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The tremendous growth of social media users interacting in onlineconversations has led to significant growth in hate speech, affecting peoplefrom various demographics. Most of the prior works focus on detecting explicithate speech, which is overt and leverages hateful phrases, with very littlework focusing on detecting hate speech that is implicit or denotes hatredthrough indirect or coded language. In this paper, we present CoSyn, acontext-synergized neural network that explicitly incorporates user- andconversational context for detecting implicit hate speech in onlineconversations. CoSyn introduces novel ways to encode these external contextsand employs a novel context interaction mechanism that clearly captures theinterplay between them, making independent assessments of the amounts ofinformation to be retrieved from these noisy contexts. Additionally, it carriesout all these operations in the hyperbolic space to account for the scale-freedynamics of social media. We demonstrate the effectiveness of CoSyn on 6 hatespeech datasets and show that CoSyn outperforms all our baselines in detectingimplicit hate speech with absolute improvements in the range of 1.24% - 57.8%.

CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network