Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English
Not on the map. Why?
Abstract (arXiv)
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 highlight the importance of structured reasoning in CoT, which enhances contextual understanding and is vital for complex tasks like satire detection.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 16 Nov 2024.
Builds on
5 of 11 references.
References · 5
- 2019Adversarial Training for Satire Detection: Controlling for Confounding Variablescited by 13
- 2021SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articlescited by 12
- 2020A Multi-Modal Method for Satire Detection using Textual and Visual Cuescited by 10
- 2019Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cuescited by 8
- 2020Fake or Real? A Study of Arabic Satirical Fake Newscited by 7
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful Humor
- 2024YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models
- 2025Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
- 2026From Native Memes to Global Moderation: Cross-Cultural Evaluation of Vision-Language Models for Hateful Meme Detection
- 2024Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection
- 2026The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection
- 2026Grounded Satirical Generation with RAG
- 2025FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis