  Social Media platforms have been seeing adoption and growth in their usage
over time. This growth has been further accelerated with the lockdown in the
past year when people's interaction, conversation, and expression were limited
physically. It is becoming increasingly important to keep the platform safe
from abusive content for better user experience. Much work has been done on
English social media content but text analysis on non-English social media is
relatively underexplored. Non-English social media content have the additional
challenges of code-mixing, transliteration and using different scripture in
same sentence. In this work, we propose an approach for abusiveness
identification on the multilingual Moj dataset which comprises of Indic
languages. Our approach tackles the common challenges of non-English social
media content and can be extended to other languages as well.
