Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech (2023)

Abstract: While many prior studies have applied computational approaches, such as machine learning, to detect and moderate hate speech, only scant attention has been paid to the task of identifying the underlying cause of hate speech. In this study, we introduce the concept of hate instigating speech, which refers to a specific type of textual posts on online platforms that stimulate or provoke others to engage in hate speech. The identification of hate instigating speech carries substantial practical implications for effective hate speech moderation. Rather than targeting individual instances of hate speech, by focusing on their roots, i.e., hate instigating speech, it becomes possible to significantly reduce the volume of content that requires review for moderation. Additionally, targeting hate instigating speech enables early prevention of the spread and propagation of hate speech, further enhancing the effectiveness of moderation efforts. However, several challenges hinder researchers from addressing the identification of hate instigating speech. First, there is a lack of comprehensive datasets specifically annotated for hate instigation, making it difficult to train and evaluate computational models effectively. Second, the subtle and nuanced nature of hate instigating speech (e.g., seemingly non-offensive texts serve as catalysts for triggering hate speech) makes it difficult to apply off-the-shelf machine learning models to the problem. To address these challenges, in this study, we have developed and released a multilingual dataset specifically designed for the task of identifying hate instigating speech. Specifically, it encompasses both English and Korean, allowing for a comprehensive examination of hate instigating speech across different linguistic contexts. We have applied existing machine learning models to our dataset and the results demonstrate that the extant models alone are insufficient for effectively detecting hate instigating speech. This finding highlights the need for further attention from the academic community to address this specific challenge. We expect our study and dataset to inspire researchers to explore innovative methods that can enhance the accuracy of hate instigating speech detection, ultimately contributing to more effective moderation and prevention of hate speech propagation online.

## introduction hate speech is a pressing issue in our society that requires significant attention. to this end, machine learning (ml) has gained a large amount of attention from both academia and industry as a valuable means to address hate speech @xcite . in general, ml methods use features extracted from textual content, such as document embeddings and pos tags, with other behavioral information, such as social interactions, to enhance hate speech classification performance. previous research @xcite has demonstrated the promising results of these approaches in identifying instances of hate speech. although the identification and moderation of hate speech hold significance per se, the paramount objective should lie in eliminating the underlying cause that gives rise to hate speech. for instance, within the context of online interactions, various forms of discourse exist that may not explicitly embody hatred itself but instead contribute to the provocation of hostility, aggression, and toxicity. these include behaviors like trolling. in this study, we refer to these behaviors as hate instigating behaviors. while it is crucial to identify and control hate instigating behaviors (or more specifically ## discussion in this study, we provide a novel concept of hate instigation speech (his) and develop a dataset that can be a catalyst for future research on hate speech moderation, particularly those based on ml-based approaches. compared to prior studies, our approach of highlighting his, which identifies and manages the root of hate speech, is a more effective way of dealing with the propagation of online hate speech. that is, by targeting the root causes of hatred, we can potentially have a greater impact on reducing the propagation and harmful effects of hate speech in online environments. furthermore, our approach is capable of addressing broader forms of discourse, which may contribute to the propagation of hate speech but were not the primary focus of moderation in previous studies. that is, while non-hate his does not explicitly manifest hatred by itself but still significantly contributes to the provocation of hostility, aggression, and toxicity, prior studies have not paid proper attention to it. by introducing such a broader concept of harmful discourse, we expect more comprehensive moderation strategies which will foster healthier and more inclusive online environments. ## limitations while we expect that our study will make significant contributions to the field of hate speech moderation, it is not without limitations. first, one potential limitation is that we did not consider the chain of hate instigation. that is, we assumed that all replies are affected by the comment associated with it. however, there might be some cases where a reply, rather than a comment, affects other replies. nonetheless, as the pioneering effort to create a human-annotated dataset for identifying hateinstigating speech provides a foundational basis for future studies that can potentially present a more nuanced and precise definition of this phenomenon. in addition, there is a possibility that our definition of his is too broad or coarse, warranting a more nuanced and granular approach. instead of simply classifying texts as either his or nhis, we may consider the multifaceted nature of hate instigating speech. this will involve categorizing his into various subcategories that correspond to different types of hatred, such as racism instigating speech, sexism instigating speech, religious his, motiveless his, and so on. lastly, it is worth nothing that our data in this study solely comprises textual information. however, incorporating a different modality of data, such as images, may provide a different angle to our problem setting.