Combating Fake News in the Digital Age: A Review of AI-Based Approaches

The increasing spread of false information requires advanced detection methods. This study provides a thorough examination of the various strategies used to identify fake news, ranging from traditional approaches to cutting-edge technologies. The strengths and limitations of artificial intelligence, deep learning, and natural language processing techniques were evaluated. The combination of multimodal analysis, artificial intelligence and graph-based models has been identified as an emerging trend that improves the effectiveness of classification systems. Challenges, such as adversarial attacks and biased training data, highlight the ongoing need for development in this area. Additionally, this paper explores new trends, including user behavior analysis and collaborative cybersecurity strategies, while discussing their potential impact on detecting counterfeit news. A comparative analysis was used to assess the suitability of these methodologies across diverse contexts. The conclusion of the review emphasizes the dynamic nature of detecting false information and underlines the crucial requirement for interdisciplinary cooperation to safeguard against deception in the constantly evolving digital realm.