@inproceedings{peng-gehao-2026-zhangpeng,
    title = "zhangpeng at {S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization",
    author = "Peng, Zhang  and
      Gehao, Lu",
    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-1.14/",
    pages = "95--100",
    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. For Subtask 1, we explored classical text representation approaches including Bag-of-Words, Word2Vec Average Vectors, and Bag-of-Centroids. Among these methods, the Bag-of-Centroids model achieved the best performance on both development and test datasets. For Subtask 2 and Subtask 3, we fine-tuned four different pre-trained language models: google-bert, FacebookAI-roberta, dccuchile-bert, and distilbert-multi. We experiment with 1) the training set data is analyzed visually, 2) multiple numbers of single models are trained on the training set data, and 3) multiple number of single models for voting weight ensemble learning. We further study the influence of different hyperparameters on the integrated model and select the best integration model for the prediction of the test set. On the official test set, our system achieved Macro-F1 scores of 0.6882 (EN) and 0.6711 (SP) for Subtask 1, 0.3752 (EN) and 0.6386 (SP) for Subtask 2, and 0.3561 (EN) and 0.4366 (SP) for Subtask 3. For the final ranking, organizers will use the Macro F1 score. These approachs has yielded good results."
}