Partisan Fact-Checkers' Warnings Can Effectively Correct Individuals' Misbeliefs About Political Misinformation

TL;DR

This paper investigates whether the political orientation of fact-checkers affects the efficacy of warnings in correcting political misinformation on social media. Using an online randomized experiment with 216 participants, it tests liberal- and conservative-leaning fact-checkers against a no-fact-checker condition across real and fake headlines. The findings show that partisan fact-checkers can reduce misbeliefs without triggering backfire, with stronger corrections for misinformation that aligns with users' ideologies, and a notable messenger-effect where incongruent sources can be especially impactful for conservatives. The results have practical implications for platform warning strategies, suggesting that explicitly labeled partisan messengers can enhance correction efficacy while maintaining trust, though perceived bias remains a consideration.

Abstract

Political misinformation, particularly harmful when it aligns with individuals' preexisting beliefs and political ideologies, has become widespread on social media platforms. In response, platforms like Facebook and X introduced warning messages leveraging fact-checking results from third-party fact-checkers to alert users against false content. However, concerns persist about the effectiveness of these fact-checks, especially when fact-checkers are perceived as politically biased. To address these concerns, this study presents findings from an online human-subject experiment (N=216) investigating how the political stances of fact-checkers influence their effectiveness in correcting misbeliefs about political misinformation. Our findings demonstrate that partisan fact-checkers can decrease the perceived accuracy of political misinformation and correct misbeliefs without triggering backfire effects. This correction is even more pronounced when the misinformation aligns with individuals' political ideologies. Notably, while previous research suggests that fact-checking warnings are less effective for conservatives than liberals, our results suggest that explicitly labeled partisan fact-checkers, positioned as political counterparts to conservatives, are particularly effective in reducing conservatives' misbeliefs toward pro-liberal misinformation.

Figures (6)

• Figure 1: Example of the experimental stimulus under the Blue Fact-Checker condition with a fake headline. The Red Fact-Checker condition is identical, except it includes a red fact-checker icon and the label "Red Fact-Checker." In the No Fact-Checker condition, no warning tags or related articles are shown and only the shared social media link with the headline is presented. The headline content remains the same across all three fact-checker conditions. For real headline stimuli, the format is the same as the No Fact-Checker condition, but displays a real headline instead.
• Figure 2: Overview of the Study Flowchart.
• Figure 3: Icons used for the Blue Fact-Checker (left) and the Red Fact-Checker (right).
• Figure 4: Average accuracy ratings across a 2 (veracity: real, fake) $\times$ 2 (news stance: congruent, incongruent) $\times$ 3 (fact-checker condition: congruent fact-checker, incongruent fact-checker, no fact-checker) factorial design in the congruency analysis. Error bars represent $\pm$ one standard error. The analysis included 68 participants in the congruent fact-checker, 72 in the incongruent fact-checker, and 76 in the no fact-checker condition.
• Figure 5: Average accuracy ratings of fake news across a 2 (veracity: real, fake) × 2 (political ideology: liberal, conservative) × 2 (news stance: pro-liberal, pro-conservative) × 3 (fact-checker condition: Blue, Red, no fact-checker) factorial design in the political stance analysis. Error bars represent $\pm$ one standard error. Among liberals, there were 38 participants in each of the Blue and no fact-checker conditions, and 34 in the Red fact-checker condition. Among conservatives, there were 38 participants in each of the Blue and no fact-checker conditions, and 30 in the Red fact-checker condition.