Social media recommendation algorithms as a tool for countering disinformation in mass communication and PR activities

Abstract

The paper examines the functioning of recommendation algorithms in the social media platforms TikTok and Instagram as tools for shaping the information environment and countering disinformation in mass communication and PR activities. The study analyzes the mechanisms of content personalization, the formation of “filter bubbles” and “echo chambers,” as well as the influence of algorithms on user behavior and information dissemination. The main differences between the algorithmic models of TikTok and Instagram are identified, including the speed of adaptation to behavioral signals, the role of the social graph, and the level of information isolation. The research proves that recommendation systems can not only intensify the risks of disinformation but also serve as effective tools for combating it through the integration of fact-checking mechanisms, increased algorithm transparency, and the development of users’ digital literacy. Directions for improving algorithmic systems are proposed through the implementation of models focused on content diversity and reducing the effect of informational isolation.