为滥用检测建模用户和在线社区：道德和可解释性的立场
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability
摘要：
互联网上的滥用是我们这个时代的一个重要社会问题。数以百万计的互联网用户在各种平台上面临骚扰、种族主义、人身攻击和其他类型的虐待。虐待对个人的心理影响可能是深刻和持久的。因此，在过去的几年里，在NLP领域，已经有了大量的研究工作来自动检测滥用语言。在这份立场文件中，我们讨论了用户和在线社区建模在滥用检测中的作用。具体来说，我们回顾和分析最先进的方法，利用用户或社区信息，以加强对滥用语言的理解和检测。然后，我们探讨了将用户和社区信息的伦理挑战，布局考虑，以指导未来的研究。最后，我们讨论的主题，在滥用语言检测的可解释性，提出属性，一个可解释的方法应该旨在展示。我们描述了如何用户和社区信息可以促进实现这些属性，并讨论了有效的操作性的可解释性的属性。
英文摘要：
Abuse on the Internet is an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various platforms. The psychological effects of abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abusive language detection in the field of NLP. In this position paper, we discuss the role that modeling of users and online communities plays in abuse detection. Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language. We then explore the ethical challenges of incorporating user and community information, laying out considerations to guide future research. Finally, we address the topic of explainability in abusive language detection, proposing properties that an explainable method should aim to exhibit. We describe how user and community information can facilitate the realization of these properties and discuss the effective operationalization of explainability in view of the properties.
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。
