用机器学习提前预测健康类讨论中的毒性强言，防患于未然。
Predictively Combatting Toxicity in Health-related Online Discussions through Machine Learning
- 基于协同过滤模型，预测用户在新冠话题中可能产生的毒性言论。
- 在相关指标上预测准确率超80%，可有效识别潜在冲突组合。
- 适合平台方用于预防性干预，提升健康社区对话质量。
在健康类话题中，线上讨论中的用户毒性常引发社会冲突或传播危险、非科学行为；现有应对方式多为检测、标记或删除已有毒言论，但往往对平台和用户均不利。本文提出一种预测性对策，预先判断用户在健康类在线讨论中可能产生毒性的互动。采用基于协同过滤的机器学习方法，预测Reddit上用户与子社区间关于新冠疫情的对话毒性，相关指标预测性能超过80%，从而实现冲突用户与子社区的提前规避。
原文摘要 · Abstract (English)
In health-related topics, user toxicity in online discussions frequently becomes a source of social conflict or promotion of dangerous, unscientific behaviour; common approaches for battling it include different forms of detection, flagging and/or removal of existing toxic comments, which is often counterproductive for platforms and users alike. In this work, we propose the alternative of combatting user toxicity predictively, anticipating where a user could interact toxically in health-related online discussions. Applying a Collaborative Filtering-based Machine Learning methodology, we predict the toxicity in COVID-related conversations between any user and subcommunity of Reddit, surpassing 80% predictive performance in relevant metrics, and allowing us to prevent the pairing of conflicting users and subcommunities.
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