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。当前用于检测有害内容的深度学习和自然语言处理模型过度依赖领域特定词汇，这削弱了它们适应泛化性仇恨言论检测的能力。这是因为这些模型往往过于狭隘地聚焦于特定语言信号或某些词语类别的使用。另一个重大挑战在于，当平台缺乏高质量标注训练数据时，需要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，该模型可在单个平台数据上训练，并泛化至多个未见过平台。为实现跨平台的良好泛化能力，一种方法是将输入表征解耦为不变特征与平台依赖特征。我们进一步论证，学习跨多样环境保持恒定的因果关系，能显著辅助理解仇恨言论中的不变表征。通过将输入解耦为平台依赖特征（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。这些特征随后被用于检测未见过平台上的仇恨言论。我们在四个平台上的大量实验表明，相较于现有最先进方法，本模型在检测泛化性仇恨言论方面展现出更优效能。