THE IMPACT OF PERSONALIZATION ALGORITHMS ON USER BEHAVIOR ON SOCIAL MEDIA AND BRAND INTERACTIONS

Abstract

Objective. The objective of this work is to identify the mechanisms by which personalization algorithms influence behavioral patterns of social media users, as well as to analyze the effectiveness of using these algorithms by brands to increase reach, enhance loyalty, and stimulate purchasing behavior. An additional task is to assess the risks associated with the formation of «information bubbles» and the decline in advertising communication effectiveness in the long term. Materials and Methods. The study is interdisciplinary in nature and combines theoretical methods (analysis of literature, concepts, comparative analysis), empirical methods (data analysis, case studies, observation), data processing methods (NLP, machine learning, Big Data), experimental methods (A/B tests, modeling), marketing and behavioral methods (behavior analysis, surveys, benchmarking). The research material comprised scientific articles and industry reports in the field of digital marketing for 2020–2025, brand cases on influencer integration, data from social network platforms (Meta, TikTok, VK) on the operation of recommendation systems, as well as the results of content analysis of native (covert) advertising. Results. It has been established that personalization algorithms analyze interaction history (likes, reposts, viewing time), generating relevant content, which leads to an increase in user engagement by 30–50% in the short term. Personalization forms impulsive consumption patterns — users more often make unplanned purchases under the influence of «perfectly tailored» offers. Simultaneously, a narrowing of cultural horizons is observed (reduced consumption of content outside habitual categories). It was revealed that brands using dynamic content adjustment to behavioral segments achieve loyalty increases 1.7 times faster. Native advertising and influencer reviews, organically integrated into personalized feeds, are perceived by users as personal recommendations, which reduces skepticism toward advertising. Conclusion. Personalization algorithms serve as a powerful tool for managing user behavior in social networks, allowing brands to multiply their reach and shorten the path to purchase through native integration and work with influencers. However, their effectiveness has a temporal limit: long-term use forms «information bubbles,» reducing audience receptivity to new stimuli and creating an illusion of choice while actually narrowing alternatives. For sustainable consumer interaction, brands are advised to combine algorithmic personalization with «bubble-breaking» elements (periodic introduction of random or contrasting content), as well as to track not only short-term engagement metrics but also long-term loyalty. Further research should be directed toward the development of ethical algorithms that minimize manipulative impact on consumer culture.