Next Article in Journal
Prevalence and Determinants of Immediate and Long-Term PTSD Consequences of Coronavirus-Related (CoV-1 and CoV-2) Pandemics among Healthcare Professionals: A Systematic Review and Meta-Analysis
Next Article in Special Issue
Preventing Violence toward Sexual and Cultural Diversity: The Role of a Queering Sex Education
Previous Article in Journal
The Impact of Neglected Tropical Diseases (NTDs) on Women’s Health and Wellbeing in Sub-Saharan Africa (SSA): A Case Study of Kenya
Previous Article in Special Issue
Development and Evaluation of an Online Education-Entertainment Intervention to Increase Knowledge of HIV and Uptake of HIV Testing among Colombian Men Who Have Sex with Men (MSM)
Article

Intimate Partner Cyberstalking, Sexism, Pornography, and Sexting in Adolescents: New Challenges for Sex Education

1
Faculty of Education and Social Work, University of Vigo, 32004 Ourense, Spain
2
Faculty of Education Sciences, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
3
Faculty of Social Sciences and Law, Carlos III of Madrid, 28903 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Jon Øyvind Odland
Int. J. Environ. Res. Public Health 2021, 18(4), 2181; https://doi.org/10.3390/ijerph18042181
Received: 15 December 2020 / Revised: 11 January 2021 / Accepted: 20 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Sex Education as Health Promotion: What Does It Take?)

Abstract

Background: Within the context of the widespread use of technologies by adolescents, the objectives of this study were to identify the perpetrators of intimate partner cyberstalking (IPCS) in adolescents; to analyze the relationship between IPCS and gender, age, sexting behaviors, pornography consumption, and ambivalent sexism; and to investigate the influence of the study variables as predictors of IPCS and determine their moderating role. Methods: Participants were 993 Spanish students of Secondary Education, 535 girls and 458 boys with mean age 15.75 (SD = 1.47). Of the total sample, 70.3% (n = 696) had or had had a partner. Results: Boys perform more sexting, consume more pornographic content, and have more hostile and benevolent sexist attitudes than girls. However, girls perpetrate more IPCS than boys. The results of the hierarchical multiple regression indicate that hostile sexism is a predictor of IPCS, as well as the combined effect of Gender × Pornography and Benevolent Sexism × Sexting. Conclusions: it is essential to implement sexual affective education programs in schools in which Information and Communication Technologies (ICT) are incorporated so that boys and girls can experience their relationships, both offline and online, in an egalitarian and violence-free way.
Keywords: intimate partner cyberstalking; sexism; sexting; pornography consumption; adolescent; sexuality education intimate partner cyberstalking; sexism; sexting; pornography consumption; adolescent; sexuality education

1. Introduction

The technological revolution has led to the increasing use of Information and Communication Technologies (ICT) by the adolescent population [1], thus establishing a new way of socializing through the virtual sphere [2]. In fact, some adolescents prefer online communication to face-to-face communication [3]. Thus, internet use, social media, and instant messaging are tools that boys and girls use routinely in both their peer and dating relationships [4,5]. Their growing impact on adolescents has become a major concern for educators and researchers in recent years [6]. As adolescents are at a crucial developmental stage in their lives in which new forms of interpersonal and affective relationships, such as falling in love, are experienced, new interests and needs emerge, as well as the first relationships, and also, the first sexual relationships [7].
Studies have identified the virtual sphere as a new space conveying many violent situations both in the peer group [8] and in dating relationships [9]. Thus, adolescents’ usage of ICT through online applications, video games, etc., should be considered useful to prevent violence and, specifically, partner violence [10]. Following the review carried out by Navarro-Pérez et al. [11] on ICT-based intervention tools, the following stand out for the prevention and intervention of Teen Dating Violence (TDV): Teen Choices program [12]; DetectAmor [13] and other mobile applications with a high level of effectiveness such as the [email protected] app [11,14], of an entertaining and educational nature, which aims to help adolescents to have egalitarian and non-toxic couple relationships, and involves having less sexist attitudes, identifying myths about love, and reducing situations of violence in their relationships.

1.1. Intimate Partner Cyberstalking in Adolescents

Cyberstalking has its roots in traditional harassment or stalking. It is defined as a type of digital practice in which the aggressor exercises domination over the victim or victims through intrusion in their intimate life. This intrusion is repetitive, disruptive, and performed against the victim’s will [15]. This harassment includes false accusations, surveillance, threats, identity theft, insulting messages, etc., that generate fear in the victims [15]. The first episodes of cyberstalking occur between ages 12 and 17 years [16]. The conceptualization of intimate partner cyberstalking (IPCS) has a marked affective and/or sexual nature [15], as it is likely to be perpetrated against the partner or to be an approach strategy toward the ex-partner [17,18]. IPCS is considered a form of gender-based violence in young people, because it includes those behaviors that, through digital means, aim at domination, discrimination, and, ultimately, abuse of the position of power where the stalker has or has had some affective and/or sexual relationship with the harassed person [15]. Studies that have focused on IPCS in adolescents indicate that the most common behaviors are usually online control, online partner monitoring or online surveillance [19,20], concepts sometimes used interchangeably in diverse studies [21,22]. However, online control is a more serious behavior than online surveillance or online monitoring. Online surveillance or online monitoring is based on observing or carefully monitoring the partner or ex-partner to obtain information due to mistrust and insecurity [23], (e.g., “I get a lot of information about my partner’s activities and friendships from looking at his/her social media pages”), but control is to go one step further, because the purpose is to dominate and manage the life of the partner or ex-partner (e.g., “I have either asked my partner to remove or block certain people from their contacts [phone or social media], because I didn’t like the person, or I have done so myself [removed/blocked the person”]) [24]. The partner is often aware of the control they suffer by their boyfriend or girlfriend, unlike surveillance, which is more cautious [24,25]. Thus, international studies identify that between 42 and 49.9% of adolescents often check whether the partner is online on social media or instant messaging apps [26,27], between 19.5 and 48.8% of adolescents send constant or exaggerated messages to know where their partner is, what they are doing, or whom their partner is with [27,28], and between 32.6 and 45% of adolescents control who their partner is talking to and who they are friends with [26,28]. Qualitative studies also show that adolescents openly acknowledge that they often constantly check their partner’s mobile [25,29], that they share their passwords as a sign of commitment and trust, and that they often create fake profiles on social media to control their partners [19,30]. These online control behaviors show that adolescents consider them appropriate or acceptable, that is, these IPCS behaviors are normalized and adolescents even tend to justify them [19,25].
As for the prevalence rates of perpetration of IPCS in adolescents, international studies show great variability in the perpetrator. Early studies identified boys as the most frequent aggressors of IPCS [31,32]. However, the most recent studies indicate that IPCS aggressors are girls who tend more frequently to control and monitor their affective partners online [25,27,30]. In this sense, studies argue that boys tend to engage more in digital threatening and pressuring of their partner, especially when they want to have sex; whereas girls engage more in controlling behaviors to gain intimacy and exclusivity in their relationship [2,30] or even to preserve their relationship [31].
In Spain, the study of IPCS in adolescents is still an incipient line of research. The few existing investigations do not identify the IPCS perpetrator. There is great variability in the prevalence rates of IPCS; between 10% [33,34] and 83.5% [35,36] of adolescents admit that they control and monitor their partners online. In terms of frequency, according to the study of Donoso, Rubio, and Vilà [37], 27% of adolescents claim that they sometimes control their partner, and 14% sometimes inspect the partner’s mobile. In fact, 12.9% of adolescents ask their partner to text them to report where they are every minute [38]. In this sense, the study of Rodríguez-Castro et al. [4] shows that behaviors such as “controlling the time of the last connection” are common in adolescent partner relationships, without their identifying these behaviors as negative. Therefore, one of the objectives of this study is to evaluate the prevalence rate of IPCS, identifying the aggressor.

1.2. Intimate Partner Cyberstalkxing in Adolescents

In order to further our knowledge of the IPCS phenomenon in adolescents, after reviewing the existing literature, other objectives of this study were to verify the relationship between IPCS and variables such as ambivalent sexism, sexting behaviors, and pornography consumption, as well as to predict which variables best explain IPCS.

1.2.1. Sexism and IPCS

We draw on the theory of Ambivalent Sexism [39], which describes ambivalent sexism as a two-dimensional construct made up of hostile and benevolent attitudes. Both sexisms function as complementary ideologies and as a reward and punishment system. Hostile sexism, with a negative tone, considers women inferior to men. Such hostile sexism is applied as a punishment to women who do not fulfill the traditional roles of wife, mother, and caregiver [40] In contrast, benevolent sexism, with a positive-affective tone, considers women to be different and, as such, it is necessary to care for and protect them, so traditional women are rewarded with benevolent sexism [41].
As international and national studies show, adolescents present ambivalent sexist attitudes, with boys having more hostile and benevolent sexist attitudes than girls [42,43]. In addition, the most sexist adolescents show more positive attitudes towards intimate partner violence [44]. In fact, studies show that both hostile sexism [45] and benevolent sexism [46,47] help explain intimate partner violence both in youth and adults [48,49].
In the online space, youth have found a new way to reproduce and perpetuate sexism [50]. Although we have found few studies that specifically link IPCS in adolescents to sexist attitudes, we can highlight the recent study of Cava et al. [33], which identified hostile sexism and relational violence as predictors of cyber-control strategies in boys, whereas myths of romantic love and verbal violence in the relationship were the main predictors of cyber-control in girls.

1.2.2. Sexting and IPCS

The exchange of erotic/sexual and intimate content such as text messages, photos, and/or videos through social networks or other electronic resources—sexting—is a normalized reality in the relationships of adolescents both in and outside of Spain [4,27]. Thus, the figures point to a range of prevalence of sexting behaviors between 14.4 and 61% for adolescents, both in the international and national context [51,52].
Sexting behaviors are part of the strategies of intimate partner violence performed through sextortion [53]. Sextortion consists of blackmailing a person by means of an intimate image of themselves that they have shared over the Internet through sexting. The purpose of this blackmail is usually the domination of the victim’s will [53]. In fact, sexting behaviors due to the partner’s coercion have become one of the main reasons for youth’s participation in this behavior, especially girls [6]. Recent research points to the relationship between sexting practices in adolescents and intimate partner violence [54] but also, more specifically, cyber-control strategies in partner relationships [55], a trend reproduced in Spanish studies, which show how sexting practices in the couple are linked to the perpetration of cyberbullying [56,57]. Thus, girls who practice sexting with their partners are usually more likely to suffer some form of cyberbullying in their relationship [57].

1.2.3. Consumption of Pornography and IPCS

Mainstream pornography has become a crucial social tool for the perpetuation of the patriarchal system because it helps shape women’s sexuality from the viewpoint of male self-interest. Through it, the patriarchal hierarchy is reproduced, confirming the attribution of a passive and silenced nature to women, and an active nature to men [58]. Through their free access to ICTs, our youth have become a consumer of pornographic content. International and national studies establish the prevalence of pornography consumption between 27 and 70.3% [59,60,61,62], with boys being more pornophile than girls [63,64]. The age range of initiation in pornography consumption is between 12 and 17 years [61,64], although some studies indicate that children are accessing pornography at increasingly younger ages, placing the first viewing at 8 years [60].
As Cobo [58] claims, the core of pornography intertwines masculine pleasure, domination, and violence. Adolescents acknowledge that pornography is violent, and 54% even admit to being influenced by it in their personal sexual experiences [61]. In fact, it has been found that boys who perform coercive behaviors and sexual abuse against their partner routinely view pornographic content [64]. However, we have not found any studies that directly relate pornography consumption to IPCS.
Taking into account this new context in which our young adolescents are socialized, the objective of this study was threefold: I. To identify IPCS perpetrators in the adolescent population; II. To analyze the relationship between IPCS and gender, age, sexting behaviors, pornography consumption, and ambivalent sexism; and III. To investigate the influence of the variables (gender, age, sexting behaviors, pornography consumption, and ambivalent sexism) as predictors of IPCS in the adolescent population.

2. Materials and Methods

2.1. Participants

Participants were 993 Spanish students of Secondary Education; 535 girls (53.9%) and 458 boys (46.1%). The age of participants ranged from 13 to 19 years, with a mean age of 15.75 years (SD = 1.47). One selection criterion of this study was to have a partner currently or to have had one in the past for at least six months. In this case, we found that of the total sample, 70.3% (n = 696) had a partner at the time of completion of the questionnaires or had had one in the past.

2.2. Instruments

An ad hoc questionnaire was used for this study. The questionnaire consisted of the following items and scales:

2.2.1. Demographic Questions

The participants indicated their gender and age.

2.2.2. Sexting Behavior

To identify sexting behaviors, we included the following question [65]: Have you ever sent sexually suggestive photos/videos or text messages of yourself? (1 = no, 2 = yes).

2.2.3. Pornography Consumption

To identify the consumption of pornography by adolescents, we included the following question: Have you ever searched for and/or viewed pornographic content over the internet? (1 = no, 2 = yes).

2.2.4. Inventory of Ambivalent Sexism in Adolescents (ISA)

The ISA [66] (based on the Scale of Ambivalent Sexism towards Women [40]) consists of 20 items that measure adolescents’ level of ambivalent sexism: 10 items measure hostile sexism and the remaining 10 items measure benevolent sexism. The response scale is a Likert-type scale ranging from 1 (strongly disagree) to 6 (strongly agree). Higher scores indicate higher levels of hostile and benevolent sexism. Cronbach’s alpha obtained in this study in the subscale of Hostile Sexism was 0.86, and in the Benevolent Sexism subscale, it was 0.85.

2.2.5. The Intimate Partner Cyberstalking Scale (IPCS-Scale)

This scale was developed “to measure specific behaviors of cyberstalking within an intimate relationship” (p.392) [24]. Examples of items include “I have checked my partner’s phone/computer history to see what they’ve been up to”, “I try to monitor my partner’s behaviors through social media”, and “I have used or have considered using phone apps to track my partner’s activities”. This scale consists of 21 items rated on a Likert-type response format ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate greater engagement in IPCS behavior. The Cronbach alpha obtained in this study was 0.91.

2.3. Procedures

Ethical approval was obtained from the PhD Program of the Education and Behavioral Sciences Ethics Committee prior to data collection. From a total of 20 public and secular Secondary Education centers of a province of northern Spain, we randomly selected 10 centers to participate in this study and, within each center, we selected the classrooms of the 2nd cycle of Compulsory Secondary Education and High School (Noncompulsory Secondary Education). The data collection process was carried out during the 2018/2019 school year. The questionnaires were applied in schools during regular school hours. The mean administration time was 25 min. Passive informed consent was received to administer the questionnaires, that is, the authorization of the academic community (directors and tutors).

2.4. Analysis

The following analyses were performed with the IBM SPSS v.21 (IBM Center, Madrid, Spain) program: first, the descriptive analyses: the mean (M) and standard deviation (SD) were calculated with Student’s t-test as a function of gender for the variables and scales studied. Cohen’s d was also used to evaluate the strength of the f2 effect size, whereby 0.02 is considered small, 0.15 is considered moderate, and 0.35 is considered large. Second, Pearson bivariate correlation coefficients (r) between the scales/subscales and the variables were calculated. Third, Hierarchical Linear Regression was used to test the regression model and interaction effects. The predictor variable was IPCS. The variables gender, age, sexting behavior, and consumption of pornography were entered in Step 1 of the regression model; next, hostile sexism and benevolent sexism were entered in Step 2. Interaction terms (Predictor x Predictor) were entered in Step 3 of the model to test the interactions between combinations of variables of the study. Beta coefficients (β) and Student’s t-test indicated the proportion of the unique effect contributed by each predictor variable. The coefficient of determination (R2), adjusted coefficient (ΔR2), ANOVA (F), and p-values were used to examine significant effects in the regression model.

3. Results

First, we compared the differences in the means of IPCS, sexting behavior, consumption of pornography, and hostile and benevolent sexism as a function of gender. As can be observed in Table 1, there were significant differences in all the scales/subscales, with a variable effect size. Boys carried out the most sexting behaviors (t = 8.07, p < 0.001, d = 0.61), consumed more pornographic content (t = 11.19, p < 0.001, d = 0.84), were more hostile sexists (t = 6.89, p < 0.001, d = 0.52), and were also more benevolent sexists (t = 3.97, p < 0.001, d = 0.30) than their female classmates. However, girls perpetrated more IPCS than boys.
All the bivariate correlations between the scales and subscales of the study (see Table 2) were significant. Gender was found to be positively related to IPCS (r = 0.10, p < 0.01) and negatively to hostile sexism (r = −0.2510, p < 0.001), benevolent sexism (r = −0.15, p < 0.001), sexting behaviors (r = −0.29, p < 0.001), and pornography consumption (r = −0.38, p < 0.001). That is, girls carried out more cyberstalking behaviors towards their partners, whereas boys were the most hostile and benevolent sexists who performed the most sexting and consumed more pornographic content.
It was also found that IPCS correlated positively with hostile sexism (r = 0.32, p < 0.01), benevolent sexism (r = 0.39, p < 0.01), sexting behaviors (r = 0.32, p < 0.01), and pornography consumption (r = 0.33, p < 0.01). That is, people with high IPCS had a higher level of hostile and benevolent sexism, practiced more sexting, and consumed more pornographic content.
In addition, sexting behaviors and pornography consumption correlated positively with age (r = 0.10, p < 0.01; r = 0.11, p < 0.01), hostile sexism (r = 0.33, p < 0.01; r = 0.36, p < 0.01), benevolent sexism (r = 0.32, p < 0.01; r = 0.34, p < 0.01), and IPCS (r = 0.32, p < 0.01; r = 0.33, p < 0.01) whereas they correlated negatively with gender (r = −0.29, p < 0.001; r = −0.38, p < 0.001). That is, the people who performed more sexting and consumed more pornography were older, the most sexist (hostile and benevolent), and performed the most cyberstalking of their partner; also, boys practiced more sexting and consumed more pornography. A positive and strong correlation was also obtained between sexting and pornography consumption (r = 0.64, p < 0.01), so those who viewed more pornographic content were also more active in sexting behaviors.
Next, the regression model was tested using hierarchical multiple regression to compare the strength of the prediction estimates of the variables (participants’ gender, age, sexting, and pornography consumption) for IPCS (see Table 3). The three variables were entered at Step 1 of the analysis, accounting for a significant 20.3% of the variance in IPCS.
At Step 2, the two predictor variables (hostile and benevolent sexism) were entered in the regression analysis, which accounted for a total of 29.5% of the variance in the model as a whole. The addition of the predictor variables accounted for an additional 9.2% of the variance in IPCS, ΔR2 = 0.092, F(2, 674) = 46.90, p < 0.001. In the final model, hostile sexism (β = 0.12, t = 2.83, p = 0.01)) was significant.
Two-way interaction terms between Gender × Pornography Consumption and Benevolent Sexism × Sexting, were entered independently in Step 3 of the model using an interaction variable (Predictor × Predictor). Two predictors in the combined effect of Gender × Pornography Consumption (β = 0.34, t = 2.01, p = 0.001) and Benevolent Sexism × Sexting (β = 0.15, t = 1.69, p = 0.01) were significant. All other combinations of interactions were non-significant.
To clarify the meaning of these two significant interactions of the hierarchical regression, a detailed analysis of the mean scores in the IPCS scale obtained by each of the groups in each of the interactions was carried out. These mean scores for each group are presented in Figure 1 and Figure 2.
As shown in Figure 1, we compared the mean scores in pairs with a t-test. These comparisons indicated that students with a high level of benevolent sexism carried out more IPCS behaviors than those with a low level of benevolent sexism, both among those who did not practice sexting (t = −3.45, p < 0.001) and those who did practice sexting (t = −6.29, p < 0.001). Likewise, students who practiced sexting scored higher in IPCS than those who did not practice it, both among those with high benevolent sexism (t = −4.92, p < 0.001) and those with low benevolent sexism (t = −2.56, p < 0.001). Therefore, the benevolent sexist students who carried out sexting behaviors scored higher in IPCS than all the other groups (that did not practice sexting). Therefore, the results indicate that the relationship between sexting practices and the perpetration of IPCS was moderated by the level of benevolent sexism.
Similarly, we compared the mean scores using t-tests in Figure 2. We note that girls obtained higher scores for IPCS than boys, both among those who did not consume pornographic content (t = −7.32, p < 0.001) and those who did consume it (t = −5.77, p < 0.001). In addition, students who consumed pornographic content, whether they were boys (t = −9.70, p < 0.001) or girls (t = −9.80, p < 0.001), performed more IPCS behaviors than those who did not consume pornography. Moreover, girls who consumed pornographic content scored higher than all the other groups in IPCS. Therefore, the results indicate that the significant relationship between pornography consumption and IPCS was moderated by gender.

4. Discussion

Numerous studies have shown the influence of isolated variables such as gender [24], personality traits [18], sexism [67,68], beliefs about love [68], sexting [57], or the consumption of pornography [69] on violence or cyber-violence in couple relationships, although mainly in the adult population and university students. To our knowledge, no study has combined the variables of this study and clarified their moderating effect on adolescents regarding IPCS.
Initially, this study analyzed the prevalence of IPCS in adolescents based on gender. Although low means were obtained in IPCS, adolescent girls claimed to perform more cyberbullying behaviors towards their partners and also stated that they would reproduce these online harassment behaviors if they had any kind of suspicions about their partner. These results are in line with international [27,30] and national [4,57] studies that show that girls perform more cyber-control of their partners. These results show a turning point in the profile of the cyber-control aggressor in couples when compared to traditional gender-based violence in adolescence when boys were the main aggressors [31,70]. Now, the girls aggress more than the boys.
Other interesting results of this study in line with international and national studies is that boys carry out more sexting behaviors than girls [63,65,71] and they also consume more pornographic content compared to girls [60,64]. We also found that older boys and girls practice the most sexting [65] and consume more pornographic content over the internet [60,61]. As our results show, pornography consumption and sexting are strongly related, such that the more pornographic content boys and girls consume, the more sexting behaviors they perform. Although few studies explored this association, the study of Stanley et al. [64], involving adolescents from five European countries, also demonstrates this strong linkage. The research of Romito and Beltramini [72] went so far as to conceptualize sexting as a means through which adolescents produced their own pornographic content that they later sent to others.
Our results show that adolescents continue to present sexist attitudes. Boys also have higher levels of ambivalent sexism (hostile and benevolent) than girls. However, the greatest differences concern hostile sexism. These results are coincident with numerous studies [42,47]. It is also interesting to note that, despite differences as a function of gender, both boys and girls increased their level of more subtle sexism (benevolent), which, due to its positive-affective tone, masks situations of discrimination against women, causing many young people to be unable to identify it. We also found that both hostile and benevolent sexism were positively related to pornography consumption and sexting behavior. Hence, boys and girls with more sexist attitudes consumed the most pornographic content and performed more sexting behaviors.
When we examined the relationship between IPCS and sexting behaviors, pornography consumption, and ambivalent sexism, we found that IPCS was positively related to every one of them. Thus, the boys and girls who exercised more cyber-control of their partners were more sexist (hostile and benevolent), performed more sexting behaviors, and also consumed more pornographic content. Various studies consider sexism, especially hostile sexism, as a predictor of violence or cyber-violence in the couple [33,73]. International literature also links sexting practices to cyberstalking in couples [6], but this is the first study to relate all these variables.
Finally, our focus was on determining the influence of gender, age, sexting behavior, pornography consumption, and ambivalent sexism as predictors of IPCS as well as confirming their moderating role in adolescents. This is the first study that examines the combination of these variables. The results obtained identified hostile sexism and interactions combining the effect of gender and pornography consumption and the effect of benevolent sexism with sexting as predictors of IPCS. It is again confirmed that the level of hostile sexism has become a key variable that predicts online control of the partner. Therefore, the most hostile sexist adolescents are more likely to perform IPCS behaviors. In this case, gender and the level of benevolent sexism modulate cyberstalking behavior in the couple. Therefore, our results show that girls who consumed more pornographic content cyberstalked their partner more. In addition, more benevolent sexist boys and girls who performed more sexting behaviors tended to cyber-monitor their partner more.
These results encourage us to take a step further and reflect on why the more benevolent sexist adolescents perform more sexting and also cyber-monitor their partners more, and why girls—greater pornography consumers—engage in more cyberstalking in their relationships than boys. It is clear that the digital scenario has become a new space to perpetrate violence through online control and surveillance of the partner [2]. Although both boys and girls admitted to controlling their partner in the virtual space, we found that girls cyber-monitored their partner more and also consumed more pornographic content. At the same time, male and female adolescents with ambivalent attitudes (hostile and benevolent)—with boys being more sexist and performing more sexting [65]—cyber-monitor their partner.
Given these results, the most plausible explanation lies in the differential socialization. Both boys and girls are educated based on gender stereotypes [74]. Thus, boys are educated as an “autonomous self”, stressing independence, power, and oriented toward competitiveness. Girls are educated in the ethics of care, emotionality, and dependence, and they build their identity based on an “I in relation” to others, on commitment to the partner, granting love a central place in their life [75,76]. This makes girls yearn to have a partner because it gives them a sense of security and a position, social recognition, and protection within the peer group [77]. Thus, adolescent girls clearly recognize the value of “being someone’s girlfriend” and are afraid of losing “the girlfriend status” in the peer group [77] (p. 208). This shows that relationships are still conditioned by patriarchy and a conception of androcentric sexuality that implies that girls "without a partner" can be attacked, rejected, or ignored by the peer group [77]. On the one hand, the fear of losing their partner possibly pushes girls to become consumers of pornographic content, in order to reproduce their total dedication to the male’s desire in their sexual practices. On the other hand, emotional dependence on their partner, coupled with jealousy and mistrust, causes violence to materialize through their cyber-control [4,19,30,53]. In fact, both boys and girls consider cyber-control as harmless, not a form of violence, and they may even regard it as play [25]. Thus, they see controlling behavior as a way to express love, care, and affection toward a partner and also as an “effective” tool to maintain their couple relationship [24,31]. Therefore, it is necessary to provide our youngsters with the necessary tools to demystify these cyber-behaviors that they have normalized in their relationships.
The main limitation of this study is related to the sample, which consisted of Secondary Education students from public and lay educational centers, discarding students of the same educational level who were enrolled in private and religious schools. It would also be interesting to incorporate new variables related to the possession and use of technologies and also to include scales of cyber-violence in the couple that can specifically detect certain behaviors such as control, online jealousy, and threats, among others. In the future, further deepening the study of intimate partner cyberstalking in the adolescent population should be addressed from a qualitative perspective in which boys and girls discuss in their own words their beliefs, attitudes, and behaviors about cyberstalking in their relationships.

5. Conclusions

In relation to the results obtained with adolescents who present sexist attitudes, consume pornography, practice sexting, and carry out behaviors of cyber-monitoring of the partner—highlighting girls’ increased participation in this type of violence—, we are faced with the need to train adolescents in the field of affective-sexual education. In Spain, the current Organic Law for the Improvement of Educational Quality [78] formally maintains the value of freedom and tolerance to promote respect and equality, although, at a practical level, it was a setback because it eliminated the academic subjects to address the contents of sex education [79].
In Spain, the most widespread sex education model is anchored in a moral/conservative model that demonizes sexuality and a risk/prevention model that uses fear and disease as keys to learning. Both these models reproduce the traditional, sexist, and heteronormative view of affective-sexual relationships [80]. The purpose of sex education should be to create a model of liberating, critical, and emancipating sexuality; for this purpose, it is necessary to have adequate comprehensive sexual training [81].
As the results of this study show, we cannot forget that the context in which young people currently live has changed drastically [82]. Thus, with the incorporation of ICTs—the Internet, social networks, etc.—on the one hand, a space is opened up to new opportunities for the promotion of sexual and reproductive health, but, on the other hand, new phenomena also arise (such as sexting, cyber-monitoring, etc.) that can make adolescents vulnerable [25,65]. Therefore, ICTs, which have encouraged dispersion of information, have become opinion-makers of the youngest population [83], and a powerful transmitter of messages, many of them erroneous or biased, about sexuality, and focused specifically on how sexual relations between men and women should be [79]. Pornography is the main vehicle for transmitting a conceptualization of androcentric and violent sexuality for younger people [58]. The increasing impact of its consumption influences their relationships, introducing certain levels of violence into sexual practices and consolidating the patriarchal imaginary of inequality between men and women [60], placing male pleasure at the center, and relegating female pleasure [58].
In short, it is essential to implement sexual education programs in schools incorporating ICTs for their safe and responsible use [84]. Several studies have tested the high effectiveness of teaching tools in version 4.0 (audiovisual materials, telephone apps, etc.) focused on the prevention of gender-based violence, which are at the service of the educational community (educators, mothers/fathers, and students) [10], such as the [email protected] mobile app to work from a playful perspective such important aspects as ambivalent sexism (hostile and benevolent), myths about love, and egalitarian relationships [10,11]. Sex education programs should be integrated into the curriculum at all levels of education as just one more subject [79], addressing essential content such as: body identity, gender identity (sexism, gender stereotypes, sexual orientation, etc.), self-esteem and self-concept, emotions, egalitarian socio-affective relationships (love, infatuation, friendship, etc.), sexual behavior, and sexual health [85] and relying on the various ICT tools of that combine learning, motivation, and fun [14]. Only in this way will the current educational system be able to respond to these new social realities generated both online and offline to allow boys and girls to live and express their interpersonal and couple relationships in an equal and violence-free way.

Author Contributions

Conceptualization, Y.R.-C. and R.M.-R.; methodology, Y.R.-C., R.M.-R. and M.V.C.-F.; formal analysis, Y.R.-C., R.M.-R. and A.A.-L.; investigation, Y.R.-C., R.M.-R., P.A.-R., M.V.C.-F. and A.A.-L.; writing—original draft preparation, Y.R.-C., R.M.-R. and P.A.-R.; writing—review and editing, Y.R.-C. and R.M.-R.; supervision, Y.R.-C. and R.M.-R.; project administration, Y.R.-C.; funding acquisition, Y.R.-C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Project INOU2014 of the County Council of Ourense in collaboration with the University of Vigo (Spain).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the PhD Program of Education and Behavioral Sciences (University of Vigo) (protocol code INOU2014, 16 November 2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. David, J.L.; Powless, M.D.; Hyman, J.E.; Purnell, D.M.; Steinfeldt, J.A.; Fisher, S. College student athletes and social media: The psychological impacts of Twitter use. Int. J. Sport Commun. 2018, 11, 163–186. [Google Scholar] [CrossRef]
  2. Stonard, K.E. “Technology was designed for this”: Adolescents’ perceptions of the role and impact of the use of technology in cyber dating violence. Comput. Hum. Behav. 2020, 105, 106211. [Google Scholar] [CrossRef]
  3. Rueda, H.A.; Lindsay, M.; Williams, L.R. “She posted It on facebook” Mexican American adolescents’ experiences with technology and romantic relationship conflict. J. Adolesc. Res. 2015, 30, 419–445. [Google Scholar] [CrossRef]
  4. Rodríguez-Castro, Y.; Alonso, P.; Lameiras, M.; Faílde, J.M. From sexting to cybercontrol among dating teens in Spain: An analysis of their arguments. Rev. Lat. Am. Psicol. 2018, 50, 170–178. [Google Scholar] [CrossRef]
  5. Van-Ouytsel, J.; Walrave, M.; Ponnet, K.; Willems, A.S.; Van Dam, M. Adolescents’ perceptions of digital media’s potential to elicit jealousy, conflict and monitoring behaviors within romantic relationships. Cyberpsychology 2019, 13. [Google Scholar] [CrossRef]
  6. Bianchi, D.; Morelli, M.; Baiocco, R.; Chirumbolo, A. Individual differences and developmental trends in sexting motivations. Curr. Psychol. 2019, 1–10. [Google Scholar] [CrossRef]
  7. Rodríguez-Castro, Y.; Lameiras, M.; Carrera, M.V.; Vallejo-Medina, P. Validación de la Escala de Actitudes hacia el Amor en una muestra de adolescentes [Validation of the Scale of Attitudes towards Love in a sample of adolescents]. Estud. Psicol. 2013, 34, 209–219. [Google Scholar] [CrossRef]
  8. Childhood Trends. Preventing Bullying and Cyberbullying: Research Based Policy Recommendations for Executive and Legislative Officials in 2017. 2017. Available online: https://www.childtrends.org/wp-content/uploads/2017/01/2017-06BullyingPolicyRecsFinal.pdf (accessed on 11 December 2020).
  9. Peskin, M.F.; Markham, C.M.; Shegog, R.; Temple, J.R.; Baumler, E.R.; Addy, R.C.; Hernandez, B.; Cuccaro, P.; Gabay, E.; Thiel, M.; et al. Prevalence and correlates of the perpetration of cyber dating abuse among early adolescents. J. Youth Adolesc. 2017, 46, 358–375. [Google Scholar] [CrossRef]
  10. Navarro-Pérez, J.J.; Carbonell, Á.; Oliver, A. Eficacia de una app psicoeducativa para reducir el sexismo en adolescentes [Efficacy of a psychoeducational app to reduce sexism in adolescents]. Rev. Psicodidáctica 2019, 24, 9–16. [Google Scholar] [CrossRef]
  11. Navarro-Pérez, J.J.; Oliver, A.; Carbonell, Á.; Schneider, B.H. Effectiveness of a Mobile App Intervention to Prevent Dating Violence in Residential Child Care. Psychosoc. Interv. 2020, 29, 59–66. [Google Scholar] [CrossRef]
  12. Levesque, D.A.; Johnson, J.L.; Welch, C.A.; Prochaska, J.M.; Paiva, A.L. Teen dating violence prevention: Cluster-randomized trial of Teen Choices, an online, stage-based program for healthy, nonviolent relationships. Psychol. Violence 2016, 6, 421–432. [Google Scholar] [CrossRef] [PubMed]
  13. App Detectamor. Available online: http://www.juntadeandalucia.es/institutodelamujer/index.php/areas-tematicas-coeducacion/app-detectamor (accessed on 4 January 2021).
  14. Navarro Pérez, J.J.; Morillo Tena, P.; Oliver Germes, A.; Carbonell Marqués, Á. Trabajo Social e interdisciplinariedad en el diseño de App interactiva para la detección de actitudes sexistas, mitos del amor romántico y prevención de violencias en relaciones sentimentales de adolescentes [Social Work and interdisciplinarity in the design of an interactive App for the detection of sexist attitudes, myths of romantic love and prevention of violence in adolescent romantic relationships]. Rev. Serv. Soc. Política Soc. 2018, 35, 37–53. [Google Scholar]
  15. Torres, A.; Robles, J.M.; De Marco, S. El Ciberacoso Como Forma de Ejercer la Violencia de Género en la Juventud: Un Riesgo en la Sociedad de la Información y del Conocimiento [Cyberbullying as a Way of Exercising Gender Violence Against Young People: A Risk in the Information and Knowledge Society]; Ministerio de Sanidad, Servicios Sociales e Igualdad, Centro de Publicaciones: Madrid, Spain, 2014. [Google Scholar]
  16. Marcum, C.D.; Higgins, G.E. Examining the effectiveness of academic scholarship on the fight against cyberbullying and cyberstalking. Am. J. Crim. Justice 2019, 44, 645–655. [Google Scholar] [CrossRef]
  17. Cavezza, C.; McEwan, T.E. Cyberstalking versus off-line stalking in a forensic sample. Psychol. Crime Law 2014, 20, 955–970. [Google Scholar] [CrossRef]
  18. March, E.; Litten, V.; Sullivan, D.H.; Ward, L. Somebody that I (used to) know: Gender and dimensions of dark personality traits as predictors of intimate partner cyberstalking. Pers. Indiv. Differ. 2020, 163, 110084. [Google Scholar] [CrossRef]
  19. Baker, C.K.; Carreño, P.K. Understanding the role of technology in adolescent dating and dating violence. J. Child Fam. Stud. 2016, 25, 308–320. [Google Scholar] [CrossRef]
  20. Strawhun, J.; Adams, N.; Huss, M.T. The assessment of cyberstalking: An expanded examination including social networking, attachment, jealousy, and anger in relation to violence and abuse. Violence Vict. 2013, 28, 715–730. [Google Scholar] [CrossRef]
  21. Borrajo, E.; Gámez-Guadix, M.; Pereda, N.; Calvete, E. The development and validation of the cyber dating abuse questionnaire among young couples. Comput. Hum. Behav. 2015, 48, 358–365. [Google Scholar] [CrossRef]
  22. Stonard, K.E.; Bowen, E.; Lawrence, T.R.; Price, S.A. The relevance of technology to the nature, prevalence and impact of adolescent dating violence and abuse: A research synthesis. Aggress. Violent Behav. 2014, 19, 390–417. [Google Scholar] [CrossRef]
  23. Tokunaga, R.S. Interpersonal surveillance over social network sites: Applying a theory of negative relational maintenance and the investment model. J. Soc. Pers. Relatsh. 2016, 33, 171–190. [Google Scholar] [CrossRef]
  24. Smoker, M.; March, E. Predicting perpetration of intimate partner cyberstalking: Gender and the Dark Tetrad. Comput. Hum. Behav. 2017, 72, 390–396. [Google Scholar] [CrossRef]
  25. Stonard, K.E.; Bowen, E.; Walker, K.; Price, S.A. “They’ll always find a way to get to you”: Technology use in adolescent romantic relationships and its role in dating violence and abuse. J. Interpers. Violence 2017, 32, 2083–2117. [Google Scholar] [CrossRef]
  26. Baker, C.K.; Helm, S. Prevalence of intimate partner violence victimization and perpetration among youth in Hawaii. Hawaii Med. J. 2011, 70, 92. [Google Scholar]
  27. Van-Ouytsel, J.; Ponnet, K.; Walrave, M. Cyber dating abuse: Investigating digital monitoring behaviors among adolescents from a social learning perspective. J. Interpers. Violence 2020, 35, 5157–5178. [Google Scholar] [CrossRef]
  28. Reed, L.A.; Tolman, R.M.; Ward, L.M. Gender matters: Experiences and consequences of digital dating abuse victimization in adolescent dating relationships. J. Adolesc. 2017, 59, 79–89. [Google Scholar] [CrossRef] [PubMed]
  29. Hellevik, P.M. Teenagers’ personal accounts of experiences with digital intimate partner violence and abuse. Comput. Hum. Behav. 2019, 92, 178–187. [Google Scholar] [CrossRef]
  30. Lucero, J.L.; Weisz, A.N.; Smith-Darden, J.; Lucero, S.M. Exploring gender differences: Socially interactive technology use/abuse among dating teens. Affil. J. Women Soc. Work. 2014, 29, 478–491. [Google Scholar] [CrossRef]
  31. Duntley, J.D.; Buss, D.M. The evolution of stalking. Sex Roles 2012, 66, 311–327. [Google Scholar] [CrossRef]
  32. Pereira, F.; Spitzberg, B.H.; Matos, M. Cyber-harassment victimization in Portugal: Prevalence, fear and help-seeking among adolescents. Comput. Hum. Behav. 2016, 62, 136–146. [Google Scholar] [CrossRef]
  33. Cava, M.J.; Martínez, B.; Buelga, S.; Carrascosa, L. Sexist attitudes, romantic myths, and offline dating violence as predictors of cyber dating violence perpetration in adolescents. Comput. Hum. Behav. 2020, 106449. [Google Scholar] [CrossRef]
  34. Muñiz-Rivas, M.; Vera, M.; Povedano-Díaz, A. Parental style, dating violence and gender. Int. J. Environ. Res. Public Health 2019, 16, 2722. [Google Scholar] [CrossRef]
  35. Sánchez, V.; Muñoz, N.; Ortega, R. “Cyberdating Q_A”: An instrument to assess the quality of adolescent dating relationships in social networks. Comput. Hum. Behav. 2015, 48, 78–86. [Google Scholar] [CrossRef]
  36. Sánchez, V.; Muñoz, N.; López, L.A.; Ortega, R. Cyber-aggression in adolescent couples: A cross-cultural study Spain-Mexico. Rev. Mex. Psicol. 2017, 34, 46–54. [Google Scholar]
  37. Donoso, T.; Rubio, M.J.; Vilá, R. Las ciberagresiones en función del género [Cyberbullying based on gender]. Rev. Investig. Educ. 2017, 35, 197–214. [Google Scholar] [CrossRef]
  38. Muñoz, N.; Sánchez, V. Cyber-aggression and psychological aggression in adolescent couples: A short-term longitudinal study on prevalence and common and differential predictors. Comput. Hum. Behav. 2020, 104, 106191. [Google Scholar] [CrossRef]
  39. Glick, P.; Fiske, S.T. Ambivalent sexism. In Advances in Experimental Social Psychology; Zanna, M.P., Ed.; Academic Press: San Diego, CA, USA, 2001; pp. 115–188. [Google Scholar]
  40. Glick, P.; Fiske, S.T. The ambivalent sexism inventory: Differentiating hostile and benevolent sexism. J. Pers. Soc. Psychol. 1996, 70, 491. [Google Scholar] [CrossRef]
  41. Rodríguez-Castro, Y.; Lameiras, M.; Carrera, M.V.; Failde, J.M. Aproximación conceptual al sexismo ambivalente: Estado de la cuestión [Conceptual approach to ambivalent sexism: State of the question]. Summa Psicológica UST 2009, 6, 131–142. [Google Scholar] [CrossRef]
  42. Gutierrez, B.C.; Halim, M.L.D.; Martinez, M.A.; Arredondo, M. The heroes and the helpless: The development of benevolent sexism in children. Sex Roles 2020, 82, 558–569. [Google Scholar] [CrossRef]
  43. Navas, M.P.; Maneiro, L.; Cutrín, O.; Gómez-Fraguela, J.A.; Sobral, J. Associations between dark triad and ambivalent sexism: Sex differences among adolescents. Int. J. Environ. Res. Public Health 2020, 17, 7754. [Google Scholar] [CrossRef] [PubMed]
  44. Ramiro-Sánchez, T.; Ramiro, M.T.; Bermúdez, M.P.; Buela-Casal, G. Sexism in adolescent relationships: A systematic review. Psychosoc. Interv. 2018, 27, 123–132. [Google Scholar] [CrossRef]
  45. Rodríguez, C.; Durán, M.; Martínez, R. Cyber aggressors in dating relationships and its relation with psychological violence, sexism, and jealousy. Health Addict. 2018, 18, 17–27. [Google Scholar]
  46. Dosil, M.; Jaureguizar, J.; Bernaras, E.; Sbicigo, J.B. Teen dating violence, sexism, and resilience: A multivariate analysis. Int. J. Environ Res. Public Health 2020, 17, 2652. [Google Scholar] [CrossRef] [PubMed]
  47. Fernández, I.; Cuadrado, I.; Martín, G. Synergy between acceptance of violence and sexist attitudes as a dating violence risk factor. Int. J. Environ. Res. Public Health 2020, 17, 5209. [Google Scholar] [CrossRef]
  48. Arbach, K.; Vaiman, M.; Bobbio, A.; Bruera, J.; Lumello, A. Ambivalent Sexism Inventory: Factorial invariance by gender and relation with intimate partner violence. Interdisciplinaria 2019, 36, 59–76. [Google Scholar]
  49. Zavala, A.G.; Bierwiaczonek, K. Male, National, and Religious Collective Narcissism Predict Sexism. Sex Roles 2020, 30, 1–21. [Google Scholar] [CrossRef]
  50. Drakett, J.; Rickett, B.; Day, K.; Milnes, K. Old jokes, new media–Online sexism and constructions of gender in Internet memes. Fem. Psychol. 2018, 28, 109–127. [Google Scholar] [CrossRef]
  51. Molla, C.; López-González, E.; Losilla, J.M. Sexting Prevalence and Socio-Demographic Correlates in Spanish Secondary School Students. Sex Res. Soc. Policy 2020, 18, 97–111. [Google Scholar] [CrossRef]
  52. Kim, S.; Martin, A.; Drossos, A.; Barbosa, S.; Georgiades, K. Prevalence and correlates of sexting behaviors in a provincially representative sample of adolescents. Can. J. Psychiatr. 2020, 65, 401–408. [Google Scholar] [CrossRef]
  53. Alonso, P.; Rodríguez, Y.; Lameiras, M.; Martínez, R. Sexting through the Spanish adolescent discourse. Saude Soc. 2018, 27, 398–409. [Google Scholar] [CrossRef]
  54. Titchen, K.E.; Maslyanskaya, S.; Silver, E.J.; Coupey, S.M. Sexting and young adolescents: Associations with sexual abuse and intimate partner violence. J. Pediatr. Adol. Gynec. 2019, 32, 481–486. [Google Scholar] [CrossRef]
  55. Wood, M.; Barter, C.; Stanley, N.; Aghtaie, N.; Larkins, C. Images across Europe: The sending and receiving of sexual images and associations with interpersonal violence in young people’s relationships. Child. Youth Serv. Rev. 2015, 59, 149–160. [Google Scholar] [CrossRef]
  56. Machimbarrena, J.M.; Calvete, E.; Fernández, L.; Álvarez-Bardón, A.; Álvarez-Fernández, L.; González, J. Internet risks: An overview of victimization in cyberbullying, cyber dating abuse, sexting, online grooming and problematic internet use. Int. J. Environ. Res. Public Health 2018, 15, 2471. [Google Scholar] [CrossRef] [PubMed]
  57. Quesada, S.; Fernández-González, L.; Calvete, E. El sexteo (sexting) en la adolescencia: Frecuencia y asociación con la victimización de ciberacoso y violencia en el noviazgo [Sexting in adolescence: Frequency and association with victimization of cyberbullying and dating violence]. Behav. Psychol. 2018, 26, 225–242. [Google Scholar]
  58. Cobo, R. Pornografía. El Placer del Poder [Pornography. The Pleasure of Power]; Ediciones B: Barcelona, Spain, 2020. [Google Scholar]
  59. Martellozzo, E.; Monaghan, A.; Davidson, J.; Adler, J. Researching the Affects That Online Pornography Has on UK Adolescents Aged 11 to 16. SAGE Open 2020, 10. [Google Scholar] [CrossRef]
  60. Ballester, L.; Orte, C.; Pozo, R. Nueva pornografía y cambios en las relaciones interpersonales de adolescentes y jóvenes [New pornography and changes in the interpersonal relationships of adolescents and young people]. In Nueva Pornografía y Cambios en las Relaciones Interpersonales de Vulnerabilidad [New Pornography and Changes in Interpersonal Relationships from Vulnerability]; Orte, C., Ballecter, L., Pozo, R., Eds.; Octaedro: Madrid, Spain, 2019; pp. 249–284. [Google Scholar]
  61. Save the Children. (Des)Información Sexual: Pornografía Y Adolescencia [Sexual Misinformation: Pornography and Adolescence]. 2020. Available online: https://www.savethechildren.es/sites/default/files/2020-09/Informe_Desinformacion_sexual-Pornografia_y_adolescencia.pdf (accessed on 11 December 2020).
  62. Efrati, Y.; Amichai-Hamburger, Y. Are adolescents who consume pornography different from those who engaged in online sexual activities? Child Youth Serv. Rev. 2020, 104843. [Google Scholar] [CrossRef]
  63. Lucić, M.; Baćak, V.; Štulhofer, A. The role of peer networks in adolescent pornography use and sexting in Croatia. J. Child Media 2019, 14, 110–127. [Google Scholar] [CrossRef]
  64. Stanley, N.; Barter, C.; Wood, M.; Aghtaie, N.; Larkins, C.; Lanau, A.; Överlien, C. Pornography, sexual coercion and abuse and sexting in young people’s intimate relationships: A European study. J. Interpers. Violence 2018, 33, 2919–2944. [Google Scholar] [CrossRef] [PubMed]
  65. Rodríguez-Castro, Y.; Alonso, P.; González, A.; Lameiras, M.; Carrera, M.V. Spanish adolescents’ attitudes towards sexting: Validation of a scale. Comput. Hum. Behav. 2017, 73, 375–384. [Google Scholar] [CrossRef]
  66. Lemus, S.; Castillo, M.; Moya, M.; Padilla, J.L.; Ryan, E. Elaboración y validación del Inventario de Sexismo Ambivalente para Adolescentes [Preparation and validation of the Inventory of Ambivalent Sexism for Adolescents]. Int. J. Clin. Health Psychol. 2008, 8, 537–562. [Google Scholar]
  67. Herrero, J.; Rodríguez, F.J.; Torres, A. Acceptability of partner violence in 51 societies: The role of sexism and attitudes toward violence in social relationships. Violence Against Women 2017, 23, 351–367. [Google Scholar] [CrossRef] [PubMed]
  68. Sánchez, M.D.; Herrera, M.D.C.; Expósito, F. Controlling Behaviors in Couple Relationships in the Digital Age: Acceptability of Gender Violence, Sexism, and Myths about Romantic Love. Psychosoc. Interv. 2020, 29, 67–81. [Google Scholar] [CrossRef]
  69. Lim, M.S.; Carrotte, E.R.; Hellard, M.E. The impact of pornography on gender-based violence, sexual health and well-being: What do we know? J. Epidemiol. Community Health 2015, 70, 3–5. [Google Scholar] [CrossRef] [PubMed]
  70. Almendros, C.; Gámez-Guadix, M.; Carrobles, J.A.; Rodríguez-Caballeira, Á.; Porrúa, C. Abuso psicológico en la pareja: Aportaciones recientes, concepto y medición [Psychological abuse in the couple: Recent contributions, concept and measurement]. Behav. Psychol. 2009, 17, 433–451. [Google Scholar]
  71. Vanden, M.; Campbell, S.W.; Eggermont, S.; Roe, K. Sexting, mobile porn use, and peer group dynamics: Boys’ and girls’ self-perceived popularity, need for popularity, and perceived peer pressure. Media Psychol. 2014, 17, 6–33. [Google Scholar] [CrossRef]
  72. Romito, P.; Beltramini, L. Factors associated with exposure to violent or degrading pornography among high school students. J. Sch. Nurs. 2015, 31, 280–290. [Google Scholar] [CrossRef]
  73. Martínez-Pecino, R.; Durán, M. I love you, but I cyberbully you: The role of hostile sexism. J. Interpers. Violence 2019, 34, 812–825. [Google Scholar] [CrossRef]
  74. Rodríguez-Castro, Y.; Lameiras, M.; Carrera, M.V.; Magalhaes, M.J. Estereotipos de género y la imagen de la mujer en los Mass Media [Gender stereotypes and the image of women in the Mass Media]. In Comunicación y Justicia en Violencia de Género [Communication and Justice in Gender Violence]; Iglesias, I.C., Lameiras, M., Eds.; Tirant lo Blanch: Valencia, Spain, 2012; pp. 37–69. [Google Scholar]
  75. Lagarde, M. Para Mis Socias de la Vida [For My Partners in Life]; Horas y Horas: Madrid, Spain, 2005. [Google Scholar]
  76. Rodríguez-Castro, Y.; Lameiras, M.; Carrera, M.V.; Vallejo, P. La fiabilidad y validez de la escala de mitos hacia el amor: Las creencias de los y las adolescentes [The reliability and validity of the scale of myths towards love: The beliefs of adolescents]. Rev. Psicol. Soc. 2013, 28, 157–168. [Google Scholar] [CrossRef]
  77. Van-Roosmalen, E. Forces of patriarchy: Adolescent experiences of sexuality and conceptions of relationships. Youth Soc. 2000, 32, 202–227. [Google Scholar] [CrossRef]
  78. Ley Orgánica 8/2013, de 9 de Diciembre, para la Mejora de la Calidad Educativa [Organic Law 8/2013, of December 9, for the Improvement of Educational Quality]. BOE 295. Available online: https://www.boe.es/buscar/pdf/2013/BOE-A-2013-12886-consolidado.pdf (accessed on 12 December 2020).
  79. Lameiras, M.; Carrera, M.V.; Rodríguez, Y. La educación sexual: Un derecho en la “lista de espera” del sistema educativo en España [Sex education: A right on the “waiting list” of the educational system in Spain]. Rev. Digit. Asoc. Convives 2019, 26, 10–16. [Google Scholar]
  80. Carrera, M.V.; Rodríguez, Y.; Lameiras, M. Yoiyo y la sexualidad: Una misión en Ultreia. La salud afectivo-sexual de la juventud en España [Yoiyo and sexuality: A mission in Ultreia. The affective-sexual health of youth in Spain]. Rev. Estud. Juv. 2019, 123, 155–170. [Google Scholar]
  81. Avery, L.; Lazdane, G. What do we know about sexual and reproductive health of adolescents in Europe? Eur. J. Contracept. Reprod. Health Care 2008, 13, 58–70. [Google Scholar] [CrossRef]
  82. Peñafiel, C.; Ronco, M.; Echegaray, L. Jóvenes, salud e Internet. Percepción, actitud y motivaciones de los jóvenes ante la información de salud [Youth, health and the Internet. Perception, attitude and motivations of young people regarding health information]. Rev. Lat. Comun. Soc. 2017, 72, 1317–1340. [Google Scholar] [CrossRef]
  83. Adá, A.; Rodríguez, Y. The presence of female athletes and non-athletes on sports media Twitter. Fem. Media Stud. 2019. [Google Scholar] [CrossRef]
  84. Wachs, S.; Jiskrova, G.K.; Vazsonyi, A.T.; Wolf, K.D.; Junger, M. A cross-national study of direct and indirect effects of cyberbullying on cybergrooming victimization via self-esteem. Psicol. Educ. 2016, 22, 61–70. [Google Scholar] [CrossRef]
  85. Lameiras, M.; Rodríguez, Y.; Ojea, M.; Dopereiro, M. Programa Coeducativo de Desarrollo Psicoafectivo y Sexual [Programa Coeducativo de Desarrollo Psicoafectivo y Sexual]; Pirámide: Madrid, Spain, 2010. [Google Scholar]
Figure 1. Moderating effect of benevolent sexism (BS) between sexting behavior and intimate partner cyberstalking.
Figure 1. Moderating effect of benevolent sexism (BS) between sexting behavior and intimate partner cyberstalking.
Ijerph 18 02181 g001
Figure 2. Moderating effect of gender on pornography consumption and intimate partner cyberstalking.
Figure 2. Moderating effect of gender on pornography consumption and intimate partner cyberstalking.
Ijerph 18 02181 g002
Table 1. Differences in the means of scales/subscales by gender.
Table 1. Differences in the means of scales/subscales by gender.
Intimate Partner CyberstalkingMean (SD)tpd-Cohen
FemaleMale
1.31 (0.41)1.23 (0.39)−2.690.007−0.20
Sexting behavior1.40 (0.49)1.69 (0.46)8.070.0010.61
Pornography consumption 1.40 (0.49)1.78 (0.41)11.190.0010.84
ISA_Hostile sexism1.66 (0.74)2.12 (0.96)6.890.0010.52
ISA_Benevolent Sexism2.11 (0.97)2.42 (1.08)3.970.0010.30
Note: SD: standard deviation; t: Student’s t-test; p: level of signification; d-Cohen: Cohen’s d effect size
Table 2. Pearson correlations between the various scales/subscales.
Table 2. Pearson correlations between the various scales/subscales.
ScalesGenderAge23456
(2) Intimate Partner Cyberstalking0.10 **0.07-
(3) ISA_Hostile Sexism−0.25 ***0.030.32 **-
(4) ISA_Benevolent Sexism−0.15 ***0.040.39 **0.58 **-
(5) Sexting behavior−0.29 ***0.10 **0.32 **0.33 **0.32 **-
(6) Pornography Consumption−0.38 ***0.11 **0.33 **0.36 **0.34 **0.64 **-
Note: ** p < 0.01; *** p < 0.001. Gender (1 = boys; 2 = girls).
Table 3. Hierarchical linear regression analysis predicting intimate partner cyberstalking.
Table 3. Hierarchical linear regression analysis predicting intimate partner cyberstalking.
PredictorsStep 1Step 2Step 3
βt (p)βt (p)βt (p)
Gender0.287.62 ***0.308.52 ***−0.17−1.26
Age0.010.700.010.100.01−0.198
Sexting Behavior0.204.58 ***0.143.34 ***−0.43−1.43
Pornography Consumption 0.316.74 ***0.235.20 ***−0.24−1.20
ISA_Hostile Sexism 0.122.83 **0.112.65 **
ISA_Benevolent Sexism 0.256.15 ***−0.68−0.491
Gender × Pornography Consumption 0.342.01 ***
Benevolent Sexism ×
Sexting Behavior
0.151.69 **
F (df, df error)42.98 (4, 676) ***46.90 (2, 674) ***24.39 (7, 667) ***
R20.2030.2950.322
ΔR20.2030.0920.028
ΔF242.98 ***43.83 ***3.89 ***
Note: ** p < 0.01; *** p < 0.001. Gender (1 = boys; 2 = girls). β: Beta coefficients. R2: coefficient of determination. ΔR2: adjusted coefficient. F: ANOVA. t: Student’s t-test and p-values.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Back to TopTop