Her work exploits data from sources such as Google, Wikipedia and Flickr, to investigate whether data from the Internet can help us measure and even predict human behaviour.
In recent studies, in collaboration with Tobias Preis, H. Eugene Stanley and colleagues, Moat has provided evidence that patterns in searches for financial information on Wikipedia and Google may have offered clues to subsequent stock market moves, and that Internet users from countries with a higher per capita GDP are more likely to search for information about years in the future than years in the past.
Moat was awarded a Ph.D. from the University of Edinburgh and won a series of prizes during her studies. Since 2011, Moat has secured £3.3 million of funding from UK, EU and US research agencies. Her work has been featured by television, radio and press worldwide, including recent pieces on CNN and the BBC.
Moat has acted as an advisor to government and public bodies on the predictive capabilities of big data. She currently co-directs a small research team working on these questions.
Come to the Computational Social Science Conference taking place in Warwick, 11-13 June 2014: excellent line-up of speakers talking about online data, mobile phone data, urban data and more
A selection of coverage by The Guardian, BBC, New Scientist, Bloomberg Businessweek and Wired
Interviews with CNN (TV), BBC (TV), Newstalk Ireland (radio) and Classic FM (radio)
The Future Orientation Index, calculated using Google data, for 2012 (rankings, map) and 2011 (rankings, map). See paper for more
Paper: Quantifying Wikipedia Usage Patterns Before Stock Market Moves
Paper: Quantifying Trading Behavior in Financial Markets Using Google Trends
Paper: Quantifying the Advantage of Looking Forward