Political Fact-Checking Efforts are Constrained by Deficiencies in Coverage, Speed, and Reach

Abstract

Fact-checking has been promoted as a key method for combating political misinformation. Comparing the spread of election-related misinformation narratives along with their relevant political fact-checks, this study provides the most comprehensive assessment to date of the real-world limitations faced by political fact-checking efforts. To examine barriers to impact, this study extends recent work from laboratory and experimental settings to the wider online information ecosystem present during the 2022 U.S. midterm elections. From analyses conducted within this context, we find that fact-checks as currently developed and distributed are severely inhibited in election contexts by constraints on their i. coverage, ii. speed, and, iii. reach. Specifically, we provide evidence that fewer than half of all prominent election-related misinformation narratives were fact-checked. Within the subset of fact-checked claims, we find that the median fact-check was released a full four days after the initial appearance of a narrative. Using network analysis to estimate user partisanship and dynamics of information spread, we additionally find evidence that fact-checks make up less than 1.2\% of narrative conversations and that even when shared, fact-checks are nearly always shared within,rather than between, partisan communities. Furthermore, we provide empirical evidence which runs contrary to the assumption that misinformation moderation is politically biased against the political right. In full, through this assessment of the real-world influence of political fact-checking efforts, our findings underscore how limitations in coverage, speed, and reach necessitate further examination of the potential use of fact-checks as the primary method for combating the spread of political misinformation.

Figures (15)

• Figure 1: Predicted probability of being fact-checked in the 2022 Midterm Election. Additional classifications are excluded for brevity but included in the SI. Points represent regression coefficients. Lines represent the 95% confidence interval. The dashed line represents the exact null effect. Black lines are statistically significant at $\alpha = .05$.
• Figure 2: Misinformation posts published before (black) and after (grey) the modal narrative was fact-checked (grouped by 12 hour totals). Fact-checks most often occur only after the typical rumor has already lost momentum, as indicated by aggregated (median) fact-check response time (vertical line).
• Figure 3: Network visualization of online discussion of false and misleading narratives and their corresponding fact-checks. Nodes represent X/Twitter users while edges represent reposts between users. In Figure A, nodes are all colored gray while edges are colored based on the content of the repost. Red edges are fact-checks of right-leaning narratives while blue edges are fact-checks of left-leaning narratives. Gray edges are all other narrative-related reposts that are not fact-checks. In Figure B, nodes are colored by the partisanship of the user (light blue is left-leaning, light red is right-leaning, green is neutral) and edges are colored according to the partisanship of the source (reposting) user. The left cluster is primarily left-leaning users while the larger cluster on the right is right-leaning. Visualization conducted using the ForceAtlas2 graph layout algorithm on Gephi network visualization software.
• Figure 4: Mechanisms of spread between users with different party leanings for posts discussing the false and misleading narratives and posts sharing fact-check links. Y-axis denotes the party (L - left-leaning, N - neutral/apolitical, R - right-leaning) of the posting user (e.g. the one who quotes, reposts, or replies), X-axis denotes the party of the original poster.
• Figure 5: The plot illustrates the expected influence of influence involvement (total users with 100K+ followers) on Fact-Check probability.