Online disinformation in the 2020 U.S. election: swing vs. safe states

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Manuel Pratelli, Marinella Petrocchi, Fabio Saracco, Rocco De Nicola
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Abstract

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.

Abstract Image

2020 年美国大选中的网络虚假信息:摇摆州与安全州
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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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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