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Abusive language is a growing concern in many social media platforms.Repeated exposure to abusive speech has created physiological effects on thetarget users. Thus, the problem of abusive language should be addressed in allforms for online peace and safety. While extensive research exists in abusivespeech detection, most studies focus on English. Recently, many smearingincidents have occurred in India, which provoked diverse forms of abusivespeech in online space in various languages based on the geographic location.Therefore it is essential to deal with such malicious content. In this paper,to bridge the gap, we demonstrate a large-scale analysis of multilingualabusive speech in Indic languages. We examine different interlingual transfermechanisms and observe the performance of various multilingual models forabusive speech detection for eight different Indic languages. We alsoexperiment to show how robust these models are on adversarial attacks. Finally,we conduct an in-depth error analysis by looking into the models' misclassifiedposts across various settings. We have made our code and models public forother researchers.

Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages