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Ensemble based high performance deep learning models for fake news detection.

Mohammed E Almandouh^1,2, Mohammed F Alrahmawy^3,4,5, Mohamed Eisa⁶

¹Portsaid University, Portsaid, Egypt. mmandouh@himc.psu.edu.eg.

Scientific Reports

|November 4, 2024

Summary

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This summary is machine-generated.

The Bi-GRU-Bi-LSTM model excels at detecting Arabic fake news, achieving high accuracy and F1 scores. This research advances the global fight against misinformation by improving automated fake news detection systems.

Area of Science:

Natural Language Processing
Artificial Intelligence
Machine Learning

Background:

Social media facilitates global communication and news dissemination but poses risks of widespread misinformation.
Automated fake news detection is crucial for mitigating the negative impact of false information online.

Purpose of the Study:

To investigate and compare machine learning, deep learning, and ensemble methods for Arabic fake news detection.
To evaluate the efficacy of hybrid deep learning models, including transformer-based architectures.

Main Methods:

Utilized FastText word embeddings with machine learning and deep learning models.
Implemented and optimized transformer models (BERT, XLNet, RoBERTa) and hybrid models (CNN-LSTM, RNN-CNN, RNN-LSTM, Bi-GRU-Bi-LSTM).

Keywords:

Deep Learning Ensemble Learning Fake News Detection FastText

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Employed two Arabic datasets (AFND and ARABICFAKETWEETS) with text preprocessing.

Main Results:

The Bi-GRU-Bi-LSTM hybrid model demonstrated superior performance across all evaluated metrics.
Achieved high precision, recall, F1 scores, and accuracy on both datasets, with results up to 0.99.

Conclusions:

The Bi-GRU-Bi-LSTM model significantly outperforms other methods for Arabic fake news detection.
This study provides a robust framework for enhancing automated misinformation detection and encourages multilingual expansion.