JointBert for Detecting Arabic Fake News

Article Mathematics, Interdisciplinary Applications

Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches

Maha Al-Yahya et al.

Summary: This study compares the use of neural network and transformer-based language models for Arabic fake news detection. The results show that transformer-based models outperform neural network solutions, significantly increasing accuracy and F1 scores. The study also highlights gaps in Arabic FND research and suggests future research directions.

COMPLEXITY (2021)

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Article Multidisciplinary Sciences

Fake News Detection Using BERT Model with Joint Learning

Wesam Shishah

Summary: In the current Internet era, the rapid spread of fake news poses a serious challenge, and traditional AI approaches have struggled to effectively detect fake news. By proposing a novel BERT approach with a joint learning framework, we observed improved performance in detecting fake news attributes on real-world datasets. This unique joint framework assigns meaningful weights to attributes, leading to better performance compared to other baseline methods.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2021)

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Article Computer Science, Information Systems

Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)

Muhammad Umer et al.

IEEE ACCESS (2020)

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Proceedings Paper Computer Science, Artificial Intelligence

Fake News Detection Enhancement with Data Imputation

Chandra Mouli Madhav Kotteti et al.

2018 16TH IEEE INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP, 16TH IEEE INT CONF ON PERVAS INTELLIGENCE AND COMP, 4TH IEEE INT CONF ON BIG DATA INTELLIGENCE AND COMP, 3RD IEEE CYBER SCI AND TECHNOL CONGRESS (DASC/PICOM/DATACOM/CYBERSCITECH) (2018)

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Article Economics