| human | Q5 |
| P2456 | DBLP author ID | 177/8082 |
| P6178 | Dimensions author ID | 012077175307.17 |
| P6634 | LinkedIn personal profile ID | suzymoat |
| P856 | official website | http://www.wbs.ac.uk/about/person/suzy-moat/ |
| P496 | ORCID iD | 0000-0001-8974-9277 |
| P4012 | Semantic Scholar author ID | 3362942 |
| P2002 | X username | suzymoat |
| P69 | educated at | University of Edinburgh | Q160302 |
| University College London | Q193196 | ||
| P108 | employer | University of Warwick | Q865528 |
| The Alan Turing Institute | Q16826821 | ||
| P735 | given name | Helen | Q13376892 |
| Helen | Q13376892 | ||
| P106 | occupation | researcher | Q1650915 |
| P21 | sex or gender | female | Q6581072 |
| P8687 | social media followers | 3868 |
| Q28646474 | Adaptive nowcasting of influenza outbreaks using Google searches |
| Q57578492 | Anticipating Stock Market Movements with Google and Wikipedia |
| Q28660019 | Characterizing the time-perspective of nations with search engine query data |
| Q57216679 | Early Signs of Financial Market Moves Reflected by Google Searches |
| Q51891111 | Error biases in inner and overt speech: evidence from tongue twisters. |
| Q57216648 | Estimating suicide occurrence statistics using Google Trends |
| Q57578488 | Estimating tourism statistics with Wikipedia page views |
| Q64095967 | Happiness is Greater in More Scenic Locations |
| Q108863686 | In search of art: rapid estimates of gallery and museum visits using Google Trends |
| Q57400732 | Manifesto de Ciência Social Computacional |
| Q57400736 | Manifesto of computational social science |
| Q114369356 | Measuring the size of a crowd using Instagram |
| Q28608227 | Modelling human mobility patterns using photographic data shared online |
| Q28647476 | Quantifying International Travel Flows Using Flickr |
| Q28608175 | Quantifying Stock Return Distributions in Financial Markets |
| Q24629584 | Quantifying Wikipedia Usage Patterns Before Stock Market Moves |
| Q28646116 | Quantifying crowd size with mobile phone and Twitter data |
| Q28650003 | Quantifying regional differences in the length of Twitter messages |
| Q57578477 | Quantifying scenic areas using crowdsourced data |
| Q28607090 | Quantifying the Impact of Scenic Environments on Health |
| Q28603764 | Quantifying the Search Behaviour of Different Demographics Using Google Correlate |
| Q24604429 | Quantifying the advantage of looking forward |
| Q28661772 | Quantifying the digital traces of Hurricane Sandy on Flickr |
| Q57578475 | Quantifying the diversity of news around stock market moves |
| Q28603432 | Quantifying the link between art and property prices in urban neighbourhoods |
| Q28661027 | Quantifying the relationship between financial news and the stock market |
| Q28244846 | Quantifying the semantics of search behavior before stock market moves |
| Q24631411 | Quantifying trading behavior in financial markets using Google Trends |
| Q28597200 | Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data |
| Q28608206 | The advantage of short paper titles |
| Q57216661 | The advantage of simple paper abstracts |
| Q28603516 | Tracking Protests Using Geotagged Flickr Photographs |
| Q94554795 | Using aircraft location data to estimate current economic activity |
| Q91342866 | Using big data to map the relationship between time perspectives and economic outputs |
| Q92092006 | Using big data to map the relationship between time perspectives and economic outputs-ERRATUM |
| Q30765251 | Using big data to predict collective behavior in the real world |
| Q38632987 | Using deep learning to quantify the beauty of outdoor places |
Search more.