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PLoS One. 2019 Mar 19;14(3):e0212834. doi: 10.1371/journal.pone.0212834. eCollection 2019.

A scalable machine learning approach for measuring violent and peaceful forms of political protest participation with social media data.

Author information

1
Department of Public Administration and Policy, School of Public and International Affairs, University of Georgia, Athens, Georgia, United States of America.
2
Department of Political Science, School of Public and International Affairs, University of Georgia, Athens, Georgia, United States of America.
3
Institute for Artificial Intelligence, University of Georgia, Athens, Georgia, United States of America.
4
College of Computing and Informatics. Drexel University, Philadelphia, Pennsylvania, United States of America.

Abstract

In this paper, we introduce a scalable machine learning approach accompanied by open-source software for identifying violent and peaceful forms of political protest participation using social media data. While violent political protests are statistically rare events, they often shape public perceptions of political and social movements. This is, in part, due to the extensive and disproportionate media coverage which violent protest participation receives relative to peaceful protest participation. In the past, when a small number of media conglomerates served as the primary information source for learning about political and social movements, viewership and advertiser demands encouraged news organizations to focus on violent forms of political protest participation. Consequently, much of our knowledge about political protest participation is derived from data collected about violent protests, while less is known about peaceful forms of protest. Since the early 2000s, the digital revolution shifted attention away from traditional news sources toward social media as a primary source of information about current events. This, along with developments in machine learning which allow us to collect and analyze data relevant to political participation, present us with unique opportunities to expand our knowledge of peaceful and violent forms of political protest participation through social media data.

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