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：社交媒体平台在促进公开讨论方面具有价值，但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特定领域的术语，这影响了它们适应通用化仇恨言论检测的能力，因为模型往往过于聚焦于特定语言信号或某一类词汇的使用。另一个重大挑战是，平台缺乏高质量标注数据用于训练，因此需要跨平台模型来适应不同的分布偏移。本研究提出了一种跨平台仇恨言论检测模型，该模型可在某一平台的数据上训练，并泛化至多个未见平台。为实现跨平台的良好泛化性，一种方法是将输入表示解耦为不变特征和平台依赖特征。我们还认为，学习跨不同环境保持不变的因果关系，能够显著增强对仇恨言论中不变表示的理解。通过将输入解耦为平台依赖特征（用于预测仇恨目标）和平台无关特征（用于预测仇恨存在），我们学习到能够抵抗分布偏移的不变表示，进而利用这些特征在未见平台上预测仇恨言论。在四个平台上的大量实验表明，与现有最先进方法相比，本模型在检测泛化仇恨言论方面具有更优性能。