Journal article
A scalable machine learning approach for measuring violent and peaceful forms of political protest participation with social media data
PloS one, v 14(3), pp e0212834-e0212834
19 Mar 2019
PMID: 30889227
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.
Metrics
Details
- Title
- A scalable machine learning approach for measuring violent and peaceful forms of political protest participation with social media data
- Creators
- Lefteris Jason Anastasopoulos - University of GeorgiaJake Ryland Williams - Drexel University
- Publication Details
- PloS one, v 14(3), pp e0212834-e0212834
- Publisher
- Public LIbrary of Science (PLOS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000461573000011
- Scopus ID
- 2-s2.0-85063285933
- Other Identifier
- 991021806421404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Political Science