Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English

Omar W. Abdalla, Aditya Joshi, Rahat Masood, Salil S. Kanhere


Abstract
Satirical news is real news combined with a humorous comment or exaggerated content, and it often mimics the format and style of real news. However, satirical news is often misunderstood as misinformation, especially by individuals from different cultural and social backgrounds. This research addresses the challenge of distinguishing satire from truthful news by leveraging multilingual satire detection methods in English and Arabic. We explore both zero-shot and chain-of-thought (CoT) prompting using two language models, Jais-chat(13B) and LLaMA-2-chat(7B). Our results show that CoT prompting offers a significant advantage for the Jais-chat model over the LLaMA-2-chat model. Specifically, Jais-chat achieved the best performance, with an F1-score of 80% in English when using CoT prompting. These results high- light the importance of structured reasoning in CoT, which enhances contextual understanding and is vital for complex tasks like satire detection.
Anthology ID:
2024.alta-1.14
Volume:
Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association
Month:
December
Year:
2024
Address:
Canberra, Australia
Editors:
Tim Baldwin, Sergio José Rodríguez Méndez, Nicholas Kuo
Venue:
ALTA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
173–178
Language:
URL:
https://aclanthology.org/2024.alta-1.14/
DOI:
Bibkey:
Cite (ACL):
Omar W. Abdalla, Aditya Joshi, Rahat Masood, and Salil S. Kanhere. 2024. Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English. In Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association, pages 173–178, Canberra, Australia. Association for Computational Linguistics.
Cite (Informal):
Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English (Abdalla et al., ALTA 2024)
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PDF:
https://aclanthology.org/2024.alta-1.14.pdf