1 research outputs found
CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network
The tremendous growth of social media users interacting in online
conversations has also led to significant growth in hate speech. Most of the
prior works focus on detecting explicit hate speech, which is overt and
leverages hateful phrases, with very little work focusing on detecting hate
speech that is implicit or denotes hatred through indirect or coded language.
In this paper, we present CoSyn, a user- and conversational-context synergized
network for detecting implicit hate speech in online conversation trees. CoSyn
first models the user's personal historical and social context using a novel
hyperbolic Fourier attention mechanism and hyperbolic graph convolution
network. Next, we jointly model the user's personal context and the
conversational context using a novel context interaction mechanism in the
hyperbolic space that clearly captures the interplay between the two and makes
independent assessments on the amounts of information to be retrieved from both
contexts. CoSyn performs all operations in the hyperbolic space to account for
the scale-free dynamics of social media. We demonstrate the effectiveness of
CoSyn both qualitatively and quantitatively on an open-source hate speech
dataset with Twitter conversations and show that CoSyn outperforms all our
baselines in detecting implicit hate speech with absolute improvements in the
range of 8.15% - 19.50%.Comment: Under review at IJCAI 202