@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Beyond Fake News Detection: A Community-based Study of the Multicultural Nature of Information Disorder
%A Gemelli, Sara
%A Di Cristina, Giulia
%A Zhang, Yiran
%A Hoque, Md Azizul
%A De La Torre Solís, Alberto
%A Behboudi Eshkiki, Mohamad Mojtaba
%A Efimov, Nikolai
%A Everstova, Mariia
%A Cappello, Caterina Maria
%A Kianimoghadam Jouneghani, Maziar
%A Latifi, Payam
%A Mahboudi, Yashar
%A Mohseni, Farzaneh
%A Placenti, Dario
%A Caselli, Tommaso
%A Sanguinetti, Manuela
%A Scarpellini, Aurora
%A Zanchi, Chiara
%A Naseem, Usman
%A Stranisci, Marco Antonio
%A Frenda, Simona
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F gemelli-etal-2026-beyond
%X 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.
%R 10.63317/4iyhqziwo6ri
%U https://aclanthology.org/2026.lrec-1.515/
%U https://doi.org/10.63317/4iyhqziwo6ri
%P 6496-6508
Markdown (Informal)
[Beyond Fake News Detection: A Community-based Study of the Multicultural Nature of Information Disorder](https://aclanthology.org/2026.lrec-1.515/) (Gemelli et al., LREC 2026)
ACL
- Sara Gemelli, Giulia Di Cristina, Yiran Zhang, Md Azizul Hoque, Alberto De La Torre Solís, Mohamad Mojtaba Behboudi Eshkiki, Nikolai Efimov, Mariia Everstova, Caterina Maria Cappello, Maziar Kianimoghadam Jouneghani, Payam Latifi, Yashar Mahboudi, Farzaneh Mohseni, Dario Placenti, Tommaso Caselli, Manuela Sanguinetti, Aurora Scarpellini, Chiara Zanchi, Usman Naseem, Marco Antonio Stranisci, and Simona Frenda. 2026. Beyond Fake News Detection: A Community-based Study of the Multicultural Nature of Information Disorder. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6496–6508, Palma de Mallorca, Spain. ELRA Language Resource Association.