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Using a case study method to frame our work, we collected over seven million tweets during a one\u2010month 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\u2010 and micro\u2010level 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 bot\u2010generated content. These findings add depth to the current understanding of bot strategies and their effectiveness. Understanding these strategies can inform efforts to combat dubious information as well as more insidious disinformation campaigns.<\/jats:p>","DOI":"10.1002\/pra2.2018.14505501028","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T16:52:19Z","timestamp":1549039939000},"page":"254-263","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["\u201cLife never matters in the DEMOCRATS MIND\u201d: Examining strategies of retweeted social bots during a mass shooting event"],"prefix":"10.1002","volume":"55","author":[{"given":"Vanessa L.","family":"Kitzie","sequence":"first","affiliation":[{"name":"University of South Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehsan","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"University of South Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Karami","sequence":"additional","affiliation":[{"name":"University of South Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,2]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"Abokhodair N. Yoo D. &Mcdonald D. W.(2015). Dissecting a social botnet: Growth content and influence in Twitter. InProceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing(pp.839\u2013851).http:\/\/doi.org\/10.1145\/2675133.2675208","DOI":"10.1145\/2675133.2675208"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.5210\/fm.v21i11.7090"},{"key":"e_1_2_8_4_1","unstructured":"Boyd D.(2018).Did media literacy backfire? Retrieved April 4 2018 fromhttps:\/\/points.datasociety.net\/did-media-literacy-backfire-7418c084d88d"},{"key":"e_1_2_8_5_1","unstructured":"Brachten F. Stieglitz S. Hofeditz L. Kloppenborg K. &Reimann A.(2017). Strategies and Influence of Social Bots in a 2017 German state election \u2010 A case study on Twitter.Proceeedings of the Australasian Conference on Information Systems (May) 1\u201312. 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