@inproceedings{liu-etal-2025-propainsight,
    title = "{P}ropa{I}nsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent",
    author = "Liu, Jiateng  and
      Ai, Lin  and
      Liu, Zizhou  and
      Karisani, Payam  and
      Hui, Zheng  and
      Fung, Yi  and
      Nakov, Preslav  and
      Hirschberg, Julia  and
      Ji, Heng",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.376/",
    pages = "5607--5628",
    abstract = "Propaganda plays a critical role in shaping public opinion and fueling disinformation. While existing research primarily focuses on identifying propaganda techniques, it lacks the ability to capture the broader motives and the impacts of such content. To address these challenges, we introduce PropaInsight, a conceptual framework grounded in foundational social science research, which systematically dissects propaganda into techniques, arousal appeals, and underlying intent. PropaInsight offers a more granular understanding of how propaganda operates across different contexts. Additionally, we present PropaGaze, a novel dataset that combines human-annotated data with high-quality synthetic data generated through a meticulously designed pipeline. Our experiments show that off-the-shelf LLMs struggle with propaganda analysis, but PropaGaze significantly improves performance. Fine-tuned Llama-7B-Chat achieves 203.4{\%} higher text span IoU in technique identification and 66.2{\%} higher BertScore in appeal analysis compared to 1-shot GPT-4-Turbo. Moreover, PropaGaze complements limited human-annotated data in data-sparse and cross-domain scenarios, demonstrating its potential for comprehensive and generalizable propaganda analysis."
}