@inproceedings{hasanain-etal-2025-propxplain,
    title = "{P}rop{X}plain: Can {LLM}s Enable Explainable Propaganda Detection?",
    author = "Hasanain, Maram  and
      Hasan, Md Arid  and
      Kmainasi, Mohamed Bayan  and
      Sartori, Elisa  and
      Shahroor, Ali Ezzat  and
      Da San Martino, Giovanni  and
      Alam, Firoj",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1296/",
    doi = "10.18653/v1/2025.findings-emnlp.1296",
    pages = "23855--23863",
    ISBN = "979-8-89176-335-7",
    abstract = "There has been significant research on propagandistic content detection across different modalities and languages. However, most studies have primarily focused on detection, with little attention given to explanations justifying the predicted label. This is largely due to the lack of resources that provide explanations alongside annotated labels. To address this issue, we propose a multilingual (i.e., Arabic and English) explanation-enhanced dataset, the first of its kind. Additionally, we introduce an explanation-enhanced LLM for both label detection and rationale-based explanation generation. Our findings indicate that the model performs comparably while also generating explanations. We will make the dataset and experimental resources publicly available for the research community (https://github.com/firojalam/PropXplain)."
}