In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the great amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not. In this work, we provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic. Classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance for two proposed tasks (binary and multi-label prediction). We make our code, models, and corpus available for further research.

翻译：近年来，由于仇恨言论的强度及其与针对受保护群体成员的暴力行为之间的关联，仇恨言论在社交网络及其他虚拟媒体中日益凸显其重要性。鉴于用户生成内容的庞大规模，研究人员在自动工具的研发上投入了大量精力，以辅助至少对最具威胁性形式的仇恨言论进行分析与审核。当前自动仇恨言论检测方法的局限性之一在于缺乏语境信息。大多数研究与资源基于无语境数据展开，即孤立消息——不包含任何类型的对话背景或讨论主题。这限制了用于判定社交网络帖子是否包含仇恨言论的可用信息。本研究基于用户对Twitter上媒体机构新闻帖子的回复，提供了一个面向语境化仇恨言论检测的新型语料库。该语料库采用里奥普拉滕斯方言变体的西班牙语采集，聚焦于与COVID-19疫情相关的仇恨言论。采用最新技术的分类实验表明，在两项提议任务（二元预测与多标签预测）中，添加语境信息可提升仇恨言论检测性能。我们将公开代码、模型及语料库以供后续研究。