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

TL;DR

The paper investigates how the U.S. presidential electoral system, via swing versus safe states, shapes online disinformation on Twitter during the 2020 pre-election week. It combines a maximum-entropy based filtering framework (BiCM), discursive-community detection, NewsGuard domain reliability labeling, and BotometerLite bot detection to map the online debate to electoral incentives and identify two main communities (Rep and Rep-Dem-Journ). Key findings show swing-state discourse accounts for ~88% of traffic and carries more untrustworthy links, with bots driving a large share of this disinformation, especially within the Rep community. The results link electoral dynamics to information diffusion, offering insights into online misinformation risks and providing a methodological blueprint applicable to other elections and platforms.

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.

Figures (6)

• Figure 1: Entropy-based filtering procedure
• Figure 2: Retweet Network after label propagation (547k nodes, 1.8M edges).
• Figure 3: Classification of links
• Figure 4: Distribution of the number of link sharing in Rep (left) and Rep-Dem-Journ (right) (see Table\ref{['table:domains-tags']}).
• Figure 5: Distribution of the number of link shared per kind of state in Rep.