@inproceedings{naseem-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization",
    author = {Naseem, Usman  and
      Geislinger, Robert  and
      Ren, Ada  and
      Kohail, Sarah  and
      Garrido Veliz, Rudy  and
      Sahil, P Sam  and
      Zhang, Yiran  and
      Stranisci, Marco Antonio  and
      Abdulmumin, Idris  and
      Alacam, {\"O}zge  and
      Acarturk, Cengiz  and
      Jabr, Aisha  and
      Anwar, Saba  and
      Ayele, Abinew Ali  and
      Tutubalina, Elena  and
      Htet, Aung Kyaw  and
      Wang, Xintong  and
      Thapa, Surendrabikram  and
      Chakraborty, Tanmoy  and
      Kodati, Dheeraj  and
      Moradizeyveh, Sahar  and
      Alam, Firoj  and
      Kyaw Thu, Ye  and
      Parida, Shantipriya  and
      Qazi, Ihsan Ayyub  and
      Wanzare, Lilian Diana Awuor  and
      Odhiambo, Nelson  and
      Siro, Clemencia  and
      Ahmad, Ibrahim Said  and
      Ali, Adem Chanie  and
      Semmann, Martin  and
      Biemann, Chris  and
      Muhammad, Shamsuddeen Hassan  and
      Yimam, Seid Muhie},
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.453/",
    pages = "3779--3796",
    ISBN = "979-8-89176-414-9",
    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 subtasks: (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 submissions on Codabench. We received final submissions from 67 teams and 69 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 and other resources for this task are publicly available."
}