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

arXiv: https://arxiv.org/abs/2411.10730 · HTML: https://mlsanity.com/paper/2411.10730/

## 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 (most cited first, 5 of 5)

- [Adversarial Training for Satire Detection: Controlling for Confounding Variables](https://mlsanity.com/paper/1902.11145/) (2019)
- [SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles](https://mlsanity.com/paper/2105.06456/) (2021)
- [A Multi-Modal Method for Satire Detection using Textual and Visual Cues](https://mlsanity.com/paper/2010.06671/) (2020)
- [Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues](https://mlsanity.com/paper/1910.01160/) (2019)
- [Fake or Real? A Study of Arabic Satirical Fake News](https://mlsanity.com/paper/2011.00452/) (2020)

## 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.