Blunt Use Among Twitter Users: Racial/Ethnic Differences in Use Patterns and Characteristics |
| Q57202946 | Characterizing JUUL-related posts on Twitter |
| Q35922306 | Characterizing Sleep Issues Using Twitter |
| Q92039195 | Characterizing Swisher Little Cigar-Related Posts on Twitter in 2018: Text Analysis |
| Q28595890 | Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection |
| Q115787660 | Characterizing and Detecting Hateful Users on Twitter |
| Q51470668 | Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study. |
| Q51416183 | Characterizing barriers to CPR training attainment using Twitter. |
| Q99584337 | Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering |
| Q60452916 | Characterizing diabetes, diet, exercise, and obesity comments on Twitter |
| Q33876085 | Characterizing the followers and tweets of a marijuana-focused Twitter handle |
| Q107523486 | Characterizing transgender health issues in Twitter |
| Q62566401 | Charities’ use of Twitter: exploring social support for women living with and beyond breast cancer |
| Q56220070 | Chatting through pictures? A classification of images tweeted in one week in the UK and USA |
| Q59762714 | Chemistry of Materials, Editors’ Choice, and Twitter |
| Q61307828 | Chinese computational propaganda: automation, algorithms and the manipulation of information about Chinese politics on Twitter and Weibo |
| Q37302099 | Chiropractic and Spinal Manipulation Therapy on Twitter: Case Study Examining the Presence of Critiques and Debates |
| Q64241631 | Christians and Buddhists Are Comparably Happy on Twitter: A Large-Scale Linguistic Analysis of Religious Differences in Social, Cognitive, and Emotional Tendencies |
| Q89717417 | Circadian mood variations in Twitter content |
| Q57724687 | Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm |
| Q59130497 | Classification of Arabic Twitter Users: A Study Based on User Behaviour and Interests |
| Q62057125 | Classification of Twitter Accounts into Automated Agents and Human Users |
| Q42374374 | Classification of Twitter Users Who Tweet About E-Cigarettes |
| Q40326250 | Classifier Ensembles That Push the State-of-the-Art in Sentiment Analysis of Spanish Tweets |
| Q54818530 | Classifying Crises-Information Relevancy with Semantics |
| Q57535707 | Classifying Twitter Topic-Networks Using Social Network Analysis |
| Q92840237 | Classifying adverse drug reactions from imbalanced twitter data |
| Q30988381 | Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll |
| Q31028777 | Climate change on Twitter: Content, media ecology and information sharing behaviour |
| Q28660285 | Climate change on Twitter: topics, communities and conversations about the 2013 IPCC Working Group 1 report |
| Q89658893 | Climatechange vs. Globalwarming: Characterizing Two Competing Climate Discourses on Twitter with Semantic Network and Temporal Analyses# |
| Q49951063 | Clinical Age-Specific Seasonal Conjunctivitis Patterns and Their Online Detection in Twitter, Blog, Forum, and Comment Social Media Posts |
| Q41579782 | Clinical research benefits go viral via Twitter. |
| Q96635902 | Clinical-Based and Expert Selection of Terms Related to Depression for Twitter Streaming and Language Analysis |
| Q55399741 | Co-constructed health narratives during a 'media event': The case of the first Dutch Twitter heart operation. |
| Q59412123 | Co-evolutionary dynamics in social networks: a case study of Twitter |
| Q60241653 | Co-following on twitter |
| Q26779616 | Codified Hashtags for Weather Warning on Twitter: an Italian Case Study |
| Q56422368 | CoffeeTweets |
| Q56422274 | CoffeeTweets: bonding around the bean on Twitter |
| Q51475689 | Cognitive Dissonance: A History in Tweets. |
| Q36189945 | Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities. |
| Q58646915 | Collecting Typhoon Disaster Information from Twitter Based on Query Expansion |
| Q37020707 | Collection and visualization of dietary behavior and reasons for eating using Twitter |
| Q89166819 | Collective Behavior of Social Bots Is Encoded in Their Temporal Twitter Activity |
| Q57813217 | Collective aspects of privacy in the Twitter social network |
| Q56894916 | College students and risk-taking behaviour on Twitter versus Facebook |
| Q91747225 | Comment on MaaniHessari, N.; van Schalkwyk, M.C.; Thomas, S.; Petticrew, M. Alcohol Industry CSR Organisations: What Can Their Twitter Activity Tell Us about Their Independence and Their Priorities? A Comparative Analysis. Int. J. Environ. Res. Publ |
| Q64055520 | Comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts" |
| Q47905317 | Comment on: "Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts". |
| Q48026600 | Comment on: "Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter". |
| Q51456166 | Comment on: Archibald M.M. & Clark A.M. (2014) Twitter and nursing research: how diffusion of innovation theory can help uptake. Journal of Advanced Nursing 70(3), e3-5. doi:10.1111/jan.12343. |
| Q88264957 | Commentary on: The Top 100 Social Media Influencers in Plastic Surgery on Twitter: Who Should You Be Following? |
| Q51477664 | Commentary: Twitter for educational networking. |
| Q57782220 | Communicating diabetes and diets on Twitter - a semantic content analysis |
| Q84415329 | Communicating in 140 Characters. How Journalists in Spain use Twitter |
| Q51834565 | Communication about childhood obesity on Twitter. |
| Q122942832 | Communication aimed at engendering trustworthiness: An analysis of CSR messages on Twitter |
| Q57864408 | Communication dynamics in twitter during political campaigns: The case of the 2011 Spanish national election |
| Q96614002 | Community Aware Models of Meme Spreading in Micro-blog Social Networks |
| Q28016870 | Company event popularity for financial markets using Twitter and sentiment analysis |
| Q58617867 | Comparing Disaster News on Twitter and Television: an Intermedia Agenda Setting Perspective |
| Q57665166 | Comparing Tag Clustering Algorithms for Mining Twitter Users' Interests |
| Q37453553 | Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers. |
| Q56537256 | Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study |
| Q100499821 | Comparing covariation among vaccine hesitancy and broader beliefs within Twitter and survey data |
| Q42376788 | Comparing human papillomavirus vaccine concerns on Twitter: a cross-sectional study of users in Australia, Canada and the UK |
| Q47172477 | Comparing the Human Papillomavirus Vaccination Opinions Trends from Different Twitter User Groups with a Machine Learning Based System and Semiparametric Nonlinear Regression |
| Q66709976 | Comparing the pulses of categorical hot events in Twitter and Weibo |
| Q38787158 | Comparing vector-based and Bayesian memory models using large-scale datasets: User-generated hashtag and tag prediction on Twitter and Stack Overflow |
| Q60359285 | Comparision of microblogging service between Sina Weibo and Twitter |
| Q28916619 | Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis |
| Q91552479 | Comparison of Twitter Use of the American Society of Anesthesiologists and the American Association of Nurse Anesthetists |
| Q104483662 | Comparison of Viral COVID-19 Sina Weibo and Twitter Contents: a Novel Feature Extraction and Analytical Workflow |
| Q50159094 | Complex dynamics in an alcoholism model with the impact of Twitter |
| Q51459966 | Complications of prostate cancer treatment. |
| Q91351619 | Comprehensive analysis of Twitter activity on #Incontinence |
| Q61963645 | Computing political preference among twitter followers |
| Q47566644 | Conditional Density Estimation of Tweet Location: A Feature-Dependent Approach |
| Q50212969 | Conference organisers swimming against the tide of Twitter |
| Q50234644 | Conflicts of interest in Twitter |
| Q59263504 | Connecting blog, Twitter and Facebook use with gaps in knowledge and participation |
| Q47664520 | Connecting in the Twitterverse: Using Twitter to satisfy unmet belonging needs |
| Q45934053 | Considering the ethics of big data research: A case of Twitter and ISIS/ISIL. |
| Q99604742 | Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak |
| Q84415076 | Construction of the Foundations of the PLE and PLN for Collaborative Learning |
| Q58822432 | Contact calls: Twitter as a dialogic social and linguistic practice |
| Q112954782 | Contemporary Media Relations: Potential of Twitter in Mediating Relationships Between Public Relations Practitioners and Journalists in New Zealand |
| Q60300315 | Content Analysis of Metaphors About Hypertension and Diabetes on Twitter: Exploratory Mixed-Methods Study |
| Q46296012 | Content Analysis of Syndromic Twitter Data |
| Q57017081 | Content Analysis of Syndromic Twitter Data |
| Q46230514 | Content Analysis of Tobacco-related Twitter Posts |
| Q56780154 | Content Analysis of a Live CDC Twitter Chat During the 2014 Ebola Outbreak |
| Q100406488 | Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019 |
| Q50422159 | Content analysis of Twitter chatter about indoor tanning. |
| Q92713980 | Content analysis of Twitter in relation to biological treatments for chronic inflammatory arthropathies: an exploratory study |
| Q60241623 | Content and Network Dynamics Behind Egyptian Political Polarization on Twitter |
| Q56889352 | Content and source analysis of popular tweets following a recent case of diphtheria in Spain |
| Q51415079 | Contents, Followers, and Retweets of the Centers for Disease Control and Prevention's Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study. |
| Q90737180 | Contextualising the 2019 E-Cigarette Health Scare: Insights from Twitter |
| Q38792372 | Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks |
| Q125240978 | Controlled experiment finds no detectable citation bump from Twitter promotion |
| Q56999396 | Conversational tagging in twitter |
| Q91944476 | Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter |
| Q93254346 | Coronavirus, ageism, and Twitter: An evaluation of tweets about older adults and COVID-19 |
| Q119958292 | Corporate social responsibility and its communication on Twitter: analysis of the discourse and feelings generated in society |
| Q90907673 | Correction Notice: Twitter as a Tool for Teaching and Communicating Microbiology: The #microMOOCSEM Initiative |
| Q60515508 | Correction: Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact |
| Q90156135 | Correction: European society for medical oncology (ESMO) 2018 Congress Twitter analysis: from ethics to results through the understanding of communication and interaction flows |
| Q45851445 | Correction: Quantifying Collective Attention from TweetStream. |
| Q101468840 | Correction: Recommendations From the Twitter Hashtag #DoctorsAreDickheads: Qualitative Analysis |
| Q55259643 | Correction: The Use of Twitter by Trauma and Orthopaedic Surgery Journals: Twitter Activity, Impact Factor, and Alternative Metrics. |
| Q89218016 | Corrigendum to: The use of Twitter to facilitate sharing of clinical expertise in urology |
| Q38650324 | Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital |
| Q50822623 | Coupling sentiment and human mobility in natural disasters: a Twitter-based study of the 2014 South Napa Earthquake |
| Q91599872 | Covariance in diurnal patterns of suicide-related expressions on Twitter and recorded suicide deaths |
| Q98655459 | Covid-19's impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data |
| Q94445490 | Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the "Chinese virus" on Twitter: Quantitative Analysis of Social Media Data |
| Q57721624 | Creating Extended Gender Labelled Datasets of Twitter Users |
| Q60485360 | Creating mobile ad hoc workflows with Twitter |
| Q57604306 | Creation of unstructured big data from customer service: the case of parcel shipping companies on Twitter |
| Q58822413 | Cricket bats, #riotcleanup, and rhubarb: Everyday creativity in Twitter interactions around Test Match Special |
| Q50995024 | Critical care training: using Twitter as a teaching tool. |
| Q60241656 | Cross-hierarchical communication in Twitter conflicts |
| Q89567794 | Cross-platform and cross-interaction study of user personality based on images on Twitter and Flickr |
| Q114995804 | Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations |
| Q57656339 | Crowd sourcing disaster management: The complex nature of Twitter usage in Padang Indonesia |
| Q40335146 | Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use. |
| Q34541980 | Crowdsourcing dialect characterization through Twitter |
| Q105037632 | Crowdsourcing functions of the living city from Twitter and Foursquare data |
| Q58589170 | Crowdsourcing the Virtual Reference Interview with Twitter |
| Q63741801 | Current Social Media Conversations about Genetics and Genomics in Health: A Twitter-Based Analysis |
| Q57403570 | Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making |
| Q57056737 | Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task |
| Q60491128 | Data-mining twitter and the autism spectrum disorder: A Pilot study |
| Q94547671 | Dataset on dynamics of Coronavirus on Twitter |
| Q89685217 | David Oliver: Racism in medicine-what ethnic minority doctors told me on Twitter |
| Q108736224 | DeBot: Twitter Bot Detection via Warped Correlation |
| Q50202579 | Decoding Twitter: Understanding the History, Instruments, and Techniques for Success. |
| Q30539150 | Decoding twitter: Surveillance and trends for cardiac arrest and resuscitation communication |
| Q59399975 | Deconstructing Social Media: An Analysis of Twitter and Facebook Use in the Publishing Industry |
| Q55413152 | Deep Gramulator: Improving Precision in the Classification of Personal Health-Experience Tweets with Deep Learning. |
| Q38379019 | Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts |
| Q91169749 | Deep learning for pollen allergy surveillance from twitter in Australia |
| Q95623368 | Defining Facets of Social Distancing during the COVID-19 Pandemic: Twitter Analysis |
| Q100682436 | Defining Facets of Social Distancing during the COVID-19 Pandemic: Twitter Analysis |
| Q57633722 | Delineating the complex use of a political podcast in South Korea by hybrid web indicators: The case of the Nakkomsu Twitter network |
| Q38655813 | Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level. |
| Q56670720 | Deriving retail centre locations and catchments from geo-tagged Twitter data |
| Q51007389 | Dermatologic concerns communicated through Twitter. |
| Q58742823 | Destinations’ response to terrorism on Twitter |
| Q56656646 | Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? |
| Q89944562 | Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis |
| Q45942981 | Detecting Bots on Russian Political Twitter. |
| Q54816493 | Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network |
| Q98686941 | Detecting Misogyny and Xenophobia in Spanish Tweets Using Language Technologies |
| Q57676056 | Detecting Real-World Influence through Twitter |
| Q92890556 | Detecting Signals of Associations between Dietary Supplement Use and Mental Disorders from Twitter |
| Q58826104 | Detecting Twitter Bots That Share SoundCloud Tracks |
| Q91253948 | Detecting and Monitoring Hate Speech in Twitter |
| Q90581132 | Detecting illicit opioid content on Twitter |
| Q57407830 | Detecting suicidality on Twitter |
| Q57403568 | Detecting tension in online communities with computational Twitter analysis |
| Q40148896 | Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat |
| Q57561651 | Detection and Extracting of Emergency Knowledge from Twitter Streams |
| Q54964843 | Detection of Adverse Drug Reactions using Medical Named Entities on Twitter. |
| Q63362337 | Detection of Stress and Relaxation Magnitudes for Tweets |
| Q47233166 | Detection of illicit online sales of fentanyls via Twitter |
| Q102062122 | Determining Public Opinion of the COVID-19 Pandemic in South Korea and Japan: Social Network Mining on Twitter |
| Q58874116 | Determining the Veracity of Rumours on Twitter |
| Q124209088 | Developing a social sensing index for monitoring place-oriented mental health issues using social media (twitter) data |
| Q57729194 | Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts |
| Q58721856 | Developing and sustaining a Community of Practice through Twitter for work-based learning |
| Q89362147 | Developing the social media presence of @NurseEducToday by using Twitter |
| Q35223181 | Development of a Twitter-based intervention for smoking cessation that encourages high-quality social media interactions via automessages |
| Q98228633 | Diabetes in the Time of COVID-19: A Twitter-Based Sentiment Analysis |
| Q93215986 | Diabetes on Twitter: A Sentiment Analysis |
| Q35620115 | Diabetes topics associated with engagement on Twitter |
| Q104456594 | Did introducing Twitter and digital professionalism as an assessed element of the nursing curriculum impact social media related incidence of 'Fitness to Practise': 12-year case review |
| Q36692852 | Differences Between People Who Use Only Facebook And Those Who Use Facebook Plus Twitter |
| Q64259118 | Differences in Emotional and Pain-Related Language in Tweets About Dentists and Medical Doctors: Text Analysis of Twitter Content |
| Q98459251 | Diffusing science through social networks: The case of breastfeeding communication on Twitter |
| Q55880162 | Diffusion Size and Structural Virality: The Effects of Message and Network Features on Spreading Health Information on Twitter |
| Q35873381 | Diffusion of Messages from an Electronic Cigarette Brand to Potential Users through Twitter |
| Q58826113 | Digital Dialogue? Australian Politicians' use of the Social Network Tool Twitter |
| Q59258876 | Digital Diplomacy 2.0? A Cross-national Comparison of Public Engagement in Facebook and Twitter |
| Q114248421 | Digital Narratives of COVID-19: A Twitter Dataset for Text Analysis in Spanish |
| Q97565973 | Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis |
| Q33579843 | Digital drug safety surveillance: monitoring pharmaceutical products in twitter |
| Q113135074 | Digital nomads, coworking, and other expressions of mobile work on Twitter |
| Q87813646 | Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology |
| Q57782228 | Disciplinary differences in Twitter scholarly communication |
| Q61537739 | Discipline and resistance on social media: Discourse, power and context in the Paul Chambers ‘Twitter Joke Trial’ |
| Q57390521 | Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis |
| Q57390527 | Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis (Preprint) |
| Q50778623 | Discourse over a contested technology on Twitter: A case study of hydraulic fracturing. |
| Q43047218 | Discovering explanatory models to identify relevant tweets on Zika |
| Q60622863 | Discriminating between Brazilian and European Portuguese National Varieties on Twitter Texts |
| Q42633525 | Discussing Occupy Wall Street on Twitter: longitudinal network analysis of equality, emotion, and stability of public discussion. |
| Q92486465 | Discussions of miscarriage and preterm births on Twitter |
| Q28648382 | Disease detection or public opinion reflection? Content analysis of tweets, other social media, and online newspapers during the measles outbreak in The Netherlands in 2013 |
| Q47608483 | Disease-specific hashtags and the creation of Twitter medical communities in hematology and oncology |
| Q53826963 | Disseminating research information through Facebook and Twitter (DRIFT): presenting an evidence-based framework. |
| Q95649189 | Disseminating the Foundations of Knowledge Translation and Patient Engagement Science and Practice: Quantitative Descriptive Evaluation of Exposure and Engagement of the KnowledgeNudge Blog and Twitter Profile |
| Q28709660 | Dissemination of health information through social networks: twitter and antibiotics |
| Q55490757 | Diurnal variations of psychometric indicators in Twitter content. |
| Q31157059 | Do Clinicians Understand Quality Metric Data? An Evaluation in a Twitter-Derived Sample |
| Q35689530 | Do Global Cities Enable Global Views? Using Twitter to Quantify the Level of Geographical Awareness of U.S. Cities |
| Q60647256 | Do Parliamentary Debates of e-Petitions Enhance Public Engagement With Parliament? An Analysis of Twitter Conversations |
| Q90659278 | Do You Follow Us (@TCSurgeon) on Twitter? |
| Q21133507 | Do altmetrics work? Twitter and ten other social web services |
| Q36308049 | Do as I tweet, not as I do: comparing physical activity data between fitness tweets and Healthy People 2020. |
| Q33765327 | Do cancer patients tweet? Examining the twitter use of cancer patients in Japan |
| Q51475394 | Do you twitter and tweet? |
| Q83544895 | Do you use Facebook or Twitter? |
| Q51448375 | Doctors take to Twitter to refute Hunt's claim that NHS has weekday working culture. |
| Q47552509 | Doctors' use of Facebook, Twitter, and WhatsApp is the focus of 28 GMC investigations |
| Q58706355 | Does Twitter language reliably predict heart disease? A commentary on Eichstaedt et al. (2015a) |
| Q34618710 | Does Twitter trigger bursts in signature collections? |
| Q35593701 | Does the type of event influence how user interactions evolve on Twitter? |
| Q45031403 | Does this Tweet make me look fat? A content analysis of weight stigma on Twitter |
| Q47584754 | Don't Tweet False Hope to Patients Desperate for a Cure |
| Q62634061 | Don’t Sleep on It: An Examination of Storm Naming and Potential Heuristic Effects on Twitter |
| Q41366982 | Drug Use in the Twittersphere: A Qualitative Contextual Analysis of Tweets About Prescription Drugs |
| Q87697131 | Drug launch on twitter breached code |
| Q51440829 | Due process in the Twitter age. |
| Q56289343 | Dynamic Debates: An Analysis of Group Polarization Over Time on Twitter |
| Q57083026 | Dynamical classes of collective attention in twitter |
| Q100534763 | E-Cigarette Advocates on Twitter: Content Analysis of Vaping-Related Tweets |
| Q101338360 | E-Cigarette Promotion on Twitter in Australia: Content Analysis of Tweets |
| Q28354215 | E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter |
| Q39001988 | E-Cigarette Topics Shared by Medical Professionals: A Comparison of Tweets from the United States and United Kingdom. |
| Q58537533 | E-RESOURCE ROUND UP: Twitter Madness and Tweeting Like Mad: How I Use Twitter to Keep Up on Developments in Web Services |
| Q92736882 | E-liquid-related posts to Twitter in 2018: Thematic analysis |
| Q61734219 | Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals |
| Q97551757 | Eating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter |
| Q58432355 | Eavesdropping on Narrowcast Self-talk and Microchats on Twitter |
| Q92866713 | Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014-2015 Ebola Epidemic |
| Q64356787 | Ebola on Instagram and Twitter: How health organizations address the health crisis in their social media engagement |
| Q44093970 | Ebola, Twitter, and misinformation: a dangerous combination? |
| Q59445421 | Ecosystem of spamming on Twitter: Analysis of spam reporters and spam reportees |
| Q51437970 | Education first: Content analysis of #erc15prague tweets. |
| Q36296180 | Effect of a Novel Engagement Strategy Using Twitter on Test Performance. |
| Q98234051 | Effect of brief encouragement to use twitter on knowledge of the critical-care literature by ICU residents: The randomized controlled IMKREASE trial |
| Q47564344 | Effective use of Twitter and Facebook in pathology practice |
| Q96617589 | Effects of Information Veracity and Message Frames on Information Dissemination: A Case Study of 2016 Zika Epidemic Discussion on Twitter |
| Q50349991 | EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets |
| Q38686860 | Electrocardiogram training for residents: A curriculum based on Facebook and Twitter |
| Q58103213 | Electronic Cigarette Themes on Twitter: Dissemination Patterns and Relations with Online News and Search Engine Queries in South Korea |
| Q37412820 | Electronic Cigarettes on Twitter - Spreading the Appeal of Flavors |
| Q37344886 | Electronic word of mouth on twitter about physical activity in the United States: exploratory infodemiology study |
| Q57646400 | Elite Tweets: Analyzing the Twitter Communication Patterns of Labour Party Peers in the House of Lords |
| Q114713651 | Elon Musk Compares Justin Trudeau to Hitler in Since-Deleted Tweet |
| Q112891706 | Embracing LOLitics: Popular Culture, Online Political Humor, and Play |
| Q57102583 | Emergency preparedness collaboration on Twitter |
| Q39223295 | Emergent use of Twitter in the 2011 Tohoku Earthquake |
| Q35625461 | Emergent user behavior on Twitter modelled by a stochastic differential equation |
| Q87065578 | Emotion Dynamics of Public Opinions on Twitter |
| Q30505134 | Emotion Intensities in Tweets |
| Q57770278 | Emotion-Corpus Guided Lexicons for Sentiment Analysis on Twitter |
| Q62043637 | Emotion-aware polarity lexicons for Twitter sentiment analysis |
| Q57813223 | Emotional Sharing on Social Media: How Twitter Replies Contribute to Increased Emotional Intensity |
| Q57391743 | Emotional Tone Detection in Arabic Tweets |
| Q47315127 | Emotional Uses of Facebook and Twitter |
| Q105587258 | Employing Twitter Hashtags and Linked Data to Suggest Trending Resources in a Digital Library |
| Q59137115 | Employing sentiment analysis for gauging perceptions of minorities in multicultural societies: An analysis of Twitter feeds on the Afrikaner community of Orania in South Africa |
| Q47145894 | Enabled backchannel: conference Twitter use by digital humanists |
| Q64467490 | Encountering #Feminism on Twitter: Reflections on a Research Collaboration between Social Scientists and Computer Scientists |
| Q34473122 | Engagement with health agencies on twitter |
| Q79936766 | Engaging for a Good Cause: Sophia's Story and Why #BugsR4Girls |
| Q57918829 | Engaging with Charities on Social Media: Comparing Interaction on Facebook and Twitter |
| Q58558930 | Engaging with the University Community on Twitter: Conference Report |
| Q47746560 | Enhanced Worldwide Dermatology-Pathology Interaction via Facebook, Twitter, and Other Social Media Platforms |
| Q40044833 | Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data |
| Q53473180 | Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses |
| Q30374716 | Enriched Audience Engagement Through Twitter: Should More Academic Radiology Departments Seize the Opportunity? |
| Q42647307 | Enriching the Toxicology Experience through Twitter |
| Q91822168 | EnrichrBot: Twitter Bot Tracking Tweets About Human Genes |
| Q30140792 | Entity Level Contextual Sentiment Detection of Topic Sensitive Influential Twitterers Using SentiCircles |
| Q38371782 | Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media |
| Q38549379 | Epilepsy in the Twitter era: a need to re-tweet the way we think about seizures |
| Q93051569 | Epistemic Networks on Twitter: A New Way To Learn |
| Q45870587 | Erratum to: Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter. |
| Q59471379 | Erratum to: Touristic site attractiveness seen through Twitter |
| Q57717038 | Erratum: Sentiment Characterization of an Urban Environment via Twitter |
| Q31032582 | Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data. |
| Q92154625 | Estimated Ages of JUUL Twitter Followers |
| Q91847841 | Estimating Determinants of Attrition in Eating Disorder Communities on Twitter: An Instrumental Variables Approach |
| Q93268360 | Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods |
| Q59613226 | Estimating local commuting patterns from geolocated Twitter data |
| Q31144763 | Estimating the Duration of Public Concern After the Fukushima Dai-ichi Nuclear Power Station Accident From the Occurrence of Radiation Exposure-Related Terms on Twitter: A Retrospective Data Analysis |
| Q92779796 | Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing |
| Q95616821 | Estimation of the number of general anesthesia cases based on a series of nationwide surveys on Twitter during COVID-19 in Japan: A statistical analysis |
| Q28604223 | Ethical issues in using Twitter for population-level depression monitoring: a qualitative study |
| Q34850534 | Ethical issues in using Twitter for public health surveillance and research: developing a taxonomy of ethical concepts from the research literature. |
| Q90060372 | European Society for Medical Oncology (ESMO) 2018 Congress Twitter analysis: from ethics to results through the understanding of communication and interaction flows |
| Q47925754 | Evaluating #VetFinals: Can Twitter help students prepare for final examinations? |
| Q39904660 | Evaluating College Students' Displayed Alcohol References on Facebook and Twitter |
| Q27818475 | Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis |
| Q51416393 | Evaluating Patient Perspectives of Provider Professionalism on Twitter in an Academic Obstetrics and Gynecology Clinic: Patient Survey. |
| Q58345169 | Evaluating Twitter and its impact on student learning in principles of economics courses |
| Q57001403 | Evaluating Twitter as a complementary data source for pharmacovigilance |
| Q94080404 | Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City |
| Q73139186 | Evaluating geo-located Twitter data as a control layer for areal interpolation of population |
| Q51468156 | Evaluating social media's capacity to develop engaged audiences in health promotion settings: use of Twitter metrics as a case study. |
| Q112933954 | Evaluating spammer detection systems for Twitter |
| Q52309716 | Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health. |
| Q40487546 | Evaluating the Use of Twitter to Enhance the Educational Experience of a Medical School Surgery Clerkship |
| Q40244332 | Evaluating the use of twitter as a tool to increase engagement in medical education |
| Q58051465 | Evaluating the utilization of Twitter messages as a source of security alerts |
| Q37716829 | Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts |
| Q36191139 | Evaluation of a multinational, multilingual vaccine debate on Twitter |
| Q40340482 | Event Detection in Twitter Microblogging |
| Q57943236 | Event detection over twitter social media streams |
| Q33706682 | Event detection using Twitter: a spatio-temporal approach |
| Q29042620 | Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots |
| Q30204895 | Evolution of a search: the use of dynamic twitter searches during superstorm sandy |
| Q59836706 | Evolutionary-based Framework for Optimizing the Spread of Information on Twitter |
| Q58204512 | Evolving Centralities in Temporal Graphs: A Twitter Network Analysis |
| Q58643396 | Examining Characteristics of Traditional and Twitter Citation |
| Q45034111 | Examining Perceived Distance and Personal Authenticity as Mediators of the Effects of Ghost-Tweeting on Parasocial Interaction. |
| Q92298356 | Examining Public Communication About Kidney Cancer on Twitter |
| Q47555279 | Examining Public Perceptions about Lead in School Drinking Water: A Mixed-Methods Analysis of Twitter Response to an Environmental Health Hazard. |
| Q100298263 | Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter |
| Q96774192 | Examining the Impact of COVID-19 Lockdown in Wuhan and Lombardy: A Psycholinguistic Analysis on Weibo and Twitter |
| Q90638959 | Examining the Phenomenon of Quarter-Life Crisis Through Artificial Intelligence and the Language of Twitter |
| Q57103598 | Examining the Role of Twitter in Response and Recovery During and After Historic Flooding in South Carolina |
| Q96222289 | Examining the Self-Harm and Suicide Contagion Effects of the Blue Whale Challenge on YouTube and Twitter: Qualitative Study |
| Q54023288 | Examining the UK higher education sector through the network of institutional accounts on Twitter |
| Q58306622 | Examining the Use of Facebook and Twitter as an Additional Social Space in a MOOC |
| Q56340747 | Expanding Opportunities for Professional Development: Utilization of Twitter by Early Career Women in Academic Medicine and Science |
| Q59192072 | Expanding Opportunities for Professional Development: Utilization of Twitter by Early Career Women in Academic Medicine and Science (Preprint) |
| Q90935766 | Expanding journal clubs beyond single institutions using Twitter |
| Q42332211 | Expanding the use of Twitter for medical education |
| Q88330948 | Experiences Of Individuals Concerning Combined Orthodontic and Orthognathic Surgical Treatment: A Qualitative Twitter Analysis |
| Q51415126 | Experiences with the Twitter Health Surveillance (THS) System. |
| Q99549968 | Expert communication on Twitter: Comparing economists and scientists' social networks, topics and communicative styles |
| Q59559214 | Explaining Sentiment Spikes in Twitter |
| Q28607237 | Exploiting Language Models to Classify Events from Twitter |
| Q66693284 | Exploiting Twitter and Wikipedia for the annotation of event images |
| Q92075648 | Exploration of Content and Reach of Physical Therapy-Related Discussion on Twitter |
| Q88695709 | Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter |
| Q35050369 | Exploratory search on Twitter utilizing user feedback and multi-perspective microblog analysis |
| Q101542522 | Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges |
| Q101125757 | Exploring Eating Disorder Topics on Twitter: Machine Learning Approach |
| Q47765453 | Exploring Identity Motives in Twitter Usage in Saudi Arabia and the UK. |
| Q92782248 | Exploring Lung Cancer Screening Discussions on Twitter |
| Q28651529 | Exploring Twitter to analyze the public's reaction patterns to recently reported homicides in London |
| Q38880147 | Exploring and modelling impacts of third molar experience on quality of life: a real-time qualitative study using Twitter |
| Q40155303 | Exploring brand-name drug mentions on Twitter for pharmacovigilance |
| Q61772509 | Exploring characteristics of suspended users and network stability on Twitter |
| Q62634078 | Exploring extreme events on social media: A comparison of user reposting/retweeting behaviors on Twitter and Weibo |
| Q92709603 | Exploring new landmarks: analysis of Twitter usage during the 41st ESNR Annual Meeting |
| Q47636971 | Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia |
| Q58899898 | Exploring political discussions by Korean twitter users |
| Q78883830 | Exploring stakeholders’ dialogue and corporate social responsibility (CSR) on Twitter |
| Q92336391 | Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics |
| Q92394223 | Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets |
| Q58503227 | Exploring the Potential of Twitter as a Research Tool |
| Q38543745 | Exploring the Role of Twitter in Promoting Women's Health in the Arab World: Lessons Learned |
| Q101573320 | Exploring the Twitter activity around the eighth meeting of the Conference of the Parties to the WHO Framework Convention on Tobacco Control |
| Q39717350 | Exploring the diffusion of tweets designed to raise the road safety agenda in Saudi Arabia |
| Q50350036 | Exploring the fine-grained analysis and automatic detection of irony on Twitter |
| Q121094830 | Exploring the impact of COVID-19 Pandemic and Vaccine Dissemination on Airbnb's Popularity and Sentiment on Twitter |
| Q84429282 | Exploring the influence of the teacher: Social participation on Twitter and academic perception |
| Q57732443 | Exploring ‘success’ in digitally augmented activism: A triangulated approach to analyzing UK activist Twitter use |
| Q91869077 | Expressions of loss predict aggressive comments on Twitter among gang-involved youth in Chicago |
| Q62634109 | Expressions of risk awareness and concern through Twitter: On the utility of using the medium as an indication of audience needs |
| Q95604037 | Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK |
| Q116215385 | Extracting Ironic Tweets using Experts Model |
| Q63246163 | Extracting health-related causality from twitter messages using natural language processing |
| Q64060408 | Extracting semantic annotations from twitter |
| Q57485996 | FCJ-190 Building a Better Twitter: A Study of the Twitter Alternatives GNU social, Quitter, rstat.us, and Twister |
| Q87833023 | FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia |
| Q58579745 | Face2face: Using Facebook, Twitter, and Other Social Media Tools to Create Great Customer Connections by David Lee King |
| Q50203133 | Facebook and Twitter join US effort to attract a million new organ donor registrations |
| Q46264244 | Facebook and Twitter vaccine sentiment in response to measles outbreaks |
| Q33447736 | Facebook and Twitter, communication and shelter, and the 2011 Tuscaloosa tornado |
| Q46054502 | Facebook and twitter saves a cat's life |
| Q88937046 | Facebook or Twitter?: Effective recruitment strategies for family caregivers |
| Q58904621 | Facilitators, characteristics, and impacts of Twitter use: Theoretical analysis and empirical illustration |
| Q100378873 | Factors Engaging Users of Diabetes Social Media Channels on Facebook, Twitter, and Instagram: Observational Study |
| Q89215140 | Factors Increasing Tweet Engagement Rate for the Journal of Vascular and Interventional Radiology Twitter Feed |
| Q42749031 | Factors that contribute to social media influence within an Internal Medicine Twitter learning community |
| Q61127142 | Fake news on Twitter during the 2016 U.S. presidential election |
| Q57256153 | Fan–Athlete Interaction and Twitter Tweeting through the Giro: A Case Study |
| Q31097950 | Feasibility of Conducting Retrospective Studies Using Hashtags and Social Media Data From Facebook and Twitter. |
| Q96121870 | Feasibility of a twitter campaign to promote HPV vaccine uptake among racially/ethnically diverse young adult women living in public housing |
| Q59163957 | Feature Engineering for Recognizing Adverse Drug Reactions from Twitter Posts |
| Q98804866 | Feature Set Ensembles for Sentiment Analysis of Tweets |
| Q60427500 | Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis |
| Q111687358 | Featured graphic: Mapping the geoweb: a geography of Twitter |
| Q104801480 | Feeling the Void: Lack of Support for Isolation and Sleep Difficulties in Pregnant Women during the COVID-19 Pandemic Revealed by Twitter Data Analysis |
| Q106112053 | Ferguson and the death of Michael Brown on Twitter: #BlackLivesMatter, #TCOT, and the evolution of collective identities |
| Q57433485 | Figuring Out #Fukushima: An Initial Look at Functions and Content of US Twitter Commentary About Nuclear Risk |
| Q51432511 | Financial Conflicts of Interest Among Hematologist-Oncologists on Twitter. |
| Q88532552 | Finding Street Gang Members on Twitter |
| Q35747167 | Finding the Twitter users who stood with Wendy |
| Q38735774 | Fine social aspiration: Twitter as a voice for cytopathology |
| Q100764730 | Five years of the #RSCPoster Twitter conference |
| Q93011531 | Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study |
| Q96115265 | Focus and social contagion of environmental organization advocacy on Twitter |
| Q51472356 | Follow Eurosurveillance on Twitter. |
| Q50902009 | Follow Me, Like Me, Tweet Me! Implementing Social Media Into Occupational Health. |
| Q51468397 | Follow us on twitter. |
| Q89504823 | Follow-Up Investigation on the Promotional Practices of Electric Scooter Companies: Content Analysis of Posts on Instagram and Twitter |
| Q57256137 | Following Celebrities’ Tweets About Brands: The Impact of Twitter-Based Electronic Word-of-Mouth on Consumers’ Source Credibility Perception, Buying Intention, and Social Identification With Celebrities |
| Q58327594 | Following brands on Twitter: an extension of theory of planned behavior |
| Q90264189 | Following the crowd: patterns of crowdsourcing on Twitter among urologists |
| Q42374927 | Forecasting the onset and course of mental illness with Twitter data |
| Q57984870 | Forgive me Father for I have Thinned: surveilling the bio-citizen through Twitter |
| Q100391726 | Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter |
| Q91354518 | Framing genetically modified mosquitoes in the online news and Twitter: Intermedia frame setting in the issue-attention cycle |
| Q50776350 | Free the tweet at scientific conferences. |
| Q59282383 | Frequent discussion of insomnia and weight gain with glucocorticoid therapy: an analysis of Twitter posts |
| Q28872272 | Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets |
| Q98174770 | From Cynic to Advocate: The Use of Twitter in Cardiology |
| Q87814174 | From DBpedia and WordNet hierarchies to LinkedIn and Twitter |
| Q58611492 | From Live TV Events to Twitter Status Updates - a Study on Delays |
| Q105083453 | From Mani stones to Twitter: Bhutan creates a unique media matrix for a 21st-century democracy |
| Q56029648 | From Street to Tweet |
| Q88317283 | From Twitter |
| Q88452493 | From Twitter |
| Q88524188 | From Twitter |
| Q88665073 | From Twitter |
| Q88885539 | From Twitter |
| Q89248280 | From Twitter |
| Q89455058 | From Twitter |
| Q95378080 | From Twitter |
| Q95555723 | From Twitter |
| Q95560054 | From Twitter |
| Q39564021 | From Twitter to Megaphones: Seven Lessons Learned about Public Health Crisis Communication |
| Q58560301 | From Unlabelled Tweets to Twitter-specific Opinion Words |
| Q59256753 | From news blogs to news on Twitter: gatewatching and collaborative news curation |
| Q105592871 | From the Blackhand Side: Twitter as a Cultural Conversation |
| Q87820375 | From twitter |
| Q87834616 | From twitter |
| Q88175048 | From twitter |
| Q88228425 | From twitter |
| Q88309306 | From twitter |
| Q88508078 | From twitter |
| Q89949128 | From twitter |
| Q38933759 | Future-oriented tweets predict lower county-level HIV prevalence in the United States |
| Q59483000 | Gaining historical and international relations insights from social media: spatio-temporal real-world news analysis using Twitter |
| Q47363956 | Gamified Twitter Microblogging to Support Resident Preparation for the American Board of Surgery In-Service Training Examination |
| Q97688855 | Gender Differences in Sports News Coverage on Twitter |
| Q90689743 | Gender Differences in Twitter Use and Influence Among Health Policy and Health Services Researchers |
| Q56220010 | Gender and image sharing on Facebook, Twitter, Instagram, Snapchat and WhatsApp in the UK |
| Q57782224 | Gender differences in the climate change communication on Twitter |
| Q35667257 | Geo-Located Tweets. Enhancing Mobility Maps and Capturing Cross-Border Movement |
| Q51458967 | Geo-located Twitter as proxy for global mobility patterns. |
| Q100443935 | Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study |
| Q47681561 | Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15. |
| Q39289503 | Geographic variations in electronic cigarette advertisements on Twitter in the United States. |
| Q98203144 | Geographical fragmentation of the global network of Twitter communications |
| Q64121099 | Geographically Resolved Rhythms in Twitter Use Reveal Social Pressures on Daily Activity Patterns |
| Q58188183 | Geography of Twitter networks |
| Q97676714 | Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2 |
| Q101235713 | Geolocated Twitter-based population mobility in Victoria, Australia, during the staged COVID-19 restrictions |
| Q104176328 | Geoscience analysis on Twitter |
| Q38611279 | Geospatial Distribution of Local Health Department Tweets and Online Searches About Ebola During the 2014 Ebola Outbreak |
| Q53063848 | Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015-2016. |
| Q31139948 | Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data |
| Q41819203 | Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis |
| Q94932190 | Global sentiments surrounding the COVID-19 pandemic on Twitter |
| Q27681552 | Good Friends, Bad News - Affect and Virality in Twitter |
| Q62593607 | Google matrix of Twitter |
| Q36907065 | Gordie Howe's "Miraculous Treatment": Case Study of Twitter Users' Reactions to a Sport Celebrity's Stem Cell Treatment |
| Q87814201 | Graph based tweet entity linking using DBpedia |
| Q100382268 | Growth of the Social #RadOnc Network on Twitter |
| Q57023737 | HIV messaging on twitter: an analysis of current practice and data-driven recommendations |
| Q93055104 | HPV vaccine coverage in Australia and associations with HPV vaccine information exposure among Australian Twitter users |
| Q90043187 | HPV vaccine, Twitter, and gay, bisexual and other men who have sex with men |
| Q41967298 | Happiness and the patterns of life: a study of geolocated tweets. |
| Q47777843 | Happiness, stress, a bit of vulgarity, and lots of discursive conversation: a pilot study examining nursing students' tweets about nursing education posted to Twitter |
| Q40211033 | Happy Twitter tweets are more likely in American states with lower levels of resident neuroticism |
| Q28477950 | Harnessing English Sentiment Lexicons for Polarity Detection in Urdu Tweets: A Baseline Approach |
| Q93175749 | Harnessing Twitter polls for multi-specialty collaboration in standardizing point-ofcare ultrasonography in nephrology |
| Q101228214 | Harnessing the Power of Medical Twitter for Mentorship |
| Q55358779 | Harvesting opinions in Twitter for sentiment analysis |
| Q57659979 | Has Robert Parker lost his hegemony as a prescriptor in the wine World? A preliminar inquiry through Twitter |
| Q47269452 | Hashtag #TBI: A content and network data analysis of tweets about Traumatic Brain Injury |
| Q112951385 | Hashtag NZPol: New Zealand Women Twitter Users and Political Participation Construction |
| Q59290578 | Hashtag Popularity on Twitter: Analyzing Co-occurrence of Multiple Hashtags |
| Q90098936 | Hashtags and heroes: perceptions of nursing on Twitter following a high profile nurse arrest |
| Q99408066 | Hashtags in healthcare: understanding Twitter hashtags and online engagement at the American Association for the Surgery of Trauma 2016-2019 meetings |
| Q116180121 | Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter |
| Q37529602 | He votes or she votes? Female and male discursive strategies in Twitter political hashtags |
| Q51455375 | Headache (@HeadacheJournal) and Twitter. |
| Q99206698 | Health Forums and Twitter for Dementia Research: Opportunities and Considerations |
| Q54107828 | Health Information Needs and Health Seeking Behavior During the 2014-2016 Ebola Outbreak: A Twitter Content Analysis. |
| Q64079335 | Health Information Technology Trends in Social Media: Using Twitter Data |
| Q36294084 | Health disparities and clinical trial recruitment: Is there a duty to tweet? |
| Q51465424 | Health organizations providing and seeking social support: a Twitter-based content analysis. |
| Q38049365 | Health tweets: an exploration of health promotion on twitter |
| Q34076757 | HealthTWITTER Initiative: Design of a Social Networking Service Based Tailored Application for Diabetes Self-Management |
| Q49981507 | Hidden in-game intelligence in NBA players' tweets |
| Q57707087 | Hierarchical Classification Approach to Emotion Recognition in Twitter |
| Q74210557 | Higgs Bison or Niels Bohrson? Twitter offers names for Fermilab’s baby bison |
| Q37232968 | High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea |
| Q51477807 | High-level twitter. |
| Q57782217 | Highly tweeted science articles: who tweets them? An analysis of Twitter user profile descriptions |
| Q102370560 | Hippocrates would be on Twitter |
| Q46372481 | Historical Personalities: Tweeting Standard Narratives in the History of Science. |
| Q105876359 | Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter |
| Q93220481 | Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis |
| Q35911100 | Hookah-Related Twitter Chatter: A Content Analysis |
| Q50239970 | How (Not) to Talk on Twitter: Effects of Politicians' Tweets on Perceptions of the Twitter Environment |
| Q47304681 | How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter |
| Q33843915 | How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets |
| Q57717041 | How Do You Understand Twitter?: Analyzing Mental Models, Understanding and Learning about Complex Interactive Systems |
| Q29394730 | How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? |
| Q59069535 | How Facebook and Twitter could be the next disruptive force in clinical trials |
| Q96577345 | How Facebook, Twitter and other data troves are revolutionizing social science |
| Q104467849 | How Healthcare Workers Wield Influence Through Twitter Hashtags: A Tale of Two Public Health Crises |
| Q47709382 | How Is a Label of Borderline Personality Disorder Constructed on Twitter: A Critical Discourse Analysis |
| Q108690341 | How Jon Stewart and Lady Gaga Made Congress Less Lame: The Impact of Social Media on the Passage of Bills Through the "Lame Duck" Session of the 111th Congress and Beyond |
| Q110627662 | How Many People Live in Political Bubbles on Social Media? Evidence From Linked Survey and Twitter Data |
| Q115179171 | How Musk’s takeover might change Twitter: what researchers think |
| Q92746503 | How Risk Communication via Facebook and Twitter Shapes Behavioral Intentions: The Case of Fine Dust Pollution in South Korea |
| Q60950248 | How Twitter Can Support the HIV/AIDS Response to Achieve the 2030 Eradication Goal: In-Depth Thematic Analysis of World AIDS Day Tweets |
| Q33855363 | How Twitter Is Studied in the Medical Professions: A Classification of Twitter Papers Indexed in PubMed |
| Q51422077 | How Twitter can move the medical education debate forward? |
| Q96949852 | How Twitter conversations using hashtags #regionalanesthesia and #regionalanaesthesia have changed in the COVID-19 era |
| Q50121716 | How Twitter has connected the colorectal community |
| Q91826575 | How Twitter is changing medical research |
| Q46459166 | How Twitter literacy can benefit conservation scientists. |
| Q40182340 | How Twitter may have helped Nigeria contain Ebola |
| Q56120104 | How Twitter was used to spread – and knock down – rumours during the riots |
| Q56413093 | How and Why Educators Use Twitter: A Survey of the Field |
| Q59650053 | How and why people Twitter |
| Q57425918 | How and why scholars cite on Twitter |
| Q62113233 | How can spreaders affect the indirect infuence on twitter? |
| Q91869110 | How can we better use Twitter to find a person who got lost due to dementia? |
| Q56781506 | How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint) |
| Q63739948 | How did Ebola information spread on twitter: broadcasting or viral spreading? |
| Q89776029 | How do Twitter users react to TV broadcasts dedicated to vaccines in Italy? |
| Q87833584 | How do Twitter, Wikipedia, and Harrison's principles of medicine describe heart attacks? |
| Q102322171 | How food companies use social media to influence policy debates: a framework of Australian ultra-processed food industry Twitter data |
| Q50048243 | How humans transmit language: horizontal transmission matches word frequencies among peers on Twitter. |
| Q106705101 | How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications |
| Q64114214 | How journalists verify user-generated content during terrorist crises. Analyzing Twitter communication during the Brussels attacks |
| Q102062850 | How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds |
| Q40055471 | How people react to Zika virus outbreaks on Twitter? A computational content analysis. |
| Q56119392 | How riot rumours spread on Twitter |
| Q92131318 | How scientists and physicians use Twitter during a medical congress |
| Q34469537 | How the scientific community reacts to newly submitted preprints: article downloads, Twitter mentions, and citations |
| Q104754019 | How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter |
| Q53818460 | How to evaluate sentiment classifiers for Twitter time-ordered data? |
| Q39379843 | How to exploit twitter for public health monitoring? |
| Q36896956 | How to normalize Twitter counts? A first attempt based on journals in the Twitter Index |
| Q50120438 | How to set up and use a Twitter account professionally |
| Q110782630 | Hunting for professional eyewitnesses- How Danish newspaper Politiken used Twitter asan information source in their coverage of the BostonMarathon bombing |
| Q54969698 | Hybrid Semantic Analysis for Mapping Adverse Drug Reaction Mentions in Tweets to Medical Terminology. |
| Q57659996 | Hyperlinks embedded in twitter as a proxy for total external in-links to international university websites |
| Q92266689 | I Tweet, Therefore I Learn: An Analysis of Twitter Use Across Anesthesiology Conferences |
| Q56673331 | I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience |
| Q55554891 | I tweet, therefore I am. |
| Q93198426 | I wake up and hit the JUUL: Analyzing Twitter for JUUL nicotine effects and dependence |
| Q114816284 | ISIS Sympathizers in U.S. Prefer Twitter Among Social Media |
| Q38653120 | Identification of Keywords From Twitter and Web Blog Posts to Detect Influenza Epidemics in Korea |
| Q98656518 | Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 |
| Q99707942 | Identifying #addiction concerns on twitter during the COVID-19 pandemic: A text mining analysis |
| Q28602684 | Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter |
| Q55562254 | Identifying Consumer Health Terms of Side Effects in Twitter Posts. |
| Q66811538 | Identifying Culture and Cooperative Behavior Pattern in Belt and Road Area: Psychological Analysis of Big Data on Twitter |
| Q50869053 | Identifying Diseases, Drugs, and Symptoms in Twitter. |
| Q94525419 | Identifying High-Risk Areas for Dengue Infection Using Mobility Patterns on Twitter |
| Q92531704 | Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic |
| Q66680273 | Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus |
| Q45944830 | Identifying Sentiment of Hookah-Related Posts on Twitter. |
| Q106861336 | Identifying Twitter Bots Using a Convolutional Neural Network |
| Q40376701 | Identifying Twitter influencer profiles for health promotion in Saudi Arabia |
| Q101061488 | Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter |
| Q58611158 | Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study |
| Q60176221 | Identifying and analyzing researchers on twitter |
| Q57680658 | Identifying behaviors that generate positive interactions between science museums and people on Twitter |
| Q104505148 | Identifying current Juul users among emerging adults through Twitter feeds |
| Q51415108 | Identifying diffusion patterns of research articles on Twitter: A case study of online engagement with open access articles. |
| Q56939283 | Identifying people with dementia on Twitter |
| Q42698370 | Identifying personal health experience tweets with deep neural networks |
| Q103809557 | Identifying public concerns and reactions during the COVID-19 pandemic on Twitter: A text-mining analysis |
| Q59256768 | Identifying the Events That Connect Social Media Users: Charting Follower Accession on Twitter |
| Q40055679 | Identifying the public's concerns and the Centers for Disease Control and Prevention's reactions during a health crisis: An analysis of a Zika live Twitter chat |
| Q55286512 | Identifying tweets of personal health experience through word embedding and LSTM neural network. |
| Q57665157 | Identifying urban crowds using geo-located Social media data: a Twitter experiment in New York City |
| Q37670543 | Ideological Consumerism in Colombian Elections, 2015: Links Between Political Ideology, Twitter Activity, and Electoral Results. |
| Q30489350 | If I tweet will you cite? The effect of social media exposure of articles on downloads and citations. |
| Q101238640 | Image-based Abuse: Debate and Reflections on the "Iveco Case" in Spain on Twitter |
| Q58188205 | Imagining Twitter as an Imagined Community |
| Q41651685 | Imbalance of opinions expressed on Twitter relating to CT radiation risk: an opportunity for increased radiologist representation |
| Q58698347 | Impact investing market on Twitter: influential users and communities |
| Q39595021 | Impact of Twitter intensity, time, and location on message lapse of bluebird's pursuit of fleas in Madagascar |
| Q39268444 | Impact of dual-polarization radar technology and Twitter on the Hattiesburg, Mississippi tornado |
| Q33740727 | Implementing Twitter in a health sciences library. |
| Q73134217 | Implementing a real-time Twitter-based system for resource dispatch in disaster management |
| Q101239258 | Importance of Educating Teenagers on Appropriate Safety Gear for E-Scooters. Comment on "Follow-Up Investigation on the Promotional Practices of Electric Scooter Companies: Content Analysis of Posts on Instagram and Twitter" |
| Q51470341 | In a twitter about social media? Patients assume you're already online. Association members share learnings from web efforts. |
| Q51471232 | In battle. Tweeting the London riots. |
| Q28596910 | In the mood: the dynamics of collective sentiments on Twitter |
| Q51469753 | In the new NHS, you are what you tweet. |
| Q58598543 | Incorporating Twitter, Instagram, and Facebook in Economics Classrooms |
| Q38429577 | Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas |
| Q27003424 | Increased use of Twitter at a medical conference: a report and a review of the educational opportunities |
| Q60138158 | Increasing Access in 140 Characters or Less: Or, What Are Archival Institutions Doing on Twitter? |
| Q55244656 | Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study. |
| Q56859822 | Indicative Tweet Generation: An Extractive Summarization Problem? |
| Q64108793 | Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement |
| Q64106486 | Infectivity enhances prediction of viral cascades in Twitter |
| Q122617475 | Inference Attack on Browsing History of Twitter Users Using Public Click Analytics and Twitter Metadata |
| Q56428059 | Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan |
| Q64060939 | Inferring Your Expertise from Twitter: Integrating Sentiment and Topic Relatedness |
| Q60241664 | Inferring international and internal migration patterns from Twitter data |
| Q60241668 | Inferring nationalities of Twitter users and studying inter-national linking |
| Q28658084 | Inferring the Origin Locations of Tweets with Quantitative Confidence |
| Q57586939 | Influence and Sentiment Homophily on Twitter Social Circles |
| Q62708758 | Influence of Twitter Activity on College Classes |
| Q60922214 | Influence of fake news in Twitter during the 2016 US presidential election |
| Q98773720 | Influence of the Pandemic Dissemination of COVID-19 on Facial Rejuvenation: A Survey of Twitter |
| Q57640148 | Influencing the Conversation About Masculinity and Suicide: Evaluation of the Man Up Multimedia Campaign Using Twitter Data |
| Q57640152 | Influencing the Conversation About Masculinity and Suicide: Evaluation of the Man Up Multimedia Campaign Using Twitter Data (Preprint) |
| Q49961660 | Influencing the Conversation About Masculinity and Suicide: Evaluation of the Man Up Multimedia Campaign Using Twitter Data. |
| Q28661279 | Influenza-like illness surveillance on Twitter through automated learning of naïve language |
| Q38333734 | Informal carers, paramedics, chronic pain, and Twitter |
| Q57092191 | Information Circulation in times of Ebola: Twitter and the Sexual Transmission of Ebola by Survivors |
| Q59290632 | Information Diffusion on Twitter: Everyone Has Its Chance, But All Chances Are Not Equal |
| Q56781511 | Information Overload, Similarity, and Redundancy: Unsubscribing Information Sources on Twitter |
| Q51443922 | Information fusion-based approach for studying influence on Twitter using belief theory. |
| Q57751429 | Information-Sharing and Community-Building: Exploring the Use of Twitter in Science Public Relations |
| Q98728952 | Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis |
| Q92844611 | Insights from Twitter about Public Perceptions of Asthma, COPD, and Exposures |
| Q57604388 | Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research |
| Q126654409 | Insights into everyday life in the library: #Fachreferatsfreitag |
| Q59637236 | Insights on the Use of Twitter at the Canadian Cardiovascular Congress |
| Q47306829 | Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas |
| Q57730746 | Institutional pain communication via Twitter by Spanish and US pain societies: Analysis of levels of use and engagement |
| Q57787099 | Integrating Geriatrics Knowledge into a Medical Student Clerkship Using Twitter Poll |
| Q66711099 | Integrating Twitter into Wiki to support informal awareness |
| Q58028740 | Intelligent Twitter Data Analysis Based on Nonnegative Matrix Factorizations |
| Q92651951 | Interdisciplinary optimism? Sentiment analysis of Twitter data |
| Q66711035 | Interest classification of Twitter users using Wikipedia |
| Q34470506 | Interest communities and flow roles in directed networks: the Twitter network of the UK riots |
| Q37053098 | Interest in a Twitter-delivered weight loss program among women of childbearing age |
| Q112908367 | Interesting discussion or tiresome bluster? Exploring the social norms of politics among young people on social media |
| Q42643993 | International Urology Journal Club via Twitter: 12-month experience. |
| Q105302711 | Internet Research Agency Twitter activity predicted 2016 U.S. election polls |
| Q36247359 | Interplay between Public Attention and Public Emotion toward Multiple Social Issues on Twitter |
| Q50233043 | Introducing @microbleeds: A pilot Twitter space for cerebral microbleeds research |
| Q57437255 | Introducing Students to Micro-Blogging through Collaborative Work: Using Twitter to Promote Cross-University Relationships and Discussions |
| Q40351521 | Introducing Twitter as an assessed component of the undergraduate nursing curriculum: case study. |
| Q46644140 | Introducing the Pediatric Urology Journal Club on Twitter |
| Q47776022 | Introducing the Twitter Impact Factor: An Objective Measure of Urology's Academic Impact on Twitter |
| Q58504394 | Investigate human behavior during ramadan through network structure: Evidence from Twitter |
| Q92730001 | Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections |
| Q58188141 | Investigating Political Polarization on Twitter: A Canadian Perspective |
| Q37622552 | Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter |
| Q98899914 | Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data |
| Q57422075 | Investigating the quality of interactions and public engagement around scientific papers on Twitter |
| Q112465058 | Investigating value propositions in social media: studies of brand and customer exchanges on Twitter |
| Q83547423 | Invisible brokers: “citizen science” on Twitter |
| Q56593123 | Iran: Downside to the "Twitter Revolution" |
| Q58188207 | Is Happiness Contagious Online? A Case of Twitter and the 2010 Winter Olympics |
| Q50218698 | Is Tweeting Important for Technical Writing? Absolutely |
| Q40439426 | Is Twitter a forum for disseminating research to health policy makers? |
| Q104104338 | Is good mentorship found on Twitter? We think so |
| Q56656648 | Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter |
| Q57745595 | Is the medium the message? Perceptions of and reactions to crisis communication via twitter, blogs and traditional media |
| Q106239979 | Issue Salience on Twitter During Swedish Party Leaders’ Debates |
| Q95942502 | It Happens in #ChurchToo: Twitter Discourse Regarding Sexual Victimization Within Religious Communities |
| Q93096479 | It is time for Turkish Cardiologists to start engaging on Twitter |
| Q51477386 | It's Twitter time. |
| Q51472552 | It's time for an epilepsy Twitter revolution. |
| Q57887264 | Italian codified hashtags for weather warning on Twitter – who is really using them? |
| Q60176211 | It’s All About Information? The Following Behaviour of Professors and PhD Students in Computer Science on Twitter |
| Q121083196 | J-Tweeters |
| Q90935187 | JNC Board Meeting: Social media endeavors and decoding Twitter |
| Q58293870 | JUDGMENT AND SHOCK ESTHETIC IN THE TWITTER AGE |
| Q91906667 | JUUL on Twitter: Analyzing Tweets About Use of a New Nicotine Delivery System |
| Q50116736 | Join the discussion on Twitter! |
| Q58554620 | Joining the Twitter Conversation |
| Q51465769 | Journal Club: Twitter as a source of vaccination information: content drivers and what they're saying. |
| Q92097471 | Journal clubs in the digital age: Twitter for continuing professional development |
| Q59256737 | Journalistic Branding on Twitter |
| Q59256793 | Journalists and Twitter: How Australian News Organisations Adapt to a New Medium |
| Q38934506 | Just What the Doctor Tweeted: Physicians' Challenges and Rewards of Using Twitter |
| Q51477754 | Just a tweet away. |
| Q57740784 | Katrin Weller / Axel Bruns / Jean Burgess / Merja Mahrt / Cornelius Puschmann (eds.) (2014): Twitter and Society. New York: Peter Lang |
| Q90066640 | Keeping up with the times: how national public health and governmental organizations communicate about cannabis on Twitter |
| Q57403611 | Knowing the Tweeters: Deriving Sociologically Relevant Demographics from Twitter |
| Q99236778 | Knowledge translation and social media: Twitter data analysis of the 2019 Canadian Guideline for Physical Activity throughout Pregnancy |
| Q57769212 | La disculpa corporativa a través de Twitter en Iberia y Banco Santander |
| Q59835147 | La literatura en un tuit |
| Q59383699 | La repercusión del movimiento MOOC en las redes sociales. Un estudio computacional y estadístico en Twitter |
| Q57864354 | Language, Twitter and Academic Conferences |
| Q57395653 | Large Scale Implementations for Twitter Sentiment Classification |
| Q115787625 | Large-scale identification and characterization of scholars on Twitter |
| Q58221443 | Learning Semantic Relationships between Entities in Twitter |
| Q28584914 | Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification |
| Q92309791 | Learning from birthing stories on Twitter |
| Q50234788 | Let Twitter and Facebook improve rather than ruin your practice |
| Q45961486 | Let's Have a Tweetup: The Case for Using Twitter Professionally. |
| Q34119411 | Let's go formative: continuous student ratings with Web 2.0 application Twitter |
| Q102990918 | Letter to Editor regarding 'COVID-19 pandemic lockdown: An emotional health perspective of Indians on Twitter' |
| Q57727556 | Let’s Chat about Brexit! A Politically-Sensitive Dialog System Based on Twitter Data |
| Q49814288 | Leveraging Google Trends, Twitter, and Wikipedia to Investigate the Impact of a Celebrity's Death From Rheumatoid Arthritis |
| Q36029967 | Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter. |
| Q45073586 | Leveraging Twitter to Maximize the Radiology Meeting Experience. |
| Q38655208 | Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew |
| Q108287021 | Leveraging fine-grained sentiment analysis for competitivity |
| Q27441129 | Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity |
| Q36170276 | Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams |
| Q33896323 | Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data |
| Q47622747 | Leveraging social media in the stem cell sector: exploring Twitter's potential as a vehicle for public information campaigns |
| Q58221445 | Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter |
| Q87833557 | Lexical Comparison Between Wikipedia and Twitter Corpora by Using Word Embeddings |
| Q35959383 | Life Satisfaction and the Pursuit of Happiness on Twitter |
| Q100406474 | Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017-2019 |
| Q58188147 | Link Sharing on Twitter during Popular Events: Implications for Social Navigation on Websites |
| Q38373710 | Link prediction on Twitter. |
| Q92910364 | Linking Survey and Twitter Data: Informed Consent, Disclosure, Security, and Archiving |
| Q74441059 | Lionfish prove a main draw on Twitter |
| Q28319955 | Little Data, Big Stories: Taking the Pulse of Large-Scaled Events on Twitter |
| Q38367601 | Live tweeting in medicine: 'Tweeting the meeting'. |
| Q47406984 | Live tweeting: a tool for learning and reflection |
| Q91940827 | Live, Love, Juul: User and Content Analysis of Twitter Posts about Juul |
| Q57813036 | Live-Tweet the Society of Thoracic Surgeons Annual Meeting: How To Leverage Twitter To Maximize Your Conference Experience |
| Q30151112 | Local Health Departments Tweeting About Ebola: Characteristics and Messaging |
| Q35687803 | Local Variation of Hashtag Spike Trains and Popularity in Twitter |
| Q47557812 | Local chatter or international buzz? Language differences on posts about Zika research on Twitter and Facebook |
| Q36840053 | Local health department use of twitter to disseminate diabetes information, United States |
| Q59428106 | Localized twitter opinion mining using sentiment analysis |
| Q58312563 | Location-specific tweet detection and topic summarization in Twitter |
| Q110883210 | Lokale Kirchengeschichte in der Coronazeit. Ein Erfahrungsbericht zur Serie #KircheninLeipzig |
| Q98853759 | Look Who’s Talking—Athletes on Twitter: A Case Study |
| Q102074673 | Look who's talking now: Cancer in primary care on Twitter |
| Q57352819 | Los Twindignados: The Rise of the Indignados Movement on Twitter |
| Q45073382 | Lung Cancer Messages on Twitter: Content Analysis and Evaluation. |
| Q57722425 | LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification |
| Q57722431 | LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English |
| Q57721620 | MISNIS: An intelligent platform for twitter topic mining |
| Q62730543 | MOA-TweetReader: Real-Time Analysis in Twitter Streaming Data |
| Q57403592 | Machine Classification and Analysis of Suicide-Related Communication on Twitter |
| Q98388508 | Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study |
| Q96114885 | Machine Learning to Detect Self-Reporting of Symptoms, Testing Access and Recovery Associated with COVID-19 on Twitter: A Retrospective Big-Data Infoveillance Study |
| Q30147776 | Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer’s Disease Stigma on Twitter |
| Q50350024 | Machine Learning-Based Sentimental Analysis for Twitter Accounts |
| Q37294563 | Maintaining a Twitter Feed to Advance an Internal Medicine Residency Program's Educational Mission |
| Q58039125 | Making Sense of Twitter |
| Q58517123 | Making Twitter Work: A Guide for the Uninitiated, the Skeptical, and the Pragmatic |
| Q121080209 | Making the most of Twitter: How technological affordances influence Swedish journalists’ self-branding |
| Q42223832 | Making the most of a brave new world: opportunities and considerations for using Twitter as a public health monitoring tool. |
| Q92525819 | Malicious Actors on Twitter: A Guide for Public Health Researchers |
| Q74211646 | Mammals face off in Twitter battles for supremacy |
| Q56687160 | Managing Participation through Modal Affordances on Twitter |
| Q108930360 | Managing your social campaign strategy using Facebook, Twitter, Instagram, YouTube & Pinterest: An interview with Dana Howard, social media marketing manager |
| Q53079951 | Mapping Engagement in Twitter-Based Support Networks for Adult Smoking Cessation. |
| Q73117276 | Mapping auroral activity with Twitter |
| Q37731352 | Mapping physician Twitter networks: describing how they work as a first step in understanding connectivity, information flow, and message diffusion |
| Q57411891 | Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election |
| Q56029904 | Mapping sociocultural controversies across digital media platforms: one week of #gamergate on Twitter, YouTube, and Tumblr |
| Q93110910 | Mapping the Dutch vaccination debate on Twitter: Identifying communities, narratives, and interactions |
| Q112065603 | Mapping the connections of health professionals to COVID-19 myths and facts in the Australian Twittersphere |
| Q115907648 | Mastodon: Datenschutzfreundliche Twitter-Alternative |
| Q47801254 | Maximizing the Tweet Engagement Rate in Academia: Analysis of the AJNR Twitter Feed. |
| Q59256747 | May the Best Tweeter Win: The Twitter Strategies of Key Campaign Accounts in the 2012 US Election |
| Q40424909 | Meaningful use of Twitter in nursing education may improve student learning and should be considered as a viable educational tool to assist in the development of digital professionalism |
| Q33826753 | Measuring Audience Engagement for Public Health Twitter Chats: Insights From #LiveFitNOLA. |
| Q36918693 | Measuring Health Information Dissemination and Identifying Target Interest Communities on Twitter: Methods Development and Case Study of the @SafetyMD Network. |
| Q62612276 | Measuring node importance on Twitter microblogging |
| Q36931670 | Measuring patient-perceived quality of care in US hospitals using Twitter |
| Q41125992 | Measuring political polarization: Twitter shows the two sides of Venezuela |
| Q57633728 | Measuring web ecology by Facebook, Twitter, blogs and online news: 2012 general election in South Korea |
| Q47483953 | Measuring, Understanding, and Classifying News MediaSympathy on Twitter after Crisis Events |
| Q57896702 | Mediatisierung und Medialität in Social Media: Das Diskurssystem „Twitter“ |
| Q57896690 | Mediatized Politics — Structures and Strategies of Discursive Participation and Online Deliberation on Twitter |
| Q36907142 | Medical Institutions and Twitter: A Novel Tool for Public Communication in Japan. |
| Q58899820 | Members of the Scottish Parliament on Twitter: good constituency men (and women)? |
| Q99412974 | Mental Health Discourses on Twitter during Mental Health Awareness Week |
| Q57717033 | Mental Models, Performance and Usability of a Complex Interactive System: The Case of Twitter |
| Q99624471 | Mental health discourse and social media: Which mechanisms of cultural power drive discourse on Twitter |
| Q38700828 | Mental health nurses' use of Twitter for professional purposes during conference participation using #acmhn2016. |
| Q91148528 | Mental illness and bipolar disorder on Twitter: implications for stigma and social support |
| Q49224386 | Messengers and Messages for Tweets That Used #thinspo and #fitspo Hashtags in 2016. |
| Q59256742 | Methodological Innovation in Precarious Spaces: The Case of Twitter |
| Q30727276 | Methodological considerations in analyzing Twitter data |
| Q33566321 | Methods for Coding Tobacco-Related Twitter Data: A Systematic Review |
| Q41936446 | Methods for computational disease surveillance in infection prevention and control: Statistical process control versus Twitter's anomaly and breakout detection algorithms |
| Q91244187 | Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab |
| Q57769207 | Metáforas y emociones en el twitter corporativo: una aproximación discursivo-lingüística a los microblogs de Acciona e Iberdrola |
| Q38448895 | Microblogging and nanotweets: Nanotechnology on Twitter. |
| Q60530718 | Microblogging for Language Learning: Using Twitter to Train Communicative and Cultural Competence |
| Q57727391 | Microblogging in Open Source Software Development: The Case of Drupal and Twitter |
| Q37256625 | Migraine tweets - what can online behavior tell us about disease? |
| Q56038776 | Mining Brand Perceptions from Twitter Social Networks |
| Q28601720 | Mining Multi-Aspect Reflection of News Events in Twitter: Discovery, Linking and Presentation |
| Q96635880 | Mining Potential Effects of HUMIRA in Twitter Posts Through Relational Similarity |
| Q58190379 | Mining Twitter Data for Improved Understanding of Disaster Resilience |
| Q95606475 | Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery |
| Q30989698 | Mining Twitter Data to Improve Detection of Schizophrenia. |
| Q41531673 | Mining Twitter Feeds for Software User Requirements |
| Q36613565 | Mining Twitter as a First Step toward Assessing the Adequacy of Gender Identification Terms on Intake Forms |
| Q96024793 | Mining Twitter to Assess the Determinants of Health Behavior towards Palliative Care in the United States |
| Q36071484 | Mining Twitter to Assess the Public Perception of the "Internet of Things" |
| Q90917937 | Mining Twitter to Inform the Design of Online Interventions for Hispanic Alzheimer's Disease and Related Dementias Caregivers |
| Q91228468 | Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States |
| Q56421918 | Mining Twitter to monitor invasive alien species — An analytical framework and sample information topologies |
| Q92370855 | Mining in Twitter for adverse events from malaria drugs: the case of doxycycline |
| Q97064236 | Mining social media divides: an analysis of K-12 U.S. School uses of Twitter |
| Q70994692 | Mining the First 100 Days: Human and Data Ethics in Twitter research |
| Q99555512 | Mining twitter to explore the emergence of COVID-19 symptoms |
| Q47110144 | Mining twitter to understand the smoking cessation barriers |
| Q29545131 | Mining twitter: A source for psychological wisdom of the crowds |
| Q57666737 | Mirror or Megaphone?: How relationships between narcissism and social networking site use differ on Facebook and Twitter |
| Q96228277 | Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis |
| Q98509018 | Misinformation dissemination in Twitter in the COVID-19 era |
| Q93623751 | Misinformation on Twitter During the Danish National Election: A Case Study |
| Q111851084 | Misinformation, social status og latterliggørelse: en undersøgelse af danskeres spredning af og reaktioner på Covid-19 misinformation på Twitter |
| Q62113235 | Modeling Indirect Influence on Twitter |
| Q92176651 | Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations |
| Q58176726 | Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty: An Example Using Twitter Data |
| Q51459357 | Modeling of information diffusion in Twitter-like social networks under information overload. |
| Q42182323 | Modeling the impact of twitter on influenza epidemics |
| Q40928079 | Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics |
| Q21135287 | Modeling users' activity on twitter networks: validation of Dunbar's number |
| Q57555807 | Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning |
| Q93200250 | Modelling and analysis of an alcoholism model with treatment and effect of Twitter |
| Q36382570 | Modular networks of word correlations on Twitter |
| Q47560742 | Monitoring Freshman College Experience Through Content Analysis of Tweets: Observational Study. |
| Q102061678 | Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach |
| Q60957039 | Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study |
| Q92510905 | Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study |
| Q57069895 | Monitoring Twitter Conversations for Targeted Recruitment in Cancer Trials in Los Angeles County: Protocol for a Mixed-Methods Pilot Study |
| Q56220024 | Monitoring Twitter strategies to discover resonating topics: The case of the UNDP |
| Q37980485 | Monitoring bacterial twitter: does quorum sensing determine the behavior of water and wastewater treatment biofilms? |
| Q89763471 | Monitoring stance towards vaccination in twitter messages |
| Q47733297 | More Than Likes and Tweets: Creating Social Media Portfolios for Academic Promotion and Tenure |
| Q35060186 | More tweets, more votes: social media as a quantitative indicator of political behavior |
| Q59446913 | Motivations for sharing information and social support in social media: A comparative analysis of Facebook, Twitter, Delicious, YouTube, and Flickr |
| Q30030173 | Movement social learning on Twitter: The case of the People’s Assembly |
| Q94672447 | Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter |
| Q62496696 | Multi-class machine classification of suicide-related communication on Twitter |
| Q106271223 | Multicultural Twitter Censorship |
| Q28275371 | Multilingual Twitter Sentiment Classification: The Role of Human Annotators |
| Q109375450 | Multimodal Entity Linking for Tweets |
| Q90623201 | Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit |
| Q62113236 | Multiple spreaders affect the indirect influence on twitter |
| Q118304293 | NBCUniversal’s Linda Yaccarino Is in Talks to Become Twitter CEO |
| Q57262142 | NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter |
| Q57239866 | Named Entity Recognition in Twitter Using Images and Text |
| Q35071058 | National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic |
| Q45944115 | National substance use patterns on Twitter. |
| Q102637184 | Natural vs. Artificially Sweet Tweets: Characterizing Discussions of Non-nutritive Sweeteners on Twitter |
| Q70757167 | Navigating precarious visibility: Ugandan sexual minorities on Twitter |
| Q59866194 | Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression |
| Q58032577 | Negotiating the Balance between Speed and Credibility in Deploying Twitter as Journalistic Tool at the Daily Nation Newspaper in Kenya |
| Q90630755 | Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects |
| Q38849774 | Network Visualization of Dementia Tweets |
| Q34404193 | Network structure and community evolution on Twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami |
| Q101565488 | Network typology, information sources, and messages of the infodemic twitter network under COVID-19 |
| Q35179980 | Networked publics and the organizing of collective action on Twitter: Examining the #Freebassel campaign |
| Q89100123 | Networking Chemical Robots Using Twitter for #RealTimeChem |
| Q90963140 | Neural attention with character embeddings for hay fever detection from twitter |
| Q89410621 | New Twitter Editor, European Society of Cardiology Journals |
| Q50066469 | New from ESC: ESC Journals Twitter |
| Q59894008 | New platform, old habits? Candidates’ use of Twitter during the 2010 British and Dutch general election campaigns |
| Q112667062 | News Classification from Social Media Using Twitter-based Doc2Vec Model and Automatic Query Expansion |
| Q100752909 | News consumption patterns on Twitter: fragmentation study on the online news media network |
| Q29029247 | Newspapers, Facebook and Twitter |
| Q58618013 | Next generation media monitoring: Global coverage of electronic nicotine delivery systems (electronic cigarettes) on Bing, Google and Twitter, 2013-2018 |
| Q102220376 | Next-Generation Scholarship: Rebranding Hematopathology Using Twitter: The MD Anderson Experience |
| Q89606294 | Nicotine patches with e-cigarettes for smoking cessation: Twitter discussion from a respirology journal club |
| Q90066017 | Nicotine patches with e-cigarettes for smoking cessation: Twitter discussion from a respirology journal club - Authors' reply |
| Q55559019 | Non-Parametric Scan Statistics for Disease Outbreak Detection on Twitter. |
| Q62628938 | Normalization of Vietnamese Tweets on Twitter |
| Q38927039 | Normative Mechanism of Rumor Dissemination on Twitter |
| Q90728805 | Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp |
| Q94563766 | Not all world leaders use Twitter in response to the COVID-19 pandemic: impact of the way of Angela Merkel on psychological distress, behaviour and risk perception |
| Q91135313 | Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data |
| Q43151945 | Novel survey disseminated through Twitter supports its utility for networking, disseminating research, advocacy, clinical practice and other professional goals |
| Q48267050 | Novel use of Twitter to disseminate and evaluate adherence to clinical guidelines by the European Association of Urology. |
| Q51457026 | Nurses and Twitter: the good, the bad, and the reluctant. |
| Q90954834 | Nursing Organizations' Health Policy Content on Facebook and Twitter Preceding the 2016 United States Presidential Election |
| Q40213820 | Nursing and Twitter: creating an online community using hashtags |
| Q51478212 | Nursing education 2.0: Twitter & tweets. Can you post a nugget of knowledge in 140 characters or less? |
| Q51470757 | O2 health. The twitter challenge. |
| Q61467956 | OMG Earthquake! Can Twitter Improve Earthquake Response? |
| Q90759483 | OMG U #Cyberbully! An Exploration of Public Discourse About Cyberbullying on Twitter |
| Q61307782 | OP0011-PARE A week to tweet: findings from young pare’s online course for twitter novices |
| Q89459518 | October's Twitter Chat Report |
| Q42710562 | Of Tweets and Men. |
| Q60140409 | Of big birds and bayonets: hybrid Twitter interactivity in the 2012 Presidential debates |
| Q88586656 | On Quantifying Diffusion of Health Information on Twitter |
| Q40970305 | On adverse drug event extractions using twitter sentiment analysis |
| Q91103276 | On first-pass, twitter response is inferior to expectations |
| Q57756727 | On predicting the popularity of newly emerging hashtags in Twitter |
| Q56865207 | On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection |
| Q98804832 | On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis |
| Q46332701 | On the relation between message sentiment and its virality on social media |
| Q55919185 | One Step, Two Step, Network Step? Complementary Perspectives on Communication Flows in Twittered Citizen Protests |
| Q47556638 | Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media |
| Q47158244 | Online Influence and Sentiment of Fitness Tweets: Analysis of Two Million Fitness Tweets. |
| Q53679228 | Online Survey Data from Twitter and Archaeology Surveys 2011-2013 |
| Q47109901 | Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter. |
| Q62708844 | Online named entity recognition method for microtexts in social networking services: A case study of twitter |
| Q42866734 | Online survey on twitter: a urological experience |
| Q90383666 | Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach |
| Q38437134 | Ontology-based automatic identification of public health-related Turkish tweets |
| Q58197876 | Ontology-based sentiment analysis of twitter posts |
| Q113610629 | Open dataset of scholars on Twitter |
| Q35100956 | Ophthalmology on social networking sites: an observational study of Facebook, Twitter, and LinkedIn |
| Q58866374 | Opinion mining about a product by analyzing public tweets in Twitter |
| Q59559218 | Opinion retrieval in Twitter using stylistic variations |
| Q90547156 | Opinion: Is Twitter a New Way to Learn? |
| Q92404049 | Opioids on Twitter: A Content Analysis of Conversations regarding Prescription Drugs on Social Media and Implications for Message Design |
| Q37679149 | Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets |
| Q58173347 | Organization or conversation in Twitter: A case study of chatterboxing |
| Q47572566 | Organizational Twitter Use: Content Analysis of Tweets during Breast Cancer Awareness Month |
| Q57352974 | Our Twitter Profiles, Our Selves: Predicting Personality with Twitter |
| Q50225407 | Our Year on Twitter: Science in #SocialMedia |
| Q57721614 | Page Rank Versus Katz: Is the Centrality Algorithm Choice Relevant to Measure User Influence in Twitter? |
| Q46823165 | Palliative care on Twitter: who to follow to get started |
| Q70757287 | Pan-Africanism as a laughing matter: (Funny) expressions of African identity on Twitter |
| Q30396927 | Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak |
| Q99568765 | Paratexts on a social network site and their relevance in the production of meaning-Results of a qualitative investigation of Twitter-Feeds |
| Q39362135 | Part-of-Speech Tagging for Twitter with Adversarial Neural Networks |
| Q55266039 | Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets |
| Q90737330 | Pathological Traits Associated to Facebook and Twitter among French Users |
| Q41582054 | Patient Participation at Health Care Conferences: Engaged Patients Increase Information Flow, Expand Propagation, and Deepen Engagement in the Conversation of Tweets Compared to Physicians or Researchers |
| Q89691977 | Patients' experiences of radiotherapy: Insights from Twitter |
| Q38693941 | Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California. |
| Q102331767 | Pediatric Program Directors Should have an Active Presence on Twitter |
| Q51475609 | Peer review: Trial by Twitter. |
| Q98897164 | Perceived Popularity and Online Political Dissent: Evidence from Twitter in Venezuela |
| Q124465686 | Perceived experts are prevalent and influential within an antivaccine community on Twitter |
| Q42674204 | Perceived realism and Twitter use are associated with increased acceptance of cosmetic surgery among those watching reality television cosmetic surgery programs |
| Q37697555 | Perceptions of Menthol Cigarettes Among Twitter Users: Content and Sentiment Analysis |
| Q37579574 | Perceptions of Secondhand E-Cigarette Aerosol Among Twitter Users. |
| Q64235494 | Perceptions of cervical cancer prevention on Twitter uncovered by different sampling strategies |
| Q57670084 | Performance Evaluation of Mahout Clustering Algorithms Using a Twitter Streaming Dataset |
| Q60241590 | Persistent Sharing of Fitness App Status on Twitter |
| Q112901023 | Personal Augmented Space: Mobile 3D Visualisation and Interaction Study with Microblogging |
| Q59890522 | Personal Branding on Twitter |
| Q112957149 | Personalised Taste Profiling in Short-Text Microblogs |
| Q44742627 | Perspectives on acne: what Twitter can teach health care providers |
| Q42660159 | Pharma twitters cautiously before advertising guidelines are released. |
| Q35570013 | Pharmacovigilance on twitter? Mining tweets for adverse drug reactions |
| Q125185132 | Phishing Detection on Twitter Streams |
| Q57727525 | Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis |
| Q51468473 | Physicians and social media: separating the tweet from the chaff. |
| Q51475517 | Physicians on Twitter. |
| Q115773841 | Picture-text walks with Robert Lorenz, regional art and product development - fashion with history(s) in and from Görlitz |
| Q58031110 | Pictures, Protests and Politics: Mapping Twitter Images during South Africa’s Fees Must Fall Campaign |
| Q37718669 | Plant twitter: ligands under 140 amino acids enforcing stomatal patterning |
| Q46886090 | Plastic surgery marketing in a generation of "tweeting". |
| Q62593596 | Poisson statistics of PageRank probabilities of Twitter and Wikipedia networks |
| Q58031423 | Poke me, I'm a journalist: The impact of Facebook and Twitter on newsroom routines and cultures at two South African weeklies |
| Q58899960 | Polarisation in political Twitter conversations |
| Q62037606 | Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences |
| Q58804723 | Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data |
| Q96938320 | Polemical speech and interaction on Twitter and on readers’ comments: Malvinas case during last CFK’s tenure |
| Q56900430 | Police message diffusion on Twitter: analysing the reach of social media communications |
| Q61631836 | Police presentational strategies on Twitter in Canada |
| Q47777725 | Policeman tweets about 16 year old kept in cell because of lack of NHS beds |
| Q56553741 | PoliticAlly: Finding political friends on twitter |
| Q57896699 | Politische Deliberation online – Twitter als Element des politischen Diskurses |
| Q61918673 | Popular YouTuber Says He Won't Cover Game After Twitter Spat |
| Q55306202 | Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies. |
| Q39493891 | Portrayal of waterpipe (shisha, hookah, nargile) smoking on Twitter: a qualitative exploration |
| Q96138581 | Postgraduate students' perspective on using Twitter as a learning resource in higher education |
| Q92366866 | Potential of Using Twitter to Recruit Cancer Survivors and Their Willingness to Participate in Nutrition Research and Web-Based Interventions: A Cross-Sectional Study |
| Q83886060 | Pound That Presentation: Connect with Twitter Users via Hashtags |
| Q84415394 | Powerful Communication Style on Twitter: Effects on Credibility and Civic Participation |
| Q51439332 | PrEP on Twitter: Information, Barriers, and Stigma. |
| Q42952487 | Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets |
| Q98178866 | Precision Medicine Diagnostics for Rare Kidney Disease: Twitter as a Tool in Clinical Genomic Translation |
| Q96135278 | Predicting COVID-19 Incidence Using Anosmia and Other COVID-19 Symptomatology: Preliminary Analysis Using Google and Twitter |
| Q55242084 | Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates. |
| Q58613715 | Predicting Elections for Multiple Countries Using Twitter and Polls |
| Q58033455 | Predicting Flu Trends using Twitter data |
| Q128123119 | Predicting Job Opportunities Through Twitter Data Using Natural Language Processing |
| Q61884703 | Predicting Personality from Twitter |
| Q29543908 | Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear” |
| Q59605358 | Predicting TV programme audience by using twitter based metrics |
| Q38603767 | Predicting age groups of Twitter users based on language and metadata features |
| Q60241672 | Predicting ideological friends and foes in Twitter conflicts |
| Q47755407 | Predicting motion picture box office performance using temporal tweet patterns |
| Q57083028 | Predicting the Political Alignment of Twitter Users |
| Q92700502 | Prediction of Personal Experience Tweets of Medication Use via Contextual Word Representations |
| Q59515350 | Prediction of employment and unemployment rates from Twitter daily rhythms in the US |
| Q40486584 | Preliminary Evidence for the Emergence of a Health Care Online Community of Practice: Using a Netnographic Framework for Twitter Hashtag Analytics |
| Q57317774 | Preliminary survey of leading general medicine journals’ use of Facebook and Twitter |
| Q46018105 | President's page: Tweet all about it: ACC charting the digital future. |
| Q90606003 | Prevalence and Characteristics of Twitter Posts About Court-Ordered, Tobacco-Related Corrective Statements: Descriptive Content Analysis |
| Q35816505 | Prevalence of Marijuana-Related Traffic on Twitter, 2012-2013: A Content Analysis |
| Q33885574 | Price-related promotions for tobacco products on Twitter |
| Q64936472 | Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US. |
| Q42692776 | Primary school children's communication experiences with Twitter: a case study from Turkey |
| Q56781519 | Privacy protection and self-disclosure across societies: A study of global Twitter users |
| Q51458253 | Privacy-preserving discovery of topic-based events from social sensor signals: an experimental study on Twitter. |
| Q38636479 | Pro-ana versus Pro-recovery: A Content Analytic Comparison of Social Media Users' Communication about Eating Disorders on Twitter and Tumblr |
| Q114761877 | Problem of Describing Digital Ephemera |
| Q28167487 | Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets |
| Q90032491 | Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines |
| Q104476328 | Promotion of Disposable Electronic Cigarette Flavors and Topics on Twitter |
| Q57787779 | Promotion of JUUL on Twitter |
| Q44217164 | Promotion of alcohol on Twitter |
| Q57817987 | Promoviendo la investigación en salud con Twitter |
| Q56342050 | Propagating and Debunking Conspiracy Theories on Twitter During the 2015-2016 Zika Virus Outbreak |
| Q46070451 | Propagation of Information About Preexposure Prophylaxis (PrEP) for HIV Prevention Through Twitter. |
| Q94913425 | Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project |
| Q84415570 | Protesting on Twitter: Citizenship and Empowerment from Public Education |
| Q50433483 | Pseudo-Twitter Format: Health Literacy Learning Activity |
| Q91012060 | Psychiatry Residents Integrating Social Media (PRISM): Using Twitter in Graduate Medical Education |
| Q97597943 | Psychological fear and anxiety caused by COVID-19: Insights from Twitter analytics |
| Q28647426 | Psychological language on Twitter predicts county-level heart disease mortality |
| Q61977085 | Public Archaeology 2.0: Facilitating engagement with Twitter |
| Q91032933 | Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study |
| Q98878668 | Public Disclosure on Social Media of Identifiable Patient Information by Health Professionals: Content Analysis of Twitter Data |
| Q97654069 | Public Opinions towards COVID-19 in California and New York on Twitter |
| Q98388522 | Public Perceptions and Attitudes Towards COVID-19 Non-Pharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data |
| Q98241612 | Public Reactions towards the COVID-19 Pandemic on Twitter in the United Kingdom and the United States |
| Q33784791 | Public Response to Obamacare on Twitter |
| Q38827809 | Public Response to Scientific Misconduct: Assessing Changes in Public Sentiment Toward the Stimulus-Triggered Acquisition of Pluripotency (STAP) Cell Case via Twitter |
| Q91090037 | Public Sense of Water Fluoridation as Reflected on Twitter 2009-2017 |
| Q99720030 | Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter |
| Q51455967 | Public health leader apologises for language in Twitter row. |
| Q35205165 | Public health surveillance of dental pain via Twitter |
| Q29012629 | Public microblogging on climate change: One year of Twitter worldwide |
| Q54989096 | Public reaction to Chikungunya outbreaks in Italy-Insights from an extensive novel data streams-based structural equation modeling analysis. |
| Q38877290 | Public reactions to e-cigarette regulations on Twitter: a text mining analysis |
| Q104509637 | Public risk perception and emotion on Twitter during the Covid-19 pandemic |
| Q58899753 | Public science communication on Twitter: a visual analytic approach |
| Q91428872 | Public's Attitudes Toward Health Information on Twitter: A Cross-sectional Survey Based on the Saudi Population |
| Q91286060 | Publicly Available Metrics Underestimate AJNR Twitter Impact and Follower Engagement |
| Q58169466 | Public’s Perception of Political Parties during the 2014 Quebec Election on Twitter |
| Q123433032 | Public’s perception on nursing education during the COVID-19 pandemic: SENTIMENT analysis of Twitter data |
| Q41576676 | Putting the methodological brakes on claims to measure national happiness through Twitter: Methodological limitations in social media analytics |
| Q29541217 | Putting twitter to the test: Assessing outcomes for student collaboration, engagement and success |
| Q36631968 | Qualitative Twitter analysis of participants, tweet strategies, and tweet content at a major urologic conference |
| Q93124522 | Qualitative and quantitative evaluation of the use of Twitter as a tool of antimicrobial stewardship |
| Q102213715 | Quality of the discussion of asthma on twitter |
| Q57302933 | Quantifying Media Influence and Partisan Attention on Twitter During the UK EU Referendum |
| Q28706322 | Quantifying collective attention from tweet stream |
| Q28646116 | Quantifying crowd size with mobile phone and Twitter data |
| Q28650003 | Quantifying regional differences in the length of Twitter messages |
| Q57887396 | Quantifying spatiotemporal dynamics of twitter replies to news feeds |
| Q36144517 | Quantifying the Twitter Influence of Third Party Commercial Entities versus Healthcare Providers in Thirteen Medical Conferences from 2011 - 2013 |
| Q30886318 | Quantifying the role of online news in linking conservation research to Facebook and Twitter |
| Q59256810 | Quantitative Approaches to Comparing Communication Patterns on Twitter |
| Q117358124 | Quitting social media: a qualitative exploration of communication outcomes |
| Q60241599 | Quote RTs on Twitter |
| Q57740805 | RESEARCHING NEWS DISCUSSION ON TWITTER |
| Q98612691 | Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter |
| Q92019701 | Radiology Program Directors Should Have an Active Presence on Twitter |
| Q47107833 | Raising Awareness About Cervical Cancer Using Twitter: Content Analysis of the 2015 #SmearForSmear Campaign. |
| Q37418537 | Randomised controlled trial evaluation of Tweet2Quit: a social network quit-smoking intervention |
| Q59701072 | Ranking twitter influence by combining network centrality and influence observables in an evolutionary model |
| Q104176292 | Rapid collaborative knowledge building via Twitter after significant geohazard events |
| Q104176297 | Rapid collaborative knowledge building via Twitter after significant geohazard events |
| Q106394836 | Rapid collaborative knowledge building via Twitter after significant geohazard events |
| Q47626725 | Rare Cancers and Social Media: Analysis of Twitter Metrics in the First 2 Years of a Rare-Disease Community for Myeloproliferative Neoplasms on Social Media-#MPNSM. |
| Q51460426 | Re-tweeting science. |
| Q51459092 | Re: Eija Laurikainen, Antti Valpas, Pauliina Aukee, et al. Five-year results of a randomized trial comparing retropubic and transobturator midurethral slings for stress incontinence. Eur Urol 2014;65:1109-14. |
| Q92958354 | Re: Stacy Loeb, Jacob Taylor, Mohit Butaney, et al. Twitter-based Prostate Cancer Journal Club (#ProstateJC) Promotes Multidisciplinary Global Scientific Discussion and Research Dissemination. Eur Urol 2019;75:881-2 |
| Q59889625 | Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure |
| Q39189929 | Reaching the global community during disasters: findings from a content analysis of the organizational use of Twitter after the 2010 Haiti earthquake |
| Q99596923 | Reaction of Juntendo Shizuoka Hospital at Izu Peninsula to Typhoon Hagibis (2019) and an Analysis of Twitter Concerning Izunokuni City |
| Q37166401 | Reaction on Twitter to a Cluster of Perinatal Deaths: A Mixed Method Study |
| Q37681371 | Reactions on Twitter to updated alcohol guidelines in the UK: a content analysis. |
| Q58009236 | Reactions to adverse incidents in the health services on Twitter: a mixed methods study |
| Q90331754 | Reading the news on Twitter: Source and item memory for social media in younger and older adults |
| Q56119157 | Reading the riots on Twitter: methodological innovation for the analysis of big data |
| Q60035219 | Reading the riots: whatwerethe police doing on Twitter? |
| Q51667313 | Real Time Sentiment Change Detection of Twitter Data Streams |
| Q59256744 | Real-Time Applications (Twitter) |
| Q57839887 | Real-Time Detection of Traffic From Twitter Stream Analysis |
| Q36098548 | Real-Time Diffusion of Information on Twitter and the Financial Markets |
| Q60704991 | Real-Time Mapping of Earthquake Perception Areas in the Italian Region from Twitter Streams Analysis |
| Q59336188 | Real-Time Monitoring of Flu Epidemics through Linguistic and Statistical Analysis of Twitter Messages |
| Q57403594 | Real-time Classification of Malicious URLs on Twitter using Machine Activity Data |
| Q62564335 | Real-time Detection of Content Polluters in Partially Observable Twitter Networks |
| Q58169261 | Real-time event detection from the Twitter data stream using the TwitterNews+ Framework |
| Q37731397 | Real-time sharing and expression of migraine headache suffering on Twitter: a cross-sectional infodemiology study |
| Q101043848 | Recommendations From the Twitter Hashtag #DoctorsAreDickheads: Qualitative Analysis |
| Q58720148 | Reconstruction of the socio-semantic dynamics of political activist Twitter networks-Method and application to the 2017 French presidential election |
| Q98853035 | Redefining virality in less broad strokes: Predicting viral behavioral intentions from motivations and uses of Facebook and Twitter |
| Q57659987 | Redes de conectividad entre empresas tecnológicas a través de un análisis métrico longitudinal de menciones de usuario en Twitter |
| Q57518443 | Redes sociales y museos: análisis de la campaña en Twitter para el Día Internacional de los Museos y Noche de los Museos |
| Q36318618 | Regarding Twitter as a Potential Data Source for Cardiovascular Disease Research |
| Q36318609 | Regarding Twitter as a Potential Data Source for Cardiovascular Disease Research-Reply. |
| Q92881113 | Regional Influenza Prediction with Sampling Twitter Data and PDE Model |
| Q31111997 | Regional Level Influenza Study with Geo-Tagged Twitter Data. |
| Q64098120 | Regional level influenza study based on Twitter and machine learning method |
| Q51416131 | Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks. |
| Q47664509 | Relevance of social media to nurses and healthcare: 'to tweet or not to tweet'. |
| Q98898238 | Reliability Index for Twitter – Twitter Handles’ Credibility Assessment |
| Q92027193 | Reply to Comment on Maani Hessari, N.; van Schalkwyk, M.C.; Thomas, S.; Petticrew, M. Alcohol Industry CSR Organisations: What Can Their Twitter Activity Tell Us about Their Independence and Their Priorities? A Comparative Analysis. Int. J. Environ. |
| Q93069958 | Reply to comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts" |
| Q59470737 | Reply trees in Twitter: data analysis and branching process models |
| Q38223708 | Representations of stem cell clinics on Twitter |
| Q57740849 | Representing Emoji Usage Using Directed Networks: A Twitter Case Study |
| Q51443327 | Response to "Discourse over a contested technology on Twitter: A case study of hydraulic fracturing"-Word choice as political speech. |
| Q43179185 | Response to "Twitter-Based Journal Clubs: Some Additional Facts and Clarifications". |
| Q51456161 | Response to: Booth & Oudshoorn's account of Archibald and Clark (2014) Twitter and nursing research: how diffusion of innovation theory can help uptake. Journal of Advanced Nursing 70(3), e3-5. |
| Q56258067 | Retweet or like? That is the question |
| Q38344339 | Review of Twitter for infectious diseases clinicians: useful or a waste of time? |
| Q105669506 | Review of gc-2019-23 by Lacassin et al. - Rapid collaborative knowledge building via Twitter after significant geohazard events |
| Q57601605 | Review: Big Data Techniques of Google, Amazon, Facebook and Twitter |
| Q30490500 | Revisiting an open access monograph experiment: measuring citations and tweets 5 years later |
| Q39736946 | Revisiting the connection between Solar eruptions and primary headaches and migraines using Twitter |
| Q106621130 | Rhetoric, Political Ideology and the Peace Process in Colombia: A Twitter® Analysis |
| Q59365335 | Rhythms in Twitter |
| Q27342965 | Rising tides or rising stars?: Dynamics of shared attention on Twitter during media events |
| Q39908502 | Risks and Threats of Social Media Websites: Twitter and the Proana Movement |
| Q46027148 | Risks and benefits of Twitter use by hematologists/oncologists in the era of digital medicine. |
| Q100994558 | Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India |
| Q47616717 | Rubbernecking Effect of Intimate Information on Twitter: When Getting Attention Works Against Interpersonal Attraction |
| Q56553742 | Rumor detection in twitter: An analysis in retrospect |
| Q35242147 | Rumor diffusion and convergence during the 3.11 earthquake: a twitter case study |
| Q62031681 | Rumour veracity detection on twitter using particle swarm optimized shallow classifiers |
| Q90457438 | Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015-2017 |
| Q106493388 | SEIZ Matters: Modelling the spread of concepts on Twitter |
| Q93149876 | SMCT - An Innovative Tool for Mental Health Analysis of Twitter Data |
| Q112926540 | SNET : a statistical normalisation method for Twitter |
| Q51453192 | Sass vs. substance. Twitter, mainstream media and academic blogs focus on surprisingly different scientific subjects. |
| Q62102059 | Scalable Privacy-Compliant Virality Prediction on Twitter |
| Q91870012 | Scaling in words on Twitter |
| Q96949362 | Scaling laws and dynamics of hashtags on Twitter |
| Q92146782 | Scaling laws in geo-located Twitter data |
| Q34812053 | Scaling-laws of human broadcast communication enable distinction between human, corporate and robot Twitter users |
| Q121322325 | Scholarly Sharing via Twitter: #icanhazpdf Requests for Health Sciences Literature |
| Q38874637 | Science Publishing, Social Media, and Nursing Research on Facebook and Twitter |
| Q37700036 | Scientific Misconduct and Social Media: Role of Twitter in the Stimulus Triggered Acquisition of Pluripotency Cells Scandal |
| Q73476017 | Scientific Twitter: The flow of paleontological communication across a topic network |
| Q51455944 | Scientific community. Who are the science stars of Twitter? |
| Q91996468 | Scientific dissemination in the Twitter era |
| Q91551459 | Scientific networks on Twitter: Analyzing scientists' interactions in the climate change debate |
| Q61440542 | Scientists on Twitter: Preaching to the choir or singing from the rooftops? |
| Q58927195 | Scientists share inspiration on Twitter with #IAmAScientistBecause and #BeyondMarieCurie |
| Q87833334 | Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts |
| Q60241716 | Secular vs. Islamist polarization in Egypt on Twitter |
| Q29542347 | Seeking and Sharing: Motivations for Linking on Twitter |
| Q51316600 | SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets |
| Q47147179 | Selecting User Influence on Twitter Data Using Skyline Query under MapReduce Framework |
| Q47696932 | Self-Expression on Social Media: Do Tweets Present Accurate and Positive Portraits of Impulsivity, Self-Esteem, and Attachment Style? |
| Q95604382 | Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource |
| Q97070290 | Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource |
| Q89629360 | Self-reported willingness to share political news articles in online surveys correlates with actual sharing on Twitter |
| Q52922577 | SemEval-2018 Task 1: Affect in Tweets |
| Q57005727 | Semantic Characterization of Tweets Using Topic Models |
| Q49811150 | Semantic Interpretation of Tweets: A Contextual Knowledge-Based Approach for Tweet Analysis |
| Q57756712 | Semi-Supervised Spam Detection in Twitter Stream |
| Q107615411 | Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter |
| Q111535553 | Semi-supervised Named Entity Recognition in noisy-text |
| Q62708618 | Semi-supervised approach based on co-occurrence coefficient for named entity recognition on Twitter |
| Q47343481 | Sense of Community on Twitter and Instagram: Exploring the Roles of Motives and Parasocial Relationships |
| Q58613979 | Sensing Trending Topics in Twitter |
| Q52312189 | Sentiment Analysis of Health Care Tweets: Review of the Methods Used. |
| Q99608771 | Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language |
| Q84358327 | Sentiment Analysis of Tweet Messages using Hybrid Approach Algorithm |
| Q125621580 | Sentiment Analysis of Tweets on the Russia-Ukraine War |
| Q57722428 | Sentiment Analysis on Monolingual, Multilingual and Code-Switching Twitter Corpora |
| Q37681514 | Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach |
| Q57763583 | Sentiment Analysis on Twitter Data for Portuguese Language |
| Q42852524 | Sentiment Classification on Twitter data using Word N Gram Model |
| Q58560739 | Sentiment Knowledge Discovery in Twitter Streaming Data |
| Q120984831 | Sentiment Lexicon Adaptation With Context And Semantics For The Social Web |
| Q35248414 | Sentiment Translation for low resourced languages: Experiments on Irish General Election Tweets |
| Q54487137 | Sentiment analysis and Twitter: a game proposal |
| Q28380956 | Sentiment analysis during Hurricane Sandy in emergency response |
| Q56234123 | Sentiment analysis of the correlation between regular tweets and retweets |
| Q56220157 | Sentiment in Twitter events |
| Q55353997 | Sentiment, Contents, and Retweets: A Study of Two Vaccine-Related Twitter Datasets. |
| Q51465723 | Severe sepsis, social media and the #Sepsis 6: 'Tweeting' national innovations. |
| Q77327168 | Sexist Slurs: Reinforcing Feminine Stereotypes Online |
| Q74211655 | Sexist review causes Twitter storm |
| Q101218781 | Sexual Victimization Among Men: A Qualitative Analysis of the Twitter Hashtag #UsToo |
| Q95279775 | Sexual transmission of Zika virus on Twitter: A depoliticised epidemic |
| Q29396128 | Shares, Pins, and Tweets |
| Q104471246 | Sharing is caring: an analysis of #FOAMed Twitter posts during the COVID-19 pandemic |
| Q104288951 | Sharing knowledge on Hepatology in times of change, or, why you should be on Twitter |
| Q96171152 | Sharing the pain: an observational analysis of Twitter and pain in Ireland |
| Q115179170 | Should I join Mastodon? A scientists’ guide to Twitter’s rival |
| Q51477244 | Should you be tweeting? |
| Q35840928 | Situations in 140 Characters: Assessing Real-World Situations on Twitter |
| Q48179634 | Smartphones, Twitter and new learning opportunities at anaesthetic conferences |
| Q98215225 | Smoking, Vaping, and Tobacco Industry During COVID-19 Pandemic: Twitter Data Analysis |
| Q30204703 | Snow Tweets: Emergency Information Dissemination in a US County During 2014 Winter Storms |
| Q102386485 | Social Bots' Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter |
| Q58221441 | Social Event Detection on Twitter |
| Q84415526 | Social Labs in Universities: Innovation and impact in Medialab UGR |
| Q36273028 | Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter |
| Q57715267 | Social Market: Stock Market and Twitter Correlation |
| Q57403634 | Social Media Analysis, Twitter and the London Olympics 2012 |
| Q38982970 | Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu. |
| Q57186984 | Social Media Mining for Birth Defects Research: A Rule-Based, Bootstrapping Approach to Collecting Data for Rare Health-Related Events on Twitter |
| Q35889429 | Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter. |
| Q59212605 | Social Media Mobilisation as a Prompt for Offline Participation? Analysing Occupy Wall Street Twitterers’ Offline Engagement with the Movement |
| Q105501588 | Social Media and Elections in Zimbabwe: Twitter War between Pro-ZANU-PF and Pro-MDC-A Netizens |
| Q33834342 | Social Media and Language Processing: How Facebook and Twitter Provide the Best Frequency Estimates for Studying Word Recognition |
| Q46581509 | Social Media and Men's Health: A Content Analysis of Twitter Conversations During the 2013 Movember Campaigns in the United States, Canada, and the United Kingdom |
| Q38594107 | Social Media and Myeloproliferative Neoplasms (MPN)--Focus on Twitter and the Development of a Disease-specific Community: #MPNSM. |
| Q38918874 | Social Media and Myeloproliferative Neoplasms (MPN): Analysis of Advanced Metrics From the First Year of a New Twitter Community: #MPNSM. |
| Q59256749 | Social Media and Niche Sports: The Netball Anz Championship and Commonwealth Games on Twitter |
| Q46898338 | Social Media and Scientific Meetings: An Analysis of Twitter Use at the Annual Meeting of the American Society of Neuroradiology |
| Q108735884 | Social Media and the Government: Why it May Be Unconstitutional for Government Officials to Moderate Their Social Media |
| Q57482212 | Social Media and the Organization of Collective Action: Using Twitter to Explore the Ecologies of Two Climate Change Protests |
| Q28081808 | Social Media and the Practicing Hematologist: Twitter 101 for the Busy Healthcare Provider |
| Q88258009 | Social Media for Global Education: Pearls and Pitfalls of Using Facebook, Twitter, and Instagram |
| Q43255525 | Social Media in Hematology in 2017: Dystopia, Utopia, or Somewhere In-between? |
| Q58206167 | Social Media in Shoulder & Elbow Surgery: An Analysis of Twitter and Instagram |
| Q55515727 | Social Medicine: Twitter in Healthcare. |
| Q99246814 | Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication |
| Q55315019 | Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ₁ Regularization. |
| Q101565485 | Social media and COVID-19: Characterizing anti-quarantine comments on Twitter |
| Q47377088 | Social media and anatomy education: Using twitter to enhance the student learning experience in anatomy. |
| Q56119994 | Social media and crisis management |
| Q62634083 | Social media and crisis management: CERC, search strategies, and Twitter content |
| Q39761707 | Social media and flu: Media Twitter accounts as agenda setters |
| Q56551356 | Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words |
| Q41229563 | Social media and medical education: Exploring the potential of Twitter as a learning tool. |
| Q96638814 | Social media and orthodontics: A mixed-methods analysis of orthodontic-related posts on Twitter and Instagram |
| Q30847277 | Social media and palliative medicine: a retrospective 2-year analysis of global Twitter data to evaluate the use of technology to communicate about issues at the end of life. |
| Q50704061 | Social media and physician learning: is it all twitter? |
| Q93358188 | Social media and psoriasis treatment: what are people saying on Twitter? |
| Q47240324 | Social media as a recruitment strategy: using Twitter to explore young people's mental health |
| Q48310740 | Social media impact factor: the top ten dermatology journals on Facebook and Twitter |
| Q62724017 | Social media in emergency management: exploring Twitter use by emergency responders in the UK |
| Q51472340 | Social media in low-resource settings: a role for Twitter and Facebook in global surgery? |
| Q50941427 | Social media in medicine: The volume that Twitter built. |
| Q51227771 | Social media in radiology: early trends in Twitter microblogging at radiology's largest international meeting. |
| Q50971285 | Social media makes global urology meetings truly global. : The influence of Twitter. |
| Q51475641 | Social media networking: Facebook and Twitter. |
| Q35006260 | Social media trends in medical history: tweets as sources in the history of contemporary science |
| Q48659358 | Social media, evidence-based tweeting, and JCEHP. |
| Q51473846 | Social media. Risky tweets? Experts urge caution when posting updates during a surgery. |
| Q42676560 | Social media: a tool to spread information: a case study analysis of twitter conversation at the Cardiac Society of Australia & New Zealand 61st annual scientific meeting 2013. |
| Q51470769 | Social networks and public health: use of Twitter by ministries of health. |
| Q47603010 | Social networks as a tool for science communication and public engagement: focus on Twitter. |
| Q51474806 | Social networks--are they good for your health? The era of Facebook and Twitter. |
| Q51460648 | Social science. Twitter offers entire data pool, but some wary of diving in. |
| Q38671890 | Social sensing of urban land use based on analysis of Twitter users' mobility patterns. |
| Q51473249 | Social-ized medicine. Tweets, texts and apps help turn chat into action. |
| Q104105140 | Social-media insights into US mental health amid the COVID-19 global pandemic: a Longitudinal analysis of publicly available Twitter data (January 22- April 10, 2020) |
| Q87813635 | SocialLink: Linking DBpedia Entities to Corresponding Twitter Accounts |
| Q87813521 | SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles |
| Q59240115 | Sociolinguistic analysis of Twitter in multilingual societies |
| Q47261581 | Sociology. Social scientists wade into the tweet stream |
| Q62677361 | Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access (Preprint) |
| Q51415068 | Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access. |
| Q102218107 | Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter |
| Q56593121 | Sparking debate? Political deaths and Twitter discourses in Argentina and Russia |
| Q57411879 | Spatial, temporal, and content analysis of Twitter for wildfire hazards |
| Q104900071 | Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr |
| Q43662744 | Spatial-based Topic Modelling using Wikidata Knowledge Base |
| Q57717026 | Spatial-temporal characterization of an urban environment using Twitter |
| Q95933217 | Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis |
| Q35035629 | Spatio-temporal variation of conversational utterances on Twitter |
| Q43419106 | Spies to use Twitter as crystal ball |
| Q59357791 | Spot the Ball: Detecting Sports Events on Twitter |
| Q56447814 | Stance Classification of Twitter Debates |
| Q47523072 | Stance Detection in Tweets Using a Majority Vote Classifier |
| Q51420186 | Stance and influence of Twitter users regarding the Brexit referendum. |
| Q58595756 | Starting A New Twitter Account @Conncollegeis: With a Report From #ALAMW14 |
| Q91986323 | State-level racial attitudes and adverse birth outcomes: applying natural language processing to Twitter data to quantify state context for pregnant women |
| Q55156817 | Statistics of co-occurring keywords in confined text messages on Twitter |
| Q50197134 | Stay connected and informed: Start tweeting |
| Q61776108 | Stepping Out of the Ivory Tower for Ocean Literacy |
| Q97542050 | Stigma: the representation of anorexia nervosa in UK newspaper Twitter feeds |
| Q91892411 | Stigma: the representation of mental health in UK newspaper Twitter feeds |
| Q101565482 | Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter |
| Q91080392 | Strategic use of Twitter as a source of health information: a pilot study with textual analysis of health tweets |
| Q64885909 | Strategies to find audience segments on Twitter for e-cigarette education campaigns. |
| Q38903499 | Strategy of robotic surgeons to exert public influence through Twitter |
| Q92888377 | Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective |
| Q50188761 | Student life - Twitter - a great medium for learning and debate |
| Q91104118 | Studying expressions of loneliness in individuals using twitter: an observational study |
| Q90280233 | Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018 |
| Q57302934 | Sub-story detection in Twitter with hierarchical Dirichlet processes |
| Q28175191 | Subjective Well-Being and Social Media: A Semantically Annotated Twitter Corpus on Fertility and Parenthood |
| Q51416476 | Substantial utilization of Facebook, Twitter, YouTube, and Instagram in the prostate cancer community. |
| Q90094994 | Sudden Infant Death Syndrome and Safe Sleep on Twitter: Analysis of Influences and Themes to Guide Health Promotion Efforts |
| Q44848521 | Suffering by comparison: Twitter users' reactions to the Victoria's Secret Fashion Show |
| Q98897042 | Summarizing sporting events using twitter |
| Q104176323 | Supplementary material to "Rapid collaborative knowledge building via Twitter after significant geohazard events" |
| Q100299616 | Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure |
| Q60536765 | Supported matrix factorization using distributed representations for personalised recommendations on twitter |
| Q99405844 | Surgeons and social media: The use of twitter hashtags at the Academic Surgical Congress 2015-2019 a cross sectional study |
| Q51432977 | Surgery on Twitter. |
| Q124079756 | Sustainability communication on Twitter: disability disclosures of listed companies in Poland |
| Q57017102 | Syndromic Classification of Twitter Messages |
| Q62031754 | Systematic literature review of sentiment analysis on Twitter using soft computing techniques |
| Q62489424 | TECLA: A temperament and psychological type prediction framework from Twitter data |
| Q36486088 | TEXT CLASSIFICATION FOR AUTOMATIC DETECTION OF E-CIGARETTE USE AND USE FOR SMOKING CESSATION FROM TWITTER: A FEASIBILITY PILOT |
| Q94178351 | THE EFFECT OF LANGUAGE ON PREPARATION FOR OLD AGE: EVIDENCE FROM CROSS-CULTURAL SURVEY AND TWITTER DATA |
| Q61834721 | TWITTER AS A NEWS SOURCE |
| Q59256779 | TWITTER AS A TECHNOLOGY FOR AUDIENCING AND FANDOM |
| Q112762504 | TWikiL - the Twitter Wikipedia Link Dataset |
| Q100739437 | Tackling acid-base disorders, one Twitter poll at a time |
| Q46093196 | Take two aspirin and tweet me in the morning: how Twitter, Facebook, and other social media are reshaping health care |
| Q59481055 | Taking social media to a university classroom: teaching and learning using Twitter and blogs |
| Q57202947 | Talking about tobacco on Twitter is associated with tobacco product use |
| Q58188131 | Talking to Twitter users: Motivations behind Twitter use on the Alberta oil sands and the Northern Gateway Pipeline |
| Q37053112 | Tanning bed burns reported on Twitter: over 15,000 in 2013. |
| Q38386081 | Teachers' perceptions of Twitter for professional development |
| Q38896648 | Teaching Tip: Developing an Intercollegiate Twitter Forum to Improve Student Exam Study and Digital Professionalism |
| Q107399377 | Technical Challenges in Developing Software to Collect Twitter Data |
| Q56873506 | Technology Firms Shape Political Communication: The Work of Microsoft, Facebook, Twitter, and Google With Campaigns During the 2016 U.S. Presidential Cycle |
| Q51476157 | Technology: Facebook, Tweets, and the Medical Record |
| Q46506530 | Teens, Tweets, and Tanning Beds: Rethinking the Use of Social Media for Skin Cancer Prevention. |
| Q98459022 | Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19-related Information on Twitter during the SARS-CoV-2 Outbreak in Europe: Infoveillance Study |
| Q55470677 | Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter. |
| Q21560906 | Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter |
| Q91837800 | Temporal trends in anti-vaccine discourse on Twitter |
| Q28649586 | Ten simple rules for effective online outreach |
| Q85188511 | Ten simple rules for getting started on Twitter as a scientist |
| Q24810428 | Ten simple rules of live tweeting at scientific conferences |
| Q105837039 | Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter |
| Q35750012 | Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science |
| Q59649818 | Testing the potential of Twitter mining methods for data acquisition: Evaluating novel opportunities for ecological research in multiple taxa |
| Q42529121 | Text Mining of Tweet for Sentiment Classification and Association with Stock Prices |
| Q51443930 | Text normalization for named entity recognition in Vietnamese tweets. |
| Q57494561 | The #BTW17 Twitter Dataset–Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag |
| Q93092877 | The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations |
| Q98782194 | The #PalliativeCare conversation on Twitter: An analysis of trends, content and caregiver perspectives |
| Q48498893 | The Anatomy of a Tweet: Social Media in Surgical Practice |
| Q84984732 | The Arab Image in Spanish Social Media: A Twitter Sentiment Analytics Approach |
| Q59256871 | The Arab Spring on Twitter |
| Q59256773 | The Arab Spring on Twitter: Language Communities in #egypt and #libya |
| Q91656743 | The Assessment of Twitter's Potential for Outbreak Detection: Avian Influenza Case Study |
| Q92221416 | The Blue Check of Credibility: Does Account Verification Matter When Evaluating News on Twitter? |
| Q35739966 | The Canary in the Coal Mine Tweets: Social Media Reveals Public Perceptions of Non-Medical Use of Opioids |
| Q56959451 | The Communication Behavior of German MPs on Twitter: Preaching to the Converted and Attacking Opponents |
| Q103985996 | The Complexity of Social Media Response: Statistical Evidence For One-Dimensional Engagement Signal in Twitter |
| Q91898180 | The Continued Rise in Professional Use of Social Media at Scientific Meetings: An Analysis of Twitter Use during the ASNR 2018 Annual Meeting |
| Q59700884 | The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election |
| Q112956498 | The Dispositif of Terror: Islamic State, Biopolitical Governance, and the Digital Conjuncture |
| Q56522957 | The Echo Chamber Effect in Twitter: does community polarization increase? |
| Q91645133 | The Effect of Social Network Size on Hashtag Adoption on Twitter |
| Q28607435 | The Effects of Twitter Sentiment on Stock Price Returns |
| Q47547224 | The Effects of Twitter Users' Gender and Weight on Viral Behavioral Intentions Toward Obesity-Related News |
| Q39149152 | The Evolution of the Journal Club: From Osler to Twitter. |
| Q48823647 | The Expanding Profile of the International Day of Radiology on Twitter |
| Q89561133 | The FDA authorization of direct-to-consumer genetic testing for three BRCA1/2 pathogenic variants: a twitter analysis of the public's response |
| Q58511685 | The Finnish Twitter war: the Winter War experienced through the #sota39 project and its implications for historiography |
| Q96937737 | The Flouting Maxim on Twitter Influencers’ Tweets |
| Q104900176 | The Geotemporal Demographics of Twitter Usage |
| Q51433531 | The Impact and Implications of Twitter for Cardiovascular Medicine. |
| Q38879166 | The Impact of Twitter and Facebook on Nursing Practice and Education: A Systematic Review of the Literature |
| Q98187529 | The Impact of Twitter: Why Should You Get Involved, and Tips and Tricks to Get Started |
| Q59201122 | The Influence of Features and Demographics on the Perception of Twitter as a Serendipitous Environment |
| Q60702617 | The Influence of Twitter on the Academic Environment |
| Q126364196 | The Interaction between Human Demand and Urban Greenspace Supply for Promoting Positive Emotions with Sentiment Analysis from Twitter |
| Q45071615 | The Journal's website: home tweet home |
| Q36907123 | The Measles Vaccination Narrative in Twitter: A Quantitative Analysis. |
| Q92628962 | The Needs of Women Treated for Ovarian Cancer: Results From a #gyncsm Twitter Chat |
| Q56887937 | The Number and Characteristics of Newspaper and Twitter Reports on Suicides and Road Traffic Deaths in Young People |
| Q59885974 | The Personal in the Political on Twitter: Towards a Typology of Politicians’ Personalized Tweeting Behaviours |
| Q57740903 | The Politics of Twitter Data |
| Q28114662 | The Potential of Social Media and Internet-Based Data in Preventing and Fighting Infectious Diseases: From Internet to Twitter |
| Q31036413 | The Potential of Twitter as a Data Source for Patient Safety |
| Q56886765 | The Pragmatics of Political Messages in Twitter Communication |
| Q53175419 | The Relationship Between Class Size and Active Twitter Participation in the Engineering Classroom |
| Q94517595 | The Relationship Between Suicide-Related Twitter Events and Suicides in Ontario From 2015 to 2016 |
| Q48059298 | The Response in Twitter to an Assisted Suicide in a Television Soap Opera |
| Q56286916 | The Rise of Twitter in the Political Campaign: Searching for Intermedia Agenda-Setting Effects in the Presidential Primary |
| Q28597203 | The Source and Credibility of Colorectal Cancer Information on Twitter |
| Q60516830 | The Source and Credibility of Colorectal Cancer Information on Twitter |
| Q89529136 | The Story of Goldilocks and Three Twitter's APIs: A Pilot Study on Twitter Data Sources and Disclosure |
| Q47847600 | The TRINITY study: Twitter discussion from a respirology journal club - Authors' reply |
| Q51426564 | The TRINITY study: Twitter discussion from a respirology journal club. |
| Q51416569 | The Top 100 Social Media Influencers in Plastic Surgery on Twitter: Who Should You Be Following? |
| Q41097204 | The Tweet Heard Round the (Health Professions) World |
| Q92518252 | The Twitter Adventure of #MyHealthRecord: An Analysis of Different User Groups During the Opt-Out Period |
| Q100529525 | The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets |
| Q28708941 | The Twitter of Babel: mapping world languages through microblogging platforms |
| Q99554721 | The Twitter parliamentarian database: Analyzing Twitter politics across 26 countries |
| Q58438050 | The Twitterization of News Making: Transparency and Journalistic Professionalism |
| Q110954822 | The Usage of Academic Libraries in Universities of Cooperation Council for the Arab Gulf States of the Characteristics of the Social Networks Facebook and Twitter in Marketing Information Services |
| Q28824101 | The Use of POS Sequence for Analyzing Sentence Pattern in Twitter Sentiment Analysis |
| Q91598483 | The Use of Twitter by Plastic Surgery Journals |
| Q39438270 | The Use of Twitter by Radiology Journals: An Analysis of Twitter Activity and Impact Factor |
| Q49724544 | The Use of Twitter by the Trauma and Orthopaedic Surgery Journals: Twitter Activity, Impact Factor, and Alternative Metrics |
| Q109501447 | The Vergecast - S9 leaks, Google Reply, and Twitter's war on bots |
| Q60944472 | The Virtual Toxicology Journal Club: the Dissemination and Discussion of Noteworthy Manuscripts Using Twitter |
| Q52603992 | The academic tweet: Twitter as a tool to advance academic surgery. |
| Q90928881 | The activity and discussion points of #Circumcision through Twitter; a microblogging platform |
| Q48386333 | The age of Twitter |
| Q57254119 | The age of Twitter: Donald J. Trump and the politics of debasement |
| Q24669520 | The age of the Twitter conference |
| Q101368091 | The association between Twitter content and suicide |
| Q39125854 | The association of suicide-related Twitter use with suicidal behaviour: a cross-sectional study of young internet users in Japan |
| Q59343720 | The augmented newsbeat: spatial structuring in a Twitterized news ecosystem |
| Q97675290 | The bald emoji effect: Alopecia and twitter |
| Q60309351 | The bursty dynamics of the Twitter information network |
| Q28608186 | The care.data consensus? A qualitative analysis of opinions expressed on Twitter |
| Q38845385 | The case of #arseniclife: Blogs and Twitter in informal peer review |
| Q57739901 | The comparison of classification analyses on the public opinion focus of biofuels based on twitter |
| Q37344901 | The complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets |
| Q95649806 | The death and life of Twitter |
| Q28597810 | The early bird catches the term: combining twitter and news data for event detection and situational awareness |
| Q50662841 | The effect of Twitter exposure on false memory formation. |
| Q56410931 | The effect of Twitter on college student engagement and grades |
| Q56902051 | The effects of generational differences on use continuance of Twitter: an investigation of digital natives and digital immigrants |
| Q57560456 | The effects of organizational Twitter interactivity on organization–public relationships |
| Q51458530 | The emerging use of Twitter by urological journals. |
| Q112888987 | The endless book - exploring the online, offline |
| Q35753921 | The gene patent controversy on Twitter: a case study of Twitter users' responses to the CHEO lawsuit against Long QT gene patents |
| Q34755498 | The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place |
| Q55447380 | The hidden Heuchera: How science Twitter uncovered a globally imperiled species in Pennsylvania, USA. |
| Q64115007 | The human geography of Twitter: Quantifying regional identity and inter-region communication in England and Wales |
| Q56517502 | The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices |
| Q62608528 | The influence of external political events on social networks: the case of the Brexit Twitter Network |
| Q59463139 | The influence of the internet for pedagogical innovation: using twitter to promote online collaborative learning |
| Q96577901 | The limited reach of fake news on Twitter during 2019 European elections |
| Q96135391 | The millennial generation plastic surgery trainees in sub-Saharan Africa and social media: A review of the application of blogs, podcasts, and twitter as web-based learning tools |
| Q59166210 | The new eye of smart city: Novel citizen Sentiment Analysis in Twitter |
| Q39235437 | The other Twitter revolution: how social media are helping to monitor the NHS reforms |
| Q35450911 | The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain |
| Q88907068 | The perception of pre- and post-natal marijuana exposure on health outcomes: A content analysis of Twitter messages |
| Q56004935 | The pragmatics of hashtags: Inference and conversational style on Twitter |
| Q58899923 | The presence of academic journals on Twitter and its relationship with dissemination (tweets) and research impact (citations) |
| Q39234431 | The professional Twitter account: creation, proper maintenance, and continuous successful operation |
| Q92132378 | The professional use of Twitter to create a global pediatric intensive care community |
| Q92130370 | The promotion of policy changes restricting access to codeine medicines on Twitter: what do national pain organizations say? |
| Q57407734 | The rate of reply and nature of responses to suicide-related posts on Twitter |
| Q34658479 | The reliability of tweets as a supplementary method of seasonal influenza surveillance |
| Q103020771 | The rise and fall of the COVID-19 aerosol box through the lens of Twitter |
| Q56403340 | The role of Twitter in the life cycle of a scientific publication |
| Q106987927 | The role of Twitter in the life cycle of a scientific publication |
| Q59343697 | The role of events in ICT adoption: same-sex marriage and Twitter |
| Q62957771 | The social amplification of risk on Twitter: the case of ash dieback disease in the United Kingdom |
| Q91869911 | The social dynamics of lung cancer talk on Twitter, Facebook and Macmillan.org.uk |
| Q106240118 | The social media election agenda: Issue salience on Twitter during the European and Swedish 2014 elections |
| Q51476926 | The social pharmacist: tweeting and posting the way to success. |
| Q50197138 | The surgeon and social media: Twitter as a tool for practicing surgeons |
| Q45077276 | The third wheel: the impact of Twitter use on relationship infidelity and divorce |
| Q30370677 | The times they are a-changin': academia, social media and the JGIM Twitter Journal Club. |
| Q91576873 | The twitter pandemic: The critical role of twitter in the dissemination of medical information and misinformation during the COVID-19 Pandemic |
| Q91595227 | The twitter warrior |
| Q38029426 | The unbearable emptiness of tweeting-About journal articles |
| Q47447844 | The use and impact of Twitter at medical conferences: Best practices and Twitter etiquette |
| Q92014766 | The use of Twitter and Zoom videoconferencing in healthcare professions seminar course benefits students at a commuter college |
| Q104136629 | The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic |
| Q46057571 | The use of Twitter to facilitate engagement and reflection in a constructionist learning environment |
| Q45949030 | The use of Twitter to facilitate sharing of clinical expertise in urology. |
| Q30402702 | The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic |
| Q88736229 | The utility of Twitter as a tool for increasing reach of research on sexual violence |
| Q55296737 | The ‘who’ and ‘what’ of #diabetes on Twitter. |
| Q87813732 | Thematic labeling of Twitter accounts using DBpedia properties |
| Q106224981 | This COVID-vaccine designer is tackling vaccine hesitancy — in churches and on Twitter |
| Q92166970 | This is not a drill: Anxiety on Twitter following the 2018 Hawaii false missile alert |
| Q92513455 | This month on Twitter |
| Q92599317 | This month on Twitter |
| Q92633667 | This month on Twitter |
| Q92872991 | This month on Twitter |
| Q93026815 | This month on Twitter |
| Q93256094 | This month on Twitter |
| Q91510141 | This month on Twitter |
| Q91871627 | This month on Twitter |
| Q91961512 | This month on Twitter |
| Q90026764 | This month on Twitter |
| Q90430485 | This month on Twitter |
| Q90433896 | This month on Twitter |
| Q90873291 | This month on Twitter |
| Q95939656 | This month on Twitter |
| Q96352250 | This month on Twitter |
| Q98199325 | This month on Twitter |
| Q101578610 | This month on Twitter |
| Q104100085 | This month on Twitter |
| Q50895048 | Thoracic Surgery Social Media Network: Bringing Thoracic Surgery Scholarship to Twitter. |
| Q50900541 | Thoracic Surgery Social Media Network: Bringing thoracic surgery scholarship to Twitter. |
| Q26796214 | Thou shalt not tweet unprofessionally: an appreciative inquiry into the professional use of social media |
| Q105841391 | Threats to Central Bank Independence: High-Frequency Identification with Twitter |
| Q58617849 | Ties, Likes, and Tweets: Using Strong and Weak Ties to Explain Differences in Protest Participation Across Facebook and Twitter Use |
| Q57821457 | Time to Get a @Handle on Twitter and Cardiovascular Medicine |
| Q51477563 | Time to tweet? |
| Q90838259 | To Tweet or Not to Tweet? A Look at Radiology Societies' Use of Twitter |
| Q40789079 | To Twitter to Woo: Harnessing the power of social media (SoMe) in nurse education to enhance the student's experience |
| Q35687312 | To tweet or not to tweet about schizophrenia systematic reviews (TweetSz): study protocol for a randomised controlled trial |
| Q90744893 | To tweet or not to tweet, that is the question: A randomized trial of Twitter effects in medical education |
| Q48066065 | To tweet or not to tweet. |
| Q56873509 | To tweet or not to tweet: Exploring the determinants of early adoption of Twitter by House members in the 111th Congress |
| Q58831361 | To tweet or not to tweet: Student perceptions of the use of Twitter on an undergraduate degree course |
| Q44094479 | To tweet or not to tweet? Nurses, social media, and patient care |
| Q100407098 | To tweet or not to tweet? Students' perceptions of twitter use in a D1 course |
| Q51470213 | To tweet or to retweet? That is the question for health professionals on twitter. |
| Q37694094 | To tweet, or not to tweet: gender differences and potential positive and negative health outcomes of adolescents' social internet use. |
| Q98897570 | To twitter or not to twitter? [Reflections] |
| Q96231701 | To what extent if any has Twitter disrupted hierarchies in forensic pathology? |
| Q92531756 | Tobacco Use Behaviors, Attitudes, and Demographic Characteristics of Tobacco Opinion Leaders and Their Followers: Twitter Analysis |
| Q39539745 | Tobacco control advocacy in the age of social media: using Facebook, Twitter and change |
| Q33843909 | Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter. |
| Q59256803 | Tools and methods for capturing Twitter data during natural disasters |
| Q98173759 | Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day |
| Q59559223 | Topic-Specific Stylistic Variations for Opinion Retrieval on Twitter |
| Q63986306 | TopicSketch: Real-Time Bursty Topic Detection from Twitter |
| Q93012503 | Topology comparison of Twitter diffusion networks effectively reveals misleading information |
| Q112465682 | Tourism boycott calls by Twitter users due to concerns for the welfare of animals |
| Q59471340 | Touristic site attractiveness seen through Twitter |
| Q57171399 | Toward Real-Time Infoveillance of Twitter Health Messages |
| Q56700276 | Toward a Description of African American Vernacular English Dialect Regions Using “Black Twitter” |
| Q28068179 | Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds |
| Q58058691 | Toward an enriched understanding of factors influencing Filipino behavior during elections through the analysis of Twitter data |
| Q36971511 | Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter. |
| Q96024642 | Towards Automatic Bot Detection in Twitter for Health-related Tasks |
| Q92778685 | Towards Automating Location-Specific Opioid Toxicosurveillance from Twitter via Data Science Methods |
| Q89116722 | Towards Large-scale Twitter Mining for Drug-related Adverse Events |
| Q87813135 | Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags |
| Q62708911 | Towards Named Entity Recognition Method for Microtexts in Online Social Networks: A Case Study of Twitter |
| Q59256758 | Towards a methodology for examining Twitter follower accession |
| Q92216416 | Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science |
| Q59478617 | Towards a standard sampling methodology on online social networks: collecting global trends on Twitter. |
| Q47097082 | Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users' Views, Online Context and Algorithmic Estimation |
| Q59445423 | Towards automated real-time detection of misinformation on Twitter |
| Q90471778 | Towards scaling Twitter for digital epidemiology of birth defects |
| Q116616384 | Tracing multisensory food experiences on Twitter |
| Q112954721 | Tracing the lines of race & health online: A mixed-methods exploration of the African-American HIV/AIDS movement on Twitter |
| Q59635341 | Tracking Dengue Epidemics Using Twitter Content Classification and Topic Modelling |
| Q97428487 | Tracking Mental Health and Symptom Mentions on Twitter During COVID-19 |
| Q28607091 | Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation |
| Q46429869 | Tracking suicide risk factors through Twitter in the US. |
| Q57815684 | Tracking the Werther Effect on social media: Emotional responses to prominent suicide deaths on twitter and subsequent increases in suicide |
| Q35921269 | Tracking the release of IPCC AR5 on Twitter: Users, comments, and sources following the release of the Working Group I Summary for Policymakers |
| Q35811690 | Trading twitter: Amateur recorders and economies of scientific exchange at the Cornell Library of Natural Sounds |
| Q100312560 | Transcript of WORLD NEUROSURGERY Twitter Chat with Walter Jean, M.D., hosted by Aliyu Ndajiwo, M.D., May 30, 2020 at 20:00 GMT |
| Q40674838 | Translating evidence to practice in the health professions: a randomized trial of Twitter vs Facebook. |
| Q37413239 | Transmissibility of the Campaign for Colorectal Cancer Awareness in Korea Among Twitter Users |
| Q104555900 | Trauma, Social Media, and #WhyIDidntReport: An Analysis of Twitter Posts About Reluctance to Report Sexual Assault |
| Q61660939 | Trending Pattern Analysis of Twitter Using Spark Streaming |
| Q106629032 | Trending Topics: A Cultural Analysis of Being Mary Jane and Black Women's Engagement on Twitter |
| Q56220125 | Trending Twitter topics in English: An international comparison |
| Q57806120 | Trends and Predictors of Retweets in Free Open Access Medical Education (#FOAMed) on Twitter (2013-7) |
| Q56004934 | Trends in Twitter Hashtag Applications: Design Features for Value-Added Dimensions to Future Library Catalogues |
| Q49916612 | Trends in Twitter use during the Annual Meeting of the Spanish Society of Allergology and Clinical Immunology (2013-2016). |
| Q41790212 | Trends in twitter use by physicians at the american society of clinical oncology annual meeting, 2010 and 2011. |
| Q56773324 | Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter |
| Q56911251 | TrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter |
| Q59663947 | Trump typhoon: A rhetorical analysis of the Donald's Twitter feed |
| Q89507339 | Trust makes Twitter plea for off-duty staff to help in A&E |
| Q118248088 | Tucker Carlson to Launch New Show on Twitter |
| Q121749186 | Tucker Carlson’s Trump Interview Showcases Potential of Tie-Up With Elon Musk’s X |
| Q35575679 | Tumor twitter: cellular communication in the breast cancer stem cell niche |
| Q36795041 | Tweaking and tweeting: exploring Twitter for nonmedical use of a psychostimulant drug (Adderall) among college students |
| Q124812926 | Tweeki: Linking Named Entities on Twitter to a Knowledge Graph |
| Q32956471 | Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter |
| Q62490210 | Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations |
| Q38883403 | Tweet Now, See You In the ED Later? Examining the Association Between Alcohol-related Tweets and Emergency Care Visits |
| Q34411878 | Tweet content related to sexually transmitted diseases: no joking matter |
| Q37740783 | Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages |
| Q35650099 | Tweet for health: using an online social network to examine temporal trends in weight loss-related posts |
| Q43379627 | Tweet for the cure: A snapshot of Twitter usage by 3 U.S. oncologic professional societies |
| Q31040874 | Tweet if you want to be sustainable: a thematic analysis of a Twitter chat to discuss sustainability in nurse education |
| Q48105019 | Tweet reach: a research protocol for using Twitter to increase information exchange in people with communication disabilities |
| Q51795397 | Tweet success? Scientific communication correlates with increased citations in Ecology and Conservation. |
| Q42412727 | Tweet this #toxicology2.0. |
| Q56287017 | Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others |
| Q47885208 | Tweet this: how advocacy for breast and prostate cancers stacks up on social media. |
| Q98280516 | Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter |
| Q51015370 | Tweet, post, share--a new school of health communication. |
| Q96136472 | Tweet, tweet |
| Q57435072 | Tweet, tweet, tweet: A content analysis of nonprofit organizations’ Twitter updates |
| Q40254768 | Tweeting About Prostate and Testicular Cancers: Do Twitter Conversations and the 2013 Movember Canada Campaign Objectives Align? |
| Q50929915 | Tweeting About Prostate and Testicular Cancers: What Are Individuals Saying in Their Discussions About the 2013 Movember Canada Campaign? |
| Q47999873 | Tweeting Creativity |
| Q46254006 | Tweeting Fever: Are Tweet Extracts a Valid Surrogate Data Source for Dengue Fever? |
| Q30488261 | Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber? |
| Q57925235 | Tweeting Government: A Case of Australian Government Use of Twitter |
| Q46103633 | Tweeting PP: an analysis of the 2015-2016 Planned Parenthood controversy on Twitter |
| Q47634677 | Tweeting Social Support Messages After a Non-Celebrity's Death: The Case of the Philippines' #Fallen44. |
| Q35971671 | Tweeting Supertyphoon Haiyan: Evolving Functions of Twitter during and after a Disaster Event. |
| Q35601598 | Tweeting about pain: comparing self-reported toothache experiences with those of backaches, earaches and headaches |
| Q37691275 | Tweeting about physical activity: can tweeting the walk help keeping the walk? |
| Q47751738 | Tweeting about sexism: The well-being benefits of a social media collective action |
| Q37715690 | Tweeting about testing: do low-income, parenting adolescents and young adults use new media technologies to communicate about sexual health? |
| Q51466469 | Tweeting all surgeons: update your facebook status, enhance your reputation, and "pin" your practice on the wild, wild web. |
| Q55399124 | Tweeting and Eating: The Effect of Links and Likes on Food-Hypersensitive Consumers' Perceptions of Tweets. |
| Q38555985 | Tweeting and Treating: How Hospitals Use Twitter to Improve Care |
| Q50201018 | Tweeting and rule breaking at conferences |
| Q51458546 | Tweeting and texting during disasters. |
| Q50246765 | Tweeting as Health Communication: Health Organizations' Use of Twitter for Health Promotion and Public Engagement |
| Q31031876 | Tweeting back: predicting new cases of back pain with mass social media data |
| Q46772231 | Tweeting badges: user motivations for displaying achievement in publicly networked environments |
| Q56567815 | Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature |
| Q47106502 | Tweeting birds: online mentions predict future citations in ornithology |
| Q38680337 | Tweeting celebrity suicides: Users' reaction to prominent suicide deaths on Twitter and subsequent increases in actual suicides |
| Q51471510 | Tweeting does not preclude the ability to communicate. |
| Q43662597 | Tweeting during conferences: educational or just another distraction? |
| Q51417306 | Tweeting for Nutrition: Feasibility and Efficacy Outcomes of a 6-Week Social Media-Based Nutrition Education Intervention for Student-Athletes. |
| Q34411855 | Tweeting for and against public health policy: response to the Chicago Department of Public Health's electronic cigarette Twitter campaign. |
| Q56593155 | Tweeting for learning: A critical analysis of research on microblogging in education published in 2008-2011 |
| Q101577650 | Tweeting from the Bench: Twitter and the Physician-Scientist Benefits and Challenges |
| Q38821091 | Tweeting influenza vaccine to cardiovascular health community |
| Q110946740 | Tweeting into the void?: creating a UK library Twitter list and analyzing best practice – successes and myths |
| Q34430097 | Tweeting it off: characteristics of adults who tweet about a weight loss attempt |
| Q38765938 | Tweeting links to Cochrane Schizophrenia Group reviews: a randomised controlled trial |
| Q45992495 | Tweeting links to cochrane schizophrenia group reviews: a randomised controlled trial. |
| Q56593106 | Tweeting nano: how public discourses about nanotechnology develop in social media environments |
| Q31146934 | Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses. |
| Q51467034 | Tweeting our way to cardiovascular health. |
| Q48818377 | Tweeting science and ethics: social media as a tool for constructive public engagement. |
| Q92494512 | Tweeting the Headache Meetings: Cross-Sectional Analysis of Twitter Activity Surrounding American Headache Society Conferences |
| Q50197989 | Tweeting the Meeting: Twitter Use at The American Society of Breast Surgeons Annual Meeting 2013-2016. |
| Q36616680 | Tweeting the meeting: A comparative analysis of an Australian emergency medicine conference over four years |
| Q90020180 | Tweeting the meeting: Quantitative and qualitative twitter activity during the 38th ESSO conference |
| Q90684032 | Tweeting the meeting: an analysis of Twitter activity at the Royal Australasian College of Surgeons Annual Scientific Congress from 2015 to 2018 |
| Q28727521 | Tweeting the meeting: an in-depth analysis of Twitter activity at Kidney Week 2011 |
| Q39673697 | Tweeting to Health: A Novel mHealth Intervention Using Fitbits and Twitter to Foster Healthy Lifestyles |
| Q59256736 | Tweeting to save the furniture: the 2013 Australian election campaign on Twitter |
| Q51415775 | Tweeting weight loss: A comparison of #thinspiration and #fitspiration communities on Twitter. |
| Q51464578 | Tweeting, blogging and Facebook: should vets be more involved? |
| Q51476886 | Tweeting, posting, and yammering: the role of social media in the OR. |
| Q100674101 | Tweetopoiesis: A peripheral review of Twitter for hematologists |
| Q100270274 | Tweets That Resonate: Information Flows and the Growth of Twitter's Universal Basic Income Discussion Space |
| Q34238749 | Tweets about hospital quality: a mixed methods study |
| Q57768393 | Tweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter |
| Q45862102 | Tweets from the emergency department |
| Q27339389 | Tweets from the forest: using Twitter to increase student engagement in an undergraduate field biology course |
| Q35228682 | Tweets on the road |
| Q57898093 | Tweets vs. Mendeley readers: How do these two social media metrics differ? |
| Q35752750 | Tweets, Apps, and Pods: Results of the 6-month Mobile Pounds Off Digitally (Mobile POD) randomized weight-loss intervention among adults |
| Q38642105 | Tweets, Gangs, and Guns: A Snapshot of Gang Communications in Detroit |
| Q55924287 | Tweets, Tweeps, and Signifyin’ |
| Q51468707 | Tweets, friends, and links: the use of social media by NICU health care providers. |
| Q54813562 | TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets |
| Q93183447 | Twelve tips for developing and implementing a medical education Twitter chat |
| Q38069213 | Twelve tips for using Twitter as a learning tool in medical education. |
| Q33708887 | TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations. |
| Q51417021 | Twitter - @WorldNeurosurg. |
| Q47608499 | Twitter 101 and beyond: introduction to social media platforms available to practicing hematologist/oncologists |
| Q111727046 | Twitter Accepts Elon Musk’s Offer to Buy Company in $44 Billion Deal |
| Q47798896 | Twitter Activity Associated With U.S. News and World Report Reputation Scores for Urology Departments |
| Q91396879 | Twitter Activity Is Associated With a Higher Research Citation Index for Academic Thoracic Surgeons |
| Q57778702 | Twitter Adoption and Use by SMEs: An Empirical Study |
| Q91354156 | Twitter Analysis of #OpenAPS DIY Artificial Pancreas Technology Use Suggests Improved A1C and Quality of Life |
| Q90035804 | Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study |
| Q45341568 | Twitter Article Mentions and Citations: An Exploratory Analysis of Publications in the American Journal of Psychiatry |
| Q93190753 | Twitter As a Noninvasive Bio-Marker for Trends in Liver Disease |
| Q115524968 | Twitter Becomes Stage for China Protests Despite Ban by Beijing |
| Q17213682 | Twitter Bungaku-shō |
| Q57782222 | Twitter Campaigns Around the Fifth IPCC Report |
| Q59124496 | Twitter Can Predict Your Next Place of Visit |
| Q57456051 | Twitter Chat Dates - time 7-8 p.m |
| Q100406486 | Twitter Communication During an Outbreak of Hepatitis A in San Diego, 2016-2018 |
| Q60231234 | Twitter Content Categorisation: A Public Library Perspective |
| Q66679204 | Twitter Conversations and English News Media Reports on Poliomyelitis in Five Different Countries, January 2014 to April 2015 |
| Q57819437 | Twitter Discussions from a Respirology Journal Club: Role of Positive Airway Pressure Therapy for Obstructive Sleep Apnea in Patients With Stroke |
| Q100664846 | Twitter Fingers and Echo Chambers: Exploring Expressions and Experiences of Online Racism Using Twitter |
| Q100730953 | Twitter Hashtag and Online Engagement During Surgical Society Meetings Over a 5-Year Period |
| Q90916481 | Twitter Hashtags for Anesthesiologists: Building Global Communities |
| Q61444170 | Twitter Health Surveillance (THS) System |
| Q30385265 | Twitter Influence on UK Vaccination and Antiviral Uptake during the 2009 H1N1 Pandemic. |
| Q37636169 | Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates |
| Q91702327 | Twitter Journal Club in Medical Radiation Science |
| Q90423615 | Twitter Journal Clubs: Medical Education in the Era of Social Media |
| Q35661030 | Twitter K-H networks in action: Advancing biomedical literature for drug search |
| Q28607537 | Twitter Language Use Reflects Psychological Differences between Democrats and Republicans |
| Q47585369 | Twitter Me This-Can Social Media Revolutionize Academic Medicine? |
| Q57065795 | Twitter Mentions and Academic Citations in the Urology Literature |
| Q113915775 | Twitter Mood as a Stock Market Predictor |
| Q28846031 | Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph |
| Q31141953 | Twitter Predicts Citation Rates of Ecological Research. |
| Q98897300 | Twitter Privacy Concern: The Effect of Twitter Profile |
| Q28531469 | Twitter Psychosis |
| Q57843136 | Twitter Sentiment Mining: A Multi Domain Analysis |
| Q36442237 | Twitter Social Media is an Effective Tool for Breast Cancer Patient Education and Support: Patient-Reported Outcomes by Survey. |
| Q37260411 | Twitter Strategies for Web-Based Surveying: Descriptive Analysis From the International Concussion Study. |
| Q45199594 | Twitter Streams Fuel Big Data Approaches to Health Forecasting |
| Q90316143 | Twitter Tailwinds - Little Capsules of Gratitude |
| Q57721636 | Twitter Topic Fuzzy Fingerprints |
| Q92114015 | Twitter Usage Using Common Reference to Tinnitus |
| Q97421112 | Twitter Use Among Departments of Surgery With General Surgery Residency Programs |
| Q62634107 | Twitter Use During a Weather Event: Comparing Content Associated with Localized and Nonlocalized Hashtags |
| Q58523937 | Twitter Use in Libraries: An Exploratory Analysis |
| Q47553073 | Twitter Use in the Hematopoietic Cell Transplantation Community. |
| Q57539008 | Twitter User Clustering Based on Their Preferences and the Louvain Algorithm |
| Q47721438 | Twitter accounts followed by Congressional health staff |
| Q96829151 | Twitter activity is associated with a higher h-index in academic thoracic surgery |
| Q57896696 | Twitter als Wahlkampfmedium |
| Q39416300 | Twitter analysis of California's failed campaign to raise the state's tobacco tax by popular vote in 2012. |
| Q39036488 | Twitter analysis of the orthodontic patient experience with braces vs Invisalign |
| Q51435921 | Twitter and Cardiovascular Disease: Useful Chirps or Noisy Chatter? |
| Q58528955 | Twitter and Facebook for User Collection Requests |
| Q106240115 | Twitter and Facebook: Populists’ double-barreled gun? |
| Q98614870 | Twitter and Gadolinium Retention: Patient-Reported Perceptions of Gadolinium-Based Contrast Agents |
| Q35974735 | Twitter and Global Political Crises: Cycles of Insecurity in #PrayforParis and #PrayforSyria |
| Q90511762 | Twitter and Haemophilia |
| Q38301217 | Twitter and Health Science Research. |
| Q56461326 | Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study |
| Q51420029 | Twitter and Non-Elites: Interpreting Power Dynamics in the Life Story of the (#)BRCA Twitter Stream. |
| Q42370442 | Twitter and Public Health (Part 1): How Individual Public Health Professionals Use Twitter for Professional Development. |
| Q42677087 | Twitter and Public Health (Part 2): Qualitative Analysis of How Individual Health Professionals Outside Organizations Use Microblogging to Promote and Disseminate Health-Related Information |
| Q58275554 | Twitter and Public Scholarship |
| Q59256762 | Twitter and Society |
| Q58899893 | Twitter and TV events: an exploration of how to use social media for student-led research |
| Q90647252 | Twitter and academic Urology in the United States and Canada: a comprehensive assessment of the Twitterverse in 2019 |
| Q91067663 | Twitter and brachytherapy: An analysis of "tweets" over six years by patients and health care professionals |
| Q53541561 | Twitter and climate change |
| Q58163085 | Twitter and its Usage for Dialogic Stakeholder Communication by MNCs and NGOs |
| Q46991431 | Twitter and mobile technology as diagnostic aids in the Democratic Republic of Congo. |
| Q51461397 | Twitter and nursing research: how diffusion of innovation theory can help uptake. |
| Q38172780 | Twitter and public health |
| Q44896260 | Twitter and resuscitation education: is this the future? |
| Q61440531 | Twitter and teargas: the power and fragility of networks protest, by Zeynep Tufekci |
| Q56120053 | Twitter and the 2011 riots in England |
| Q104510817 | Twitter and the Credibility of Disseminated Medical Information During the COVID-19 Pandemic |
| Q58498829 | Twitter and the Library: Thoughts on the Syndicated Lifestyle |
| Q51467064 | Twitter and the health reforms in the English National Health Service. |
| Q96764585 | Twitter and the pursuit of global health-care during COVID-19 pandemic |
| Q100455342 | Twitter and the pursuit of global health-care during COVID-19 pandemic |
| Q41544451 | Twitter and traumatic brain injury: A content and sentiment analysis of tweets pertaining to sport-related brain injury |
| Q110946515 | Twitter as a Marketing Tool for Libraries |
| Q57821456 | Twitter as a New Core Competency for Cardiologists |
| Q36146709 | Twitter as a Potential Data Source for Cardiovascular Disease Research. |
| Q30203093 | Twitter as a Potential Disaster Risk Reduction Tool. Part I: Introduction, Terminology, Research and Operational Applications |
| Q30203087 | Twitter as a Potential Disaster Risk Reduction Tool. Part II: Descriptive Analysis of Identified Twitter Activity during the 2013 Hattiesburg F4 Tornado |
| Q30203084 | Twitter as a Potential Disaster Risk Reduction Tool. Part III: Evaluating Variables that Promoted Regional Twitter Use for At-risk Populations During the 2013 Hattiesburg F4 Tornado |
| Q30203080 | Twitter as a Potential Disaster Risk Reduction Tool. Part IV: Competency-based Education and Training Guidelines to Promote Community Resiliency |
| Q53538278 | Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons |
| Q48615512 | Twitter as a Tool for Forecasting Stock Market Movements: A Short-window Event Study |
| Q54485631 | Twitter as a Tool for Health Research: A Systematic Review. |
| Q37468457 | Twitter as a Tool for Teaching and Communicating Microbiology: The #microMOOCSEM Initiative |
| Q39690712 | Twitter as a communication tool for orthopedic surgery |
| Q59256745 | Twitter as a first draft of the present |
| Q47686668 | Twitter as a means to study temporal behaviour |
| Q88457173 | Twitter as a place where people meet to make suicide pacts |
| Q93162581 | Twitter as a powerful tool for communication between pain physicians during COVID-19 pandemic |
| Q96161521 | Twitter as a promising microblogging application for psychiatric consultation - Understanding the predictors of use, satisfaction and e-health literacy |
| Q55509340 | Twitter as a reporting tool for breaking news |
| Q34818310 | Twitter as a sentinel in emergency situations: lessons from the Boston marathon explosions |
| Q89969438 | Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy |
| Q43758428 | Twitter as a source of vaccination information: content drivers and what they are saying |
| Q51435851 | Twitter as a survey tool for real-time unbiased snapshots of personal sentiment in population level. |
| Q56399771 | Twitter as a teaching practice to enhance active and informal learning in higher education: The case of sustainable tweets |
| Q48055716 | Twitter as a tool for communication and knowledge exchange in academic medicine: A guide for skeptics and novices |
| Q56410876 | Twitter as a tool for conservation education and outreach: what scientific conferences can do to promote live-tweeting |
| Q57614777 | Twitter as a tool for delivering improved quality of life for people with chronic conditions |
| Q38049418 | Twitter as a tool for ophthalmologists |
| Q89594967 | Twitter as a tool for social movement: An analysis of feminist activism on social media communities |
| Q39201575 | Twitter as a tool to enhance student engagement during an interprofessional patient safety course |
| Q51470638 | Twitter as a tool to promote composite tissue allotransplants. |
| Q51477169 | Twitter as an educational tool. |
| Q38910345 | Twitter as an in-class backchannel tool in a large required pharmacy course |
| Q51469052 | Twitter at a nursing conference. |
| Q46676043 | Twitter at the 2014 and 2015 Annual Meetings of the American Academy of Ophthalmology. |
| Q64975267 | Twitter au service des infirmières en oncologie? |
| Q50140212 | Twitter big data and infectious disease conferences |
| Q58971549 | Twitter bootstrap and AngularJS: Frontend frameworks to expedite science gateway development |
| Q57898008 | Twitter bot surveys |
| Q74438333 | Twitter buzz about papers does not mean citations later |
| Q51452238 | Twitter campaign highlights top women in global health. |
| Q50194788 | Twitter can enhance the medical conference experience |
| Q115787588 | Twitter changed science — what happens now it’s in turmoil? |
| Q35014822 | Twitter chatter about marijuana |
| Q37165447 | Twitter classification model: the ABC of two million fitness tweets |
| Q47145230 | Twitter content classification |
| Q59460127 | Twitter data analysis as contribution to strategic foresight-The case of the EU Research Project “Foresight and Modelling for European Health Policy and Regulations” (FRESHER) |
| Q58899746 | Twitter data analytics – or: the pleasures and perils of studying Twitter |
| Q59256764 | Twitter data: What do they represent? |
| Q94960112 | Twitter debate: controversies in functional gastrointestinal disorders |
| Q38638448 | Twitter discussions about the predicaments of robots in geriatric nursing-Forecast of Nursing Robotics in Aged Care |
| Q101061483 | Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach |
| Q47979422 | Twitter discussions from a respirology journal club |
| Q51450890 | Twitter discussions from a respirology journal club - authors' reply. |
| Q109570231 | Twitter earthquake detection: earthquake monitoring in a social world |
| Q29619470 | Twitter evolution: converging mechanisms in birdsong and human speech |
| Q41295098 | Twitter expands the reach and engagement of a national scientific meeting: the Irish Society of Urology. |
| Q99616926 | Twitter followers' details reveal the power of a paper |
| Q51425364 | Twitter for doctors - promising educational occurrences. |
| Q42702533 | Twitter for neurosurgeons |
| Q51471385 | Twitter for plastic surgeons. |
| Q51476970 | Twitter for scientists. |
| Q38778892 | Twitter for travel medicine providers |
| Q98897811 | Twitter friend repertoires: Introducing a methodology to assess patterns of information management on Twitter |
| Q91303136 | Twitter helped me decide that I'm not for resuscitation |
| Q89715493 | Twitter impact on a community trauma: An examination of who, what, and why it radiated |
| Q34524932 | Twitter improves influenza forecasting |
| Q57683486 | Twitter in Special Libraries: A Distributed Social Media Strategy |
| Q50772020 | Twitter in academic medicine. |
| Q51470402 | Twitter in emergencies. Follow our roadmap. |
| Q93004127 | Twitter in the Veterinary Diagnostic Imaging Classroom: Examination Outcomes and Student Views |
| Q51474162 | Twitter in the classroom: twenty-first century flash cards. |
| Q30208895 | Twitter in the cross fire--the use of social media in the Westgate Mall terror attack in Kenya |
| Q40248231 | Twitter in urology and other surgical specialties at global conferences |
| Q47202339 | Twitter journal clubs and continuing professional development: An analysis of a #MedRadJClub tweet chat |
| Q91562362 | Twitter message types, health beliefs, and vaccine attitudes during the 2015 measles outbreak in California |
| Q90657763 | Twitter metrics complement traditional conference evaluations to evaluate knowledge translation at a National Emergency Medicine Conference |
| Q40176549 | Twitter mining for fine-grained syndromic surveillance |
| Q91989597 | Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance |
| Q43662959 | Twitter mood predicts the stock market |
| Q95421490 | Twitter offers nurses opportunity for care feedback |
| Q48071329 | Twitter opens its cage. |
| Q55983242 | Twitter power: Tweets as electronic word of mouth |
| Q92041468 | Twitter promotion predicts citation rates of cardiovascular articles: a preliminary analysis from the ESC Journals Randomized Study |
| Q57210799 | Twitter reciprocal reply networks exhibit assortativity with respect to happiness |
| Q64114823 | Twitter response to the 2018 US Preventive Services Task Force guidelines on prostate cancer screening |
| Q34435780 | Twitter response to the United States Preventive Services Task Force recommendations against screening with prostate-specific antigen. |
| Q101558633 | Twitter reveals human mobility dynamics during the COVID-19 pandemic |
| Q34552055 | Twitter sentiment around the Earnings Announcement events |
| Q105531638 | Twitter sentiment as a weak signal in venture capital financing |
| Q90746515 | Twitter sentiment classification for measuring public health concerns |
| Q35223129 | Twitter sentiment predicts Affordable Care Act marketplace enrollment |
| Q98292206 | Twitter social bots: The 2019 Spanish general election data |
| Q51458656 | Twitter storm forces Chimerix's hand in compassionate use request. |
| Q100420327 | Twitter talk on COVID-19: A temporal examination of topics, trends and sentiments |
| Q84352822 | Twitter to Transport: Geo-Spatial Sentiment Analysis of Traffic Tweets to Discover People’s Feelings for Urban Transportation Issues |
| Q92124526 | Twitter to engage, educate, and advocate for global antibiotic stewardship and antimicrobial resistance |
| Q50916217 | Twitter triumph: connect, communicate, and learn. |
| Q44480531 | Twitter tweets and twaddle: twittering at AHPA. National Health Promotion Conference |
| Q96167615 | Twitter usage about autism spectrum disorder |
| Q51467329 | Twitter usage at Clinical Congress rises markedly over two years. |
| Q44408016 | Twitter use at a family medicine conference: analyzing #STFM13. |
| Q49843352 | Twitter use at the 2016 Conference on the Science of Dissemination and Implementation in Health: analyzing #DIScience16. |
| Q61963644 | Twitter use by the U.S. Congress |
| Q51471876 | Twitter use during emergency medicine conferences. |
| Q30375952 | Twitter use in physics conferences |
| Q57092803 | Twitter use in scientific communication revealed by visualization of information spreading by influencers within half a year after the Fukushima Daiichi nuclear power plant accident |
| Q36246619 | Twitter users' reaction to a chain pharmacy's decision to end tobacco sales |
| Q47823480 | Twitter weather warnings: Communicating risk in 140 characters-the impact of imperative and declarative message style on weather risk perception and behavioral intentions. |
| Q56845533 | Twitter ye not! Health Sciences Librarians are using it /2.0 for themselves |
| Q66142047 | Twitter – en termometer på samhällstemperaturen |
| Q58506563 | Twitter's Complex Simplicity: How Small Is the Next Big Thing? |
| Q119626980 | Twitter's inability to pay its rent is more urgent than building 'Twitter 2.0' |
| Q96577356 | Twitter's saddest fortnight and bizarre quantum matter in space |
| Q51429452 | Twitter, #alternativefacts, careless whispers and Rheumatology. |
| Q91686285 | Twitter, Folklore and Evidence-Based Medicine: The Tale of Distal Radial Access |
| Q38869070 | Twitter, Millennials, and Nursing Education Research |
| Q56220127 | Twitter, MySpace, Digg |
| Q51476858 | Twitter, facebook and youtube: the TMA turns to social media to engage members. |
| Q56004839 | Twitter, information sharing and the London riots? |
| Q47409887 | Twitter, the lost years and the geeks. |
| Q50283962 | Twitter--an indispensable tool for all. |
| Q57740777 | Twitter-Archive und die Herausforderungen von »Big Social Data« für die Medien- und Kommunikationswissenschaft |
| Q45949451 | Twitter-Augmented Journal Club: Educational Engagement and Experience So Far. |
| Q28647238 | Twitter-Based Analysis of the Dynamics of Collective Attention to Political Parties |
| Q52944734 | Twitter-Based Detection of Illegal Online Sale of Prescription Opioid. |
| Q57069891 | Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study |
| Q43178792 | Twitter-Based Journal Clubs: Additional Facts and Clarifications. |
| Q96606544 | Twitter-Based Journal Clubs: Bringing Critical Appraisal to the Social Table |
| Q104068164 | Twitter-Based Social Support Added to Fitbit Self-Monitoring for Decreasing Sedentary Behavior: Protocol for a Randomized Controlled Pilot Trial With Female Patients From a Women's Heart Clinic |
| Q89534398 | Twitter-Characterized Sentiment Towards Racial/Ethnic Minorities and Cardiovascular Disease (CVD) Outcomes |
| Q36441033 | Twitter-Delivered Behavioral Weight-Loss Interventions: A Pilot Series |
| Q64117182 | Twitter-based Prostate Cancer Journal Club (#ProstateJC) Promotes Multidisciplinary Global Scientific Discussion and Research Dissemination |
| Q58236719 | Twitter-based information distribution during the 2009 Red River Valley flood threat |
| Q93254207 | Twitter-based learning for continuing medical education? |
| Q91796909 | Twitter-based measures of neighborhood sentiment as predictors of residential population health |
| Q63473960 | Twitter-derived measures of sentiment towards minorities (2015-2016) and associations with low birth weight and preterm birth in the United States |
| Q47153480 | Twitter-derived neighborhood characteristics associated with obesity and diabetes |
| Q60241638 | Twitter: A Digital Socioscope |
| Q35737871 | Twitter: A Novel Tool for Studying the Health and Social Needs of Transgender Communities |
| Q90300631 | Twitter: A Platform for Dissemination and Discussion of Scientific Papers in Radiation Oncology |
| Q50253346 | Twitter: An Application to Encourage Information Seeking Among Nursing Students |
| Q98898352 | Twitter: Social communication in the Twitter era |
| Q73478818 | Twitter: What's All the Chirping About? |
| Q94525476 | Twitter: a complementary tool to monitor seasonal influenza epidemic in France ? |
| Q31149166 | Twitter: a good place to detect health conditions |
| Q40702144 | Twitter: a viable medium for daily pain diaries in chronic orofacial pain? |
| Q51474751 | Twitter: an essential tool for every physician leader. |
| Q46522120 | Twitter: an opportunity for public health campaigns. |
| Q51457838 | Twitter: big data opportunities--response. |
| Q51457846 | Twitter: big data opportunities. |
| Q45845354 | Twitter: chat or watch? |
| Q51476686 | Twitter: consider the possibilities for continuing nursing education. |
| Q51431083 | Twitter: the new PubMed? |
| Q35848296 | Twitter=quitter? An analysis of Twitter quit smoking social networks |
| Q58169284 | TwitterNews+: A Framework for Real Time Event Detection from the Twitter Data Stream |
| Q51415059 | TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications. |
| Q36444457 | Twittering About Research: A Case Study of the World's First Twitter Poster Competition |
| Q112883757 | Twittering Libraries: How and Why New Zealand Public Libraries Use Micro-Blogging |
| Q58588221 | Twittering Pupae of Papilionid and Nymphalid Butterflies (Lepidoptera): Novel Structures and Sounds |
| Q84163305 | Twittering about anything, everything, and even health |
| Q51477493 | Twittering healthcare: social media and medicine. |
| Q47824143 | Twittering on about mental health: is it worth the effort? |
| Q84962852 | Twittering urology away |
| Q114795324 | Twitter’s Lawsuit Against Elon Musk: Five Main Takeaways |
| Q120122813 | Twitter’s New Chief Eases Into the Hot Seat |
| Q58491351 | Twitter’s diffusion in sports journalism: Role models, laggards and followers of the social media innovation |
| Q50658027 | Twitter™ as a study prompt: engaging adult learners on the go. |
| Q58921915 | Two Applications of Clustering Techniques to Twitter: Community Detection and Issue Extraction |
| Q38839519 | Two Studies on Twitter Networks and Tweet Content in Relation to Amyotrophic Lateral Sclerosis (ALS): Conversation, Information, and 'Diary of a Daily Life'. |
| Q41860276 | Two types of well followed users in the followership networks of Twitter. |
| Q50877655 | Type 1 Diabetes in Twitter: Who All Listen To? |
| Q45252741 | US Presidential Election 2012 Prediction using Census Corrected Twitter Model |
| Q106421411 | USA’s udenrigspolitik under Trump – på afveje eller bare på Twitter? |
| Q44977171 | USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders |
| Q44977353 | USFD: Twitter NER with Drift Compensation and Linked Data |
| Q57493409 | Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study |
| Q90844920 | Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics |
| Q90236801 | Under his thumb the effect of president Donald Trump's Twitter messages on the US stock market |
| Q59808860 | Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach |
| Q37700028 | Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study |
| Q96641024 | Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study |
| Q35686091 | Understanding Human Mobility from Twitter. |
| Q92780110 | Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter |
| Q57813230 | Understanding Popularity, Reputation, and Social Influence in the Twitter Society |
| Q98215957 | Understanding Public Perception of COVID-19 Social Distancing on Twitter |
| Q51416665 | Understanding health food messages on Twitter for health literacy promotion. |
| Q103827016 | Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams |
| Q92760198 | Understanding public opinion to the introduction of minimum unit pricing in Scotland: a qualitative study using Twitter |
| Q63440010 | Understanding student use of twitter and online forums in higher education |
| Q62611452 | Understanding the Twitter usage of humanities and social sciences academic journals |
| Q57411872 | Understanding the spatio-temporal characteristics of Twitter data with geotagged and non-geotagged content: two case studies with the topic of flu and Ted (movie) |
| Q38546418 | Unveiling self-harm behaviour: what can social media site Twitter tell us about self-harm? A qualitative exploration. |
| Q38925211 | Upgrading a Social Media Strategy to Increase Twitter Engagement During the Spring Annual Meeting of the American Society of Regional Anesthesia and Pain Medicine. |
| Q90364944 | Urban isolation and daytime neighborhood social composition from Twitter data |
| Q57403548 | Us and them: identifying cyber hate on Twitter across multiple protected characteristics |
| Q56903150 | Use of Facebook, Tuenti, Twitter and Myspace among young Spanish people |
| Q98073368 | Use of Hadoop for Sentiment Analysis on Twitter’s Big Data |
| Q42976359 | Use of Moodle, ExamSoft, and Twitter in a first-year pharmacy course |
| Q38377035 | Use of Twitter Polls to Determine Public Opinion Regarding Content Presented at a Major National Specialty Society Meeting |
| Q38388759 | Use of Twitter at a major national pharmacy conference |
| Q64910455 | Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic. |
| Q93027076 | Use of Twitter in communicating living solid organ donation information to the public: An exploratory study of living donors and transplant professionals |
| Q96637569 | Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale |
| Q49923671 | Use of Twitter to Assess Viewer Reactions to the Medical Drama, Code Black. |
| Q57937242 | Use of Twitter to Educate and Engage Surgeons in Infectious Diseases and Antimicrobial Stewardship |
| Q91883309 | Use of Twitter to Promote Awareness of Familial Hypercholesterolemia |
| Q39225740 | Use of Twitter to document the 2013 Academic Surgical Congress |
| Q42839584 | Use of Twitter to encourage interaction in a multi-campus pharmacy management course |
| Q34440988 | Use of Twitter to monitor attitudes toward depression and schizophrenia: an exploratory study. |
| Q37042889 | Use of hangeul twitter to track and predict human influenza infection |
| Q36667737 | Use of new media by Turkish fans in sport communication: facebook and twitter |
| Q90030199 | Use of social media to support student nurses: an EBN Twitter chat led by Daniel Gooding and Alison Twycross |
| Q37134253 | Use of twitter among local health departments: an analysis of information sharing, engagement, and action |
| Q59474302 | Use of twitter data for waste minimisation in beef supply chain |
| Q66710943 | User Interests Identification on Twitter Using a Hierarchical Knowledge Base |
| Q87813655 | User Modeling on Twitter with WordNet Synsets and DBpedia Concepts for Personalized Recommendations |
| Q40190857 | User and Content Characteristics of Public Tweets Referencing Little Cigars |
| Q45758809 | User-Customizable Health Pattern Detector Framework: Twitter AnalysisExample. |
| Q41141725 | Users' participation and social influence during information spreading on Twitter |
| Q56287018 | Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat |
| Q35822783 | Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter). |
| Q52568775 | Using Social Media to Generate and Collect Primary Data: The #ShowsWorkplaceCompassion Twitter Research Campaign. |
| Q50639240 | Using Tweets to Facilitate the Conversation About Evidence-Based Psychiatric and Mental Health Care. |
| Q31019169 | Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study. |
| Q64057398 | Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation |
| Q61226518 | Using Twitter To Generate Signals For The Enhancement Of Syndromic Surveillance Systems: Semi-Supervised Classification For Relevance Filtering in Syndromic Surveillance |
| Q39196990 | Using Twitter and other social media platforms to provide situational awareness during an incident |
| Q50227789 | Using Twitter as a tool for CPD. |
| Q58285873 | Using Twitter data for demographic research |
| Q105031086 | Using Twitter data in urban green space research: A case study and critical evaluation |
| Q42376625 | Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing |
| Q73121909 | Using Twitter for Geolocation Purposes During the Hanse Sail 2016 in Rostock |
| Q40034205 | Using Twitter for Teaching and Learning in an Oral and Maxillofacial Radiology Course |
| Q34509383 | Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month |
| Q99584473 | Using Twitter for crisis communications in a natural disaster: Hurricane Harvey |
| Q51468680 | Using Twitter for prenatal health promotion: encouraging a multivitamin habit among college-aged females. |
| Q108170286 | Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines |
| Q56400034 | Using Twitter for teaching |
| Q42511481 | Using Twitter in Clinical Education and Practice |
| Q84415523 | Using Twitter in Higher Education in Spain and the USA |
| Q59412562 | Using Twitter sentiment and emotions analysis of Google Trends for decisions making |
| Q51426836 | Using Twitter to Amplify Your Presence During Radiology Meetings. |
| Q39018744 | Using Twitter to Assess the Public Response to the United States Preventive Services Task Force Guidelines on Lung Cancer Screening with Low Dose Chest CT. |
| Q47549631 | Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA. |
| Q64229344 | Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study |
| Q45759696 | Using Twitter to Detect and Investigate DiseaseOutbreaks |
| Q91352191 | Using Twitter to Engage Online RN-to-BSN Students in Health Care Policy |
| Q57471583 | Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study |
| Q47102452 | Using Twitter to Explore (un)Healthy Housing: Learning from the #Characterbuildings Campaign in New Zealand |
| Q30490846 | Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project |
| Q36607331 | Using Twitter to Measure Public Discussion of Diseases: A Case Study. |
| Q59212609 | Using Twitter to Mobilise Protest Action: Transnational Online Mobilisation Patterns and Action Repertoires in the Occupy Wall Street, Indignados and Aganaktismenoi Movements |
| Q56399646 | Using Twitter to Reinforce Classroom Concepts |
| Q49483174 | Using Twitter to Teach Evidence-Based Practice in Doctor of Nursing Practice Degree Program |
| Q94676303 | Using Twitter to Track Unplanned School Closures: Georgia Public Schools, 2015-17 |
| Q30253035 | Using Twitter to Understand Public Perceptions Regarding the #HPV Vaccine: Opportunities for Public Health Nurses to Engage in Social Marketing |
| Q92611006 | Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics |
| Q47556375 | Using Twitter to access the human right of communication for people who use Augmentative and Alternative Communication (AAC). |
| Q90541210 | Using Twitter to advocate for British livestock farming |
| Q64448881 | Using Twitter to assess attitudes to schizophrenia and psychosis |
| Q40829320 | Using Twitter to communicate conservation science from a professional conference. |
| Q50268390 | Using Twitter to expand the reach and engagement of allergists |
| Q94567780 | Using Twitter to improve child healthcare |
| Q48505226 | Using Twitter to increase content dissemination and control educational content with Presenter Initiated and Generated Live Educational Tweets (PIGLETs). |
| Q127043386 | Using Twitter to investigate discourse on immigration: the role of values in expressing polarized attitudes toward asylum seekers during the closure of Roxham Road |
| Q34688410 | Using Twitter to investigate opinions about multiple sclerosis treatments: a descriptive, exploratory study |
| Q57247836 | Using Twitter to investigate seasonal variation in physical activity in urban green space |
| Q60491122 | Using Twitter to learn about the autism community |
| Q43700009 | Using Twitter to measure behavior patterns |
| Q94541322 | Using Twitter to promote a youth mental health agenda |
| Q92591460 | Using Twitter to promote awareness of disabilities in medicine |
| Q35087661 | Using Twitter to promote continuous education and health research in Peru |
| Q88907830 | Using Twitter to recruit participants for health research: An example from a caregiving study |
| Q95941576 | Using Twitter to surveil the opioid epidemic in North Carolina: An exploratory study |
| Q45039075 | Using Twitter to survey alcohol use in the San Francisco Bay Area |
| Q50589178 | Using Twitter to teach problem-based learning |
| Q64433331 | Using Twitter trust network for stock market analysis |
| Q38862778 | Using Twitter™ to drive research impact: A discussion of strategies, opportunities and challenges |
| Q57721630 | Using Unstructured Profile Information for Gender Classification of Portuguese and English Twitter Users |
| Q53947101 | Using a Bayesian Method to Assess Google, Twitter, and Wikipedia for ILI Surveillance. |
| Q104467105 | Using a Twitter chat to rapidly identify barriers and policy solutions for metastatic breast cancer care: A qualitative study |
| Q102075280 | Using alt text to make science Twitter more accessible for people with visual impairments |
| Q60535966 | Using data from online social networks in conservation science: which species engage people the most on Twitter? |
| Q97092502 | Using deep learning and Twitter data to identify outpatient antibiotic misuse |
| Q57721626 | Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions |
| Q35255801 | Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm |
| Q45953409 | Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter. |
| Q21999474 | Using social media for support and feedback by mental health service users: thematic analysis of a twitter conversation |
| Q38493856 | Using social media to learn and communicate: it is not about the tweet |
| Q39319228 | Using social media to monitor mental health discussions - evidence from Twitter |
| Q91596552 | Using social media to promote academic research: Identifying the benefits of twitter for sharing academic work |
| Q35601595 | Using support vector machine ensembles for target audience classification on Twitter. |
| Q39032552 | Using twitter in health professional education: a case study |
| Q37134335 | Using twitter to examine smoking behavior and perceptions of emerging tobacco products |
| Q58164835 | Uso de Twitter ® y sus implicaciones en las reuniones y congresos de la Asociación Española de Cirujanos |
| Q33774392 | Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users |
| Q58549286 | Utilizing Twitter data for analysis of chemotherapy |
| Q56403625 | Utilizing Twitter for Concept Learning |
| Q104130578 | Utilizing a multi-class classification approach to detect therapeutic and recreational misuse of opioids on Twitter |
| Q104678870 | Utilizing deep learning and graph mining to identify drug use on Twitter data |
| Q52609188 | Vaccine Images on Twitter: Analysis of What Images are Shared. |
| Q47790943 | Vaccine opponents' use of Twitter during the 2016 US presidential election: Implications for practice and policy |
| Q31096504 | Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality |
| Q55268485 | Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump. |
| Q48247761 | Vape, quit, tweet? Electronic cigarettes and smoking cessation on Twitter |
| Q57492293 | Vaping associated with healthy food words: A content analysis of Twitter |
| Q28552584 | Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter |
| Q62634101 | Variability in Twitter Content Across the Stages of a Natural Disaster: Implications for Crisis Communication |
| Q57745547 | Venting Negative Emotions on Twitter and The Number of Followers and Followees |
| Q62031752 | Verifying information with multimedia content on twitter |
| Q104139359 | Video kills the sentiment-Exploring fans' reception of the video assistant referee in the English premier league using Twitter data |
| Q47557551 | Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter. |
| Q56349607 | Virtual Zika transmission and spread on Twitter |
| Q47970985 | Virtual attendance at an international physical activity meeting using Twitter: how can data visualisation provide a presence? |
| Q37065007 | Virtual colleagues, virtually colleagues--physicians' use of Twitter: a population-based observational study |
| Q39417788 | Virtual communication about psychiatric intensive care units: Social actor representatives claim space on Twitter |
| Q114795491 | Visualising Topical Sentiment in Twitter Streams |
| Q47946064 | Vitamin D supplementation in patients with COPD: Twitter discussions on behalf of the University of Toronto Respirology and Sleep Journal Club |
| Q47946053 | Vitamin D supplementation in patients with COPD: Twitter discussions on behalf of the University of Toronto Respirology and Sleep Journal Club--Authors' Reply |
| Q108875432 | Von Reisen und Residenzen, von Alltag und Kultur – Thomas Lang |
| Q40279111 | WA47 Tweets, hashtags and palliative care: a workshop for social media newbies looking to join the digital revolution |
| Q49152075 | Walter Benjamin and Biz Stone: the scientific paper in the Age of Twitter |
| Q56258073 | Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network |
| Q114457197 | We Are #altgov : Social Media Resistance From The Inside |
| Q41381367 | We feel: mapping emotion on Twitter |
| Q56248891 | Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate |
| Q37702990 | Weight loss support seeking on twitter: the impact of weight on follow back rates and interactions |
| Q28912412 | Weighted argumentation for analysis of discussions in twitter |
| Q57712142 | What Affects the Spiral of Silence and the Hard Core on Twitter? An Analysis of the Nuclear Power Issue in Japan |
| Q33867467 | What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention. |
| Q43376078 | What Can Tweets Tell Us About a Person's Sleep? |
| Q89878045 | What Can Twitter Tell Us about Skin Cancer Communication and Prevention on Social Media? |
| Q37681042 | What Can We Learn About Mental Health Needs From Tweets Mentioning Dementia on World Alzheimer's Day? |
| Q39164590 | What Do Patients Tweet About Their Mammography Experience? |
| Q38547058 | What Do People Like to "Share" About Obesity? A Content Analysis of Frequent Retweets About Obesity on Twitter |
| Q51417522 | What Drives Health Professionals to Tweet About #HPVvaccine? Identifying Strategies for Effective Communication. |
| Q113135282 | What Drives U.S. Congressional Members’ Policy Attention on Twitter? |
| Q106111641 | What Kind of Movement is Black Lives Matter? The View from Twitter |
| Q40085723 | What Makes a Tweet Fly? Analysis of Twitter Messaging at Four Infection Control Conferences. |
| Q92252111 | What Twitter teaches us about patient-provider communication on pain |
| Q88688508 | What advice is given to newly qualified doctors on Twitter? An analysis of #TipsForNewDocs tweets |
| Q58929665 | What all those scientists on Twitter are really doing |
| Q34458347 | What are health-related users tweeting? A qualitative content analysis of health-related users and their messages on twitter |
| Q47672870 | What are people tweeting about orthodontic retention? A cross-sectional content analysis |
| Q51468100 | What are we 'tweeting' about obesity? Mapping tweets with Topic Modeling and Geographic Information System. |
| Q64992106 | What can Twitter offer oncology nurses? |
| Q36113057 | What can we learn about the Ebola outbreak from tweets? |
| Q33821795 | What do computer scientists tweet? Analyzing the link-sharing practice on Twitter |
| Q57195555 | What do people study when they study Twitter? Classifying Twitter related academic papers |
| Q59442626 | What is Twitter, a social network or a news media? |
| Q51416955 | What is Twitter? How Do I Get Started? Why Should I Become a User? |
| Q44095062 | What makes people talk about Ebola on social media? A retrospective analysis of Twitter use. |
| Q34034423 | What makes people talk about antibiotics on social media? A retrospective analysis of Twitter use. |
| Q102210136 | What people share about the COVID-19 outbreak on Twitter? An exploratory analysis |
| Q89503091 | What the hashtag? Using twitter and podcasting to extend your scientific reach |
| Q105835121 | When Celebrities Speak: A Nationwide Twitter Experiment Promoting Vaccination In Indonesia |
| Q57653877 | When Citizens and Journalists Interact on Twitter |
| Q104566697 | When Health Organization Answers the Question: Differential Effects of Dialogic Messages in Website and Twitter through Social Presence and Psychological Distance |
| Q121083219 | When Journalists Tweet: Disclosure, Participatory, and Personal Transparency |
| Q106993702 | When Shopping Turns Scary: Twitter Conversations about Violent Crime in Johannesburg Malls |
| Q56798301 | When countries become the talking point in microblogs: Study on country hashtags in Twitter |
| Q92826840 | Where Do People Vape? Insights from Twitter Data |
| Q58710780 | Where in the world is my tweet: Detecting irregular removal patterns on Twitter |
| Q63362312 | Which image types do universities tweet? |
| Q37638943 | Who Are the Most Influential Emergency Physicians on Twitter? |
| Q90599416 | Who Tweets About Diabetic Foot on Twitter and Which Tweets Are More Attractive? |
| Q36257766 | Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter |
| Q39638657 | Who gives a tweet: assessing patients' interest in the use of social media for health care |
| Q45949528 | Who has Australia's most-followed Twitter accounts in health and medicine? |
| Q92147065 | Who is Saying What on Twitter: An Analysis of Messages with References to HIV and HIV Risk Behavior |
| Q94998767 | Who is interacting with researchers on Twitter? A survey in the field of Information Science |
| Q60034209 | Who is responsible for Twitter’s echo chamber problem? Evidence from 2016 U.S. election networks |
| Q59408480 | Who is the better player? Off-field battle on Facebook and Twitter |
| Q56656650 | Who is tweeting on Twitter |
| Q55385023 | Who tweets about cancer? An analysis of cancer-related tweets in the USA. |
| Q64065603 | Who tweets in academia? An overview of Twitter use in higher education |
| Q28652984 | Who tweets? Deriving the demographic characteristics of age, occupation and social class from twitter user meta-data |
| Q50247716 | Why Follow JCRP and AACVPR on Twitter and Facebook? |
| Q48270387 | Why I Live-Tweeted Residency |
| Q50951774 | Why I blog and tweet. |
| Q57256151 | Why We Follow: An Examination of Parasocial Interaction and Fan Motivations for Following Athlete Archetypes on Twitter |
| Q60622558 | Why do college students prefer Facebook, Twitter, or Instagram? Site affordances, tensions between privacy and self-expression, and implications for social capital |
| Q36293920 | Why do people use electronic nicotine delivery systems (electronic cigarettes)? A content analysis of Twitter, 2012-2015. |
| Q24651531 | Why do shrews twitter? Communication or simple echo-based orientation |
| Q98207278 | Why not use "Twitter" of core clinical journals for rapid dissemination of medical information during the COVID-19 pandemic? |
| Q29041706 | Why we twitter: understanding microblogging usage and communities |
| Q57768598 | Wiki-MID: A Very Large Multi-domain Interests Dataset of Twitter Users with Mappings to Wikipedia |
| Q66711044 | Wikipedia based semantic smoothing for twitter sentiment classification |
| Q42012555 | Will pharma twitter? |
| Q56554668 | Will the revolution be tweeted? A conceptual framework for understanding the social media and the Arab Spring |
| Q55509844 | Women and Kidney Disease: A Twitter Conversation for One and All. |
| Q53161542 | Word usage mirrors community structure in the online social network Twitter |
| Q101450350 | World Antibiotic Awareness Week and European Antibiotic Awareness Day, November 2018: An analysis of the impact of Twitter activity |
| Q60194664 | World Pneumonia Day 2011–2016: Twitter contents and retweets |
| Q92071960 | World leaders' usage of Twitter in response to the COVID-19 pandemic: a content analysis |
| Q40742397 | Worldlex: Twitter and blog word frequencies for 66 languages |
| Q41689563 | X app |
| Q107615676 | XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond |
| Q29365194 | Yellow fever outbreaks and Twitter: Rumors and misinformation |
| Q92265818 | Yonder: Medication adherence, Twitter, head and neck cancer, and knitting |
| Q96684324 | Yonder: Vitamin D screening, startups, social distancing, and world leaders on Twitter |
| Q32907717 | You Are What You Tweet: A New Hybrid Model for Sentiment Analysis |
| Q35762433 | You Are What You Tweet: Connecting the Geographic Variation in America's Obesity Rate to Twitter Content |
| Q50222662 | You mean the Twitter nurse? |
| Q36717476 | Young Adults' Exposure to Alcohol- and Marijuana-Related Content on Twitter |
| Q90491310 | Your @Attending Will #Tweet You Now: Using Twitter in Medical Education |
| Q84415632 | Youth impact on the public sphere in Press and Twitter: The dissolution of the Spanish Youth Council |
| Q64904486 | Zika discourse in the Americas: A multilingual topic analysis of Twitter. |
| Q33639843 | Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts. |
| Q42998600 | Zika live Twitter chat |
| Q51449352 | [Benefits and risks of Twitter use for urologists]. |
| Q51454410 | [Exponential use of social media in medicine: example of the interest of Twitter(©) in urology]. |
| Q51473454 | [Facebook and Twitter, are they already in the pediatrician's office? Survey on the use of social networks]. |
| Q51453944 | [Facebook and Twitter: new references for Chilean medical journals]. |
| Q91780535 | [French academic dermatology on Twitter] |
| Q94946756 | [Medicine on Twitter: A question of confraternity or a danger?] |
| Q100573521 | [Perceptions regarding health rights for migrants in Chile: Twitter data analysis] |
| Q90485101 | [The Annales de Pathologie are now on Facebook and Twitter!] |
| Q51477285 | [The journal on Twitter] |
| Q51434132 | [Twitter activity at the German Society of Urology congress #DGU16]. |
| Q95404504 | [Twitter but don't forget the patients – The Medical Association's advice on social media use needs to be supplemented] |
| Q97530566 | [Twitter in cardiology : Tips and tricks] |
| Q51444332 | [Use of Twitter(®) in a congress: First experience for French internal medicine]. |
| Q93193381 | [Usefulness of Twitter and #DocTocToc in assessing a cutaneous condition] |
| Q51452976 | [Using Twitter in oncology. Research, continuing education, and advocacy]. |
| Q34768044 | ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
| Q51475276 | iNurse: Facebook, Twitter, and LinkedIn, oh my! |
| Q64790997 | just setting up my twttr |
| Q58611500 | nowUP: A System that Automatically Creates TV Summaries Based on Twitter Activity |
| Q113307711 | rtweet: Collecting and analyzing Twitter data |
| Q56220065 | “@God please open your fridge!” Twitter messages to @God in content analysis: Hopes, humor, spirituality, and profanities |
| Q74210715 | “A field of crop scientists” — Twitter delivers collective nouns for researchers |
| Q57701401 | “Come Together!”: Interactions of Language Networks and Multilingual Communities on Twitter |
| Q56591736 | “Fuck Your Body Image”: Teen Girls’ Twitter and Instagram Feminism in and Around School |
| Q57399567 | “Thank You, Black Twitter”: State Violence, Digital Counterpublics, and Pedagogies of Resistance |
| Q106111644 | “Where We Are, Resistance Lives”: Black Women, Social Media, and Everyday Resistance in Higher Education |
| Q108298861 | Haine en ligne : Twitter sommé d’agir en toute transparence |
| Q60647696 | Twitter rend-il fou ? |
| Q11251198 | Togetter |
| Q96938255 | Twitter como espacio (¿anti?)político: análisis discursivo de los tuits del presidente Mauricio Macri |
| Q17315793 | Лентапедия / Twitter |
