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.
In June 2014, I chaired Europe's first Computational Social Science Conference with Mark Carrigan and Tobias Preis.
Sign up to hear about future runs of our FutureLearn MOOC, Big Data: Measuring and Predicting Human Behaviour!
Teaching in 2016-2017
IM9030: Complexity in the Social Sciences
IB9AP0: Behavioural Sciences for the Manager
IB9CS0: Big Data Analytics
IB9CSB: Big Data Analytics
Suzy Moat is an Associate Professor of Behavioural Science at Warwick Business School, where she co-directs the Data Science Lab. Her research investigates whether data on our usage of the Internet, from sources such as Google, Wikipedia and Flickr, can help us measure and even predict human behaviour in the real world.Guardian (2013)
Moat's work touches on problems as diverse as linking online behaviour to stock market moves (with Preis, Curme, Stanley, et al.), estimating crowd sizes (with Botta and Preis) and evaluating whether the beauty of the environment we live in might affect our health (with Seresinhe and Preis). The results of her research have been featured by television, radio and press worldwide, by outlets such as CNN, BBC, The Guardian, Wall Street Journal, New Scientist and Wired.
Moat studied Computer Science at UCL and Psychology at 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. With her collaborator and Data Science Lab co-director Tobias Preis, she recently led an online course on using big data to measure and predict human behaviour which attracted over 15,000 learners. Suzy has also acted as an advisor to government and public bodies on the predictive capabilities of big data.
Selected 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"
"Wikipedia page views could predict stock market changes"
Wall Street Journal (2014)
"Do politics-themed Google searches predict stock activity?"
"Crowds 'could be counted' with phone and Twitter data
CNN (TV interview, 2013)
BBC (TV interview, 2013)
Interview with Women in Business magazine (2013)
Interview with TechMix magazine (2014)
You can follow Suzy on Twitter at @suzymoat
Alanyali, M., Preis, T. and Moat, H. S. (2016) "Tracking protests using geotagged Flickr photographs", PLoS One, 11, 3, 1-8, e0150466
Letchford, A., Preis, T. and Moat, H. S. (2016) "Quantifying the search behaviour of different demographics using Google Correlate", PLoS One, 11, 2, 1-11, e0149025
Moat, H. S., Olivola, C. Y., Chater, N. and Preis, T. (2016) "Searching choices : quantifying decision-making processes using search engine data", Topics in Cognitive Science
Seresinhe, C. I., Preis, T. and Moat, H. S. (2016) "Quantifying the link between art and property prices in urban neighbourhoods", Royal Society Open Science, 3, 4, 1-7, 160146
Botta, F., Moat, H. S. and Preis, T. (2015) "Quantifying crowd size with mobile phone and Twitter data", Royal Society Open Science , Volume 2, Number 5, 150162-150162
Alis, C. M., Lim, M. T., Moat, H. S., Barchiesi, D., Preis, T. and Bishop, S. R. (2015) "Quantifying regional differences in the length of Twitter messages", PLoS One, Volume 10, Number 4, Article number e0122278
Letchford, A., Moat, H. S. and Preis, T. (2015) "The advantage of short paper titles", Royal Society Open Science , 2, 8, 1-6, 150266
Barchiesi, D., Preis, T., Bishop, S. R. and Moat, H. S. (2015) "Modelling human mobility patterns using photographic data shared online", Royal Society Open Science , 2, 8, 1-8, 150046
Barchiesi, D., Moat, H. S., Alis, C. M., Bishop, S. R. and Preis, T. (2015) "Quantifying international travel flows using Flickr", PLoS One, 10, 7, 1-8, e0128470
Seresinhe, C. I., Preis, T. and Moat, H. S. (2015) "Quantifying the impact of scenic environments on health", Scientific Reports, 5, 16899
Botta, F., Moat, H. S., Stanley, H. E. and Preis, T. (2015) "Quantifying stock return distributions in financial markets", PLoS One, 10, 9, 1-10, e0135600
Letchford, A., Preis, T. and Moat, H. S. (2015) "The advantage of simple paper abstracts", Journal of Informetrics, 10, 1, 1-8
Noguchi, T., Stewart, N., Olivola, C. Y., Moat, H. S. and Preis, T. (2014) "Characterizing the time-perspective of nations with search engine query data", PLoS One, Volume 9, Number 4, Article number e95209
Curme, C., Preis, T., Stanley, H. E. and Moat, H. S. (2014) "Quantifying the semantics of search behavior before stock market moves", Proceedings of the National Academy of Sciences of the United States of America, Volume 111, Number 32, 11600-11605
Preis, T. and Moat, H. S. (2014) "Adaptive nowcasting of influenza outbreaks using Google searches", Royal Society Open Science , Volume 1, Number 2, Article number 140095
Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. and Chater, N. (2014) "Using big data to predict collective behavior in the real world", Behavioral and Brain Sciences, Volume 37, Number 01, 92-93
Alanyali, M., Moat, H. S. and Preis, T. (2013) "Quantifying the relationship between financial news and the stock market", Scientific Reports, Volume 3, Article number 3578
Preis, T., Moat, H. S., Bishop, S. R., Treleaven, P. and Stanley, H. E. (2013) "Quantifying the digital traces of Hurricane Sandy on Flickr", Scientific Reports, Volume 3, Article: 3141
Preis, T., Moat, H. S. and Stanley, H. E. (2013) "Quantifying trading behavior in financial markets using Google Trends", Scientific Reports, 3, 1684
Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E. and Preis, T. (2013) "Quantifying Wikipedia usage patterns before stock market moves", Scientific Reports, 3, 1801
Preis, T., Moat, H. S., Stanley, H. E. and Bishop, S. R. (2012) "Quantifying the advantage of looking forward", Scientific Reports, Vol.2, Article no. 350
Conte, R., Gilbert, G. N., Bonelli, G., Cioffi-Revilla, C. A., Deffuant, G., Kertész, J., Loreto, V., Moat, H. S., Nadal, J., Sanchez, A., Nowak, A., Flache, A., San Miguel, M. and Helbing, D. (2012) "Manifesto of computational social science", The European Physical Journal Special Topics, Volume 214, Number 1, 325-346
Corley, M., Brocklehurst, P. H. and Moat, H. S. (2011) "Error biases in inner and overt speech : evidence from tongue twisters.", Journal of Experimental Psychology : Learning, Memory, and Cognition, Volume 37, Number 1, 162-175