Control of false information in social networks: A short review of communication mechanisms and management strategies

Abstract

In the digital era, social networks serve as critical platforms for information dissemination but are also plagued by the spread of false information, which can undermine public trust and incite societal discord. This study examines the dynamics of false information dissemination on social networks, including its types, influential factors, and detection and management strategies. We explore various forms of false informationsuch as impersonation, misleading content, and AI-generated forgeriesand analyze the role of user interactions, network topology, and macro factors in the spread of misinformation. Detection methods are reviewed, highlighting advancements in technologies like deep learning, and management strategies are proposed, including user behavior regulation and dissemination path control. Challenges related to legal, ethical, and privacy issues are discussed, alongside the complexities of user behavior and future research directions. The findings underscore the need for comprehensive, adaptive approaches to safeguard the integrity of online information ecosystems.