We increasingly rely on networked computer systems and smart cards to support our everyday activities, and everything we do generates data – whether buying bread at the supermarket, taking a ride on the Tube, or calling a friend for a chat.
This data is opening up a new era for our understanding of human behaviour – and also for policy making and business processes which depend upon this understanding.
As a computational social scientist, my research investigates how the vast amounts of data generated by our everyday use of technology can help us understand and even predict how humans behave.
Find out more about the Computational Social Science Conference, taking place at Warwick Business School from Wed 11th June - Fri 13th June 2014.
Chairs: Suzy Moat, Mark Carrigan and Tobias Preis.
Teaching in 2014-2015
IB9ES0: Advanced Communication Skills for Data Science Research
IB9AP0: Behavioural Sciences for the Manager
IB9CS0: Big Data Analytics
IB9CSB: Big Data Analytics
IM9030: Complexity in the Social Sciences
IB9ER0: Designing and Managing Data Science Research
Suzy Moat is an Assistant Professor of Behavioural Science at Warwick Business School. 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.Guardian (2013)
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 data science research team working on these questions.
Recent media coverage of her work includes:
"Which countries are the most forward thinking? See it visualised"
New Scientist (2012)
"Online searches for future linked to economic success"
"Google searches predict market moves"
Bloomberg Businessweek (2013)
"'Big Data' Researchers Turn to Google to Beat the Markets"
"Wikipedia page views could predict stock market changes"
Wall Street Journal (2014)
"Do politics-themed Google searches predict stock activity?"
CNN (TV interview, 2013)
BBC (TV interview, 2013)
Classic FM (radio interview, 2013)
Newstalk Ireland (radio interview, 2013)
Interview with Women in Business magazine (2013)
You can follow Suzy on Twitter at @suzymoat
Moat, H. S., Olivola, C. Y., Chater, N,. & Preis, T.
"Searching choices: Quantifying decision making processes using search engine data"
Topics in Cognitive Science
in press (2015)
Alis, C. M., Lim, M. T., Moat, H. S., Barchiesi, D., Preis, T. & Bishop, S. R.
"Quantifying regional differences in the length of Twitter messages"
10 (2015): e0122278.
Curme, C., Preis, T., Stanley, H. E., & Moat, H. S.
"Quantifying the semantics of search behavior before stock market moves"
Proceedings Of The National Academy Of Sciences (USA)
111 (2014): 11600-11605.
Moat, H. S., Preis, T., Olivola, C. Y., Liu, C., & Chater, N.
"Using big data to predict collective behavior in the real world"
Behavioral And Brain Sciences
37 (2014): 92-93.
Noguchi, T., Stewart, N., Olivola, C. Y., Moat, H. S., & Preis, T.
"Characterizing the time-perspective of nations with search engine query data"
9 (2014): e95209.
Preis, T. & Moat, H. S.
"Adaptive nowcasting of influenza outbreaks using Google searches"
Royal Society Open Science
1 (2014): 140095.
Preis, T., Moat, H. S., & Stanley, H. E.
"Quantifying trading behavior in financial markets using Google Trends"
3 (2013): 1684.
Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E., & Preis, T.
"Quantifying Wikipedia usage patterns before stock market moves"
3 (2013): 1801.
Preis, T., Moat, H. S., Bishop, S.R., Treleaven, P., & Stanley, H. E.
"Quantifying the digital traces of Hurricane Sandy on Flickr"
3 (2013): 3141.
Alanyali, M., Moat, H. S., & Preis, T.
"Quantifying the relationship between financial news and the stock market"
3 (2013): 3578.
Conte, R., Gilbert, N., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., Loreto, V., Moat, S., Nadal, J.-P., Sanchez, A., Nowak, A., Flache, A., San Miguel, M., & Helbing, D.
"Manifesto of computational social science"
European Physical Journal
214 (2012): 325-346.
Preis, T., Moat, H. S., Stanley, H. E., & Bishop, S. R.
"Quantifying the advantage of looking forward"
2 (2012): 350.
Corley, M., Brocklehurst, P. H., & Moat, H. S.
"Error biases in inner and overt speech: Evidence from tongue twisters"
Journal of Experimental Psychology: Learning, Memory, and Cognition
37 (2010): 162-175.
Preis, T. & Moat, H. S.
"Early signs of financial market moves reflected by Google Searches"
Social Phenomena: From Data Analysis to Models
Moat, H.S., Curme, C., Stanley, H. E., & Preis, T.
"Anticipating stock market movements with Google and Wikipedia"
Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale