A Review on Fake News Detection using Deep Learning Methods

Abstract

The proliferation of fake news in online media platforms poses a significant threat to the integrity of information dissemination and public discourse. In response, researchers have increasingly turned to deep learning techniques to develop effective solutions for detecting and mitigating the spread of fake news. This review paper provides a comprehensive overview of recent advances in fake news detection using deep learning methodologies. We survey the literature on various deep learning architectures and approaches employed for fake news detection, including supervised, semi-supervised, and unsupervised learning methods. We discuss the challenges associated with data preprocessing, feature extraction, and model evaluation, and examine the ethical considerations and societal implications of deploying deep learning models for fake news detection. Furthermore, we identify emerging trends and future research directions in the field, with a focus on addressing the evolving nature of fake news and enhancing the robustness and interpretability of detection systems. This review contributes to the ongoing discourse on fake news detection and provides valuable insights for researchers, practitioners, and policymakers working in the domain of information integrity and online media governance.