"Life never matters in the DEMOCRATS MIND": Examining Strategies of Retweeted Social Bots During a Mass Shooting Event
Vanessa L. Kitzie, Ehsan Mohammadi, Amir Karami

TL;DR
This study analyzes social bot strategies on Twitter during a mass shooting, revealing diverse tactics beyond disinformation and highlighting human amplification of bot content, which informs counter-disinformation efforts.
Contribution
It provides a detailed analysis of social bot strategies during a mass shooting event, showing their diversity and impact on information dissemination.
Findings
Bots use diverse strategies including baiting and sharing information.
Humans primarily disseminate bot-generated content.
Bots amplify conversation about mass shootings.
Abstract
This exploratory study examines the strategies of social bots on Twitter that were retweeted following a mass shooting event. Using a case study method to frame our work, we collected over seven million tweets during a one-month period following a mass shooting in Parkland, Florida. From this dataset, we selected retweets of content generated by over 400 social bot accounts to determine what strategies these bots were using and the effectiveness of these strategies as indicated by the number of retweets. We employed qualitative and quantitative methods to capture both macro- and micro-level perspectives. Our findings suggest that bots engage in more diverse strategies than solely waging disinformation campaigns, including baiting and sharing information. Further, we found that while bots amplify conversation about mass shootings, humans were primarily responsible for disseminating…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMisinformation and Its Impacts · Social Media and Politics · Hate Speech and Cyberbullying Detection
