A new study published in the Journal of Psychopathology and Clinical Science has identified the most robust risk factors for problematic pornography use by analyzing 74 preexisting self-report data sets using machine learning. The findings shed light on a topic that has garnered increasing clinical and scientific attention over the past several decades.
Pornography consumption is widespread, with studies indicating that 70-94% of adults and 42-98% of adolescents have viewed pornography in the past 20 years. While many use pornography without issues, a subset of users experience problematic pornography use (PPU), characterized by uncontrollable consumption patterns leading to significant distress and functional impairment.
Estimates suggest that 1-38% of adults and 5-14% of adolescents may struggle with PPU. Understanding the risk factors for PPU is essential for developing effective prevention and treatment strategies, especially as compulsive sexual behavior disorder, which includes PPU, has been officially recognized in the 11th revision of the International Classification of Diseases.
“PPU seems to be as prevalent as other well-established mental health issues (e.g., depression), but it has received significantly less scientific attention in the past. For example, even though we have some empirical evidence about risk and protective factors concerning PPU, our knowledge is quite limited,” study author Beáta Bőthe, an assistant professor of psychology at the University of Montreal and director of the Sexuality, Technology, and Addictions Research Laboratory (STAR Lab).
“At the same time, theoretical models in our field propose that several different factors may contribute to the development of PPU, and these factors might be in interaction with each other. With the emergence of artificial intelligence-based data analytic methods (compared to traditional statistical methods), we were able to examine these complex questions and include hundreds of potential risk and protective factors in our study.”
For their study, the researchers solicited data from 98 laboratories worldwide, ultimately including 74 datasets from 16 countries. These datasets, comprising over 112,000 participants, included both published and unpublished data and assessed PPU using various validated scales such as the Problematic Pornography Consumption Scale, the Cyber-Pornography Use Inventory, and the Brief Pornography Screen.
To analyze these datasets, the researchers employed random forest models, a machine learning method that builds on classification and regression trees. This method allows for the simultaneous consideration of numerous variables and their complex interactions, providing a robust way to identify key predictors of PPU.
The random forest models were applied to each dataset individually, with the PPU score as the dependent variable and various potential predictors as independent variables. The researchers then combined the results from these models using meta-analytic methods to ensure the findings were generalizable and reliable.
The most robust predictor was the frequency of pornography use. Regular consumption of pornography was found to be strongly associated with PPU, suggesting that frequent users are more likely to experience problematic patterns of use.
“Individuals who use pornography more frequently may be at a higher risk of experiencing problems with their use,” Bőthe told PsyPost. “Yet, it is important to note that high-frequency pornography use may appear without PPU in some cases (e.g., due to strong sexual desire), and self-perceived PPU may be present even with low-frequency pornography use (e.g., due to moral disapproval of pornography use). Therefore, information about someone’s pornography use frequency in itself is not enough to decide whether they have problems with their use.”
Emotional avoidance motivation was another critical predictor. Individuals who used pornography to avoid negative emotions, such as stress or anxiety, were more likely to develop PPU. This finding highlights the role of pornography as a coping mechanism for managing emotional distress.
Moral incongruence emerged as a significant predictor as well. This refers to the conflict individuals feel when their pornography use contradicts their personal values or moral beliefs. Those experiencing higher levels of moral incongruence were more likely to report PPU, indicating that internal conflicts about pornography use can contribute to problematic patterns.
Sexual shame was also identified as a key predictor. Individuals who felt ashamed of their sexual behaviors, including their use of pornography, were more likely to develop PPU. This suggests that feelings of shame and guilt can exacerbate problematic use patterns.
Stress reduction motivation was another significant predictor. Using pornography as a way to cope with stress was strongly linked to PPU. This finding underscores the importance of addressing stress and developing healthier coping mechanisms to prevent the development of problematic use.
The findings indicate that “individuals who experience more negative emotions and use pornography to regulate them may experience higher levels of PPU,” Bőthe said.
Other notable predictors included the duration of pornography use per session, fantasy-driven motivations, and feelings of guilt. General psychological factors such as anxiety and depression symptoms were also significant predictors.
Consistent with previous research, the study found that men were more likely to experience PPU compared to women. But while gender was a (statistically) significant predictor, it was a relatively weak one.
“Based on previous findings, we expected that gender would be an important predictor of PPU (i.e., this issue is usually more common among men compared to women and gender-diverse individuals),” Bőthe explained. “Yet, somewhat surprisingly, gender did not emerge as such an important predictor in this study, it was not even among the top 10 predictors. These findings highlight the importance of being inclusive in pornography research and not focusing only on the experience of men if we want to better understand this phenomenon.”
But, as with all research, there are some caveats to consider. The reliance on self-report data can introduce biases, such as recall bias, and the overrepresentation of data from Western, educated, industrialized, rich, and democratic (WEIRD) countries limits the generalizability of the findings. Future research should strive to include more diverse populations to enhance the applicability of the results.
Additionally, the study faced considerable heterogeneity in its findings, suggesting that further exploration is needed to fully understand the factors contributing to PPU. Longitudinal studies, which follow participants over time, could provide more detailed information about how PPU develops and changes.
“We aim to widen the scope of our work and include more underserved and underrepresented groups in our studies,” Bőthe said.
The study, “Uncovering the Most Robust Predictors of Problematic Pornography Use: A Large-Scale Machine Learning Study Across 16 Countries,” was authored by Beáta Bőthe, Marie-Pier Vaillancourt-Morel, Sophie Bergeron, Zsombor Hermann, Krisztián Ivaskevics, Shane W. Kraus, Joshua B. Grubbs, and the Problematic Pornography Use Machine Learning Study Consortium.
