@inproceedings{st-ahelin-etal-2026-fine,
    title = "Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas",
    author = "St{\textbackslash}{''}ahelin, Lukas  and
      Solopova, Veronika  and
      Upravitelev, Max  and
      Kaplan, David  and
      Sahitaj, Premtim  and
      Sahitaj, Ariana  and
      Jakob, Charlott  and
      M{\textbackslash}{''}oller, Sebastian  and
      Schmitt, Vera",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.139/",
    pages = "2887--2902",
    ISBN = "979-8-89176-395-1",
    abstract = "Propaganda detection in social media is challenging due to noisy, short texts and low annotation agreements. We introduce a new intent-focused taxonomy of propaganda techniques and compare it against an established, higher-agreement schema. Along three dimensions (model portfolio, schema effects, and prompting strategy) we evaluate the taxonomies as a classification task with the help of four language models (GPT-4.1-nano, Phi-4 14B, Qwen2.5-14B, Qwen3-14B). Our results show that fine-tuning is essential, since it transforms weak zero-shot baselines into competitive systems and reveals methodological differences that are hidden using base models. Across schemas, the Qwen models achieve the strongest overall performance, and Phi-4 14B consistently outperforms GPT-4.1-nano. Our hierarchical prompting method (HiPP), which predicts fine-grained techniques before aggregating them, is especially beneficial after fine-tuning and on the more ambiguous, low-agreement taxonomy, while remaining competitive on the simpler schema. The HQP dataset, annotated with the new intent-based labels, provides a richer lens on propaganda{'}s strategic goals and a challenging benchmark for future work on robust, real-world detection."
}