  Abusive language is a growing concern in many social media platforms.
Repeated exposure to abusive speech has created physiological effects on the
target users. Thus, the problem of abusive language should be addressed in all
forms for online peace and safety. While extensive research exists in abusive
speech detection, most studies focus on English. Recently, many smearing
incidents have occurred in India, which provoked diverse forms of abusive
speech 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 multilingual
abusive speech in Indic languages. We examine different interlingual transfer
mechanisms and observe the performance of various multilingual models for
abusive speech detection for eight different Indic languages. We also
experiment to show how robust these models are on adversarial attacks. Finally,
we conduct an in-depth error analysis by looking into the models' misclassified
posts across various settings. We have made our code and models public for
other researchers.
