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Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as "early warning signs" of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior. ; We thank Didier Sornette, Dirk Helbing, and Steven R. Bishop for comments. This work was partially supported by the German Research Foundation Grant PR 1305/1-1 (to T. P.). This work was also supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC00285 and by the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Defense Threat Reduction Agency (DTRA). The U. S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government. (PR 1305/1-1 - German Research Foundation; D12PC00285 - Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC); National Science Foundation (NSF); Office of Naval Research (ONR); Defense Threat Reduction Agency (DTRA)) ; Published version

Internet search data may offer new possibilities to improve forecasts of collective behavior, if we can identify which parts of these gigantic search datasets are relevant. We introduce an automated method that uses data from Google and Wikipedia to identify relevant topics in search data before large events. Using stock market moves as a case study, our method successfully identifies historical links between searches related to business and politics and subsequent stock market moves. We find that the predictive value of these search terms has recently diminished, potentially reflecting increasing incorporation of Internet data into automated trading strategies. We suggest that extensions of these analyses could help draw links between search data and a range of other collective actions.

Society's increasing interactions with technology are creating extensive "digital traces" of our collective human behavior. These new data sources are fuelling the rapid development of the new field of computational social science. To investigate user attention to the Hurricane Sandy disaster in 2012, we analyze data from Flickr, a popular website for sharing personal photographs. In this case study, we find that the number of photos taken and subsequently uploaded to Flickr with titles, descriptions or tags related to Hurricane Sandy bears a striking correlation to the atmospheric pressure in the US state New Jersey during this period. Appropriate leverage of such information could be useful to policy makers and others charged with emergency crisis management. ; T.P., H. S. M., S. R. B. and P. T. acknowledge the support of Research Councils UK via Grant EP/K039830/1. HES thanks NSF Grant CMMI 1125290. TP, HSM and HES were also supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC00285. The U. S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U. S. Government. (EP/K039830/1 - Research Councils UK; CMMI 1125290 - NSF; D12PC00285 - Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC); EP/J005207/1 - Engineering and Physical Sciences Research Council; EP/K039830/1 - Engineering and Physical Sciences Research Council) ; Published version