Cyber-pornography use among a sample of Chinese university students: utilizing machine learning and network analysis

Due to the popularity and convenience of the internet, cyber-pornography use among youth has attracted the widespread attention of scholars. Cyber-pornography use among Chinese university students remains rarely investigated, especially based on ecological systems theory using machine learning and network analysis approaches. Therefore, the present study examined cyber-pornography use through machine learning and network analysis based on ecological systems theory among a sample of Chinese university students.

A sample of 1322 university students in different provinces of China were surveyed in a cross-sectional study. The survey included cyber-pornography use variables, demographic information, psychological variables, family variables, and school factors.

Of 1332 participants, 25 students were classed as high-risk cyber-pornography users (1.9%). The XGBoost model of machine learning identified 15 important features of cyber-pornography use including social ostracism, moral disengagement, gender, teacher fairness, year of study, age, autonomy, self-control, depression, father and mother's parenting style, relatedness, experience of being left behind, loneliness, and family closeness. In the network analysis, social ostracism was the strongest node in the total sample and among males, whereas depression was the strongest node in females.

Psychological, family, school, and demographic factors (e.g. gender) may directly or indirectly be associated with cyber-pornography use among Chinese university students. The results suggest that ecological systems theory, including the microsystem, mesosystem, macrosystem, and chronosystem, may be considered as one of the core theories in understanding cyber-pornography use. In addition, the interaction of related factors also needs to be examined deeply to aid prevention and intervention of cyber-pornography use in the future.