从不同角度设计有毒物质含量分类
Designing Toxic Content Classification for a Diversity of Perspectives
摘要：
在这项工作中，我们展示了如何现有的分类识别有毒的评论在线未能推广到互联网用户的不同关注。我们调查了17，280名参与者，以了解用户对有毒内容的期望如何因人口统计，信仰和个人经历而异。我们发现，历史上有骚扰风险的群体-例如LGBTQ+或年轻人-更有可能将Reddit，Twitter或4chan的随机评论标记为有毒，过去亲身经历过骚扰的人也是如此。根据我们的研究结果，我们展示了当前的一刀切毒性分类算法，如Jigsaw的Perspective API，通过个性化模型调整，平均可以将准确率提高86%。最后，我们强调当前的陷阱和新的设计方向，可以提高所有用户的有毒内容分类器的公平性和有效性。
英文摘要：
In this work, we demonstrate how existing classifiers for identifying toxic comments online fail to generalize to the diverse concerns of Internet users. We survey 17,280 participants to understand how user expectations for what constitutes toxic content differ across demographics, beliefs, and personal experiences. We find that groups historically at-risk of harassment - such as people who identify as LGBTQ+ or young adults - are more likely to to flag a random comment drawn from Reddit, Twitter, or 4chan as toxic, as are people who have personally experienced harassment in the past. Based on our findings, we show how current one-size-fits-all toxicity classification algorithms, like the Perspective API from Jigsaw, can improve in accuracy by 86% on average through personalized model tuning. Ultimately, we highlight current pitfalls and new design directions that can improve the equity and efficacy of toxic content classifiers for all users.
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。
