The Influence of Social Media Recommendation Algorithms on Information-Seeking Behavior and Filter Bubble among Generation Z Users in Indonesia

Social media platforms increasingly function as algorithmic information systems that shape how young users access, select, and interpret information. Recommendation algorithms on TikTok, Instagram, YouTube, Facebook, and X personalize feeds based on interaction patterns, interests, and digital activity histories. Although personalization can improve relevance, it may also narrow information exposure and encourage filter bubbles. This study examines the relationships among perceived social media recommendation algorithms, algorithmic awareness, filter bubble formation, and information-seeking behavior among Generation Z users in Indonesia. An explanatory quantitative design was employed using an online questionnaire distributed to active social media users aged 18-27 years. The data were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using a reflective measurement model and 5,000 bootstrap subsamples. From 257 recorded data rows, 137 valid responses were analyzed after 120 spreadsheet rows with no questionnaire answers were removed. The results indicate that perceived recommendation algorithms are positively and significantly associated with filter bubbles (β = 0.544; p < 0.001), while filter bubbles are positively and significantly associated with information-seeking behavior (β = 0.401; p = 0.001). Perceived recommendation algorithms also show a significant negative association with information-seeking behavior (β = -0.426; p < 0.001), indicating that the proposed positive-direction hypothesis is not supported. Algorithmic awareness is not significantly associated with either filter bubbles or information-seeking behavior. These findings suggest that algorithmic personalization is associated with homogeneous information exposure and lower active information seeking when users rely heavily on recommended feeds. However, because the study uses cross-sectional self-reported survey data, the results should be interpreted as statistical associations rather than causal effects. The study highlights the need for algorithmic literacy that encourages users to verify sources, compare perspectives, and manage recommendation preferences critically

recommendation algorithm, social media, information-seeking behavior, filter bubble, Generation Z

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