# Arabic Fake News Detection Based on Deep Contextualized Embedding Models

arXiv: https://arxiv.org/abs/2205.03114 · HTML: https://mlsanity.com/paper/2205.03114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 10 of 10)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [Machine Generation and Detection of Arabic Manipulated and Fake News](https://mlsanity.com/paper/2011.03092/) (2020)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [Fake News Detection as Natural Language Inference](https://mlsanity.com/paper/1907.07347/) (2019)
- [A review of sentiment analysis research in Arabic language](https://mlsanity.com/paper/2005.12240/) (2020)
- [Fake or Real? A Study of Arabic Satirical Fake News](https://mlsanity.com/paper/2011.00452/) (2020)
- [AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset](https://mlsanity.com/paper/2105.03143/) (2021)
- [Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis](https://mlsanity.com/paper/2112.00534/) (2021)

## Built on by (most cited first, 1 of 1)

- [Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models](https://mlsanity.com/paper/2304.00636/) (2023)

## Abstract (arXiv)

Social media is becoming a source of news for many people due to its ease and freedom of use. As a result, fake news has been spreading quickly and easily regardless of its credibility, especially in the last decade. Fake news publishers take advantage of critical situations such as the Covid-19 pandemic and the American presidential elections to affect societies negatively. Fake news can seriously impact society in many fields including politics, finance, sports, etc. Many studies have been conducted to help detect fake news in English, but research conducted on fake news detection in the Arabic language is scarce. Our contribution is twofold: first, we have constructed a large and diverse Arabic fake news dataset. Second, we have developed and evaluated transformer-based classifiers to identify fake news while utilizing eight state-of-the-art Arabic contextualized embedding models. The majority of these models had not been previously used for Arabic fake news detection. We conduct a thorough analysis of the state-of-the-art Arabic contextualized embedding models as well as comparison with similar fake news detection systems. Experimental results confirm that these state-of-the-art models are robust, with accuracy exceeding 98%.