  Social media platforms serve as accessible outlets for individuals to express
their thoughts and experiences, resulting in an influx of user-generated data
spanning all age groups. While these platforms enable free expression, they
also present significant challenges, including the proliferation of hate speech
and offensive content. Such objectionable language disrupts objective discourse
and can lead to radicalization of debates, ultimately threatening democratic
values. Consequently, organizations have taken steps to monitor and curb
abusive behavior, necessitating automated methods for identifying suspicious
posts. This paper contributes to Hate Speech and Offensive Content
Identification in English and Indo-Aryan Languages (HASOC) 2023 shared tasks
track. We, team Z-AGI Labs, conduct a comprehensive comparative analysis of
hate speech classification across five distinct languages: Bengali, Assamese,
Bodo, Sinhala, and Gujarati. Our study encompasses a wide range of pre-trained
models, including Bert variants, XLM-R, and LSTM models, to assess their
performance in identifying hate speech across these languages. Results reveal
intriguing variations in model performance. Notably, Bert Base Multilingual
Cased emerges as a strong performer across languages, achieving an F1 score of
0.67027 for Bengali and 0.70525 for Assamese. At the same time, it
significantly outperforms other models with an impressive F1 score of 0.83009
for Bodo. In Sinhala, XLM-R stands out with an F1 score of 0.83493, whereas for
Gujarati, a custom LSTM-based model outshined with an F1 score of 0.76601. This
study offers valuable insights into the suitability of various pre-trained
models for hate speech detection in multilingual settings. By considering the
nuances of each, our research contributes to an informed model selection for
building robust hate speech detection systems.
