  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.
