@inproceedings{khubaib-etal-2025-narrativeminers,
    title = "{N}arrative{M}iners at {S}em{E}val-2025 Task 10: Combating Manipulative Narratives in Online News",
    author = "Khubaib, Muhammad  and
      Khursheed, Muhammad Shoaib  and
      Khurram, Muminah  and
      Samad, Abdul  and
      Kumar, Sandesh",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.215/",
    pages = "1639--1644",
    ISBN = "979-8-89176-273-2",
    abstract = "Our team, Narrative Miners, participated in SemEval-2025 Task 10 to tackle the challenge of detecting manipulative narratives in online news, focusing on the Ukraine-Russia war and climate change. We worked on three key subtasks: classifying entity roles, categorizing narratives and subnarratives, and generating concise narrative explanations. Using transformer-based models like BART, BERT, GPT-2, and Flan-T5, we implemented a structured pipeline and applied data augmentation to enhance performance. BART-CNN proved to be our best-performing model, significantly improving classification accuracy and explanation generation. Despite challenges like dataset limitations and class imbalance, our approach demonstrated the effectiveness of hierarchical classification and multilingual analysis in combating online disinformation. We made use of different data augmentation techniques to cover the class imbalances present in the dataset. We had different evaluation metrics set for each subtask, specifically focusing on the need of that particular outcome. With this paper, we hope to play our part in mitigating the impact of harmful disinformation."
}