# Multi-Stage Training for Abusive Comment Detection in Indic Languages

arXiv: https://arxiv.org/abs/2605.22380 · HTML: https://mlsanity.com/paper/2605.22380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 1 of 1)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)

## Abstract (arXiv)

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.