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Contending hate speech in social media is one of the most challenging socialproblems of our time. There are various types of anti-social behavior in socialmedia. Foremost of them is aggressive behavior, which is causing many socialissues such as affecting the social lives and mental health of social mediausers. In this paper, we propose an end-to-end ensemble-based architecture toautomatically identify and classify aggressive tweets. Tweets are classifiedinto three categories - Covertly Aggressive, Overtly Aggressive, andNon-Aggressive. The proposed architecture is an ensemble of smaller subnetworksthat are able to characterize the feature embeddings effectively. Wedemonstrate qualitatively that each of the smaller subnetworks is able to learnunique features. Our best model is an ensemble of Capsule Networks and resultsin a 65.2% F1 score on the Facebook test set, which results in a performancegain of 0.95% over the TRAC-2018 winners. The code and the model weights arepublicly available athttps://github.com/parthpatwa/Hater-O-Genius-Aggression-Classification-using-Capsule-Networks.

Hater-O-Genius Aggression Classification using Capsule Networks