Machine Generation and Detection of Arabic Manipulated and Fake News
El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed,, Tariq Alhindi, Hasan Cavusoglu

TL;DR
This paper introduces a new method for generating and detecting manipulated Arabic news, providing a large dataset and models that improve detection accuracy of fake news in Arabic.
Contribution
It presents a novel Arabic news manipulation generation method, a large POS-tagged dataset, and state-of-the-art detection models for Arabic fake news.
Findings
Human annotation reveals effects of machine manipulation on veracity
Detection models achieve macro F1=70.06, outperforming previous methods
The dataset and models are publicly available for future research
Abstract
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…
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Taxonomy
TopicsMisinformation and Its Impacts · Spam and Phishing Detection · Hate Speech and Cyberbullying Detection
