Abstract:
In the current era, social media has emerged as a very useful and reliable means of
communication between different people and communities. However, with the leverage
of communication platforms and billions of social media users, it became more challeng ing to stop hateful, abusive, or offensive content spread by extremists that are various
aspects of Anti-social Behavior (ASB). Multiple users from several regions use different
languages (a mix of native, local and other languages) to express their emotions. In
the South Asia region, the frequently used languages on these platforms are Roman
Urdu-English and Roman Hindi-English. Therefore, the ASB detection with multi lingual model settings represents a wide area of interest for all kinds of social media
platforms. Failing to properly address this issue over time on a global scale has already
led to morally questionable real-life events, human deaths, and the perpetuation of hate
itself. In this thesis, we perform a sentimental analysis of the Roman Urdu-English and
Roman Hindi-English languages using Transformer based mBERT and XLM-R mod els. Moreover, we process the negatively classified sequences for detecting/analyzing the
ASB.
