Mapping Violence: Developing an Extensive Framework to Build a Bangla Sectarian Expression Dataset from Social Media Interactions
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917643339956224 |
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| author | Tasnim, Nazia Gupta, Sujan Sen Shihab, Md. Istiak Hossain Juee, Fatiha Islam Tahsin, Arunima Ghum, Pritom Fatema, Kanij Haque, Marshia Farzana, Wasema Nasir, Prionti KhudaBukhsh, Ashique Sadeque, Farig Sushmit, Asif |
| author_facet | Tasnim, Nazia Gupta, Sujan Sen Shihab, Md. Istiak Hossain Juee, Fatiha Islam Tahsin, Arunima Ghum, Pritom Fatema, Kanij Haque, Marshia Farzana, Wasema Nasir, Prionti KhudaBukhsh, Ashique Sadeque, Farig Sushmit, Asif |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_11752 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mapping Violence: Developing an Extensive Framework to Build a Bangla Sectarian Expression Dataset from Social Media Interactions Tasnim, Nazia Gupta, Sujan Sen Shihab, Md. Istiak Hossain Juee, Fatiha Islam Tahsin, Arunima Ghum, Pritom Fatema, Kanij Haque, Marshia Farzana, Wasema Nasir, Prionti KhudaBukhsh, Ashique Sadeque, Farig Sushmit, Asif Computation and Language Computers and Society 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. |
| title | Mapping Violence: Developing an Extensive Framework to Build a Bangla Sectarian Expression Dataset from Social Media Interactions |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2404.11752 |