Unraveling the impact of cyberporn motivations on mental health: insights from Chinese college students

Abstract

Background

Internet pornography use is prevalent among college students, yet the impact of internet pornography consumption on mental health remains contentious, with debates on whether it is positive or negative. This study aims to explore the complex relationship between internet pornography consumption and mental health from the perspective of motives for internet pornography use.

Methods

Using a convenience sampling method, this study surveyed 2,938 college students, collecting data on their demographics, depression, anxiety, sleep quality, social interaction anxiety, and motives for internet pornography use. Descriptive statistics and network analysis methods were employed to investigate the relationship between motives for internet pornography use and mental health among college students.

Results

The results revealed that among college students with motives for internet pornography use, the prevalence rates of depression and anxiety were 55.31% and 36.25%, respectively. Motives for internet pornography use were positively correlated with depression, anxiety, sleep quality, and social interaction anxiety. Network analysis indicated that internet pornography behaviors based on enhancement and coping motives might be associated with increased symptoms of depression and anxiety, whereas behaviors based on social motives might be related to decreased symptoms of depression and anxiety.

Conclusions

This study partially explains the varying impacts of internet pornography consumption on mental health. The findings highlight the importance for policymakers to understand better the potential impact of different motives for internet pornography use on the mental health of college students when regulating internet pornography materials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-025-02901-y.

Introduction

With the rapid development of the internet, various types of pornographic content are proliferating online [1]. The world’s largest pornographic website has become one of the most visited sites, offering users interactive live pornographic content, virtual reality pornographic content with realistic situational experiences, and pornographic content that caters to various niche preferences [2]. This greatly satisfies users’ pursuit of sexual gratification and significantly increases their dependence on online pornographic materials [3]. Surveys in Western countries have found that the lifetime usage rate of pornographic materials is 96% among men and 61% among women [4]. Similarly, in a sample of Chinese adults, 96.6% of male participants reported watching pornographic materials, compared to 77.7% of female participants [5]. These data indicate that the consumption of internet pornography has become a common behavior. Internet pornography use is a common practice among university students [6], and it has increasingly replaced traditional forms of pornography use [7]. Data shows that up to 89.8% of university students have accessed sexual information online, and 76.5% reported engaging in online sexual entertainment activities [8].

The use of internet pornography has a wide range of positive and negative effects on physical and mental health [9]. Some studies have indicated that internet pornographic behavior can, to some extent, promote the physical and mental well-being of users. One of the most significant benefits is that internet pornographic material increases users’ sexual knowledge and attitudes [10, 11], thereby enhancing users’ sexual confidence [9]. Secondly, internet pornographic products can satisfy users’ sexual curiosity without any emotional commitment, avoiding the fear of rejection or abandonment [12]. Additionally, viewing pornographic content can improve personal mood and help escape daily stressors [13], as pornographic material induces pleasure [14]. Finally, internet pornography use can also foster individual interpersonal networks, such as through online pornographic interactions [15], increasing their sense of belonging [16] and reducing feelings of loneliness [17], while simultaneously enhancing their perception of interpersonal intimacy [18].

Despite the incomplete exploration of the negative impacts of cyberporn, numerous studies have highlighted the adverse consequences associated with excessive use of internet pornographic materials or pornography addiction. From a physiological perspective, the use of internet pornography may lead to sexual dysfunction [19] and a loss of sexual interest in real-life partners [20]. However, some studies indicate that pornographic materials generally do not significantly affect male sexual function [21]. This contradiction may stem from whether the studies emphasize excessive use of cyberporn. Furthermore, since internet pornography consumption typically occurs at midnight, excessive browsing may delay sleep time, leading to sleep disorders [22].

From a psychological perspective, internet pornographic content may cause dissatisfaction with one’s body image, resulting in low self-esteem or confusion [23], and increasing emotional distress and suffering [24]. For students, the consumption of internet pornography negatively impacts learning and educational outcomes [25]. Moreover, studies have shown a positive correlation between pornographic behavior and depressive symptoms [26], with higher levels of depression making individuals more likely to engage in pornographic behavior [27]. The comorbidity of pornographic behavior and depression is also relatively common. Research estimates that 28% of males in a sample of sex addiction also suffer from depression, a proportion significantly higher than the 12% in the general male population [28]. Additionally, the use of internet pornography is closely related to anxiety levels [29]. These pieces of evidence suggest an inseparable relationship between internet pornography motivation and mental health.

Although internet pornography use may be associated with various negative health outcomes, college students continue to engage in this behavior, with reasons attributable to physiological, psychological, and social factors. First, college students are in a stage of physical maturity characterized by increased secretion of sex hormones, which heightens their sexual needs [30, 31]. Opportunities to fulfill these needs are often constrained by real-world factors (e.g., romantic relationships, social skills), making internet pornography an appealing alternative due to its accessibility, affordability, anonymity, and novelty [32, 33]. Second, the pleasure derived from dopamine release can temporarily alleviate stress or negative emotions, particularly for individuals experiencing psychological distress or singlehood [34]. For instance, among Chinese college students, only 27.61% are in romantic relationships, and 41.93% have no intention of seeking a partner, suggesting that singlehood may exacerbate reliance on internet pornography use [35, 36], while discordant real-life sexual experiences may also drive individuals toward the virtual world [37]. Finally, social factors play a significant role, as studies indicate that in certain college student communities, sharing internet pornography or discussing related topics serves as a social strategy to enhance group identity [38–40]. In conclusion, given that internet pornography use is driven by a confluence of physiological, psychological, and social factors, completely prohibiting it may prove challenging. Thus, investigating the motivations behind internet pornography use could provide a theoretical foundation for developing effective intervention strategies.

Furthermore, the use of internet pornography can be interpreted through the Digital Life Balance (DLB) framework [41]. The DLB framework emphasizes that maintaining a balance between online and offline life is critical to individual mental health [42]. According to this framework, internet pornography use may serve as a compensatory mechanism, helping individuals cope with unmet psychological needs in real life [43]. Specifically, when college students face setbacks, loneliness, or psychological stress in real life, unmet psychological needs may drive them to turn to online activities such as pornography to seek emotional relief [41]. For instance, negative emotions such as depression and anxiety may prompt individuals to rely on internet pornography for short-term emotional regulation; however, this behavior may further exacerbate psychological issues, creating a vicious cycle [44, 45]. Moreover, excessive use of internet pornography not only disrupts digital life balance but may also trigger negative emotions such as shame and guilt, thereby increasing individuals’ psychological burden [42]. This evidence suggests that a bidirectional relationship may exist between internet pornography motivations and mental health, on one hand, psychological issues drive individuals toward internet pornography; on the other hand, excessive use of internet pornography may worsen psychological problems. Therefore, understanding this bidirectional relationship could provide important theoretical guidance for targeted interventions.

In summary, the primary motivations for engaging in internet pornography may be simply to satisfy sexual needs driven by physiological hormones or functions; as a compensatory mechanism, to seek temporary sexual pleasure to escape stress, or to engage in social activities related to cyberporn. It is worth noting that sexual behavior, as a fundamental physiological need, is not only related to the lowest level of Maslow’s hierarchy—the physiological needs—but also to higher-level needs, such as social needs, esteem needs, and self-actualization needs [46]. When college students are unable to obtain sexual satisfaction offline, the use of internet pornography becomes the primary means of fulfilling their sexual needs, making it potentially unavoidable.

Although previous studies have explored the overall relationship between internet pornography use and mental health, there remains a lack of in-depth analysis regarding how specific motivations (e.g., seeking sexual gratification, stress relief, or social needs) interact with particular symptoms (e.g., restlessness in anxiety or hopelessness in depression). For instance, motivations driven by social needs may be associated with a reduction in mental health symptoms, whereas those rooted in sexual gratification or avoidance might contribute to the emergence of such symptoms. This complexity likely stems from a bidirectional influence between motivations and symptoms: on one hand, specific mental health symptoms may drive individuals to use internet pornography for varying reasons; on the other hand, different motivations for pornography use may exacerbate certain symptoms while alleviating others. However, existing research has predominantly focused on aggregate effects, overlooking the heterogeneity at the symptom level. Consequently, investigating these symptom-specific associations not only enhances our understanding of the underlying mechanisms of internet pornography use but also provides a theoretical foundation for targeted prevention and intervention strategies.

Methods

Participants

In June 2023, a survey was conducted involving 2,938 university students from two universities in Chongqing, China. The survey was administered through the online platform Wenjuanxing (www.wjx.cn), and the generated QR code for the questionnaire was distributed to students by university counselors. Although the survey was anonymous, the section on motives for internet pornography might have caused discomfort to participants due to ethical review standards. Therefore, this section was optional. Only those university students who had experience with internet pornography and were willing to report their motives provided relevant data. Ultimately, complete data were collected from only 320 university students. The relatively low number of participants reporting their internet pornography motivation might be attributed to China’s relatively conservative sexual culture [47], the legal ban on pornography, and the burden of completing the questionnaire.

Sample size

Although some recent studies have advocated the use of the Monte Carlo Method for sample size estimation for network analysis, which is an optimal parameter-based sample size estimation method [48], in our research context, with parameters set to achieve a sensitivity of 0.6 with a probability of 0.8, the recommended sample size is 3,019. This sample size far exceeds the conditions of our investigation. Therefore, sample size estimation was performed in this study based on a priori conditions [49, 50]. According to the potential estimation parameter (23*22/2 + 23), the presupposed sample size is at least higher than the estimation parameter, i.e., greater than 253. The methods section details the estimation of the regularized Gaussian graphical models (GGM) by combining Glasso with Extended Bayesian Information Criterion (EBIC) model selection to obtain stable results.

Measures

Demographic information includes gender, age, class, place of household registration, subjective family economic status, internet usage time, and primary purposes of internet use. The Cybersex Motives Questionnaire (CMQ) consists of 14 items designed to investigate the motives of college students for engaging in cyberporn [51]. In this study, Cronbach’s α for the CMQ is 0.98. The depression symptoms were assessed using the Chinese version of the Patient Health Questionnaire (PHQ-9) [52]. In this study, Cronbach’s α for the PHQ-9 is 0.91. The anxiety symptoms were assessed using the Chinese version of the Generalized Anxiety Disorder Scale (GAD-7) over two weeks [53]. In this study, the Cronbach’s α is 0.95. The social interaction anxiety subscale of the social media users’ social anxiety scale [54] was used to measure the social anxiety experienced. The short version of the insomnia severity index [55] was used to assess the sleep quality of college students. Detailed information on measurement tools can be found in the supplementary material.

Data analysis

In this study, we employed Gaussian network analysis and Bayesian network analysis. Network analysis is a detailed symptom-based analysis method [56] that visually presents the relationships between these symptoms and related variables through network structure diagrams. This method has been widely used in the study of addictive behaviors [57] or mental disorders [58]. While traditional statistical methods, such as structural equation modeling (SEM), have been widely used in psychology as a powerful multivariate statistical approach [59], we opted for network analysis for several key reasons. First, network analysis allows us to explore complex associations at the item (symptom) level rather than imposing predefined causal pathways, which is a fundamental assumption in SEM [60]. When estimating a large number of observed variables, SEM models tend to become overly complex. Using latent variables can simplify the model, but this may obscure the original meaning of individual items [61]. Second, Bayesian network modeling provides probabilistic insights into the potential priority relationships among variables [62]. Unlike SEM, which heavily depends on theoretical assumptions for model construction and interpretation, Bayesian network analysis is particularly useful when existing theories do not provide clear causal directions, allowing for the exploration of plausible causal connections. Overall, network analysis offers a more exploratory, data-driven approach that is well-suited for uncovering subtle association patterns among multiple items without imposing strict causal assumptions.

First, we conducted descriptive statistical analysis, covering the demographic information of the sample and the levels of mental illness, as well as Spearman correlation analysis. This helped us better understand the characteristics of the current sample. Second, considering the conceptual overlap of sleep quality and social interaction anxiety with depression and generalized anxiety symptoms, we used network analysis methods to visualize the relationship between internet pornography motivation and depression and anxiety. This aimed to obtain clearer relationship results. The reason for constructing two separate network models is that depression and anxiety typically exhibit a very high correlation, which may affect the structure of the network. This study focuses on the nuanced relationships between online internet pornography motivation and depression and anxiety, rather than examining the relationships between depression and anxiety alone or merely analyzing the total scores of these variables. This approach is necessary because while correlation analysis, regression, or structural equation modeling may suffice to explore the relationships among the total scores of these variables, a more nuanced examination is required to understand the complex interplay between online sexual motivation and mental health outcomes. All analyses were conducted using R version 4.4.0.

The network analysis employed GGM to estimate connections between nodes, where edges represented partial correlations after accounting for all other nodes. Spearman’s rho correlation matrices were utilized, and regularization was performed using the graphical LASSO algorithm to enhance both interpretability and stability [63]. A hyperparameter of 0.5 for the EBIC was chosen to balance sensitivity and specificity in edge extraction [49]. For visualization, the Fruchterman-Reingold algorithm was applied via the R-package qgraph [64, 65].

Node expected influence, indicating the importance of nodes within the network, was computed using qgraph [64]. Additionally, bridge expected influence, which highlights potential contagion risks to other communities, was calculated using networktools [66, 67]. The robustness of the network was assessed using bootnet (nboot = 2000), with edge weight accuracy evaluated through non-parametric bootstrap methods and node influence stability measured by the correlation stability coefficient [50].

Directed Acyclic Graph (DAG) analysis based on cross-sectional data can provide directionality to nodes from an algorithmic perspective, thereby establishing priority relationships between nodes. This study utilized the R package bnlearn and the hill-climbing algorithm to compute DAG of internet pornography motives with depression and anxiety symptoms, respectively [68]. The algorithm continuously adds, removes, and reverses edge directions until the best fit is achieved according to the Bayesian Information Criterion (BIC) [69]. This involves an iterative process that includes randomly restarting the procedure, attempting different possible edge connections between node pairs, perturbing the system, and using 50 different random restarts to avoid local maxima. Following previous studies [70, 71], we performed 100 perturbations (i.e., attempts to insert, delete, or reverse an edge) in each restart. To ensure network stability, we adopted a two-step approach. First, we determined the frequency of edge appearances across 5,000 bootstrap networks [70, 71]. Then, we applied the optimal cutoff method proposed by Scutari and Nagarajan [72] to retain edges, generating a network with both high sensitivity and high specificity. Second, when estimating edge directions, an edge was retained as directional if it appeared in the same direction in 51% or more of the 5,000 bootstrap networks. To enhance the interpretability of the DAG, we generate two distinct visualizations. In the first visualization, the thickness of each edge corresponds to the change in the BIC value when removing that specific edge from the graph, thereby indicating its relative importance. In the second visualization, the edge thickness represents the posterior probability of the edge’s directionality, reflecting confidence in the inferred causal relationship. Nodes positioned closer to upstream have a greater influence on the entire network. All the data and code used for analysis can be accessed on the open science framework, https://osf.io/pktex/?view_only=958276675e7744b38b56667939893670.

Results

Participant characteristics

Figure 1 illustrates the main characteristics of the participants included in the final analysis. Although the sample consisted of university students from four different grades, the majority were freshmen, accounting for 82.81% (see Fig. 1a). Most of these students came from rural areas and towns, with only 20% holding urban household registrations (see Fig. 1b). The primary reported purpose for internet use among participants was for studying, making up 21.56% (see Fig. 1c). Additionally, 36.36% of participants reported mild or higher levels of anxiety symptoms, and 55.31% reported mild or higher levels of depressive symptoms (see Fig. 1d). Spearman correlation analysis revealed that internet pornography motivation was significantly positively correlated with sleep quality, social interaction anxiety, depression, and anxiety, but not with subjective family economic status and internet usage time (see Fig. 1e and f). Furthermore, the mean depression score was 5.98 (SD = 5.48), the mean anxiety score was 3.75 (SD = 4.69), and the mean internet pornography motivation score was 23.32 (SD = 13.89).

Fig. 1.

Demographic characteristics of participants and correlation analysis of the variables. (a) Distribution of grade; (b) Distribution of hometown; (c) Distribution of internet usage purposes; (d) Prevalence of anxiety and depression among participants; (e and f) Correlation analysis of the variables (Eco: Subjective economic level; Time: Daily mobile phone usage time; SQ: Sleep quality; SIA: Social interaction anxiety; Dep: Depression; Anx: Anxiety; CM: Cybersex Motives)

Cyberporn motivations and depression network (CD network)

Figure 2a and c display the network structure and expected influence, as well as the bridge expected influence centrality of CD, respectively. Network analysis results indicated that out of the possible 253 edges, 118 were connected, with a connection ratio of 46.64%. The network exhibited two distinct communities: the depression community and the internet pornography motivation community. Edge weight analysis revealed that the strongest connections within each community were CM4-CM7 (weight = 0.48) for the internet pornography motivation community and D1-D2 (weight = 0.44) for the depression community. Additionally, two notable negative edges were found between the communities, namely CM7-D4 and CM4-D7. Expected influence centrality analysis of the nodes showed that CM9 (“To get a ‘high’ feeling”) and CM11 (“because it helps me when I’m depressed or nervous”) had the highest centrality scores, while the node representing suicidal ideation had the lowest centrality score. The nodes with the highest bridge expected influence scores were D8 (“Motor”) and CM5 (“In order to forget my problems or worries”). The stability coefficient for edge weights in the CD network was 0.67, for expected influence metrics it was 0.59, and for bridge expected influence metrics it was 0.21. Despite the bridge expected influence metric being slightly below the recommended value of 0.25, all other metrics demonstrated good stability [50].

Fig. 2.

Network structures and centrality indices of each node. (a) Network structure of CD; (b) Network structure of CA; (c) Expected influence and bridge expected influence centrality indices of each node in the CD network; (d) Expected influence and bridge expected influence centrality indices of each node in the CA network. Note: D1: Anhedonia; D2: Sadness; D3: Sleeping; D4: Fatigue; D5: Appetite; D6: Failure; D7: Concentration; D8: Motor; D9: Suicidality ideation; A1: Nervousness; A2: Controlling worries; A3: Worry too much; A4: Trouble relaxing; A5: Restlessness; A6: Irritability; A7: Feeling afraid; CM1: To get entertained; CM2: To meet somebody; CM3: Because I like the feeling; CM4: Because I need to exchange with other people; CM5: In order to forget my problems or worries; CM6: Because it’s exciting; CM7: For being sociable and appreciated by others; CM8: For watching; CM9: To get a “high” feeling; CM10: For masturbation; CM11: Because it helps me when I’m depressed or nervous; CM12: Simply because it’s fun; CM13: Because it makes a social gathering more enjoyable; CM14: It comforts me when I’m in a bad mood

Cyberporn motivation and anxiety network (CA network)

Figure 2b and d display the network structure and expected influence, as well as the bridge expected influence centrality of CA, respectively. Out of the possible 210 edges in the CA network, 101 were connected, resulting in a connection ratio of 48.09%. The network displayed two distinct communities: the anxiety community and the internet pornography motivation community. Edge weight analysis showed that the strongest connections within these communities were CM4-CM7 (weight = 0.48) in the internet pornography motivation community and A4-A5 (weight = 0.35) in the anxiety community. Furthermore, two notable negative edges were identified between the communities, specifically CM7-A4 and CM13-A1. Expected influence centrality analysis indicated that the nodes CM9 (To get a ‘high’ feeling”) and CM11 (“Because it helps me when I’m depressed or nervous”) had the highest centrality scores, while the node “To meet somebody” had the lowest centrality score. The nodes with the highest bridge expected influence scores were CM5 (“In order to forget my problems or worries”) and A2 (“Controlling worries”). The stability coefficient for edge weights in the CA network was 0.67, for expected influence metrics it was 0.44, and for bridge expected influence metrics, it was 0.28. All metrics exhibited good stability [50].

DAG

Figure 3a and b illustrate the edge importance and probability of the DAG of the CD network, respectively, and Fig. 3c and d depict the edge importance and probability of the DAG of the CA network, respectively. We primarily examined the priority relationships between internet pornography motives and depression and anxiety, temporarily excluding the internal priority order of variables. Specifically, in the CD network structure representing importance, four edges point from internet pornography motives to depression symptoms: CM11-D4 (Bayesian Information Criterion [BIC] = -24.03), CM4-D1 (BIC = -3.85), CM12-D5 (BIC = -1.26), and CM6-D3 (BIC = 0.37); one edge points from depression symptoms to internet pornography motives: D7-CM-13 (BIC = -2.88). The network representing edge probability shows that the thickest edge connects CM6-D3 (0.54; i.e., this edge is directed in this way in 2,700 of the 5,000 bootstrap networks and in the opposite direction in the remaining 2,300 networks). The BIC values and probabilities for each edge in the DAG of the CD network can be found in Table S1 of the supplementary materials. Structurally, multiple internet pornography motives are located upstream in the entire DAG network, directly or indirectly influencing other depression symptoms. Additionally, we observed similar results in the DAG of the CA network. In the CA network structure representing importance, three edges point from internet pornography motives to anxiety symptoms: CM9-A6 (BIC = -6.95), CM4-A2 (BIC = -2.75), and CM7-A7 (BIC = 0.04); no edges point from anxiety symptoms to internet pornography motives (Table S2). Furthermore, the network representing edge probability shows that the thickest edge connects CM9-A6 (probability of 65%). Structurally, internet pornography motives similarly take precedence over anxiety symptoms.

Fig. 3.

Directed acyclic graph (DAG) for CD and CA networks. a and c illustrate the edge thickness indicating the edge’s importance to the overall network structure of CD and CA, respectively; b and d illustrate the thicker lines indicating a greater probability of directionality. See Fig. 2 for label names

Discussion

Internet pornography behavior is highly prevalent among college students [6], yet research on this behavior among Chinese college students is limited. Previous studies have highlighted the potential relationship between internet pornography and declines in mental health [73]. However, the relationship between the motivations for using internet pornography and mental health remains unclear. This study explored the fine-grained relationships between internet pornography motivation and symptoms of depression and anxiety among Chinese college students. Overall, we found that the motivations for using internet pornography were significantly positively correlated with total scores of depression and anxiety, but the relationships varied depending on the specific motivations. In terms of overall scores, our research findings are consistent with previous studies, indicating that strong motivations for internet pornography use are negatively correlated with mental health. This negative correlation may stem from the fact that strong motivations for internet pornography use can easily lead individuals to excessively consume internet pornography content, subsequently developing an addiction to it. Studies suggest that internet pornography addiction is closely associated with symptoms of depression and anxiety, and excessive reliance on internet pornography may exacerbate psychological distress by reducing social interaction, increasing feelings of loneliness, and triggering shame [74, 75]. Therefore, internet pornography addiction may be a significant factor contributing to the decline in mental health. From the perspective of nodes and symptoms, socially-based motivations were negatively correlated with symptoms of depression and anxiety, whereas coping-based motivations were positively correlated with symptoms of depression and anxiety.

The edge weight results of the network analysis indicate a negative connection between internet pornography motivation and symptoms related of depression and anxiety. This suggests that not all internet pornography motivation promote depression and anxiety. On the contrary, certain specific motivations may alleviate these symptoms. For example, in the CD network, social motivations CM4 (“Because I need to exchange with other people”) and CM7 (“For being sociable and appreciated by others”) are negatively related to D7 (Concentration) and D4 (Fatigue), respectively. These socially driven motivations may alleviate depressive symptoms. Research suggests that when individuals are strongly motivated to exchange information with others, they may exert more effort to achieve this exchange, thereby increasing their concentration [76]. For instance, to obtain information from others, individuals may continuously browse related internet pornography content until they find information that can be used for exchange or sharing. This contrasts with the inability to concentrate seen in depressive symptoms [77], as individuals in a depressive state typically exhibit lower motivation levels [78]. This suggests that in the intervention of depression, encouraging individuals’ proactivity, rather than merely having them passively receive the intervention, may be more effective. Additionally, when individuals seek others’ appreciation, they may exert more effort [79] and display more energy. This further supports the potential approach of using social motivation to improve symptoms of depression and anxiety.

Similar results were observed in the CA network, based on the social motivation nodes CM7: “For being sociable and appreciated by others” and CM13: “Because it makes a social gathering more enjoyable”, which were negatively correlated with anxiety symptoms A4: “Trouble relaxing” and A1: “Nervousness”. Firstly, when individuals gain appreciation from others, it can provide happiness and satisfaction, thereby neutralizing tension and anxiety, and helping individuals to relax [80]. Secondly, appreciation from others can also boost self-confidence, enabling individuals to better cope with stress and challenges, thus reducing feelings of tension [81]. Lastly, appreciation from others is a form of social support, which can enhance an individual’s psychological resilience, allowing them to better cope with stress [82]. Overall, the use of internet pornography for these social purposes may provide a social buffer, thereby reducing stress and anxiety, and mitigating the onset of psychological disorders [83].

The expected influence of network analysis results indicates that enhancement motive (“To get a ‘high’ feeling”) and coping motives (“Because it helps me when I’m depressed or nervous”) serve as central nodes, playing a crucial role in the activation and maintenance of internet pornography motivation and symptoms of depression and anxiety. Firstly, similar to most addictive behaviors, the consumption of internet pornography can bring about a “pleasant feeling” [84, 85], further promoting the consumption of internet pornography and potentially leading to internet pornography addiction [86]. Secondly, individuals with depression typically exhibit anhedonia [78], which may drive them to consume internet pornography [87]. Lastly, when individuals feel depressed or anxious, they may use internet pornography to experience a “pleasant feeling,” thereby temporarily avoiding the negative impacts of these stressors [88]. However, it is important to note that we found a positive correlation between avoidant internet pornography motivation and symptoms of depression and anxiety. This suggests that when individuals use internet pornography to avoid stressors, these stressors do not disappear; the negative impacts of the stressors persist once the pleasure from internet pornography fades, potentially exacerbating depression and anxiety symptoms [89, 90]. A simple example is when college students facing the stress of graduation exams use internet pornography to experience a “pleasant feeling,” temporarily forgetting the exam stress. However, the graduation exams will not disappear due to internet pornography motivation, and the students will need to face both the exam stress and the negative impacts of internet pornography behavior. Therefore, avoidant internet pornography behavior may be a risk factor for depression and anxiety. In this study, this perspective can be explained through the Digital Life Balance (DLB) theoretical framework [41]. According to the DLB framework, individuals facing unmet needs—particularly social isolation or stress in real life—may turn to online activities to seek psychological comfort. This process may temporarily alleviate their emotional stress, but in the long term, excessive reliance on online activities could exacerbate mental health issues in real life, such as symptoms of depression and anxiety [41]. Specifically, motivations for using internet pornography, such as escaping real-life stress or seeking immediate gratification, may be regarded as a compensatory mechanism that helps individuals cope with life stressors in the short term, though this approach may negatively impact mental health [91]. Therefore, maintaining a harmonious balance between online and offline life is crucial for psychological well-being.

The coping motives node CM5 (“In order to forget my problems or worries”) serves as a common bridge node and a node with temporal priority in two networks in two networks, playing a crucial role in the dissemination of internet pornography motivation and symptoms of depression and anxiety. The nodes most closely linked to CM5 are all coping motive nodes, indicating that consumption of internet pornography for coping-based purposes exacerbates symptoms of depression and anxiety. This situation can easily lead to repeated consumption of internet pornography when facing stressors, further deepening addictive behavior [92, 93]. Such addictive behavior, in turn, has a range of negative impacts on physical and mental health [21]. Therefore, avoiding the use of internet pornography triggered by stressors may be a potential pathway to reducing both internet pornography motivation and symptoms of depression or anxiety. Interventions through reasonable stress relief methods, such as increasing social group sports activities or exercise [94], and fostering resilience in college students might enhance their ability to cope with risks, thereby reducing internet pornography [95].

Regarding the prioritization of internet pornography motives in the DAG network, the underlying reasons may be related to China’s conservative sexual culture background. First, traditional Chinese sexual culture emphasizes subtle and conservative attitudes toward sex, typically avoiding open discussions about sex-related topics [47, 96]. Particularly among young university students, the lack of open and healthy sex education channels may drive individuals to seek alternative ways to fulfill their sexual needs [97]. Internet pornography, as an easily accessible and highly private option, may become a substitute for satisfying sexual desires. However, this approach does not fundamentally resolve the conflict between sexual needs and socio-cultural norms; rather, it may exacerbate psychological distress [92]. Second, within the context of China’s sexual culture, strict societal norms regarding sexual behavior may cause university students to experience shame or guilt when confronting their sexual needs [98]. The consumption of internet pornography may be seen as a means of escaping real-life pressures, especially when sexual satisfaction cannot be obtained through socially accepted avenues. While internet pornography can temporarily alleviate emotional stress, this behavior often fails to effectively address psychological difficulties and may instead intensify feelings of anxiety and depression. In particular, the shame and guilt associated with traditional sexual culture may lead individuals to experience internal conflict and psychological burden after enjoying the short-term pleasure brought by internet pornography, thereby negatively impacting their mental health [42]. Therefore, when understanding the relationship between internet pornography consumption and mental health, the influence of China’s conservative sexual culture must be fully considered. Future research could further explore how culture shapes individuals’ attitudes toward internet pornography and the psychological adaptation mechanisms within this cultural context, providing a theoretical basis for developing targeted psychological intervention strategies.

Overall, we observe that college students tend to engage in internet pornography activities when coping with stress, which is closely associated with the increase in their depressive and anxiety symptoms; while in pursuit of social interaction, they tend to alleviate these psychological symptoms through internet pornography activities. These findings guide for managing college students’ internet pornography behavior. Specifically, given that engaging in internet pornography activities based on social motives may have potential psychological health benefits but may also increase the risk of pornography addiction, we suggest seeking alternative social activities to meet this need, such as encouraging college students to participate in various school interest groups actively. For example, joining the school basketball team not only helps to improve physical fitness but also reduces consumption of pornographic content and enhances overall physical and mental health [99]. On the other hand, for students who engage in avoidant internet pornography behavior due to stress, cultivating psychological resilience can be strengthened to enhance their ability to cope with challenges and stress [100].

Limitations

This study has some limitations. Firstly, we did not investigate the frequency of engagement in internet pornography among different individuals, thus preventing comparisons of motivational differences across varying frequencies. Secondly, the sample size was small, and most participants were unwilling to disclose their motivations for engaging in cyberporn, possibly due to the conservative sexual culture in China. Furthermore, in this study, participants were recruited from two universities in Chongqing, located in southwestern China. While this sample encompasses a diverse group of college students, it may not fully represent the broader population of Chinese university students due to cultural differences in sexual attitudes and sex education across regions. For instance, significant variations exist in the level of sex education, sociocultural backgrounds, and attitudes toward and acceptance of internet pornography across different regions, all of which may influence patterns and motivations of internet pornography use. Therefore, the generalizability of the study’s findings should be interpreted with caution. Future research should include samples from diverse regions and cultural backgrounds to enhance the universality of the conclusions and provide a more comprehensive understanding of the relationship between internet pornography use and mental health.

Thirdly, the relationships between internet pornography motivation and depression and anxiety appear relatively weak, highlighting the issues related to the sample size and statistical power. In the future, the reliability of the results should be verified in a large sample size. From a real-world perspective, the findings of this study may offer some inspiration for individual mental health interventions. For example, if the relationship between motivations for internet pornography use and mental health symptoms persists, even with a small effect, it could still have an impact on certain high-risk populations. Therefore, future research could further explore whether potential mediating or moderating variables—such as individual psychological resilience or social support—might strengthen this relationship. Given the significant relationship between coping motives and mental health issues, future research could consider the beneficial effects of interventions related to coping motives. For example, interventions such as stress management programs aimed at improving well-being could be explored for their effectiveness in reducing internet pornography use. This, in turn, would help mitigate the mental health problems associated with internet pornography use. Additionally, exploring internet pornography motivation among adolescents and older individuals may enhance our understanding of the development of such behaviors. However, it is crucial to pay special attention to the protection of underage populations during this inquiry.

Moreover, since this study employed a cross-sectional design, the results cannot support causal inferences. Although Bayesian modeling was used to analyze the admissible causal relationships, the causal relationships between variables should still be interpreted with caution. Future research should consider employing longitudinal designs and rigorously controlled experimental designs to verify these causal relationships. Lastly, the reliance on cross-sectional self-report data may introduce biases. For instance, social desirability bias could lead participants to underreport or overreport their internet pornography motivation and mental health to align with perceived social norms. Likewise, recall bias may result in inaccurate reporting of past experiences, potentially leading to an overestimation or underestimation of actual behaviors. Future studies could incorporate objective measures (e.g., digital tracking or behavioral assessments) to complement self-reported data and enhance the validity of the results.

Conclusions

Although internet pornography is widely believed to be associated with mental health issues, this study delved into this relationship from a motivational perspective. The research found that internet pornography behavior based on coping motives was positively correlated with increased symptoms of depression and anxiety, while behavior based on social motives was negatively correlated with these symptoms. This serves as a guideline for managing internet pornography among college students.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviations

CMQ

Cybersex Motives Questionnaire

GAD-7

Seven-item Generalized Anxiety Disorder Scale

PHQ-9

Nine-item Patient Health Questionnaire

DGA

Directed Acyclic Graph

CD

Cyberporn Motivation and Depression

CA

Cyberporn Motivation and Anxiety

CM

Cyberporn Motivation

SD

Standard Deviation

Author contributions

L.Y. and R.L. collected the data and K.L. and Z.F. proposed conceptualization and L.L., K.L. and R.F. wrote the main manuscript text and C.L., L.R. prepared Figures 1-2. All authors reviewed the manuscript.

Funding

This study was supported by the Chongqing Municipal Education Commission (Project No. KJQN202203812) and the Key Scientific Research Project of Chongqing Water Resources and Electric Engineering College (Grant No. K202207).

Data availability

Data is provided on the OSF platform, https://osf.io/pktex/?view_only=958276675e7744b38b56667939893670.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. All participants received informed consent before filling out the questionnaire. This study used an online questionnaire to collect data, and participants could opt to complete the questionnaire within one week. The study procedure was submitted to the Ethics Committee of the Chongqing Key Laboratory of Psychological Diagnosis and Educational Technology for Children with Special Needs for ethical approval. The submitted materials included the research design, questionnaire content, data collection methods, informed consent forms, participant recruitment procedures, and potential risk assessments. The Ethics Committee evaluated the ethicality, safety, and scientific validity of the study and eventually granted ethics approval (Ethics Number: CSTJ-RE-20230620004).

Consent for publication

A comprehensive description of the study was provided to participants, and informed consent was obtained from each participant.

Competing interests

The authors declare no competing interests.