揭示成年读者在评估社交媒体帖子可信度时所呈现的复杂性
Revealing complexities when adult readers engage in the credibility evaluation of social media posts
AI总结：
本研究通过844名众包工人对健康社交媒体帖子的评估发现，先验信念一致性和来源专业知识对感知可信度影响最大，而证据质量影响较小，且数据收集平台对评估结果有显著影响。
摘要：
本研究调查了来源特征、证据质量、众包平台以及对主题的先验信念在成年读者对简短健康相关社交媒体帖子进行可信度评估中的作用。研究人员通过操纵帖子中的来源特征（来源的专业知识、性别和种族）、声明的准确性以及证据质量（研究证据、证言、共识和个人经验），设计了涉及五个健康主题的帖子内容。随后，计算机生成了在这些其他受操纵方面有所变化的准确和不准确的帖子。从两个平台招募的众包工人（N = 844）被要求评估十条社交媒体帖子的可信度，共产生8380次评估。在进行可信度评估之前，评估了参与者对帖子主题的先验信念。结果表明，在控制帖子主题后，先验信念一致性和来源专业知识对准确和不准确社交媒体帖子的感知可信度影响最大。相比之下，支持健康声明的证据质量影响相对较小。此外，数据收集平台具有显著影响，即与另一个平台相比，包含不准确声明的帖子在一个平台上更有可能被评为较高级别。讨论了对可信度评估理论和研究的启示。
英文摘要：
The present study investigates the role of source characteristics, evidence quality, crowdsourcing platform, and prior beliefs of the topic in adult readers' credibility evaluations of short healthrelated social media posts. Researchers designed content for the posts concerning five health topics by manipulating source characteristics (source's expertise, gender, and ethnicity), accuracy of the claims, and evidence quality (research evidence, testimony, consensus, and personal experience) in the posts. Then, accurate and inaccurate posts varying in these other manipulated aspects were computer-generated. Crowdworkers (N = 844) recruited from two platforms were asked to evaluate the credibility of ten social media posts, resulting in 8380 evaluations. Before credibility evaluation, participants' prior beliefs on the topics of the posts were assessed. Results showed that prior belief consistency and source expertise most affected the perceived credibility of accurate and inaccurate social media posts after controlling for the topic of the post. In contrast, the quality of evidence supporting the health claim mattered relatively little. In addition, the data collection platform had a notable impact, such that posts containing inaccurate claims were much more likely to be rated higher on one platform compared to the other. Implications for credibility evaluation theory and research are discussed.
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。
