Abstract
Mass shootings, like other extreme events, have long garnered public curiosity and, in turn, significant media coverage. The media framing, or topic focus, of mass shooting events typically evolves over time from details of the actual shooting to discussions of potential policy changes (e.g., gun control, mental health). Such media coverage has been historically provided through traditional media sources such as print, television, and radio, but the advent of online social networks (OSNs) has introduced a new platform for accessing, producing, and distributing information about such extreme events. The ease and convenience of OSN usage for information within society’s larger growing reliance upon digital technologies introduces potential unforeseen risks. Social bots, or automated software agents, are one such risk, as they can serve to amplify or distort potential narratives associated with extreme events such as mass shootings. In this paper, we seek to determine the prevalence and relative importance of social bots participating in OSN conversations following mass shooting events using an ensemble of quantitative techniques. Specifically, we examine a corpus of more than 46 million tweets produced by 11.7 million unique Twitter accounts within OSN conversations discussing four major mass shooting events: the 2017 Las Vegas concert shooting, the 2017 Sutherland Springs church chooting, the 2018 Parkland School Shooting and the 2018 Santa Fe school shooting. This study’s results show that social bots participate in and contribute to online mass shooting conversations in a manner that is distinguishable from human contributions. Furthermore, while social bots accounted for fewer than 1% of total corpus user contributors, social network analysis centrality measures identified many bots with significant prominence in the conversation networks, densely occupying many of the highest eigenvector and out-degree centrality measure rankings, to include 82% of the top-100 eigenvector values of the Las Vegas retweet network.
Highlights
Mass shootings have become their own distinct phenomenon separate from the likes of general homicide and mass murder due to their continued prevalence and the natural draw of media attention to extreme events (Schildkraut et al, 2018)
Through the application of social network analysis (SNA) techniques to the mass shooting event retweet networks, we present our findings of directional conversation interactions in the ‘Intra-group and cross-group conversational patterns’ subsection and the centrality analysis rankings of bots in relation to humans in the ‘Relative importance of social bots in online mass shooting conversations’ subsection
This work contributes to the greater understanding of online social networks (OSNs) as a primary news source and the ability of them, along with new content providers, to frame a sustained, participative conversation surrounding the potentially contentious narrative focused on mass shootings
Summary
Mass shootings have become their own distinct phenomenon separate from the likes of general homicide and mass murder due to their continued prevalence and the natural draw of media attention to extreme events (Schildkraut et al, 2018). This study expands the literature by introducing quantitative ensemble methods to observe social bot activity in large-scale online conversations by examining suspected social bots within Twitter conversations associated with four recent mass shooting events: the Las Vegas concert shooting (October 1, 2017), the Sutherland Springs church shooting (November 5, 2017), the Parkland School Shooting (February 14, 2018) and the Santa Fe school shooting (May 18, 2018). We analysed the presence and contribution patterns of social bots in relation to human users in an effort to determine potential cross-conversational norms of bot behaviour within the highly polarised conversation topic of mass shooting events. The ‘Results and discussion’ section presents the findings of our employed methods to answer the study’s research questions, followed by the ‘Conclusion’ section
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