Social media platforms, despite their value in promoting open discourse, are often exploited to spread harmful content. Current deep learning and natural language processing models used for detecting this harmful content overly rely on domain-specific terms affecting their capabilities 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 significant 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. To achieve good generalizability across platforms, one way 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 features (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 extensive experiments across four platforms highlight our model's enhanced efficacy compared to existing state-of-the-art methods in detecting generalized hate speech.

翻译：社交媒体平台尽管在促进公开讨论方面具有价值，却常被滥用于传播有害内容。当前用于检测此类有害内容的深度学习与自然语言处理模型过度依赖领域特定术语，这限制了其适应通用仇恨言论检测的能力。原因在于，这些模型往往过于聚焦特定语言信号或某类词语的使用。另一个重大挑战是，平台缺乏高质量标注数据用于训练，从而亟需能够适应不同分布偏移的跨平台模型。本研究提出一种跨平台仇恨言论检测模型，该模型能在单一平台数据上训练，并泛化至多个未见平台。为实现跨平台良好泛化性，一种方法是将输入表示解耦为不变特征与平台相关特征。我们同时论证，学习跨不同环境保持恒定的因果关系，可显著促进对仇恨言论中不变表征的理解。通过将输入解耦为平台相关特征（用于预测仇恨目标）与平台无关特征（用于预测仇恨存在），我们学习到能抵抗分布偏移的不变表征，进而利用这些特征在未见平台上预测仇恨言论。我们在四个平台上开展的广泛实验表明，相较于现有最先进方法，本模型在检测泛化仇恨言论方面具有更优效能。