Communal violence in online forums has become extremely prevalent in South Asia, where many communities of different cultures coexist and share resources. These societies exhibit a phenomenon characterized by strong bonds within their own groups and animosity towards others, leading to conflicts that frequently escalate into violent confrontations. To address this issue, we have developed the first comprehensive framework for the automatic detection of communal violence markers in online Bangla content accompanying the largest collection (13K raw sentences) of social media interactions that fall under the definition of four major violence class and their 16 coarse expressions. Our workflow introduces a 7-step expert annotation process incorporating insights from social scientists, linguists, and psychologists. By presenting data statistics and benchmarking performance using this dataset, we have determined that, aside from the category of Non-communal violence, Religio-communal violence is particularly pervasive in Bangla text. Moreover, we have substantiated the effectiveness of fine-tuning language models in identifying violent comments by conducting preliminary benchmarking on the state-of-the-art Bangla deep learning model.

自动检测孟加拉文网络内容中的社群暴力特征的首个综合框架的数据统计和基准测试表明，除了非社群暴力类别外，宗教社群暴力在孟加拉文本中尤为普遍；此外，通过对最先进的孟加拉深度学习模型进行初步基准测试，我们证实了微调语言模型在识别暴力评论方面的有效性。

暴力图谱：基于社交媒体互动构建孟加拉宗派表达数据集的广泛框架