理解社区标签与社区标签对超党派错误信息感知准确性的影响
Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation
摘要：
超党派的错误信息已成为公众关注的主要问题。为了研究哪种类型的错误信息标签可以减轻社交媒体上超党派的错误信息共享，我们在COVID-19健康信息的背景下进行了一项4（标签类型：算法，社区，第三方事实检查器和无标签）X 2（后意识形态：自由派与保守派）的受试者在线实验（N = 1，677）。结果表明，对于自由派用户来说，无论帖子的意识形态如何，所有标签都会降低虚假帖子的感知准确性和可信度。相比之下，对于保守派用户来说，标签的有效性取决于帖子是否在意识形态上一致：与社区标签相比，算法标签在降低虚假保守派帖子的感知准确性和可信度方面更有效，而所有标签都能有效降低他们对自由派帖子的信任。我们的研究结果揭示了依赖于人们的政治意识形态的各种错误信息标签的不同影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
