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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.22380 |
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| _version_ | 1866918516395868160 |
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| author | Rastogi, Pranshu Mathur, Madhav S, Ramaneswaran Mohan, Kshitij |
| author_facet | Rastogi, Pranshu Mathur, Madhav S, Ramaneswaran Mohan, Kshitij |
| contents | In recent years social media has become an increasingly popular tool for communication. People use it to share their ideas, exchange information, and discuss thoughts. Given its prevalence and widespread reach, social media must remain a safe space for people. Content generated on social media can be abusive and it has become increasingly important to detect such content. In this paper, we use a language-based preprocessing and an ensemble of several models and analyze their performance of abusive comment detection. Through extensive experimentation, we propose a pipeline that minimizes the false-positive rate (marking non-abusive as abusive) so that these systems can detect abusive comments without undermining the freedom of expression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22380 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Stage Training for Abusive Comment Detection in Indic Languages Rastogi, Pranshu Mathur, Madhav S, Ramaneswaran Mohan, Kshitij Computation and Language Machine Learning In recent years social media has become an increasingly popular tool for communication. People use it to share their ideas, exchange information, and discuss thoughts. Given its prevalence and widespread reach, social media must remain a safe space for people. Content generated on social media can be abusive and it has become increasingly important to detect such content. In this paper, we use a language-based preprocessing and an ensemble of several models and analyze their performance of abusive comment detection. Through extensive experimentation, we propose a pipeline that minimizes the false-positive rate (marking non-abusive as abusive) so that these systems can detect abusive comments without undermining the freedom of expression. |
| title | Multi-Stage Training for Abusive Comment Detection in Indic Languages |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.22380 |