@inproceedings{gemelli-etal-2026-beyond,
    title = "Beyond Fake News Detection: A Community-based Study of the Multicultural Nature of Information Disorder",
    author = "Gemelli, Sara  and
      Di Cristina, Giulia  and
      Zhang, Yiran  and
      Hoque, Md Azizul  and
      De La Torre Sol{\'i}s, Alberto  and
      Behboudi Eshkiki, Mohamad Mojtaba  and
      Efimov, Nikolai  and
      Everstova, Mariia  and
      Cappello, Caterina Maria  and
      Kianimoghadam Jouneghani, Maziar  and
      Latifi, Payam  and
      Mahboudi, Yashar  and
      Mohseni, Farzaneh  and
      Placenti, Dario  and
      Caselli, Tommaso  and
      Sanguinetti, Manuela  and
      Scarpellini, Aurora  and
      Zanchi, Chiara  and
      Naseem, Usman  and
      Stranisci, Marco Antonio  and
      Frenda, Simona",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.lrec-1.515/",
    doi = "10.63317/4iyhqziwo6ri",
    pages = "6496--6508",
    abstract = "Recognizing disinformation is a challenging task for humans and AI systems. News can be false, misleading, or harmful, and its interpretation often depends on the cultural context of the audience. However, existing datasets rarely account for these contextual and cultural differences, as they are typically not designed from the perspective of news consumers. To address this gap, in this paper, we present the Information Disorder (InDor) corpus, a multilingual dataset of news articles in English, Farsi, Italian, and Russian, annotated for information disorder detection and explanation. The corpus was developed through a participatory process involving contributors from diverse cultural and professional backgrounds, who engaged in data collection, annotation, and evaluation of Large Language Model (LLM) performance on the task. Our findings highlight that false and manipulated news manifest differently across cultural settings, and that current LLMs fail to adequately capture this complexity. This underscores the need for culturally aware computational approaches in the study of information disorder."
}