@inproceedings{niyigaba-etal-2026-kinyaprop,
    title = "{K}inya{P}rop: Fine-Grained Propaganda Annotation in {K}inyarwanda",
    author = "Niyigaba, Manzi Fabrice  and
      Yang, Ivory  and
      Vosoughi, Soroush",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1580/",
    pages = "34230--34243",
    ISBN = "979-8-89176-390-6",
    abstract = "Propaganda is a widely used approach for shaping public opinion and disseminating misinformation in news media. While it has recently gained significant attention within the NLP community, research on fine grained propaganda detection remains heavily concentrated in high resource languages. To bridge this gap, we introduce KinyaProp, the first fine-grained propaganda dataset of its kind for Kinyarwanda and, to our knowledge, the first such resource created for a Bantu language. Using this dataset, we evaluate whether state-of-the-art LLMs can function as reliable annotators in a genuinely low resource and culturally grounded setting. Our results show that current multilingual LLMs do not reliably approximate human annotation behavior. Instead, they behave as conservative annotators whose performance is largely limited to lexically explicit cues, substantially under-identifying propaganda and exhibiting extremely low and unstable performance on discourse-level techniques. Our findings highlight an important limitation of recent successes in LLM based annotation reported for high resource languages, demonstrating that such results do not readily transfer to low resource settings, where scalable annotation would be most valuable. We release KinyaProp to support future research on fine grained propaganda detection and to enable more robust evaluation of multilingual models in underrepresented languages."
}