Boosting Arabic Fake Reviews Detection by Integrating Textual and Metadata Features: A Transformer-Based Model
Ibrahim Amin, Ismail Fakhr, Mohamed Waleed Fakhr, Rasha Kashef · IEEE Access · 2025
Fake reviews present a significant threat to e-businesses and content providers, lowering consumer trust and damaging brand reputation. As such, the detection and prevention of fake reviews is essential for maintaining the integrity and success of e-businesses. On the other hand, the Arabic language presents unique challenges due to its complex linguistic structure and the wide variety of dialects spoken across different regions. However, the availability of Arabic datasets for fake review detection remains limited, where the available ones either suffer from small sample sizes or are translated from English to Modern Standard Arabic, failing to capture the natural, colloquial language typically used in reviews. Moreover, most Arabic fake reviews research has focused mainly on the textual content of the reviews and has not considered the metadata. Therefore, there has been no comprehensive research investigating the benefits and effects of integrating metadata features with textual content for classifying Arabic fake reviews. To this end, this paper is two-fold. Firstly, a balanced Egyptian Arabic dataset has been created, translated from the YelpZip English dataset using transformers, which includes the metadata. Secondly, a comprehensive and comparative study is conducted to investigate the effects of augmenting the textual content with the metadata features. Baseline experiments with textual content only and fine-tuned pre-trained Arabic BERT models achieved an F1-score of around 69%. Combining pre-trained Arabic language model embeddings with handcrafted metadata features significantly boosts performance, with the best-performing system achieving an F1-score of 77% without the user and product IDs as features and as high as 87% with the inclusion of user and product IDs.