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Spikes and Variance: Using Google Trends to Detect and Forecast Protests

Published online by Cambridge University Press:  08 April 2021

Joan C. Timoneda Open the ORCID record for Joan C. Timoneda [Opens in a new window]  and
Erik Wibbels
Joan C. Timoneda*
Affiliation:
Department of Political Science, Purdue University, 2230 Beering Hall, 100 North University Street, West Lafayette, IN47907, USA. Email: timoneda@purdue.edu
Erik Wibbels
Affiliation:
Department of Political Science, Duke University, 280 Gross Hall, 140 Science Drive, Durham, NC27708, USA
*
Corresponding author Joan C. Timoneda
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Abstract

Google search is ubiquitous, and Google Trends (GT) is a potentially useful access point for big data on many topics the world over. We propose a new ‘variance-in-time’ method for forecasting events using GT. By collecting multiple and overlapping samples of GT data over time, our algorithm leverages variation both in the mean and the variance of a search term in order to accommodate some idiosyncracies in the GT platform. To elucidate our approach, we use it to forecast protests in the United States. We use data from the Crowd Counting Consortium between 2017 and 2019 to build a sample of true protest events as well as a synthetic control group where no protests occurred. The model’s out-of-sample forecasts predict protests with higher accuracy than extant work using structural predictors, high frequency event data, or other sources of big data such as Twitter. Our results provide new insights into work specifically on political protests, while providing a general approach to GT that should be useful to researchers of many important, if rare, phenomena.

Keywords

Google trendsforecastingprotestbig data methods
Type
Article
Information
Political Analysis , Volume 30 , Issue 1 , January 2022 , pp. 1 - 18
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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Timoneda and Wibbels Dataset

Dataset

https://doi.org/10.7910/DVN/WXZH8C
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