利用社区语境丰富滥用语言检测
Enriching Abusive Language Detection with Community Context
摘要：
使用贬义词可以是善意的，也可以是积极的。当滥用检测模型将这些表达错误地归类为贬义时，它们无意中审查了边缘化群体进行的富有成效的对话。与非主导观点互动的一种方法是在对话中加入语境。以前的研究已经利用了用户级和线程级的功能，但它往往忽略了有效对话发生的空间。我们的论文强调了社区背景如何提高滥用语言检测的分类结果。我们为此作出两项主要贡献。首先，我们表明，在线社区集群的性质，他们对虐待受害者的支持。其次，我们建立了社区环境如何提高准确性，降低最先进的辱骂性语言分类器的误报率。这些发现为语境感知模型在辱骂性语言研究中的应用提供了一个有希望的方向。
英文摘要：
Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with non-dominant perspectives is to add context around conversations. Previous research has leveraged user- and thread-level features, but it often neglects the spaces within which productive conversations take place. Our paper highlights how community context can improve classification outcomes in abusive language detection. We make two main contributions to this end. First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse. Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers. These findings suggest a promising direction for context-aware models in abusive language research.
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。
