Fake News Detection: A Comprehensive Taxonomy of Text, Image, Video, and Multi-Modal Techniques
Keywords:
fake news detection, deep learning, machine learningAbstract
The widespread dissemination of fake news across digital platforms has posed significant challenges for information integrity, social stability, and public trust. Traditional fake news detection approaches, primarily based on text analysis, are no longer sufficient, as misinformation now integrates multi-modal content, including images, videos, and manipulated metadata. This paper presents a comprehensive taxonomy of fake news detection techniques, categorizing existing methods into text-based, image-based, video-based, and multi-modal approaches. We review the evolution of detection methodologies, from traditional machine learning models to advanced deep learning architectures, including transformers, convolutional neural networks (CNN), and hybrid AI models. Additionally, we analyze the growing challenge of adversarial attacks, where malicious actors manipulate text, images, and videos to bypass detection systems. Finally, we highlight emerging research directions, such as adversarial-resilient AI models, cross-modal fact verification, and human-AI hybrid fact-checking systems, which are crucial for developing trustworthy, explainable, and robust fake news detection frameworks. This study serves as a foundation for researchers and practitioners in advancing multi-modal misinformation detection and strengthening AI-driven fact-checking mechanisms.
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Copyright (c) 2025 Hussein Al-Kaabi (Author)
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