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This paper is currently in pre-print form.

7/ ✨ These findings suggest that algorithmic literacy,as a contextual influence on privacy rules, may serve as a protective factor, allowing fatigued users to recognize and resist sharing information with algorithms.

5/ Key finding: individuals with high algorithmic literacy are generally leas willing to co-own private information with platforms. Their understanding empowers them to make informed choices about data sharing in the social media algorithm context.

4/ I used Latent Moderated Structural equations (LMS) to test the relationship between algorithmic literacy, privacy fatigue, and users’ privacy management behaviors on social media.

3/ This study addresses a paradox: though users dislike these privacy violations, they often continue sharing personal info due to privacy fatigue—when managing privacy becomes overwhelming, exhausting or leads to cynicism. Or at least, thats one possibility.

2/ 🔍 As platforms rely on increasingly personalized algorithms, users (un)knowingly cultivate feeds based on their human-algorithm interplay and private information, often leading to unexpected insights or ads that reveal details users may not have intended to share.