Identifying Bangla Political Hate Speech using Transformer-based Approach

In recent times, Bangladesh has experienced significant political changes, sparking debates and discussions across various platforms, particularly on social media. As political opinions continue to proliferate, the rise of political hate speech targeting political groups and leaders has become increasingly evident. Such hate speech often includes undemocratic language or incitements to violence, which can destabilize social harmony. This study focuses on detecting political hate speech in Bangla text, providing valuable insights for social media moderation and public sentiment analysis. It highlights the challenges of hate speech detection in low-resource languages (LRLs) like Bangla, particularly in the context of recent political unrest. To tackle this issue, we introduced a novel dataset, BanglaPoliHaS-D, specifically designed for identifying political hate speech in Bangla. We explored a range of machine learning (ML), deep learning (DL), and transformer-based approaches and the results turned out to be quite promising. While most ML, DL, and transformer-based models demonstrated strong performance, Bangla-BERT outperformed all other approaches, attaining the highest accuracy of 92.41%.