Hybrid deep learning models for fake news detection: case study on Arabic and English languages

TL;DR

This paper presents a deep learning model for detecting fake news in Arabic and English, achieving high accuracy on benchmark datasets.

Contribution

The novel hybrid model combines CNN and BiLSTM with FastText for improved fake news detection in multilingual settings.

Findings

The model achieved 94.43% accuracy on the AFND Arabic dataset.

It outperformed existing methods with 98.85% accuracy on the WELFake English dataset.

The approach effectively handles linguistic challenges in Arabic fake news detection.

Abstract

Fake news has become a significant threat to public discourse due to the swift spread of online content and the difficulty of detecting and distinguishing it from real news. This challenge is further amplified by society's increasing dependence on online social networks. Many researchers have developed machine learning and deep learning models to combat the spread of misinformation and identify fake news. However, the studies focused on a single language, and the performance analysis achieved a low accuracy, especially for Arabic, which faces challenges due to resource constraints and linguistic intricacies.

This paper introduces an effective deep-learning technique for fake news detection (FND) in Arabic and English. The proposed model integrates a multi-channel Convolutional Neural Network (CNN) and dual Bidirectional Long Short-Term Memory (BiLSTM), parallelly capturing semantic and local textual features embedded by a pre-trained FastText model. Subsequently, a global max-pooling layer was added to reduce dimensionality and extract salient features from the sequential output. Finally, the model classifies news as fake or real. Moreover, the model is trained and evaluated on three benchmark datasets, AFND and ANS, Arabic datasets, and WELFake, an English dataset.

Experimental results highlight the model's effectiveness and performance superiority over state-of-the-art (SOTA) approaches, with (94.43 ± 0.19) %, (71.63 ± 1.45) %, and (98.85 ± 0.03) %, accuracy on AFND, ANS and WELFake, respectively.

This work provides a robust approach to combating misinformation, offering practical applications in enhancing the reliability of information on social networks.

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