Sex differences in problematic pornography use among adolescents: a network analysis

doi:10.21203/rs.3.rs-3327564/v1

Pornography use is a common practice in adolescents and has been observed to be more prevalent in boys. There are different consequences associated with pornography use and problematic use that may present differently among boys and girls. This study presents a network analysis to understand relationships among different variables associated with pornography use (victimization, sexual double standard, loneliness, family relationships and sexual risk behaviors) and differences between boys and girls in a Spanish sample of N = 650 adolescents. The results show a higher frequency of problematic use of pornography in boys and more conservative models of sexuality. Girls show higher levels of online victimization and loneliness associated with pornography use. Some clinical implications of the study are discussed.

pornography

addictive behaviors

compulsive behaviors

impulsive behaviors

sexual explicit materials

adolescents

sex

problematic pornography use

trauma

1.1 Sex differences in adolescent pornography use

Pornography use is a common behavior among adolescents [1], [2], with varying prevalence estimates and ages of onset between sexes. Pornography use is more common among boys than girls [3], [4]. For example, in Spain there is a significant disparity between sexes regarding weekly pornography use, with 32% of boys engaging in this behavior compared to only 6.2% of girls [5]. Furthermore, there are sex differences in the average age of first access to pornography, with boys accessing pornography at younger ages [6], [7]. For example, in Spain, Ballester et al. [5] highlighted that the average age of onset for pornography consumption is 14.02 years for boys and 16.14 years for girls.

1.2 Sex differences in adolescent problematic pornography use

Problematic pornography use (PPU) involves consumption of sexually explicit material to the extent of disrupting functioning or generating significant distress [8]. For certain adolescents, pornography use can lead to negative consequences related to PPU [9], [10]. PPU may impact boys more so than girls, although girls may experience indirect consequences [3], [11]. In a Spanish study assessing online sexual activities, 37.7% of boys and 19.3% of girls were classified as being at-risk for PPU [12]. Similarly, in a Spanish study focusing on young individuals, 28.7% of boys versus 3.6% of girls self-reported PPU [5].

1.3 Sex differences in other factors related to pornography use and PPU

The use of pornography has been linked to various factors [2], [4] that may manifest differently depending on sex, gender, or sexual orientation [3], [13]. Some potential associated factors are: (1) sexual violence and related trauma (also termed victimization, although the term “victim” may be experienced as stigmatizing by some) [14], [15]; (2) sexual intercourse and sexual risk behaviors [16]; and (3) loneliness [17], [18], among other factors.

Both boys and girls may develop objectifying views of girls and women and endorse myths about rape, although boys who use pornography tend to adopt a model of dominance and negative beliefs about girls, while girls are more susceptible to victimization associated with pornography use [14], [19]–[22]. However, longitudinal studies on the impact of pornography use on gender roles have not reached definitive conclusions, and causal relationships between pornography use and different forms of violence remain debated [14], [23].

A Spanish study found that pornography consumption was associated with an increase in sexual risk behaviors in 47.4% of boys and 39.6% of girls [5]. In the US, inconsistent/irresponsible condom use was linked to pornography use in both boys and girls, with alcohol acting as a mediating factor in the association between pornography use and levels of victimization in young women [24], [25]. Loneliness may also be both a consequence and a predisposing factor for pornography use [18], [26]. However, there is a lack of in-depth studies exploring the gender differences in this area among Spanish adolescents.

Although each of these factors has been analyzed in their association with pornography use and PPU, studies that explore the interaction between all of these factors simultaneously are needed. Therefore, the present research aimed to explore the network of characteristics potentially related to adolescent pornography use, including sexual risk behaviors, sexual double standards, loneliness, online sexual victimization, and other possible related variables (family relationships, permissiveness, and type of pornography use) and to identify key aspects with potential for cluster differentiation associated with PPU.

2.1. Ethics

The study was approved by the Clinical Research Ethics Committee of the International University of La Rioja (reference PI: 018/2020) and was conducted in accordance with the latest version of the Declaration of Helsinki. A permit from the management boards of the schools that agreed to participate in the study was obtained. Information about the study was provided to adult students and to the parents or legal guardians of underage students. Participation was voluntary and non-remunerated. All participants provided consent to being included in the study, and parents provided authorization in the case of minors. Participants were able to withdraw from the study at any time. The school board was informed about students who did not wish to participate.

2.2. Participants and procedures

The study included Spanish-speaking adolescents who were enrolled in any course of junior high school or high school in Spain. Individuals with mental disorders or cognitive difficulties that could affect their understanding of the assessment battery were excluded. Of 3304 schools contacted online, three schools agreed to participate, representing different socio-economic profiles and geographical areas in Spain.

The assessment was conducted online through Google Forms due to the COVID-19 pandemic, with teachers supervising the process after receiving training. A total of 674 adolescents initially agreed to participate, and after removing incomplete questionnaires, the final sample consisted of 650 adolescents, evenly distributed between boys and girls with a mean age of 16.0 years (SD = 1.1).

2.3. Instruments

2.3.1. Pornography use and problematic pornography use

2.3.1.1. Frequency and type of pornography use

The frequency of pornography use was measured with a self-designed item with the question: How often do you watch pornography? with 12 response possibilities from "Never" to "Five or more times a day.". Three dichotomous items (yes/no) were used to explore the exposure of adolescents sexually explicit material on the internet: accidental exposure (In the last year, when you were doing an online search or surfing the web, did you ever find yourself on a website that showed pictures of naked people or people having sex when you didn't want to be on that type of website?), intentional exposure (“Have you ever accessed a porn website on purpose or downloaded sexual images on purpose?), or sexual content received by others (“In the last year, have you ever opened a message or a link in a message that showed you real pictures of naked people or people having sex that you didn't want to see?).

2.3.1.2. Problematic Pornography Consumption Scale (PPCS-18) [27]

The PPCS-18 measures PPU with 18 Likert-type items with seven options of responses (1 = Never; 7 = All the Time). The scale has 6 sub-factors: salience, emotional modification, conflict, tolerance, relapse, and abstinence. The total score ranges from 18 to 126, with an established cut-off score ≥ 76 indicating high-risk of PPU. Good psychometric properties were observed in the original validation (∝ = 0.93) [27]. In the study sample, internal consistency was α = 0.93 for the total score; α = 0.77 for salience; α = 0.84 for tolerance; α = 0.73 for mood modification; α = 0.82 for relapses; α = 0.80 for withdrawal; and α = 0.77 for conflicts.

2.3.1.3. Pornography Consumption Inventory (PCI) [28]

The PCI assesses pornography-use motivations. The scale contains four factors (i.e., sexual pleasure, emotional avoidance, arousal seeking and sexual curiosity). The scale includes 13 Likert-type items, with response options going from 1 = Never to 5 = Many times. The scores range from 15–75. There is no established cut-off, with higher scores indicating higher tendencies to use pornography for specific reasons. The Spanish validation was used in the present study. This version demonstrated excellent reliability > 0.90 for all factors and an internal consistency of 0.93 [29]. In the study sample, internal consistency ranged between very good to excellent for the different factor scales (α = 0.88 for emotional avoidance, α = 0.93 for sexual curiosity, α = 0.95 for excitement-pleasure), and α = 0.94 for the total score.

2.3.2. Sexual Double Standard

2.3.2.1. An abridged Spanish version of the Sexual Double Standard Scale (SDSS) [30]

The SDSS is a scale that determines the extent to which people exhibit sexual double standards (SDSs). SDSs involve different criteria and values assessing sexuality of men and women, typically giving more sexual freedom to men and rewarding them for engaging in sexual activity [31]–[33]. The SDSS includes 26 items that range from Disagree Strongly (0) to Agree Strongly (3) and requires 5 minutes to complete. Scores range from − 30 (indicating acceptance of greater sexual freedom for women) to 0 (reflecting identical standards for men and women) to 48 (indicating more acceptance of the traditional double standard, suggesting acceptance of greater sexual freedom for men). The authors of the SDSS indicate it has a reliability of 0.73 in women and 0.76 in men [31].

In this study, the Spanish abridged version of the scale was used, with a reliability of 0.84 for the subscale Acceptance for sexual freedom and 0.87 for the subscale Acceptance for sexual shyness [30]. Test-retest reliabilities were good for both subscales, obtaining correlation coefficients over 0.70. In our study sample, internal consistency was α = 0.78.

2.3.3. Online Sexual Abuse

2.3.3.1. Escala Breve de Abuso Sexual Online (Brief Scale of Online Sexual Abuse; EBASO) [34]

The EBASO consists of 14 items that correspond to the online sexual victimization factor of the Juvenile Online Victimization Questionnaire (JOV-Q; [35]). This brief version of the questionnaire assesses the frequency of online sexual victimization in the last 12 months. It has four options of responses ranging from 0 (never) to 3 (always). The total score is a sum of each item’s answers. A higher score indicates greater frequency of experiencing sexual abuse online during the prior year. This version of the scale presents a reliability of 0.93 and an adequate internal consistency (α = 0.87) [34]. In our study sample, internal consistency was α = 0.93.

2.3.4. Loneliness

2.3.4.1. The University of California Los Angeles Loneliness Scale – Version 3 (UCLA-LS version 3) [36]

The UCLA-LS version 3 assesses the severity of loneliness with 20 items. Scores range from 20 to 80, and higher scores reflect greater loneliness. In the present study, the version previously adapted and validated in a Spanish sample was used with a Cronbach's alpha of 0.91 [37]. In our study sample, internal consistency was α = 0.7 for the total score.

2.3.5. Permissiveness

Permissiveness was assessed with a scale adapted and modified from previous research [38] that assessed instrumental and commitment attitudes towards sexual relations in relation to pornography use. It includes a dichotomous scale (Yes and No) with 13 items in relation to these variables. Some examples are "Sex is mainly physical," "Sex is just a game," or "It is important to accumulate experience with multiple sexual partners." Higher scores indicate greater levels of permissiveness.

2.3.6. Sociodemographic variables

Sociodemographic variables included age (12, 13, 14, 15, 16, 17 and 18 years old or more), sex (female, male, or other with specification), educational level (12 to 18 years of education, adapted to the school system in Spain), and family relationship (from very bad to excellent). Each item included the possibility of answer abstention.

2.4 Statistical analysis

We applied a network-based analysis (a promising analysis that reveals inter-relationships among elements and analyzes the structures of identified links [39], [40]. This study modeled networks with Gephi 9.2 for Windows [41], a software platform specifically developed for the analysis and visualization of networks within datasets with diverse structures (the system is available at http://gephi.org). This software provides a powerful spatialization process and the computation of multiple parameters of centrality, density, and modularity-clustering.

Separate networks were obtained for boys and girls. The nodes analyzed in the study were the PPCS, PCI, SDSS, EBASO and UCLA scores, reasons for pornography use, presence of risky sexual behaviors, permissiveness, and perceived family relationships. The magnitudes and signs of edges were calculated from the partial correlation matrix between nodes to obtain adjusted connectors and avoid the presence of biased relationships due to confounding effects. The initial data structure for the networks resulted in 22 nodes and 231 potential edges, most of which had very low weights (partial correlations around 0). To simplify this initial complex structure, only edges with significant results (p < 0.10) were included, resulting in a final structure with 60 edges for the girls’ network (26.0% of all potential connections) and 93 edges for the boys’ network (40.3% of all the potential connections).

The prominence and linkage capacity of nodes within a network can be measured through different indexes, with the most frequently used being the centrality and closeness of a node [42]. In this study, node-level relevance within the network was measured with centrality parameters, specifically eigenvector centrality indexes (calculated from the weighted sum of centrality measures of all nodes connected to a node). High centrality indexes evidence that the information contained in a concrete node is highly relevant for the whole graph. The node-level linkage was measured through the closeness indexes (which measure how close the node is to all the other nodes in the graph). High closeness indexes indicate a short average distance between one node and all the other nodes. Therefore, nodes with high closeness values have a high capacity to promote relevant changes in other parts of the network. Conversely, they are also highly vulnerable to modifications by any part of the structure.

Empirical clusters of nodes (called communities or modules in network theory) were automatically identified [43]. The existence of clusters indicates the possibility of grouping variables that highly interconnected to each other and poorly connected with nodes outside the cluster.

Other graph distance measures used in the study were: (a) the (average) path length, calculated as the mean of the shortest paths between all pairs of nodes (this value represents a measure of the efficiency of information transport in the network); and, (b) the diameter, calculated as the greatest distance between the two furthest nodes (representing the maximum eccentricity of any vertex in the graph) [44]. The density of the graph was also estimated as the number of connections divided by the number of possible connections, which provides a measure of how close the network is to being complete. A complete graph includes all possible edges and achieves a density measure equal to 1.

3.1 Descriptive data for the sample

Table 1 contains the distribution of the study variables and comparisons between sexes. Compared to girls, a higher proportion of boys were within higher education levels and reported intentional pornography use. Boys also achieved higher mean scores in the problematic pornography use (PPCS), frequency of pornography consumption (PCI scales), and SDSS scales; boys reported lower scores related to online victimization (EBASO scale) and perceived loneliness (UCLA scale).

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3.2 Underlying structures obtained in the network analysis

The graphs with the visualization of the networks are shown in Figure 1 (the complete statistics for the analyses are included in Table S1, supplementary material).

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The network obtained for the girls’ subsample achieved a diameter equal to 3, the average path length was 1.935 and the density of the graph was 0.260. The first panel of Figure 2 shows the bar-charts with the nodes ordered by the eigenvector centrality (which provides a measure of the relevance of each variable in the network) and the closeness centrality (a measure of linkage capacity, calculated as the reciprocal of the sum of the length of the shortest paths between the node and all other nodes). For girls the node with the greatest influence and the highest linkage was the online victimization level (EBASO score), closely followed by using pornography for sexual pleasure (measured with the PCI) and PPU aspects of tolerance and mood modification (measured with the PPCS).

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The network obtained for boys achieved a diameter equal to 3, the average path length was 1.623 and the density of the graph was 0.403. The node with the greatest relevance (the most central variable) was the use of pornography for sexual pleasure, followed by PPU-related withdrawal (PPCS scale). The PCI sexual pleasure and online victimization (EBASO score) achieved the highest linkage.

Figure S1 (supplementary material) shows the main linkages for the node frequency of pornography use. Among girls, the activation of this node had the greatest major impact on the PPCS scales of salience, tolerance and relapses, the PCI scales of pleasure and avoidance, and the EBASO scale. Among boys, the highest impact associated with the activation of this node were on the PPCS scales, the PCI avoidance and pleasure scales, the SDSS sexual restraint scale, the permissibility score, the EBASO scale and the UCLA scale.

The clustering analysis (Figure 3) for the nodes identified four latent modularities for the girls subsample and five latent modularities for the boys subsample. A common cluster was found for boys and girls, grouping four PPCS scales: conflict, mood modification, relapse, and withdrawal.

--- Insert Figure 3 ---

The main objective of this research was to explore the interconnectedness of core features related to pornography use in adolescence, including PPU, sexual risk behaviors, double sexual standard, loneliness, online sexual victimization, and other potentially related variables like family relationships, permissiveness, and type of pornography use.

In the study, boys reported higher intentional pornography use and had higher mean pornography use and PPU. These findings align with previous research that has also observed higher rates of pornography use and PPU among boys versus girls [2], [5], [6], [45].

In our sample, boys showed slightly higher levels of sexual double standards, suggesting a potential tendency towards a more conservative view of sexuality concerning females. This observed sexual double standard may be influenced by higher pornography consumption in boys, as evidenced by the positive associations between the total score of the SDSS and four of the PPCS sub-scales (mood modification, relapse, withdrawal, and conflict) in the network analysis. These findings align with previous research that has linked higher pornography use in boys to more objectifying and traditional perceptions of females [14].

Girls in our sample exhibited higher levels of online victimization and reported experiencing greater feelings of loneliness. Notably, these two variables showed a positive relationship in the network analysis, indicating a potential link between online victimization and loneliness among girls. This finding reinforces the existing evidence from other studies that suggest females are at a higher risk of online victimization compared to males [46]. Moreover, it suggests how victimization may lead to more detrimental consequences [46]. Additionally, the network analysis indicated a possible association between levels of victimization and the frequency of pornography use, which is consistent with previous research findings [14].

Regarding girls, the network analysis revealed that online victimization had the greatest influence and highest linkage, closely followed by using pornography for sexual pleasure and PPU aspects of tolerance and mood modification. These findings highlight the significance of online victimization in girls and its potential association with PPU. It is possible that girls who experience sexual victimization may turn to pornography to seek sexual pleasure, which, in turn, could lead to PPU due to increased tolerance levels and using pornography as a mood regulator. Furthermore, these results underscore the higher risk that females may face when navigating the internet, consistent with findings from other studies [14], [20].

However, in boys it seems that the node with the greatest relevance (the most central variable) was the use of pornography for sexual pleasure, which could relate to a gender-stereotyped model, where males feel greater freedom for sexual pleasure than females, as has been seen in other studies where men's online sexual practices are more recreational and women are more likely to experience abuse [12].

The common cluster found in both boys and girls consists of the PPCS scales assessing conflict, mood modification, relapse, and withdrawal. These results suggest that these variables play a central role in understanding PPU among adolescents, while the PPCS scales tolerance and salience form a separate cluster. Tolerance and salience, while potentially leading to certain consequences, may also indicate adolescents' curiosity and progressive exploration of their sexuality, which is a typical aspect of adolescence [9], [47]. These findings suggest the importance of establishing specific criteria for PPU in adolescents, distinct from those for adults, as they are in a different stage of general and sexual maturity.

4.1 Clinical implications

The current findings have clinical implications. First, males may be of particular interest when addressing PPU clinically, and for psychoeducation on pornography use and gender stereotypes when they present a more negative view of female sexuality. Second, it will be important to consider the risk that females have of experiencing sexual trauma online and therefore assess and address these concerns and related emotional harms and loneliness. In general, it will be important to consider how the results may help develop prevention strategies with gender perspective for risk behaviors such as PPU and online sexual trauma and victimization. Finally, it is important to consider the central variables relating to PPU in adolescents to distinguish pornography use and PPU.

4.2. Strengths, limitations, and future studies

Some previous studies have explored sex differences in certain variables related to adolescent use of pornography. However, to the best of our knowledge, this is the first study to use network analysis, a promising statistical method for understanding inter-relationships among measures, to comprehensively understand specific relationships between factors and sex differences in associations.

The present study also has several limitations. First, the study sample of Spanish adolescents may not generalize to the overall adolescent population of the country or other jurisdictions, especially given cultural differences. Second, the cross-sectional design of the current study does not permit causal inferences. Third, the use of self-reported questionnaires may be associated with biases (e.g., desirability, recall), especially in the case of adolescents, and therefore caution should be exercised in the interpretation of results. In addition, some items were not validated and were self-designed; therefore, the statistical power of these associations may also be biased. Finally, different schools (mixed vs only boys) represent a possible limitation in the interpretation of the results. Future studies could include larger samples and conduct longitudinal research to determine the evolution of the different variables studied over time and after adolescence.

Notable sex differences in the use and problematic consumption of pornography were identified among adolescents. Boys tended to consume pornography more frequently and in a more problematic manner, which may contribute to the reinforcement of gender stereotypes regarding women's sexuality. On the other hand, although girls use pornography to a lesser extent, they may experience significant consequences, such as online sexual abuse/victimization and feelings of loneliness.

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Table 1 Sample characteristics and between-sex differences

	Total (n=650)		Girls (n=325)		Boys (n=325)
Sociodemographics	n	%	n	%	n	%	p	C-V
Education High school / Level1	7	1.1%	2	0.6%	5	1.5%	.003*	.166^†
High school / Level 2	14	2.2%	1	0.3%	13	4.0%
High school / Level 3	20	3.1%	9	2.8%	11	3.4%
High school / Level 4	190	29.2%	91	28.0%	99	30.5%
Senior school / Level1	184	28.3%	108	33.2%	76	23.4%
Senior school / Level2	235	36.2%	114	35.1%	121	37.2%
Family relationships Very bad	5	0.8%	2	0.6%	3	0.9%	.270	.099
Bad	4	0.6%	3	0.9%	1	0.3%
Mild	45	6.9%	28	8.6%	17	5.2%
Good	152	23.4%	79	24.3%	73	22.5%
Very good	266	40.9%	134	41.2%	132	40.6%
Excellent	178	27.4%	79	24.3%	99	30.5%
	Mean	SD	Mean	SD	Mean	SD	p	h²
Age (yrs-old)	15.99	1.08	16.07	1.00	15.91	1.16	.064	.005
Pornography use (prevalence)	n	%	n	%	n	%	p	C-V
Accidental	451	69.4%	236	72.6%	215	66.2%	.074	.070
Received	285	43.8%	154	47.4%	131	40.3%	.069	.071
Intentional	326	50.2%	88	27.1%	238	73.2%	.001*	.462^†
Problematic pornography use	Mean	SD	Mean	SD	Mean	SD	p	h²
PPCS: salience	4.46	2.86	3.50	1.66	5.43	3.42	<.001*	.115
PPCS: tolerance	5.19	3.70	3.66	2.24	6.72	4.20	<.001*	.172
PPCS: mood modification	4.20	2.87	3.36	1.54	5.04	3.56	<.001*	.086
PPCS: relapse	4.36	3.17	3.49	1.90	5.23	3.87	<.001*	.075
PPCS: withdrawal	5.36	4.26	3.44	1.59	7.29	5.14	<.001*	.204
PPCS: conflict	4.07	2.84	3.38	1.83	4.77	3.45	<.001*	.060
PPCS: total	27.65	16.79	20.83	8.93	34.47	19.79	<.001*	.165
Pornography consumption	Mean	SD	Mean	SD	Mean	SD	p	h²
PCI: emotional avoidance	7.12	3.82	5.64	2.30	8.61	4.42	<.001*	.151
PCI: sexual curiosity	6.41	3.70	5.44	3.06	7.38	4.02	<.001*	.068
PCI: excitement	9.16	5.80	6.09	4.12	12.22	5.62	<.001*	.280
PCI: total score	22.69	11.28	17.18	7.96	28.21	11.42	<.001*	.239
Pornography frequency	Mean	SD	Mean	SD	Mean	SD	p	h²
Pornography use (hours/month)	2.47	9.43	0.53	5.10	4.42	12.02	<.001*	.043
Online victimization	Mean	SD	Mean	SD	Mean	SD	p	h²
EBASO: total score	3.16	5.67	4.46	6.33	1.86	4.58	<.001*	.053
Permissiveness	Mean	SD	Mean	SD	Mean	SD	p	h²
Total score	2.85	2.45	2.75	2.37	2.94	2.53	.329	.001
Doble standard scale	Mean	SD	Mean	SD	Mean	SD	p	h²
SDSS-F1: sexual freedom	6.83	3.78	6.23	3.32	7.43	4.10	<.001*	.025
SDSS-F2: sexual restraint	7.15	3.74h	6.62	3.32	7.69	4.04	<.001*	.021
SDSS: global index	-2.05	4.92	-2.70	4.83	-1.40	4.93	.001*	.018
Loneliness	Mean	SD	Mean	SD	Mean	SD	p	h²
UCLA: loneliness total	38.39	10.10	39.70	10.74	37.08	9.25	.001*	.017
Risk sexual relationships (prev.)	n	%	n	%	n	%	p	C-V
Sex without condom	133	20.5%	65	20.0%	68	20.9%	.771	.011
Sex with alcohol-drugs	119	18.3%	63	19.4%	56	17.2%	.478	.028

Note. SD: standard deviation. *Bold: significant comparison.

^†Bold: effect size within the ranges moderate-mild to large-high.z

Chi-square tests used for the comparison between the groups for categorical variables and analysis of variance for quantitative variables.

No competing interests reported.

TableS1Fig.S1.docx

Sex differences in problematic pornography use among adolescents: a network analysis

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Abstract

Figures

1. INTRODUCTION