🤖 AI Summary
This study addresses the high false positive rates in detecting abusive comments in Indic languages on social media platforms, which often risk infringing on freedom of expression. To tackle this challenge, the authors propose a novel framework that integrates language-specific preprocessing, multi-model ensembling, and multi-stage training, carefully tailored to the linguistic characteristics of Indic languages. The approach significantly improves the accuracy of abusive content detection while markedly reducing misclassification of non-abusive texts. Experimental results across multiple Indic-language datasets demonstrate that the proposed framework effectively balances detection performance with false positive control, offering a new paradigm for multilingual content moderation that harmonizes accuracy with the preservation of free speech.
📝 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.