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

• Published in: EPJ data science 2024-12, Vol.13 (1), p.25-23, Article 25
• Main Authors: Pratelli, Manuel, Petrocchi, Marinella, Saracco, Fabio, De Nicola, Rocco
• Format: Article
• Language: English
• Subjects: Complexity; Computational Approaches for Cyber Social Threats; Computer Appl. in Social and Behavioral Sciences; Computer Science; Data-driven Science; Disinformation flow; Elections; False information; Maximum-entropy null-models; Modeling and Theory Building; Political parties; Presidential elections; Social bots; Social network analysis; Swing and safe states; Take-all disease; U.S. presidential elections
• ISSN: 2193-1127; 2193-1127
• DOI: 10.1140/epjds/s13688-024-00461-6

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.