Personality, Algorithmic Awareness, and Addictive Symptoms of TikTok Use in University Students
Abstract
(1) Background: Problematic social media use has increasingly been conceptualized as a non-clinical addictive-like behavior characterized by impaired control and negative functional consequences. Despite the rapid growth of TikTok and its algorithm-driven content delivery, the contribution of individual psychological factors and users’ awareness of algorithmic processes to addictive symptoms remains insufficiently understood, particularly in Latin American contexts. This study examined the associations between personality traits, algorithmic awareness, and addictive symptoms of TikTok use among university students. (2) Methods: A quantitative, cross-sectional design was conducted with a convenience sample of 238 university students from Ecuador. Participants completed self-report measures of social media addiction, algorithmic media content awareness, and Big Five personality traits. Spearman correlations and hierarchical multiple regression analyses were performed, controlling for age and sex. (3) Results: Algorithmic awareness dimensions were not significant predictors of addictive symptoms. Demographic variables explained minimal variance, whereas personality traits accounted for the largest increase in explained variance in the final model. Neuroticism and Extraversion were positively associated with addictive symptoms, while Conscientiousness and Openness to Experience were negatively associated. (4) Conclusions: Personality traits were more informative than algorithmic awareness in explaining addictive-like TikTok use among university students, underscoring the relevance of self-regulatory and affective dispositions for prevention and intervention strategies.
Fields of Study & Subjects
Impact of Technology on Adolescents
Digital Mental Health Interventions
Communication and COVID-19 Impact
Publication details
- Journal / source
- Journalism and Media
- Publisher
- MDPI AG
- Volume
- 7
- Issue
- 2
- Pages
- 110–110
- Publication date
- 20 May 2026
- Language
- EN