跨语言评估维基百科引用来源可靠性，靠编辑持久性预测可信度。
Language-Agnostic Modeling of Source Reliability on Wikipedia
- 基于编辑活动数据构建跨语言可靠性模型，捕捉域名在文章中留存时间。
- 英语等高资源语言F1宏得分约0.80，中等资源语言达0.65。
- 适合维基编辑者与内容社区可信度验证，尤其关注低资源语言适配。
近年来，验证信息来源的可信度成为应对虚假信息的关键需求。本文提出一种跨语言模型，用于评估多语言维基百科中网页域名作为引用来源的可靠性。该模型利用编辑活动数据，在气候变迁、新冠疫情、历史、媒体和生物学等不同争议性话题的文章中评估域名可靠性。通过构建反映域名在各类文章中使用模式的特征，模型能有效预测可靠性：英语等高资源语言的F1宏评分约为0.80；中资源语言为0.65；低资源语言表现不一。在所有情况下，域名在文章中持续存在的时长（即“持久性”）是最具预测性的特征之一。研究揭示了在不同资源水平语言间保持性能一致性的挑战，并表明从高资源语言迁移模型可提升效果。这些发现有助于维基编辑者核查引用，也为其他用户生成内容社区提供参考。
原文摘要 · Abstract (English)
Over the last few years, verifying the credibility of information sources has become a fundamental need to combat disinformation. Here, we present a language-agnostic model designed to assess the reliability of web domains as sources in references across multiple language editions of Wikipedia. Utilizing editing activity data, the model evaluates domain reliability within different articles of varying controversiality, such as Climate Change, COVID-19, History, Media, and Biology topics. Crafting features that express domain usage across articles, the model effectively predicts domain reliability, achieving an F1 Macro score of approximately 0.80 for English and other high-resource languages. For mid-resource languages, we achieve 0.65, while the performance of low-resource languages varies. In all cases, the time the domain remains present in the articles (which we dub as permanence) is one of the most predictive features. We highlight the challenge of maintaining consistent model performance across languages of varying resource levels and demonstrate that adapting models from higher-resource languages can improve performance. We believe these findings can assist Wikipedia editors in their ongoing efforts to verify citations and may offer useful insights for other user-generated content communities.
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