HYBRID DEEP LEARNING FRAMEWORK FOR EARLY DETECTION OF FAKE NEWS IN SOCIAL MEDIA PLATFORMS - M Rajeshkumar,; John Grasias S,; Chandralekha P, (The Bioscan, 2026)
The rapid growth of social media platforms has significantly accelerated the spread of information across the globe. However, this accessibility has also facilitated the widespread dissemination of misinformation and fake news, which can negatively influence public opinion, political stability, and social harmony. Detecting fake news at an early stage is therefore a critical challenge for modern information systems. Traditional machine learning approaches often struggle to capture complex linguistic patterns and contextual relationships present in social media content. To address this issue, this paper proposes a Hybrid Deep Learning Framework for Early Detection of Fake News in Social Media. The proposed framework integrates semantic feature extraction using Bidirectional Encoder Representations from Transformers, contextual sequence modeling through Long Short-Term Memory, and feature refinement using Convolutional Neural Network layers. This hybrid architecture captures both global contextual semantics and local linguistic patterns present in news content and social media posts. The system performs data preprocessing, feature extraction, hybrid model training, and classification to distinguish between fake and genuine news articles. Experimental evaluation demonstrates that the proposed model achieves improved detection accuracy, reduced false positives, and faster identification of misinformation compared with traditional machine learning approaches. The framework is particularly effective for early- stage detection where limited propagation information is available. The proposed method provides a scalable and efficient solution for combating misinformation in modern social media ecosystems.