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News and Geolocated Social Media Accurately Measure Protest Size Variation

Published online by Cambridge University Press:  30 June 2020

Yale University
University of California, Los Angeles
University of California, Los Angeles
University of California, Los Angeles
Anton Sobolev, Postdoctoral Associate, Leitner Program on Political Economy, Yale University,
M. Keith Chen, Professor, Anderson School of Management, University of California, Los Angeles,
Jungseock Joo, Assistant Professor, Department of Communication, University of California, Los Angeles,
Zachary C. Steinert-Threlkeld, Assistant Professor, Luskin School of Public Affairs, University of California, Los Angeles,


Larger protests are more likely to lead to policy changes than small ones are, but whether or not attendance estimates provided in news or generated from social media are biased is an open question. This letter closes the question: news and geolocated social media data generate accurate estimates of protest size variation. This claim is substantiated using cellphone location data from more than 10 million individuals during the 2017 United States Women’s March protests. These cellphone estimates correlate strongly with those provided in news media as well as three size estimates generated using geolocated tweets, one text-based and two based on images. Inferences about protest attendance from these estimates match others’ findings about the Women’s March.

© The Author(s), 2020. Published by Cambridge University Press on behalf of the American Political Science Association.

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We thank Pablo Barberá, Brett Carter, Benjamin A.T. Graham, Will Hobbs, Nazita Lajevardi, Natalia Lamberova, John McCarthy, Clark McPhail, Jeremy Pressman, Michael Shalev, and Thomas Zeitzoff for their valuable feedback. The editor and anonymous reviewers also provided constructive criticism. Ryan Fox Squire and Aaron Hoffman assisted with the SafeGraph data. Alexander Chan and Christina Indudhara assisted with detecting duplicate faces. This work is supported by NSF RIDIR #1831848, the UCLA Big Data Transdisciplinary Seed Grant, and a UCLA California Center for Population Research Seed Grant. All remaining errors remain inadvertently. Replication files are available at the American Political Science Review Dataverse:


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