Content moderation faces a challenging task as social media's ability to
spread hate speech contrasts with its role in promoting global connectivity.
With rapidly evolving slang and hate speech, the adaptability of conventional
deep learning to the fluid landscape of online dialogue remains limited. In
response, causality inspired disentanglement has shown promise by segregating
platform specific peculiarities from universal hate indicators. However, its
dependency on available ground truth target labels for discerning these nuances
faces practical hurdles with the incessant evolution of platforms and the
mutable nature of hate speech. Using confidence based reweighting and
contrastive regularization, this study presents HATE WATCH, a novel framework
of weakly supervised causal disentanglement that circumvents the need for
explicit target labeling and effectively disentangles input features into
invariant representations of hate. Empirical validation across platforms two
with target labels and two without positions HATE WATCH as a novel method in
cross platform hate speech detection with superior performance. HATE WATCH
advances scalable content moderation techniques towards developing safer online
communities.

使用基于置信度的重新权重和对比正则化的方法，本研究提出了 HATE WATCH，这是一种弱监督因果分解的新型框架，可以绕过显式目标标签的需求，并有效地将输入特征分解为对仇恨言论具有不变性的表示。在两个带有目标标签和两个没有目标标签的平台上进行的实证验证将 HATE WATCH 定位为一种新的跨平台仇恨言论检测方法，并展现出卓越的性能。HATE WATCH 推动了可扩展的内容管理技术，以发展更加安全的在线社区。