Important Dates | POLAR @ 2026

The Task

Polarization refers to the division of opinions into two sharply contrasting groups, often accompanied by hostility, intolerance, or exclusion. In today's digital era, polarization is intensifying across platforms and geographies, influencing public discourse, exacerbating conflicts, and contributing to societal fragmentation.

This shared task is the first SemEval initiative focused on polarization, aiming to advance the computational understanding of how polarization manifests in text across multiple languages, cultures, and event types. Participants will develop models capable of detecting and interpreting polarization in a variety of online contexts.

The task centers on textual data collected from real-world events such as elections, international conflicts, social protests, and ideological debates. The primary goal is to evaluate systems’ ability to identify polarized content and classify its targets.

Multilingual, Multicultural, and Multievent Scope

To promote global inclusivity and cross-cultural representation, the dataset encompasses multiple languages, including many mid/low-resource and underrepresented ones in mainstream NLP research.

Task Format and Subtasks

Participants may choose to compete in one or more of three subtasks:

  1. Subtask 1: Polarization Detection – Binary classification to determine whether a post contains polarized content (Polarized or Not Polarized).
  2. Subtask 2: Polarization Type Classification – Identify the target of polarization, including political groups, religious groups, racial/ethnic communities, gender identities, sexual orientations, or other domain-specific targets.
  3. Subtask 3: Manifestation Identification – Classify how polarization is expressed; multiple labels possible, such as stereotyping, vilification, dehumanization, deindividuation, extreme language, lack of empathy, invalidation.

Data Description

The dataset is sourced from news websites, Reddit, blogs, Bluesky, and regional forums, covering event types like elections, conflicts, gender rights, migration, and more. Each language includes between 3,000 and 5,000 annotated instances.

Annotation tools used include Label Studio, Prolific, Potato, and Mechanical Turk.

Research Contributions

This task aims to advance socially responsible AI by supporting NLP for low-resource languages and fostering explainable and inclusive NLP systems. It will help establish multilingual benchmarks for polarization detection, promoting fair and transparent computational approaches to understanding societal divisions.

Timeline

Who Should Participate?

This task welcomes participation from:

Organizing Team

The POLAR task is organized by researchers from:

Contact and Community

For questions or to join the community, reach out via email at polarization-semeval-2026-organisers@googlegroups.com. Stay connected through the Discord channel, mailing list, and GitHub repository (links to be announced soon).