Exploring Factors Influencing Adolescent Short Video Addiction: A Latent Profile Analysis and Machine Learning Approach
DOI:
https://doi.org/10.54097/aa4z4608Keywords:
short video addiction; machine learning; latent profile analysis; random forest algorithm; nonlinear relationships.Abstract
The proliferation of short-form video platforms has precipitated mounting concerns regarding addiction among adolescents. This investigation examines Short-Form Video Addiction (SFVA) through the integration of Latent Profile Analysis (LPA) and machine learning techniques. Analysis of data from 11,687 adolescents revealed four distinct user profiles: low-risk self-controlled (18.03%), high-impact low-awareness (24.60%), high-risk dependent (25.12%), and moderate-risk potential (32.25%) groups. Subsequent machine learning analysis, utilizing random forest algorithm with 89% accuracy, identified peer attachment, parental overprotection, and parenting styles as paramount predictors of SFVA development. These findings illuminate the multifaceted nature of adolescent SFVA and provide empirically-grounded recommendations for targeted intervention strategies.
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References
[1] Allen, J. P., & Tan, J. (2016). The multiple facets of attachment in adolescence. In J. Cassidy & P. Shaver (Eds.), Handbook of attachment (3rd ed., pp. 399–415). New York NY: Guilford.
[2] Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, Potenza MN. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neurosci Biobehav Rev. 2019 Sep; 104:1-10.
[3] Choi, S. B., & Lim, M. S. (2016). Effects of social and technology overload on psychological well-being in young South Korean adults: The mediatory role of social network service addiction. Computers in Human Behavior, 61, 245–254.
[4] Koning IM, Peeters M, Finkenauer C, van den Eijnden RJJM. Bidirectional effects of Internet-specific parenting practices and compulsive social media and Internet game use. J Behav Addict. 2018 Sep 1;7(3):624-632.
[5] Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary educational psychology, 61, 101860.
[6] Valkenburg, P. M., & Peter, J. (2009). Social consequences of the Internet for adolescents: A decade of research. Current Directions in Psychological Science, 18(1), 1–5.
[7] Van den Eijnden, R. J., Lemmens, J. S., & Valkenburg, P. M. (2016). The social media disorder scale. Computers in human behavior, 61, 478-487.
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