A Secure Framework for Detecting “Fake News in Social Media Networks”

Abstract

In today's digital era, the proliferation of fake news poses significant challenges to societal trust, political stability, and public perception. This study develops a comprehensive framework for enhancing fake news detection, leveraging advanced machine learning techniques, privacy-preserving methods, and dynamic threat modeling. Key objectives include improving detection accuracy, ensuring user data privacy, and adapting to evolving misinformation tactics. By integrating ensemble learning methods such as Random Forests and Gradient Boosting, along with Natural Language Processing (NLP) techniques, the framework offers superior performance in identifying fake news. Additionally, privacy-preserving techniques like differential privacy and federated learning help address growing concerns over user data confidentiality. The research highlights the importance of ensuring compatibility with major social media platforms to maximize effectiveness and scalability. Comprehensive performance evaluations underscore the robustness of the proposed system. Recommendations include fostering collaboration among stakeholders, strengthening user engagement in evaluation processes, and advancing the framework's adaptability to dynamic misinformation tactics. This research contributes significantly to the ongoing fight against misinformation, promoting a more informed and resilient society through an efficient, privacy-focused detection system.