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

Paper · 2026 · Usman Naseem, Robert Geislinger, Ada Ren et al.

Summary

This shared task targets automatic detection of online polarization, meaning sharp antagonism between social, political or identity groups, in many languages, cultures and events. Earlier computational work mostly covered high-resource languages and single events. The organizers released POLAR, a multi-label dataset of more than 110K social media, news-comment and forum texts in 22 languages from seven language families. Some items were re-annotated from existing toxicity or hate speech corpora. Labeling combined crowdworkers on Mechanical Turk and Prolific with trained community annotators for low-resource languages, using guidelines adapted to each culture. The task has three levels: whether a text is polarized, the social dimension involved, and the rhetorical strategy used. Systems were ranked by macro F1 on Codabench. It was the most popular of the 12 SemEval-2026 tasks. Strong results came from different approaches:

  • LoRA-tuned LLMs
  • LLM stacking ensembles
  • ensembles of multilingual encoders

No single method dominated. Agreement between annotators differed widely across languages, which suggests that polarization is partly a matter of perspective. The public dataset is a benchmark for cross-lingual and culturally aware polarization research.

Key points

  • POLAR contains 110,656 instances: 73,681 train, 3,687 dev and 33,288 test. Russian and Polish are included, but Ukrainian is not.
  • The three subtasks are POLARDETECT (binary), POLARTYPE and POLARMANIFEST.
    • POLARTYPE labels: political, racial/ethnic, religious, gender/sexual, other.
    • POLARMANIFEST uses six labels: invalidation, a lack of empathy, dehumanization, vilification, stereotype, and extreme language.
    • POLARMANIFEST covers 18 languages, leaving out mya, ita, pol and rus.
  • Participation exceeded 1,000 people and 10k Codabench submissions. Final submissions came from 67 teams, with 69 system papers. The 548 test-phase submissions split 267 / 161 / 120 across the three subtasks.
  • Inter-annotator agreement ranged from 0.10 (German, Krippendorff's α) and 0.13 (Burmese) up to 0.83 (Khmer).
  • The top teams used different methods:
    • UTokyo Tsuruoka Lab fine-tuned Gemma-3 with LoRA and used single-forward-pass inference.
    • NYCU-NLP stacked Gemma-3 27B, Mistral Small 3.2 and Phi-4, and had the most top-3 placements (38).
    • SMASH combined mDeBERTa, XLM-R and mBERT with per-class threshold calibration and placed first in Subtask 3.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Hamad bin Khalifa UniversityDoha · Qatar141 works · 14,175 citations🎓Macquarie UniversitySydney · Australia42 works · 5,776 citations🎓University of TurinTurin · Italy33 works · 6,637 citations🎓Universität HamburgHamburg · Germany79 works · 5,422 citations🎓Virginia TechBlacksburg · United States36 works · 43,036 citations🎓Bielefeld UniversityBielefeld · Germany35 works · 1,196 citations🎓Bahir Dar UniversityBahir Dar · Ethiopia11 works · 261 citations🎓University of PretoriaPretoria · South Africa9 works · 755 citations🎓Zayed UniversityAbu Dhabi · United Arab Emirates6 works · 67 citations🎓Bayero University KanoKano · Nigeria5 works · 1,285 citations🎓Lahore University of Management SciencesLahore · Pakistan3 works · 61 citations🎓Maseno UniversityKisumu · Kenya2 works · 61 citations

Also on the byline: Indian Institute of Information Technology Senapati, Manipur.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksSociology and Political Science · 577 works

🧭 Topics 1Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 828 works

Cite

Cite as Usman Naseem, Robert Geislinger, Ada Ren, Sarah Kohail, Rudy Garrido Veliz, P Sam Sahil, Yiran Zhang, Marco Antonio Stranisci, Idris Abdulmumin, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Elena Tutubalina, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Tanmoy Chakraborty, Dheeraj Kodati, Sahar Moradizeyveh, Firoj Alam, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo, Clemencia Siro, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad and Seid Muhie Yimam. 2026. SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.semeval-1.453

@inproceedings{naseem2026semeval,
  title = {{SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization}},
  author = {Usman Naseem, Robert Geislinger, Ada Ren, Sarah Kohail, Rudy Garrido Veliz, P Sam Sahil, Yiran Zhang, Marco Antonio Stranisci, Idris Abdulmumin, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Elena Tutubalina, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Tanmoy Chakraborty, Dheeraj Kodati, Sahar Moradizeyveh, Firoj Alam, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo, Clemencia Siro, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad and Seid Muhie Yimam},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.453},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.453}
}

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