@inproceedings{kongqiang-qingli-2026-wangkongqiang-semeval,
    title = "wangkongqiang at {S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization",
    author = "Kongqiang, Wang  and
      Qingli, Tan",
    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-1.20/",
    pages = "133--139",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system developed for the SemEval-2026 Task 9: Detecting Multilingual,Multicultural and Multievent Online Polarization. on Subtask 1: Multilingual Text Classification Challenge - Polarization Detection. on Subtask 2: Multilingual Text Classification Challenge - Polarization Type Classification. on Subtask 3: Multilingual Text Classification Challenge - Manifestation Identification. To this end, we focus on English and Spanish language use two different pre-trained languages models: models{--}google-bert{--}bertbase-uncased, and models{--}microsoft{--}debertav3-base. We experiment with 1) the training set data is analyzed visually, 2) use the gemma-3-27b-it generative model to perform data augmentation on the training dataset through prompts, and 3) multiple numbers of single models are trained on the training set data. We further study the influence of different hyperparameters on the single model and select the best single model for the prediction of the test set. Our submission achieved the good ranking place in the test set. All subtasks evaluated using Macro F1 score across different languages and cultural contexts. For Subtask 1, the English and Spanish language tasks are Macro F1 Score 0.7805 and 0.7155 respectively. For Subtask 2, the English and Spanish language tasks are Macro F1 Score 0.2603 and 0.4647 respectively. For Subtask 3, the English and Spanish language tasks are Macro F1 Score 0.2766 and 0.3322 respectively. For the final ranking, organizers will use the Macro F1 score. Even so, my approach has yielded good results from an overall perspective."
}