Cartwright, Nancy 2017. Philosophy of Science in Practice.
Gillings, Michael R. Hilbert, Martin and Kemp, Darrell J. 2016. Information in the Biosphere: Biological and Digital Worlds. Trends in Ecology & Evolution, Vol. 31, Issue. 3, p. 180.
Kristoufek, Ladislav Moat, Helen Susannah and Preis, Tobias 2016. Estimating suicide occurrence statistics using Google Trends. EPJ Data Science, Vol. 5, Issue. 1,
Letchford, Adrian Preis, Tobias Moat, Helen Susannah and Perc, Matjaz 2016. Quantifying the Search Behaviour of Different Demographics Using Google Correlate. PLOS ONE, Vol. 11, Issue. 2, p. e0149025.
Letchford, Adrian Preis, Tobias and Moat, Helen Susannah 2016. The advantage of simple paper abstracts. Journal of Informetrics, Vol. 10, Issue. 1, p. 1.
Moat, Helen Susannah Olivola, Christopher Y. Chater, Nick and Preis, Tobias 2016. Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data. Topics in Cognitive Science, Vol. 8, Issue. 3, p. 685.
Nardo, Michela Petracco-Giudici, Marco and Naltsidis, Minás 2016. WALKING DOWN WALL STREET WITH A TABLET: A SURVEY OF STOCK MARKET PREDICTIONS USING THE WEB. Journal of Economic Surveys, Vol. 30, Issue. 2, p. 356.
Seresinhe, Chanuki Illushka Preis, Tobias and Moat, Helen Susannah 2016. Quantifying the link between art and property prices in urban neighbourhoods. Royal Society Open Science, Vol. 3, Issue. 4, p. 160146.
Tkachenko, Nataliya Procter, Rob and Jarvis, Stephen 2016. Predicting the impact of urban flooding using open data. Royal Society Open Science, Vol. 3, Issue. 5, p. 160013.
Alis, Christian M. Lim, May T. Moat, Helen Susannah Barchiesi, Daniele Preis, Tobias Bishop, Steven R. and Braunstein, Lidia Adriana 2015. Quantifying Regional Differences in the Length of Twitter Messages. PLOS ONE, Vol. 10, Issue. 4, p. e0122278.
Barchiesi, Daniele Moat, Helen Susannah Alis, Christian Bishop, Steven Preis, Tobias and Perc, Matjaz 2015. Quantifying International Travel Flows Using Flickr. PLOS ONE, Vol. 10, Issue. 7, p. e0128470.
Barchiesi, Daniele Preis, Tobias Bishop, Steven and Moat, Helen Susannah 2015. Modelling human mobility patterns using photographic data shared online. Royal Society Open Science, Vol. 2, Issue. 8, p. 150046.
Botta, Federico Moat, Helen Susannah and Preis, Tobias 2015. Quantifying crowd size with mobile phone andTwitterdata. Royal Society Open Science, Vol. 2, Issue. 5, p. 150162.
Botta, Federico Moat, Helen Susannah Stanley, H. Eugene Preis, Tobias and Chen, Yanguang 2015. Quantifying Stock Return Distributions in Financial Markets. PLOS ONE, Vol. 10, Issue. 9, p. e0135600.
Letchford, Adrian Moat, Helen Susannah and Preis, Tobias 2015. The advantage of short paper titles. Royal Society Open Science, Vol. 2, Issue. 8, p. 150266.
Seresinhe, Chanuki Illushka Preis, Tobias and Moat, Helen Susannah 2015. Quantifying the Impact of Scenic Environments on Health. Scientific Reports, Vol. 5, p. 16899.
Sloan, Luke Morgan, Jeffrey and Preis, Tobias 2015. Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter. PLOS ONE, Vol. 10, Issue. 11, p. e0142209.
Sloan, Luke Morgan, Jeffrey Burnap, Pete Williams, Matthew and Preis, Tobias 2015. Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data. PLOS ONE, Vol. 10, Issue. 3, p. e0115545.
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, Vol. 111, Issue. 32, p. 11600.
Noguchi, Takao Stewart, Neil Olivola, Christopher Y. Moat, Helen Susannah Preis, Tobias and Perc, Matjaž 2014. Characterizing the Time-Perspective of Nations with Search Engine Query Data. PLoS ONE, Vol. 9, Issue. 4, p. e95209.
Recent studies provide convincing evidence that data on online information gathering, alongside massive real-world datasets, can give new insights into real-world collective decision making and can even anticipate future actions. We argue that Bentley et al.’s timely account should consider the full breadth, and, above all, the predictive power of big data.
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