Work

For you vs. for everyone: The effectiveness of algorithmic personalization in driving social media engagement

Year: 2025

Type: article

Abstract: Social media platforms increasingly use algorithmic personalization, raising concerns about potential uncontrolled usage. However, these concerns remain partly speculative as evidence for the effectiveness of algorithmic personalization in driving user engagement is limited. Therefore, the present study investigated how TikTok users’ behavior and experiences would change if their feeds were no longer personalized based on their interests. In this preregistered study, 88 TikTok users participated in a two-week within-subjects design: a baseline week (default highly personalized feed), followed by an experimental week (less personalized feed). Daily experiences were assessed through daily surveys, and objective TikTok usage data was obtained through screenshots. We found that both daily frequency and duration of TikTok use decreased, self-regulation increased, and participants derived less enjoyment from their use. These findings highlight the critical role of algorithmic personalization in sustaining user engagement and suggest that reducing feed personalization may be a promising, though currently limited, approach to address uncontrolled social media use.

Source: Telematics and Informatics

DOI: 10.1016/j.tele.2025.102300

Authors: Cynthia A. Dekker, Susanne E. Baumgartner, Sindy R. Sumter

Institution: University of Amsterdam

Language: English

Topic: Impact of Technology on Adolescents

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,020