C o S yn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network (2023)

Abstract: The tremendous growth of social media users interacting in online conversations has led to significant growth in hate speech affecting people from various demographics. Most of the prior works focus on detecting explicit hate speech, which is overt and leverages hateful phrases, with very little work focusing on detecting hate speech that is implicit or denotes hatred through indirect or coded language. In this paper, we present CoSyn, a context synergized neural network that explicitly incorporates user- and conversational-context for detecting implicit hate speech in online conversations. CoSyn introduces novel ways to encode these external contexts and employs a novel context interaction mechanism that clearly captures the interplay between them, making independent assessments of the amounts of information to be retrieved from these noisy contexts. Additionally, it carries out all these operations in the hyperbolic space to account for the scale-free dynamics of social media. We demonstrate the effectiveness of CoSyn on 6 hate speech datasets and show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%. We make our code available.

## introduction hate speech is defined as the act of making utterances that can potentially offend, insult, or threaten a person or a community based on their caste, religion, sexual orientation, or gender @xcite . for social media companies like twitter, facebook, and reddit, appropriately dealing with hate speech has been a crucial challenge @xcite . hate speech can take the form of overt abuse, also known as explicit hate speech @xcite , or can be uttered in coded or indirect language, also known as in #loc, some believers have been going to cow shelters once a week to cover their bodies in cow dung and urine in the hope it will boost their immunity against, or help them recover from, covid-19. | #name implicit hate speech @xcite . fig. 1 illustrates a twitter conversation where users convey hate implicitly through sarcasm and conversational context. ## related work prior research on identifying hate speech on social media has proposed systems that can flag online hate speech with remarkable accuracy @xcite . however, as mentioned hyperbolic networks have been explored earlier for tasks that include modeling user interactions in social media, like suicide ideation detection @xcite , fake news detection @xcite , online time stream modeling @xcite , etc. all these works show that modeling the hierarchical and scale-free nature of social networks and data generated online benefits from modeling in the hyperbolic space over euclidean space. ## conclusion in this paper, we present cosyn, a novel learning framework to detect implicit hate speech in online conversations. cosyn jointly models the conversational context and the author's historical and social context in the hyperbolic space to classify whether a target utterance is hateful. leveraging these contexts allows cosyn to effectively detect implicit hate speech ubiquitous in online social media. of all its components. therefore, as a direction for future research, our focus will be on enhancing the performance of individual components.