SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization

TL;DR

SemEval-2026 Task 9 introduces a multilingual online polarization detection challenge with extensive annotations, fostering research across languages and polarization types.

Contribution

It presents a large, multilingual dataset and a shared task framework for polarization detection, analysis, and classification.

Findings

Baseline results provided for all subtasks.

Top-performing systems employ diverse NLP techniques.

Multilingual and multi-label aspects pose unique challenges.

Abstract

We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labeled with the presence of polarization, polarization type, and polarization manifestation. Participants were asked to predict labels in three sub-tasks: (1) detecting the presence of polarization, (2) identifying the type of polarization, and (3) recognizing the polarization manifestation. The three tasks attracted over 1,000 participants worldwide and more than 10k submission on Codabench. We received final submissions from 67 teams and 73 system description papers. We report the baseline results and analyze the performance of the best-performing systems, highlighting the most common approaches and the most effective methods across different subtasks and languages. The dataset of this task is publicly available.