mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
Spotting inflammatory speech across 22 languages before it turns toxic
Researchers built an AI system to detect polarizing content online across 22 languages by finetuning large language models with a technique that keeps computational costs manageable. They strengthened the system by training it on multiple versions of the same text—anonymized, capitalized differently, and with character substitutions—making it more likely to catch polarization even when people use tricks to avoid detection.
Online polarization often escalates into hate speech and social division. Catching inflammatory rhetoric early, across languages and cultures, gives platforms a practical tool to intervene before discussions turn hostile. The approach also shows how to build multilingual AI systems efficiently, without needing expensive computational resources.