论文标题
了解算法与社区标签对超党派错误信息的精度的影响
Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation
论文作者
论文摘要
超级党派的错误信息已成为主要的公众关注。 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.结果表明，对于自由用户，所有标签都降低了虚假职位的可感知准确性和可信度，而不论帖子的意识形态如何。相比之下，对于保守的用户而言，标签的功效取决于帖子在意识形态上是否一致：算法标签在降低伪造的保守派帖子的可感知准确性和可信度与社区标签相比更有效，而所有标签都可以有效地减少在自由帖子中的信念。我们的结果揭示了各种错误信息标签的不同影响，取决于人们的政治意识形态。
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.
