Multi-Stage Training for Abusive Comment Detection in Indic Languages
Summary
This paper proposes a multi-stage training pipeline using language-based preprocessing and an ensemble of models to detect abusive comments in Indic languages, aiming to minimize false positives while preserving freedom of expression.
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# Multi-Stage Training for Abusive Comment Detection in Indic Languages Source: [https://arxiv.org/abs/2605.22380](https://arxiv.org/abs/2605.22380) [View PDF](https://arxiv.org/pdf/2605.22380) > Abstract: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\. ## Submission history From: Pranshu Rastogi \[[view email](https://arxiv.org/show-email/a923246b/2605.22380)\] **\[v1\]**Thu, 21 May 2026 12:09:53 UTC \(486 KB\)
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