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Detecting coordinated and bot-like behavior in Twitter: the Jürgen Conings case 检测 Twitter 中的协调行为和机器人行为:Jürgen Conings 案例
Pub Date : 2024-06-05 DOI: 10.1140/epjds/s13688-024-00477-y
Bart De Clerck, J. C. Fernández Toledano, Filip Van Utterbeeck, Luis E. C. Rocha
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引用次数: 0
Online disinformation in the 2020 U.S. election: swing vs. safe states 2020 年美国大选中的网络虚假信息:摇摆州与安全州
Pub Date : 2024-02-28 DOI: 10.48550/arXiv.2402.18664
Manuel Pratelli, M. Petrocchi, F. Saracco, R. Nicola
For U.S. presidential elections, most states use the so-called winner-take-all system, in which the state’s presidential electors are awarded to the winning political party in the state after a popular vote phase, regardless of the actual margin of victory. Therefore, election campaigns are especially intense in states where there is no clear direction on which party will be the winning party. These states are often referred to as swing states . To measure the impact of such an election law on the campaigns, we analyze the Twitter activity surrounding the 2020 US preelection debate, with a particular focus on the spread of disinformation. We find that about 88% of the online traffic was associated with swing states. In addition, the sharing of links to unreliable news sources is significantly more prevalent in tweets associated with swing states: in this case, untrustworthy tweets are predominantly generated by automated accounts. Furthermore, we observe that the debate is mostly led by two main communities, one with a predominantly Republican affiliation and the other with accounts of different political orientations. Most of the disinformation comes from the former.
在美国总统选举中,大多数州都采用所谓的 "赢者通吃 "制度,即在普选阶段结束后,无论实际胜负如何,该州的总统选举人都将被授予该州的获胜政党。因此,在一些没有明确胜负方向的州,竞选活动尤为激烈。这些州通常被称为摇摆州。为了衡量这种选举法对竞选活动的影响,我们分析了围绕 2020 年美国大选前辩论的推特活动,尤其关注虚假信息的传播。我们发现,约 88% 的网络流量与摇摆州有关。此外,在与摇摆州相关的推文中,分享不可靠新闻来源链接的现象明显更为普遍:在这种情况下,不可信的推文主要由自动账户生成。此外,我们还观察到,这场辩论主要由两个主要群体主导,一个是以共和党人为主的群体,另一个是不同政治倾向的账户。大部分虚假信息来自前者。
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引用次数: 0
Adaptation of student behavioural routines during Covid-19: a multimodal approach 在 Covid-19 期间调整学生行为常规:一种多模式方法
Pub Date : 2023-06-14 DOI: 10.1140/epjds/s13688-023-00429-y
Nicolo A. Girardini, Simone Centellegher, Andrea Passerini, Ivano Bison, Fausto Giunchiglia, B. Lepri
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引用次数: 0
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EPJ Data Science
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