Darius Plikynas, Vilnius University, Lithuania

The proliferation of fake news, propaganda, and disinformation (FNPD) in the era of generative AI and information warfare poses significant challenges to societal cohesion and democratic processes. This systematic review examines recent advances in machine learning (ML) techniques for detecting and assessing the social impact of FNPD. Employing the PRISMA framework, we analyze promising ML/DL methodologies and hybrid approaches in combating the spread of conspiracy theories, echo chambers, and filter bubbles that contribute to social polarization and radicalization. Our findings highlight the potential of AI-driven solutions in identifying malicious social media accounts, organized troll networks, and bot activities that target specific demographics and manipulate public discourse. We also explore future research directions for developing more robust FNPD detection systems and mitigating the fragmentation of social networks of trust and cooperation. This review provides valuable insights for researchers and policymakers addressing the complex challenges of information integrity in the digital age.

Machine Learning, Deep learning, Propaganda and Disinformation, Social Impact Analysis, PRISMA Systematic Review