@inproceedings{kongqiang-qingli-2026-wangkongqiang,
    title = "wangkongqiang at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark- Psycholinguistic Conspiracy Marker Extraction and Detection",
    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-1.2/",
    pages = "8--14",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system developed for the SemEval-2026 Task 10: PsyCoMark Psycholinguistic Conspiracy Marker Extraction and Detection. on Subtask 1: Conspiracy Marker Extraction. on Subtask 2: Conspiracy Detection. To this end, we focus on English language use four different pre-trained languages models: models{--}distilbert{--}distilbert-base uncased, models{--}distilbert{--}distilbert-base-multilingual-cased, models{--}lxyuan{--}distilbert-base-multilingual-cased-sentiments-student, and models{--}microsoft{--}deberta-v3-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 for Subtask 2: Conspiracy Detection, 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 leaderboard. For Subtask 1, the evaluation criteria for this task mainly consist of the aggregate results of the four markers: Actor, Action, Effect, and Victim, and they are measured using the Macro F1 score. For Subtask 2, this task is essentially a binary classification task for text. Performance will be evaluated using macro-averaged F1 score. In other words, this subtask evaluated using Weighted F1 score across different sentences and cultural contexts. For Subtask 1 and Subtask 2, our best approach is to obtain the results are Macro F1 score 0.1587 and Weighted F1 score 0.7411 separately. For the final ranking, organizers will use the aggregate results of Macro F1 score and Weighted F1 score. Even so, our approach has yielded good results."
}