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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Lifan Zhang
Universiti Teknologi MARA
Computer Science & IT

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Rumor detection based on deep learning techniques: a systematic review Lifan Zhang; Shafaf Ibrahim; Ahmad Firdaus Ahmad Fadzil
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25929

Abstract

The rise of social media platforms has led to an increase in the flow and dissemination of information, but it has also made generating and spreading rumors easier. Rumor detection requires understanding the context and semantics of text, dealing with the evolving nature of rumors, and processing vast amounts of data in real-time. Deep learning (DL)-based techniques exhibit a higher accuracy in detecting rumors on social media compared to many traditional machine learning approaches. This study presents a systematic review of DL approaches in rumor detection, analyzing datasets, pre-processing methods, feature taxonomy, and frequently used DL methods. In the context of feature selection, we categorize features into three areas: text-based, user-based, and propagation-based. Besides, we surveyed the trends in DL models for rumor detection and classified them into convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), and other methods based on the model structure. It offers insights into effective algorithms and strategies, aiming to guide researchers, developers, social media users, and governments in detecting and preventing the spread of false information. The study contributes to enhancing research in this field and identifies potential areas for future exploration.
Co-Authors Ahmad Firdaus Ahmad Fadzil Shafaf Ibrahim