Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning

作     者：Plikynas, Darius Rizgeliene, Ieva Korvel, Grazina 

作者机构：Vilnius Univ Fac Math & Informat Inst Data Sci & Digital Technol Vilnius 10257 Lithuania 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：17583-17629页

核心收录：

基　　金：Lithuanian Government Priority Research Program "Building Societal Resilience and Crisis Management in the Context of Contemporary Geopolitical Developments"(Implemented through the Lithuania Research Council) [S-VIS-23-8] 

主　　题：Fake news Social networking (online) Systematic literature review Feature extraction Content management Classification algorithms Analytical models Accuracy Security Machine learning deep learning propaganda and disinformation fake news PRISMA systematic review authors' analysis content analysis social impact analysis social impact analysis 

摘      要：In recent years, the world has witnessed a global outbreak of fake news, propaganda and disinformation (FNPD) flows on online social networks (OSN). In the context of information warfare and the capabilities of generative AI, FNPDs have proliferated. They have become a powerful and quite effective tool for influencing people s social identities, attitudes, opinions and even behavior. Ad hoc malicious social media accounts and organized networks of trolls and bots target countries, societies, social groups, political campaigns and individuals. As a result, conspiracy theories, echo chambers, filter bubbles and other processes of fragmentation and marginalization are polarizing, radicalizing, and disintegrating society in terms of coherent politics, governance, and social networks of trust and cooperation. This systematic review aims to explore advances in using machine and deep learning to detect FNPD in OSNs effectively. We present the results of a combined PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) review in three analysis domains: 1) propagators (authors, trolls, and bots), 2) textual content, 3) social impact. This systemic research framework integrates meta-analyses of three research domains, providing an overview of the wider research field and revealing important relationships between these research domains. It not only addresses the most promising ML/DL research methodologies and hybrid approaches in each domain, but also provides perspectives and insights on future research directions.