Causality Guided Disentanglement for Cross-Platform Hate Speech Detection

Abstract

espite their value in promoting open discourse, social media plat-forms are often exploited to spread harmful content. Current deep learning and natural language processing models used for detect-ing this harmful content rely on domain-specific terms affecting their ability to adapt to generalizable hate speech detection. This is because they tend to focus too narrowly on particular linguistic signals or the use of certain categories of words. Another signifi-cant challenge arises when platforms lack high-quality annotated data for training, leading to a need for cross-platform models that can adapt to different distribution shifts. Our research introduces a cross-platform hate speech detection model capable of being trained on one platform's data and generalizing to multiple unseen platforms. One way to achieve good generalizability across plat-forms is to disentangle the input representations into invariant and platform-dependent features. We also argue that learning causal relationships, which remain constant across diverse environments, can significantly aid in understanding invariant representations in hate speech. By disentangling input into platform-dependent fea-tures (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts. These features are then used to predict hate speech across unseen platforms. Our ex-tensive experiments across four platforms highlight our model's enhanced efficacy compared to existing state-of-The-Art methods in detecting generalized hate speech