Department of Public Administration and Policy, School of Public and International Affairs, University of Georgia, Athens, Georgia, United States of America.
Department of Political Science, School of Public and International Affairs, University of Georgia, Athens, Georgia, United States of America.
PLoS One. 2019 Mar 19;14(3):e0212834. doi: 10.1371/journal.pone.0212834. eCollection 2019.
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.
在本文中，我们介绍了一种可扩展的机器学习方法，并提供了开源软件，用于使用社交媒体数据识别暴力和和平形式的政治抗议参与。虽然暴力政治抗议在统计上是罕见事件，但它们常常塑造公众对政治和社会运动的看法。部分原因是，相对于和平抗议参与，暴力抗议参与会得到广泛且不成比例的媒体报道。过去，当少数媒体集团作为了解政治和社会运动的主要信息来源时，收视率和广告商的需求促使新闻机构专注于暴力形式的政治抗议参与。因此，我们对政治抗议参与的了解很大程度上来自于关于暴力抗议的数据，而对和平抗议形式的了解则较少。自 21 世纪初以来，数字革命将注意力从传统新闻来源转移到社交媒体，将其作为了解时事的主要信息来源。再加上机器学习的发展，使我们能够收集和分析与政治参与相关的数据，这为我们提供了独特的机会，通过社交媒体数据扩大我们对和平和暴力形式的政治抗议参与的了解。
