不完美自动事实核查何时适得其反：虚假信息下的社会学习研究
When Imperfect Automated Fact-Checking Backfires: Social Learning in the Presence of Misinformation
AI 导读
一项arXiv研究用序贯社会学习模型分析社交媒体上廉价但不完美的自动事实核查工具何时会适得其反。研究发现两种相反效应：核查工具帮助智能体摆脱错误级联，但其错误可能误导本可精确验证的用户。理论证明当真实文章信息量不足且人工核查成本较高时，该工具对中等可信度文章会严格降低福利；模拟显示其平均优于纯人工核查仅在准确率超过某一阈值时成立。
正文 · 原文
Abstract:Automated fact-checking tools, cheap and fast but imperfect, are increasingly available on social platforms. We study when such tools can backfire in a sequential social learning model of misinformation. Agents observe an article's reliability and their predecessors' actions, which indicate whether they liked or disliked the article. They may also pay to fact-check it. We examine how welfare, defined as the probability of a correct cascade, changes when a free, error-prone automated tool supplements costly but perfectly accurate manual fact-checking. Two opposing effects arise. Broader use of fact-checking helps agents escape wrong cascades. At the same time, errors from the automated tool can mislead agents who would otherwise have verified the article perfectly, reducing the likelihood of correct cascades. We prove that adding the tool strictly lowers welfare for articles of intermediate reliability when truthful articles are not too informative and manual checking is sufficiently costly. Simulations show that the tool can help on either side of the intermediate range, and that it improves on manual-only checking on average only above a threshold accuracy.
| Comments: | 8 pages, 5 figures |
| Subjects: | Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2611.08053 [cs.AI] |
| (or arXiv:2611.08053v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08053 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zixuan Liu [view email]
[v1]
Thu, 5 Nov 2026 14:10:05 UTC (283 KB)
来源：arXiv cs.AI · arxiv.org