Exploring Factors Influencing Adolescent Short Video Addiction: A Latent Profile Analysis and Machine Learning Approach
Year: 2024
Type: article
Study design: Observational Study
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.
DOI: 10.54097/aa4z4608
Authors: Huiying Chen, Chengyi Zhao, Ting Jiang, Yueyang Wang, Yizhen Ren
Institutions: Beijing Normal University, Guangzhou University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4405842819