A Hybrid Framework for Identifying Fake News and Deceptive Information in Social Media with Mitigation Strategy

Abstract

The rapid proliferation of fake news and deceptive information across social media platforms poses significant challenges to society, politics, and the global economy. This paper presents a hybrid framework for identifying and mitigating fake content, integrating principles of digital forensics with advanced detection strategies. The study highlights the scope of digital forensics—including disk, network, email, wireless, database, malware, and memory forensics—demonstrating their role in preserving, retrieving, and documenting digital evidence for legal and investigative purposes. Beyond forensic applications, the research underscores the disruptive influence of misinformation on democratic processes, financial markets, and public trust. A key focus is placed on the development of a Fake Content Detection System that leverages both forensic techniques and computational intelligence to address the evolving nature of online deception. The study also emphasizes the role of mitigation strategies, where people, governments, digital firms, and civil society collectively contribute to combating misinformation through coordinated policies and technological safeguards. By analyzing the mechanisms of social media communication and unverified content dissemination, the framework offers practical pathways for strengthening trust, accountability, and resilience in digital ecosystems. The findings emphasize the urgency of updating technological solutions to ensure accurate information dissemination and mitigate the adverse societal impacts of fake news.