Operating principles of social media algorithmic systems

Abstract

This study investigates the operating principles of social media algorithms to develop evidence-based recommendations for enhancing marketing strategies amid the digital transformation of the economy. By reconstructing the algorithms' logic, derived from the platforms' economic objectives, a conceptual model of content ranking is proposed. This model frames ranking as a function of weighted factors operating within a set of predefined constraints. The study systematizes the types of content prioritized by these algorithms to achieve their goals: relevant, engaging, interaction-provoking, and loyalty-fostering content. The findings demonstrate that an effective SMM strategy must be oriented toward creating content that is simultaneously relevant to the target audience's interests and aligned with the platform's economic goals of user retention and attention monetization. The proposed model provides a theoretical foundation for further applied research aimed at identifying and verifying key ranking factors.