Uncovering the most robust predictors of problematic pornography use : a large-scale machine learning study across 16 countries

Abstract

Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in the past two decades, but most prior studies were characterized by several shortcomings (e.g., using homogenous, small samples), resulting in crucial knowledge gaps and a limited understanding concerning empirically based risk factors for PPU. This study aimed to identify the most robust risk factors for PPU using a preregistered study design. Independent laboratories’ 74 preexisting self-report data sets (Nparticipants = 112,397; Ncountries = 16) were combined to identify which factors can best predict PPU using an artificial intelligence-based method (i.e., machine learning). We conducted random forest models on each data set to examine how different sociodemographic, psychological, and other characteristics predict PPU, and combined the results of all data sets using random-effects meta-analysis with meta-analytic moderators (e.g., community vs. treatment-seeking samples). Predictors explained 45.84% of the variance in PPU scores. Out of the 700+ potential predictors, 17 variables emerged as significant predictors across data sets, with the top five being (a) pornography use frequency, (b) emotional avoidance pornography use motivation, (c) stress reduction pornography use motivation, (d) moral incongruence toward pornography use, and (e) sexual shame. This study is the largest and most integrative data analytic effort in the field to date. Findings contribute to a better understanding of PPU’s etiology and may provide deeper insights for developing more efficient, cost-effective, empirically based directions for future research as well as prevention and intervention programs targeting PPU.