SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
arXiv:2604.06817v2 Announce Type: replace 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
The proliferation of online content and the increasing sophistication of AI for content generation and analysis make the detection of polarization more critical and feasible now.
Understanding and detecting online polarization is crucial for societal stability, information integrity, and the development of more robust AI systems that can navigate complex social dynamics.
The ability to automatically detect and classify online polarization across multiple languages and events evolves, moving from theoretical understanding to practical, large-scale application.
- · Social media platforms
- · NLP researchers
- · Governments
- · Fact-checking organizations
- · Disinformation campaigns
- · Extremist groups
- · Unregulated online content
Improved detection of harmful online rhetoric across various linguistic and cultural contexts will become possible.
This improved detection could lead to more targeted interventions against coordinated efforts to spread polarization and misinformation.
Societies might develop greater resilience to information warfare, potentially altering geopolitical influence dynamics in the digital sphere.
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Read at arXiv cs.CL