# Machine Generation and Detection of Arabic Manipulated and Fake News

arXiv: https://arxiv.org/abs/2011.03092 · HTML: https://mlsanity.com/paper/2011.03092/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [AraT5: Text-to-Text Transformers for Arabic Language Generation](https://mlsanity.com/paper/2109.12068/) (2021)
- [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://mlsanity.com/paper/2212.10758/) (2022)
- [GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://mlsanity.com/paper/2305.14976/) (2023)
- [Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks](https://mlsanity.com/paper/2403.01031/) (2024)
- [Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2312.08400/) (2023)
- [JASMINE: Arabic GPT Models for Few-Shot Learning](https://mlsanity.com/paper/2212.10755/) (2022)
- [Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks](https://mlsanity.com/paper/2411.01192/) (2024)
- [AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking](https://mlsanity.com/paper/2104.13559/) (2021)
- [Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation](https://mlsanity.com/paper/2310.16127/) (2023)
- [NSINA: A News Corpus for Sinhala](https://mlsanity.com/paper/2403.16571/) (2024)
- [ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination](https://mlsanity.com/paper/2201.06496/) (2022)
- [USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification](https://mlsanity.com/paper/2312.10536/) (2023)
- [Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding](https://mlsanity.com/paper/2205.10687/) (2022)
- [Arabic Fake News Detection Based on Deep Contextualized Embedding Models](https://mlsanity.com/paper/2205.03114/) (2022)
- [On the importance of Data Scale in Pretraining Arabic Language Models](https://mlsanity.com/paper/2401.07760/) (2024)
- [Combating Misinformation in the Arab World: Challenges & Opportunities](https://mlsanity.com/paper/2506.05582/) (2025)

## Abstract (arXiv)

Fake news and deceptive machine-generated text are serious problems threatening modern societies, including in the Arab world. This motivates work on detecting false and manipulated stories online. However, a bottleneck for this research is lack of sufficient data to train detection models. We present a novel method for automatically generating Arabic manipulated (and potentially fake) news stories. Our method is simple and only depends on availability of true stories, which are abundant online, and a part of speech tagger (POS). To facilitate future work, we dispense with both of these requirements altogether by providing AraNews, a novel and large POS-tagged news dataset that can be used off-the-shelf. Using stories generated based on AraNews, we carry out a human annotation study that casts light on the effects of machine manipulation on text veracity. The study also measures human ability to detect Arabic machine manipulated text generated by our method. Finally, we develop the first models for detecting manipulated Arabic news and achieve state-of-the-art results on Arabic fake news detection (macro F1=70.06). Our models and data are publicly available.