Predictors of compulsive cyberporn use: A machine learning analysis

•Limited research exists on factors predicting or related to CCU.•According to the subjects' CCU scores, 21.96% showed signs of CCU.•ML analysis identified the most important determinants of CCU scores.•The most important predictor is the users’ strength of craving for pornography experiences....

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Vydané v:	Addictive behaviors reports Ročník 19; s. 100542
Hlavní autori:	Ben Brahim, Farah, Courtois, Robert, Vera Cruz, Germano, Khazaal, Yasser
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier Ltd 01.06.2024 Elsevier
Predmet:	Addiction Cognitive science Compulsive cyberporn use Cyberporn Machine learning Psychiatric/Mental Health Psychology CUD Addiction CCU ANOVA XGBoost Machine learning FCU Cyberporn MSE Compulsive cyberporn use RQ ML frequency of cyberporn use addiction cyberporn cyberporn use duration machine learning compulsive cyberporn use research question mean squared error Extreme Gradient Boosting algorithm analysis of variance
ISSN:	2352-8532, 2352-8532
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Shrnutí:	•Limited research exists on factors predicting or related to CCU.•According to the subjects' CCU scores, 21.96% showed signs of CCU.•ML analysis identified the most important determinants of CCU scores.•The most important predictor is the users’ strength of craving for pornography experiences. Compulsive cyberporn use (CCU) has previously been reported among people who use cyberporn. However, most of the previous studies included convenience samples of students or samples of the general adult population. Research examining the factors that predict or are associated with CCU are still scarce. In this study, we aimed to (a) assess compulsive cyberporn consumption in a broad sample of people who had used cyberporn and (b) determine, among a diverse range of predictor variables, which are most important in CCU scores, as assessed with the eight-item Compulsive Internet Use Scale adapted for cyberporn. Overall, 1584 adult English speakers (age: 18–75 years, M = 33.18; sex: 63.1 % male, 35.2 % female, 1.7 % nonbinary) who used cyberporn during the last 6 months responded to an online questionnaire that assessed sociodemographic, sexual, psychological, and psychosocial variables. Their responses were subjected to correlation analysis, analysis of variance, and machine learning analysis. Among the participants, 21.96% (in the higher quartile) presented CCU symptoms in accordance with their CCU scores. The five most important predictors of CCU scores were related to the users’ strength of craving for pornography experiences, suppression of negative emotions porn use motive, frequency of cyberporn use over the past year, acceptance of rape myths, and anxious attachment style. From a large and diverse pool of variables, we determined the most important predictors of CCU scores. The findings contribute to a better understanding of problematic pornography use and could enrich compulsive cyberporn treatment and prevention.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2352-8532 2352-8532
DOI:	10.1016/j.abrep.2024.100542