SARNET: SARCASM VS TRUE-HATE DETECTION NETWORK

Harsh Mittal, Kartikeya Singh Chauhan, Anil Singh Parihar, Kavinder Singh, Ashutosh Pandey

Keywords: Game Theory, Hate Speech, Sarcasm, Nash Equilibrium, Prisoner's Dilemma

TL;DR: This research paper focuses on quasi-ternary classification of hate and sarcasm in a tweet using game theory, Nash Equilibrium and deep learning.

Abstract: At times hate speech detection classifiers miss the context of a sentence and flag a sarcastic tweet incorrectly. To tackle this problem by emphasising on the context of a tweet we propose SarNet. SarNet is a two-fold deep learning based model which follows a quasi-ternary labelling strategy and contextually classifies a tweet as hate, sarcastic or neither. The first module of SarNet is an ANN-BiLSTM based Pyramid Network used to calculate the hate and sarcastic probabilities of a sentence. The second module of the SarNet is the Nash Equalizer which stems from the concept of game theory and prisoner’s dilemma. It treats hate and sarcasm as two prisoners. A payoff matrix is constructed to calculate the true hate of the tweet. True hate considers the hate part of a tweet excluding the sarcastic part of the tweet. Thus, this gives a true estimate of the hate content in a tweet thereby decreasing the number of sarcastic tweets being falsely flagged as hate. Our proposed model is trained on state-of-the-art hate speech and sarcasm datasets in the English language. The precision, recall and F1 score of our proposed model is 0.93, 0.84 and 0.88 respectively. Comparison with state-of-the-art architectures demonstrated better performance of SarNet by a significant margin.

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