An In-Depth Comparative Analysis of Fake News Detection Approaches in Social Media: Methodologies, Challenges, and Future Directions

The proliferation of fake news on social networks poses significant challenges for trust, security, and societal well-being. In this paper, we present a comprehensive study of fake news detection approaches and techniques, introducing a novel framework for news construction comprising four elements: news content, news context, news propagation, and news environment. We propose a new taxonomy of fake news detection techniques categorized into two primary types—individual methods (content-based, context-based, and propagation-based) and frameworks (hybrid and perception-aware methods). We highlight their strengths, weaknesses, and applicability by analyzing ۱۴ state-of-the-art detection methods across platforms such as Twitter, Facebook, and Sina-Weibo. Furthermore, we address critical research gaps by identifying future directions, including early fake news detection, unsupervised learning, multimodal datasets, adversarial attacks on algorithms, multi-lingual platforms, and AI-generated content detection. Our findings and recommendations aim to serve as a foundation for developing new robust, scalable, and impactful fake news detection systems.

کلیدواژه ها:

Fake news detection ، Content-based methods ، Context-based methods ، Propagation-based methods ، Hybrid methods ، Perception-aware frameworks.