  Hyper-partisan misinformation has become a major public concern. In order to
examine what type of misinformation label can mitigate hyper-partisan
misinformation sharing on social media, we conducted a 4 (label type:
algorithm, community, third-party fact-checker, and no label) X 2 (post
ideology: liberal vs. conservative) between-subjects online experiment (N =
1,677) in the context of COVID-19 health information. The results suggest that
for liberal users, all labels reduced the perceived accuracy and believability
of fake posts regardless of the posts' ideology. In contrast, for conservative
users, the efficacy of the labels depended on whether the posts were
ideologically consistent: algorithmic labels were more effective in reducing
the perceived accuracy and believability of fake conservative posts compared to
community labels, whereas all labels were effective in reducing their belief in
liberal posts. Our results shed light on the differing effects of various
misinformation labels dependent on people's political ideology.
