解决在线滥用问题：自动滥用检测方法调查
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods
摘要：
互联网上的虐待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网络平台上面临骚扰、种族主义、人身攻击和其他类型的虐待。这种虐待对个人的心理影响可能是深刻和持久的。因此，在过去的几年中，在自然语言处理（NLP）领域中已经有了大量的研究工作来实现自动滥用检测。在本文中，我们提出了一个全面的调查的方法，已提出的日期，从而为这一领域的进一步发展提供了一个平台。我们描述了现有的数据集，并回顾了滥用检测的计算方法，分析了它们的优势和局限性。我们讨论了出现的主要趋势，强调了仍然存在的挑战，概述了可能的解决方案，并提出了道德和可解释性的指导方针
英文摘要：
Abuse on the Internet represents an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse on online platforms. The psychological effects of such abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abuse detection in the field of natural language processing (NLP). In this paper, we present a comprehensive survey of the methods that have been proposed to date, thus providing a platform for further development of this area. We describe the existing datasets and review the computational approaches to abuse detection, analyzing their strengths and limitations. We discuss the main trends that emerge, highlight the challenges that remain, outline possible solutions, and propose guidelines for ethics and explainability
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。
