用删除讨论分析维基内容删改逻辑，发现删除预测更易但用户标签不靠谱。
Wikipedia is Not a Dictionary, Delete! Text Classification as a Proxy for Analysing Wiki Deletion Discussions
- 构建多语言维基删除讨论数据库，用于评估大模型预测能力。
- 删除类讨论预测准确率更高，但用户自标标签反而降低分类效果。
- 适合研究内容审核机制或社会性标注偏误的学者参考。
协作式知识库（如维基百科、维基数据）的自动化内容治理是一项重要且具挑战性的任务。本文构建了一个涵盖多个维基站点及三种语言的删除讨论数据库，用于评估多种大模型在不同任务上的表现，包括预测讨论结果、识别评论中隐含的政策依据等。结果显示，导致删除的讨论更易预测；令人意外的是，用户自动生成的标签（保留、删除或重定向）并未始终提升分类器性能，可能源于评论中用户表达的犹豫与权衡。该研究为理解维基社区决策机制提供了实证基础。
原文摘要 · Abstract (English)
Automated content moderation for collaborative knowledge hubs like Wikipedia or Wikidata is an important yet challenging task due to multiple factors. In this paper, we construct a database of discussions happening around articles marked for deletion in several Wikis and in three languages, which we then use to evaluate a range of LMs on different tasks (from predicting the outcome of the discussion to identifying the implicit policy an individual comment might be pointing to). Our results reveal, among others, that discussions leading to deletion are easier to predict, and that, surprisingly, self-produced tags (keep, delete or redirect) don't always help guiding the classifiers, presumably because of users' hesitation or deliberation within comments.
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