Operation gridlock: opposite sides, opposite strategies.

IF 2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Journal of Computational Social Science Pub Date : 2022-01-01 Epub Date: 2021-07-20 DOI:10.1007/s42001-021-00133-9
Matthew Babcock, Kathleen M Carley
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引用次数: 2

Abstract

Twitter and other social media platforms are important tools for competing groups to push their preferred messaging and respond to opposing views. Special attention has been paid to the role these tools play in times of emergency and important public decision-making events such as during the current COVID-19 pandemic. Here, we analyze the Pro- and Anti-Protest sides of the Twitter discussion surrounding the first few weeks of the anti-lockdown protests in the United States. We find that these opposing groups mirror the partisan divide regarding the protests in their use of specific phrases and in their sharing of external links. We then compare the users in each group and their actions and find that the Pro-Protest side acts more proactively, is more centrally organized, engages with the opposing side less, and appears to rely more on bot-like or troll-like users. In contrast, the Anti-Protest side is more reactive, has a larger presence of verified account activity (both as actors and targets), and appears to have been more successful in spreading its message in terms of both tweet volume and in attracting more regular type users. Our work provides insights into the organization of opposing sides of the Twitter debate and discussions over responses to the COVID-19 emergency and helps set the stage for further work in this area.

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行动僵局:对立的双方,对立的策略。
Twitter和其他社交媒体平台是相互竞争的团体发布自己喜欢的信息和回应对立观点的重要工具。特别关注这些工具在紧急情况和重要公共决策事件中发挥的作用,例如在当前的COVID-19大流行期间。在这里,我们分析了围绕美国反封锁抗议活动最初几周的推特讨论中支持和反对抗议的两方。我们发现,这些对立团体在使用特定用语和分享外部链接方面反映了对抗议活动的党派分歧。然后,我们比较了每个组中的用户和他们的行为,发现亲抗议方的行为更主动,更集中组织,与对方的接触更少,似乎更多地依赖于机器人或巨魔式的用户。相比之下,反抗议一方反应更积极,有更多的认证账户活动(作为参与者和目标),在推特数量和吸引更多普通用户方面,似乎在传播信息方面更成功。我们的工作有助于深入了解推特上针对COVID-19紧急情况的辩论和讨论的对立双方的组织情况,并有助于为这一领域的进一步工作奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Social Science
Journal of Computational Social Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
6.20
自引率
6.20%
发文量
30
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