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Article

FoMO as a Predictor of Cyber Dating Violence Among Young Adults: Understanding Digital Risk Factors in Romantic Relationships

by
Alessandra Ragona
1,
Mara Morelli
2,
Lia Ruggeri
3,
Elena Cattelino
3 and
Antonio Chirumbolo
4,*
1
Department of Developmental and Social Psychology, Sapienza University of Rome, 00185 Rome, Italy
2
Department of Dynamic and Clinical Psychology, and Health Studies, Sapienza University of Rome, 00185 Rome, Italy
3
Department of Human and Social Science, University of Valle d’Aosta, 11100 Aosta, Italy
4
Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Societies 2025, 15(9), 258; https://doi.org/10.3390/soc15090258
Submission received: 12 May 2025 / Revised: 9 September 2025 / Accepted: 11 September 2025 / Published: 14 September 2025
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Abstract

Cyber Dating Violence (CDV) is a prevalent and concerning phenomenon among young people, characterized by abusive behaviors toward a romantic partner through digital platforms. Fear of Missing Out (FoMO), defined as the anxiety of being excluded from rewarding experiences, is closely linked to excessive digital engagement and the need for constant connectivity. While previous studies have associated FoMO with bullying and cyberstalking, its relationship with dating violence remains unexplored. This study’s aim is to bridge this gap by analyzing the association between FoMO and both perpetration and victimization in CDV. The sample consisted of 911 young adults aged 18 to 30 (Mage = 22.0; SDage = 2.57; 74% women), recruited via an online survey. Findings indicate that FoMO significantly predicts both the perpetration and victimization of CDV. Additionally, for perpetration, significant interactions emerged with gender and sexual orientation, suggesting that these factors may moderate the relationship between FoMO and abusive behaviors in digital dating contexts. These results highlight the need for further research to better understand CDV and inform prevention and intervention strategies focused on digital platform use and healthy relationship dynamics.

1. Introduction

Digital media have become a constant presence in everyone’s daily life, offering new opportunities but also risks and challenges. Their widespread diffusion has thus enabled them to become new channels through which violent behaviors in relationships can be carried out. It has been hypothesized by several authors [1,2,3] that, due to its characteristics, the digital environment is more likely to predispose individuals to engage in online aggression and lead to more negative consequences for the victim [4]. In particular, the online world exposes victims more, as content can spread to a huge audience, unlike face-to-face violence [3,5]. Furthermore, the perception of anonymity allows perpetrators to engage in violent behaviors with a heightened sense of anonymity and impunity [6]. In recent years, scholars have increasingly examined which variables contribute to risk for interpersonal aggression, shedding light on the complex relationship between violent behaviors and personal and social factors [7,8,9].

1.1. Cyber Dating Violence

In its in-person form, violent behaviors in romantic relationships have received considerable attention in the literature, both to better understand the phenomenon and to design intervention strategies [10,11,12]. Dating violence is a major public health concern that emerges as early as adolescence and continues to affect young adults, leading to significant social, physical, and psychological consequences [13]. Dating violence can include relational abuse (humiliation or isolation), verbal-emotional abuse (manipulation or aggression), psychological abuse (undermining self-esteem), physical abuse (inflicting pain or suffering), and sexual abuse (non-consensual sexual acts or comments) [14]. Studies show that verbal-emotional abuse is the most common and accepted form of violence among young couples [15,16,17,18,19,20], with relational violence often preceding both verbal-emotional and physical violence [21,22].
In recent years, the online context has given rise to new forms of abuse in adolescent relationships, occurring through mobile devices and social networks, such as digital control and abuse. Recent studies have identified these as the most common forms of aggression, surpassing direct forms of violence [23,24]. Studies indicate that cyber-control behaviors are more frequent than direct aggression carried out through digital channels, such as mobile applications, webcams, social media platforms, text messages, and emails [4]. This trend indicates that the subtle nature of cyber-monitoring actions makes them more easily accepted by young individuals, often being misinterpreted as expressions of love or romantic jealousy [23,25]. Another important dimension of dating violence in the online context is relational aggression, which involves attempts to damage a partner’s social relationships. This can include spreading false rumors or actively working to turn others against them, ultimately leading to social isolation and emotional distress [6,26]. Given the pervasive nature of digital communication, these behaviors can have a lasting impact, exacerbating the psychological and social consequences of dating violence.
Several authors have referred to this form of violence that occurs in intimate relationships among young people as “online dating violence” [27,28,29,30] or “Cyber Dating Violence” [26]. A recent review [31] highlights that this form of violence in dating relationships is labeled in various ways, including cyber dating abuse, electronic dating violence, online dating abuse, digital dating abuse, and cyber aggression, with ‘cyber dating abuse’ being the most commonly used term [32].
Statistics from studies on the prevalence of Cyber Dating Violence (CDV) indicate that this issue may impact a significant number of adolescents and young adults [33,34,35]. Research indicates that CDV is growing worldwide [36]. Addressing this phenomenon in young adults is particularly crucial, because the risk of intimate partner violence is particularly elevated within this age group [37], aligning with the period when more committed romantic relationships are typically formed [38], as well as the widespread use of ICTs for communication [39]. According to Borrajo et al. [23], almost half of college students have experienced some form of CDV. Regarding its severity, around 93% reported exposure to less severe behaviors, such as verbal insults or swearing, while 12–13% encountered more severe abuse, including threats or public humiliation [40]. However, prevalence rates of both perpetration and victimization differ considerably across studies. Cyber Dating Violence (CDV) victimization generally has profound negative impacts on youth’s psychosocial well-being, leading to emotional distress, lowered self-esteem, feelings of loneliness, identity confusion, as well as increased anxiety, depression, and suicidal ideation [27,41,42,43].
The existing literature suggests that, in terms of behavioral involvement prevalence, young individuals experience this form of violence as victims at rates ranging from 14% to 73%, while perpetration rates range from 12% to 67% [26,30,44,45]. The literature on gender differences in CDV presents mixed findings. Regarding perpetration, some studies report no significant gender differences [46,47], while others suggest that gender is associated with specific forms of CDV: girls seem to be more likely to engage in non-sexual forms (e.g., verbal, psychological, and relational aggression), whereas boys report higher rates of sexual CDV perpetration [48]. In terms of victimization, research indicates that girls experience more severe forms of aggression, such as sexual coercion, and suffer greater psychological distress compared to boys [49,50]. However, findings on gender differences in CDV prevalence remain inconsistent, with some studies reporting significant disparities, e.g., [30,51], while others find no significant differences [4,52].
Regarding sexual orientation, several studies have highlighted that young individuals from sexual minority groups face a significantly higher risk of experiencing bullying, cyberbullying, and dating violence [53,54,55]. This increased vulnerability is often linked to social stigma, discrimination, and lower levels of social support [53,55]. LGBTQ+ youth are more likely to be targeted due to their identity, experiencing hostility both online and offline [56]. Additionally, internalized stigma and fear of rejection can hinder help-seeking behaviors, increasing their risk of victimization [57].

1.2. Fear of Missing Out

A growing concern in today’s digital landscape is the phenomenon known as FoMO (Fear of Missing Out), which has gained prominence alongside the widespread use of social media [58]. FoMO is characterized by a sense of anxiety driven by the fear that others are participating in rewarding or enjoyable activities while one is left out. This feeling often leads to a constant desire to stay connected and informed about the lives of others, especially close friends, to avoid the discomfort of feeling left behind or disconnected [59].
Recent studies have established a strong relationship between FoMO and problematic engagement with social media [60,61]. Digital platforms and social media, which continuously share updates about others’ lives and activities, represent a significant factor in the development of FoMO. In addition to contributing to excessive or compulsive social media use, FoMO has been connected to a range of negative online behaviors, including cyberbullying [62], aggravated sexting [63], harmful patterns of communication on smartphones and social media [64], and problematic internet use [65]. Neurobiological studies on FoMO suggest that individuals with higher levels of FoMO are more likely to seek approval and focus on social inclusion [66], with feelings of social pain or distress arising from the perception or experience of exclusion from important social groups or close relationships [67]. This is particularly relevant for emerging adults, who, as they begin to separate from primary caregivers, place increasing importance on peer groups for socialization and identity development [68]. Although research exploring the connection between FoMO and violence in romantic relationships is limited, existing literature clearly identifies perceived rejection and social exclusion as significant risk factors for aggressive behavior [69].
Even if emerging literature has highlighted that FoMO may be associated with various violent behaviors among peers [61,63,70], to our knowledge no study has explored the potential relationship between FoMO and CDV among young adults.

1.3. Aims of the Present Study

This evolving body of research has significantly advanced the understanding of the mechanisms through which media use influences aggression. While much of the existing literature has focused on adolescents, it is crucial to extend this investigation to young adults, especially as romantic relationships are now evolving to last longer and become more complex over time. Building on these studies, the present research aims to fill a critical gap in the literature by exploring the potential link between Fear of Missing Out (FoMO) and CDV perpetration and victimization, a connection that, to date, remains unexplored. This study will contribute to the growing body of evidence identifying key risk factors for aggression and violence [71,72,73], furthering the understanding of how digital media use may impact these behaviors. In doing so, it will play an important role in informing public health initiatives and interventions designed to mitigate the harmful effects of media on young adults and reduce the risk of relationship violence.
Based on the existing literature and considering the bidirectional nature of CDV and the lack of a clearly defined distinction between victim and perpetrator roles [74,75,76,77], it was hypothesized that FoMO predicts both the perpetration and victimization of CDV. Lastly, given that previous research has highlighted the influence of gender, age, and sexual orientation on dating violence [6,78,79], these variables were incorporated into our regression analyses as additional predictors to better understand their impact. Despite conflicting findings in the literature and some overlap between males and females in engaging in dating violence, it was hypothesized that males might be more prone to this behavior [74], with older individuals potentially being more involved as well [80]. Furthermore, based on existing research, it is expected that the relationship between FoMO and various forms of Cyber Dating Violence behaviors will be stronger among sexual minorities compared to their heterosexual participants [53].

2. Materials and Methods

2.1. Participants and Procedure

The study involved 911 young adults aged between 18 and 30 (Mage = 22.0; SDage = 2.57; 74% women), recruited through snowball sampling via the distribution of a questionnaire on digital platforms. The questionnaire was created using Google Forms and included the study participation information sheet and the administered instruments. To reach as many participants as possible, we employed procedures commonly used in online studies on sensitive topics, e.g., [27,60,63], such as fully anonymous administration, clear and standardized instructions, and dissemination across multiple digital platforms (e.g., Instagram, Facebook, Whatsapp). In addition, participants could share the questionnaire link with their contacts, allowing for broader participation. This procedure was chosen, in line with other research, to minimize social desirability bias as much as possible. The sample includes both heterosexual and individuals belonging to sexual minorities (29.6% non-exclusively heterosexual; n = 269). To be eligible for this study, participants were required to either be currently in a relationship or have experienced one in the past. Regarding relationship status, 64.3% of participants reported being in a relationship at the time of the survey, while 35.7% indicated they had been in a relationship in the past. Participants engaged in the study by completing an online questionnaire, indicating their consent by choosing ‘Yes, I agree to participate’ on the first page of the survey. The study was conducted in accordance with the ethical guidelines set forth in the Declaration of Helsinki and was approved by the University’s ethics committee of the Department of Dynamic and Clinical Psychology, and Health Studies of Sapienza University of Rome.

2.2. Measures

2.2.1. Demographics Information

Participants shared details about their gender, age, sexual orientation, and relationship status. To assess sexual orientation, the Kinsey scale [81] was employed, allowing participants to self-rate on a 7-point scale, where 1 represented exclusively heterosexual and 7 indicated non-exclusively heterosexual. Consistent with the methodology in previous research (e.g., [6,82]), participants were categorized into two groups: participants who identified as exclusively heterosexual were coded as 0, while those belonging to sexual minorities were coded as 1. Gender identity was assessed with multiple options, including ‘cisgender man,’ ‘cisgender woman,’ ‘transgender man,’ ‘transgender woman,’ and ‘nonbinary.’ However, as no participants identified with gender categories outside of ‘cisgender man’ or ‘cisgender woman,’ all participants were classified accordingly. For clarity and consistency, the terms ‘men’ and ‘women’ will be used throughout the results and discussion sections.

2.2.2. Cyber Dating Violence

The Cyber Dating Violence Inventory (CDVI; [26]) was used to measure Cyber Dating Violence, assessing both perpetration and victimization experiences in digital romantic relationships. The scale uses a 4-point Likert scale ranging from 0 (Never) to 4 (6 times or more) to assess two main aspects: Psychological violence (6 items) and Relational violence (5 items). Psychological violence involves verbal and emotional forms of abuse, such as using insults, threatening to break up, or creating jealousy in the partner (e.g., ‘I threatened to end the relationship through SMS/email/Facebook’). Relational violence refers to actions aimed at damaging the partner’s social relationships, such as spreading negative information about them to their friends through SMS/email/Facebook in an attempt to turn them against the partner (e.g., ‘I told his/her friends things to make them dislike him/her’). In this study, both the perpetration and victimization dimensions demonstrated good reliability, with McDonald’s ω coefficients of 0.83 and 0.87, respectively.

2.2.3. Fear of Missing out

To assess Fear of Missing Out, the Fear of Missing Out Scale (FoMOs; [59]; Italian validation by [83]) was used. The scale measures feelings of fear, anxiety, and irritability related to the fear of missing out on meaningful experiences others are having, as well as the need to stay connected with others. Participants rate each of the 10 items using a Likert scale, with 1 representing ‘Not at all true for me’ and 5 indicating ‘Extremely true for me’. An example item is “I get worried when I find out that my friends are having fun without me”. The scale demonstrated strong reliability indexes, 0.88 for McDonald’s ω and 0.87 for Cronbach’s α.

2.3. Data Analysis

Given the non-normal distribution and the overdispersion features of the Cyber Dating Violence variables, a negative binomial regression analysis was conducted. To examine the relationship with Cyber Dating Violence, both perpetration and victimization, each dimension was regressed onto the following variables: age, gender (coded as 0 = male; 1 = female), and sexual orientation (coded as 0 = exclusively heterosexual; 1 = sexual minorities). Additionally, FoMO was included as a predictor and tested with three interaction terms: FoMO*age, FoMO*gender, and FoMO*sexual orientation.
As previously noted, due to the distributional characteristics of the CDV variables, negative binomial regression was identified as the most appropriate statistical technique. This model is specifically designed to analyze count data, i.e., the frequency with which an event occurs within a defined time frame or context (such as the occurrence of specific behaviors). Negative binomial regression is particularly suited for behavioral data [84], which often deviate from a normal distribution and frequently exhibit overdispersion, a condition where the variance exceeds the mean. In our data, this was clearly the case, as CDV-perpetration showed a mean of 3.41 and a variance of 17.6, while CDV-victimization had a mean of 3.73 and a variance of 24.1. These values confirm the presence of overdispersion, justifying the use of the negative binomial model over more restrictive alternatives such as Poisson regression.
To further justify the use of the negative binomial, a model comparison between the negative binomial and the Poisson model was used, supporting the choice of the negative binomial over the Poisson one for both CDV perpetration and victimization. In fact, with regard to CDV perpetration, the negative binomial model provided lower AIC (negative binomial AIC = 4246.45; Poisson model AIC = 6186.52) and BIC (negative binomial BIC 4289.75; Poisson model BIC = 6215.41), with ΔAIC = 1940.07 and ΔBIC = 925.66. Moreover, the Pearson χ2/df for the negative-binomial was 0.842, indicating adequate fit, while the Poisson model showed strong overdispersion (χ2/df = 5.03). Similarly, the negative binomial model for CDV victimization reported a lower AIC (negative binomial AIC = 4350.25; Poisson model AIC = 6900.14) and BIC (negative binomial BIC = 4393.57; Poisson model BIC = 6929.02), with ΔAIC = 2549.89 and ΔBIC = 2535.45. For the negative binomial, the Pearson χ2/df was 0.891, indicating good fit, compared to 6.27 for the Poisson model. In summary, the negative binomial model presents an adequate fit for both forms of CDV.
For the analyses, Version 2.5.5 of jamovi [85] with the GAMLj3 module [86] was used.

3. Results

Descriptive statistics and the correlation among study variables are presented in Table 1.
The negative binomial regression model for the perpetration dimension explained 2.5% of the variance, with R2 = 0.25, χ2 (7) = 25.4, p < 0.001. A positive association was found exclusively between Cyber Dating Violence perpetration and FoMO, with B = 0.03, SE = 0.01, Exp(B) = 1.03, p < 0.001. Furthermore, a statistically significant interaction was observed between FoMO and gender identity, B = 0.03, SE = 0.01, Exp(B) = 1.03, p = 0.02, indicating that with each unit increase in this interaction, the probability of Cyber Dating Violence perpetration rises by a factor of 1.03. The relevant statistics are presented in Table 2.
A slope analysis was carried out to investigate the interaction direction by plotting the values of CDV perpetration against FoMO, distinguishing between the two gender groups (0 = men, 1 = women). The results revealed that for men, the relationship was significantly stronger, with a coefficient of B = 0.40, SE = 0.01, Exp(B) = 1.05, p < 0.001, indicating a 1.05-fold increase in the frequency of CDV perpetration for each unit increase in FoMO. In contrast, the relationship for women was weaker, with B = 0.01, SE = 0.01, Exp(B) = 1.02, p = 0.02, resulting in a 1.02-fold increase per unit of FoMO. These findings highlight that the association between FoMO and CDV perpetration is more pronounced among men. The slope analysis is represented in Figure 1.
Additionally, another significant interaction was observed between FoMO and sexual orientation, with B = 0.02, SE = 0.01, Exp(B) = 1.02, p = 0.02. To better understand the interaction effect, a slope analysis was performed to visualize the association between FoMO and CDV perpetration across the two categories of sexual orientation, 0 = exclusively heterosexual, 1 = sexual minorities. The analysis revealed a more pronounced and statistically significant positive relationship for sexual minorities, B = 0.04, SE = 0.01, Exp(B) = 1.04, p < 0.001, in comparison to exclusively heterosexual individuals, B = 0.02, SE = 0.01, Exp(B) = 1.02, p = 0.01. The slope analysis is presented in Figure 2.
Regarding the overall model for Cyber Dating Violence victimization, it explained the 2.1% of variance, R2 = 0.2, χ2(7) = 21.0, p = 0.004. Cyber Dating Violence victimization showed an association only with the FoMO dimension, B = 0.02, SE = 0.01, Exp(B) = 1.02, p < 0.001, and this suggests that for every additional unit of FoMO, the occurrence of Cyber Dating Violence victimization rises by a factor of 1.02. Relevant statistics are presented in Table 1.

4. Discussion

The aim of this study was to investigate the relationship between Fear of Missing Out (FoMO) and both the perpetration and victimization of Cyber Dating Violence among young adults, while controlling for gender, age, and sexual orientation, as no studies have been conducted on this relevant topic so far.
Regarding the main hypothesis, results indicate a relationship between FoMO and both CDV perpetration and victimization. Consistently with the expectation, FoMO seems to play an important role in influencing CDV dynamics. The association with perpetration can be explained by the fact that FoMO, characterized by the fear of missing out on experiences, is closely linked to insecurity in relationships, which may contribute to maladaptive behaviors [87,88]. Individuals experiencing FoMO might be more prone to obsessively monitoring their partner’s activities on social media, fearing they might miss important information about their partner’s life [89]. Moreover, FoMO is strongly associated with feelings of jealousy and personal insecurity, often rooted in the fear of being replaced or excluded. These emotions could manifest in controlling behaviors, such as monitoring messages, emotional coercion, or even the dissemination of private information to exert control over the relationship. This heightened vigilance can escalate into abusive behaviors such as online stalking [61], persistent demands for updates, or unauthorized access to the partner’s digital devices, eventually leading to the destruction of the partner’s social network, isolating and weakening them, making them even more vulnerable and dependent on the perpetrator. Future studies are needed to confirm these preliminary hypotheses and further deepen the understanding of this topic.
At the same time, higher levels of FoMO are associated with increased time spent online (e.g., three or more hours per day on WhatsApp and Instagram, especially in women), greater insecurity in relationships [90], and it is also linked to sleep disorders, mental health issues, stress and life dissatisfaction [91], which may heighten an individual’s vulnerability to dating violence victimization. The need for constant social connectivity and reassurance could lead individuals to engage in riskier online interactions, lower their boundaries in relationships, or tolerate harmful behaviors out of fear of exclusion or abandonment. Indeed, individuals experiencing FoMO may exhibit a heightened emotional dependency on their partner, making them more likely to tolerate abusive behaviors to avoid exclusion or the end of the relationship. Moreover, the persistent need to stay connected and updated increases digital exposure, making individuals more susceptible to online abuse, such as surveillance, digital control, or cyber harassment. Additionally, the constant search for approval associated with FoMO may lead individuals to comply with harmful requests—such as sharing intimate images or personal information—to gain validation and maintain their sense of connection within the relationship.
Although this study considers perpetration and victimization of CVD as two distinct outcomes, it should be noted that roles are often not so clearly defined and that there is circularity in the dynamics of violence, with perpetrators possibly becoming victims and vice versa [74,77], and this is also confirmed by the high correlation found between the two dimensions in the present study (r = 0.78, p < 0.001). From these preliminary findings, FoMO emerges as a risk factor for CVD overall, while other studies should propose models that refine this view and consider the overlap of roles. For greater interpretative openness, different possible mechanisms underlying the two outcomes have been proposed, including potential psychological mechanisms (e.g., jealousy, emotional dependence) that could link FoMO to specific roles, but these should be understood as theoretical possibilities to be further explored. From these findings, it cannot be argued that FoMO is a separate predictor of perpetration and victimization of CVD, but rather an overall risk factor that, in interaction with other variables, contributes to the experience of CVD.
Regarding the link between individual socio-demographic variables and CDV, some significant interaction effect has been found only for the perpetration dimension, but not for the victimization one. In particular gender and sexual orientation show an interaction effect with FoMO for the perpetration of CDV. Specifically, regarding the gender variable, the findings indicate that this association is more pronounced for men compared to women. As FoMO levels increase, men tend to exhibit a more pronounced increased in CDV perpetration, suggesting that they may be more susceptible to engaging in these behaviors as a response to heightened fears of social exclusion [92,93,94]. In contrast, while the relationship remains significant for women, the increase in CDV perpetration is less pronounced. These results suggest that FoMO may play a differential role in shaping dating behaviors across genders, although it is important to note that, with regard specifically to FoMO, some authors have not found gender differences [95,96], highlighting the need for further research to explore the underlying mechanisms driving these variations.
The moderating effect of sexual orientation is consistent with existing literature [6,53], which indicates that LGB individuals face a higher risk of experiencing dating violence. This vulnerability appears to be further exacerbated by heightened levels of FoMO, suggesting a complex interplay between minority stress, relational insecurities, and digital behaviors that may contribute to increased exposure to Cyber Dating Violence [53,55].

Limitation and Future Perspectives

Despite the innovative findings that provide useful insights for improving prevention programs, this study has several limitations. First, the cross-sectional design restricts the ability to establish causal links between the variables, highlighting the need for future research utilizing more complex models to enhance our understanding of these dynamics. Additionally, the explained variance in the models was relatively low, which, while consistent with prior studies also conducted in a cross-cultural perspective and involving similar variables (e.g., [63,97,98]), can be attributed to the complexity of the behaviors under investigation. Despite the low variance explained by the models, it is believed that the theoretical model is solid and that it could prompt reflection on the need to further investigate the role of FoMO in violent behavior. As such, it is anticipated that future studies incorporating a broader range of variables and more sophisticated methodologies will provide valuable insights into this phenomenon. As the first study to explore this topic, it provides a foundation for future research and deeper investigation into these dynamics.
Future research should explore the differential impact of FoMO on various subtypes of CDV, as distinct mechanisms may underlie each form of digital aggression. Examining these nuanced relationships could provide deeper insights into the psychological pathways linking FoMO to CDV perpetration and victimization, ultimately informing targeted intervention strategies. Furthermore, more complex models should consider the possible circularity of roles in perpetration and victimization of CDV and explore whether FoMO operates primarily as a shared risk factor or whether it contributes to distinct pathways that differentially predict the two outcomes. These findings underscore the importance of directing greater attention to the dynamics of relationships among young people, particularly within the online context. It is anticipated that such attention will facilitate the development of prevention and intervention programs aimed at improving self-esteem and fostering the ability to cultivate relationships grounded in mutual respect, both in digital and offline environments. The results suggest the importance of implementing prevention programs, starting with adolescents, focused on emotional education to teach young people how to establish and build healthy romantic relationships.

5. Conclusions

In summary, this article addresses the possible influence of a digital risk factor, such as FoMO, on the experience of CDV. The results show that FoMO is a significant predictor of both perpetration and victimization of CDV and therefore constitutes an overall risk factor for both outcomes. Regarding hypotheses concerning the role of gender, age, and sexual orientation, it appears that males are more prone to perpetration behaviors, especially in the presence of FoMO. Furthermore, sexual minorities, as previously demonstrated in the literature, appear to show greater vulnerability to engaging in risky and CDV behaviors, especially in the presence of high levels of FoMO. The implications of this preliminary study concern the possibility of paying greater attention to an underestimated risk factor and, hopefully, a greater understanding of the phenomenon of CDV.

Author Contributions

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

Funding

Study partially funded by the project “Sostenibilità e resilienza nei territori montani: istruzione, economia, disuguaglianza, e autonomie regionali” PR FSE+2021/2027 della Regione Autonoma della Valle d’Aosta, Codice avviso 24AJ—Codice progetto FSE.44406.24AJ.0.0001—CUP B63C24001040008, denominazione specifica del programma di ricerca “Violenza di coppia e nuove tecnologie di comunicazione”—Codice: UNIVDA/FAR4/06/2024 [“Sustainability and Resilience in Mountain Areas: Education, Economy, Inequality, and Regional Autonomies” PR FSE+2021/2027 of the Autonomous Region of Valle d’Aosta, Call Code 24AJ—Project Code FSE.44406.24AJ.0.0001—CUP B63C24001040008, specific research program title: "Intimate Partner Violence and New Communication Technologies”—Code: UNIVDA/FAR4/06/2024].

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Ethics Committee of the Department of Dynamic, Clinical and Health Psychology, Sapienza University of Rome (approval code: No. 0000591, approval date: 2022-04-11).

Informed Consent Statement

Written informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to ethical reasons.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Kranenbarg, M.W.; Van Gelder, J.L.; Barends, A.J.; de Vries, R.E. Is there a cybercriminal personality? Comparing cyber offenders and offline offenders on HEXACO personality domains and their underlying facets. Comput. Hum. Behav. 2023, 140, 107576. [Google Scholar] [CrossRef]
  2. Khawrin, M.K.; Nderego, E.F. Psychological challenges of cyberspace: A systematical review of meta-analysis. Indian. J. Health Wellbeing 2022, 13, 294–300. [Google Scholar]
  3. Sontag, L.M.; Clemans, K.H.; Graber, J.A.; Lyndon, S.T. Traditional and cyber aggressors and victims: A comparison of psychosocial characteristics. J. Youth Adolesc. 2011, 40, 392–404. [Google Scholar] [CrossRef] [PubMed]
  4. Caridade, S.; Braga, T.; Borrajo, E. Cyber dating abuse (CDA): Evidence from a systematic review. Aggress. Violent Behav. 2019, 48, 152–168. [Google Scholar] [CrossRef]
  5. Schoffstall, C.L.; Cohen, R. Cyber aggression: The relation between online offenders and offline social competence. Soc. Dev. 2011, 20, 587–604. [Google Scholar] [CrossRef]
  6. Morelli, M.; Nappa, M.R.; Chirumbolo, A.; Wright, P.J.; Pabian, S.; Baiocco, R.; Cattelino, E. Is adolescents’ cyber dating violence perpetration related to problematic pornography use? The moderating role of hostile sexism. J. Health Commun. 2024, 39, 3134–3144. [Google Scholar] [CrossRef]
  7. Hedrick, A. A meta-analysis of media consumption and rape myth acceptance. J. Health Commun. 2021, 26, 645–656. [Google Scholar] [CrossRef]
  8. Hust, S.J.T.; Rodgers, K.B.; Cameron, N.; Li, J. Viewers’ perceptions of objectified images of women in alcohol advertisements and their intentions to intervene in alcohol-facilitated sexual assault situations. J. Health Commun. 2019, 24, 328–338. [Google Scholar] [CrossRef]
  9. Wright, P.J.; Paul, B.; Herbenick, D. Preliminary insights from a US probability sample on adolescents’ pornography exposure, media psychology, and sexual aggression. J. Health Commun. 2021, 26, 39–46. [Google Scholar] [CrossRef] [PubMed]
  10. Cascardi, M.; Avery-Leaf, S. Gender differences in dating aggression and victimization among low-income, urban middle school students. Partn. Abus. 2015, 6, 383–402. [Google Scholar] [CrossRef]
  11. Muñoz-Rivas, M.J.; Redondo, N.; Zamarrón, D.; González, M.P. Violence in dating relationships: Validation of the Dominating and Jealous Tactics Scale in Spanish youth. An. Psicol./Ann. Psychol. 2019, 35, 11–18. [Google Scholar] [CrossRef]
  12. Ybarra, M.L.; Thompson, R.E. Predicting the emergence of sexual violence in adolescence. Prev. Sci. 2018, 19, 403–415. [Google Scholar] [CrossRef]
  13. Park, S.; Kim, S.H. The power of family and community factors in predicting dating violence: A meta-analysis. Aggress. Violent Behav. 2018, 40, 19–28. [Google Scholar] [CrossRef]
  14. Ali, P.; McGarry, J.; Dhingra, K. Identifying signs of intimate partner violence. Emerg. Nurse 2016, 23, 25–29. [Google Scholar] [CrossRef] [PubMed]
  15. Carrascosa, L.; Cava, M.J.; Buelga, S. Perfil psicosocial de adolescentes españoles agresores y víctimas de violencia de pareja. Univ. Psychol. 2018, 17, 183–193. [Google Scholar] [CrossRef]
  16. Cornelius, T.L.; Resseguie, N. Primary and secondary prevention programs for dating violence: A review of the literature. Aggress. Violent Behav. 2007, 12, 364–375. [Google Scholar] [CrossRef]
  17. Herbert, J.L.; Bromfield, L. Better together? A review of evidence for multi-disciplinary teams responding to physical and sexual child abuse. Trauma. Violence Abus. 2019, 20, 214–228. [Google Scholar] [CrossRef]
  18. Pazos, M.; Oliva, A.; Hernando, A. Violencia en relaciones de pareja jóvenes y adolescentes. Rev. Latinoam. Psicol. 2014, 46, 148–159. [Google Scholar] [CrossRef]
  19. Rey Anacona, C.A. Prevalence, risk factors, and problems associated with dating violence: A literature review. Av. En Psicol. Latinoam. 2008, 26, 227–241. Available online: http://repository.urosario.edu.co/handle/10336/15974 (accessed on 20 January 2025).
  20. Wolfe, D.A.; Wekerle, C.; Scott, K.; Straatman, A.L.; Grasley, C. Predicting abuse in adolescent dating relationships over 1 year: The role of child maltreatment and trauma. J. Abnorm. Psychol. 2004, 113, 406. [Google Scholar] [CrossRef]
  21. Magdol, L.; Moffitt, T.E.; Caspi, A.; Newman, D.L.; Fagan, J.; Silva, P.A. Gender differences in partner violence in a birth cohort of 21-year-olds: Bridging the gap between clinical and epidemiological approaches. J. Consult. Clin. Psychol. 1997, 65, 68. [Google Scholar] [CrossRef]
  22. Schwartz, J.P.; Magee, M.M.; Griffin, L.D.; Dupuis, C.W. Effects of a group preventive intervention on risk and protective factors related to dating violence. Group. Dyn. Theory Res. Pract. 2004, 8, 221. [Google Scholar] [CrossRef]
  23. 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]
  24. De Los Reyes, V.; Jaureguizar, J.; Redondo, I. Cyberviolence in young couples and its predictors. Behav. Psychol./Psicol. Conduct. 2022, 30, 391–410. [Google Scholar] [CrossRef]
  25. Ollen, E.W.; Ameral, V.E.; Palm Reed, K.; Hines, D.A. Sexual minority college students’ perceptions on dating violence and sexual assault. J. Couns. Psychol. 2017, 64, 112–119. [Google Scholar] [CrossRef]
  26. Morelli, M.; Bianchi, D.; Chirumbolo, A.; Baiocco, R. The cyber dating violence inventory: Validation of a new scale for online perpetration and victimization among dating partners. Eur. J. Dev. Psychol. 2018, 15, 464–471. [Google Scholar] [CrossRef]
  27. Borrajo, E.; Gámez-Guadix, M. Abuso” online” en el noviazgo: Relación con depresión, ansiedad y ajuste diádico. Behav. Psychol. 2016, 24, 221. Available online: http://hdl.handle.net/10486/679217 (accessed on 17 January 2025).
  28. Draucker, C.B.; Martsolf, D.S. The role of electronic communication technology in adolescent dating violence. J. Child Adolesc. Psychiatr. Nurs. 2010, 23, 133–142. [Google Scholar] [CrossRef] [PubMed]
  29. Fernet, M.; Lapierre, A.; Hébert, M.; Cousineau, M.M. A systematic review of literature on cyber intimate partner victimization in adolescent girls and women. Comput. Hum. Behav. 2019, 100, 11–25. [Google Scholar] [CrossRef]
  30. Zweig, J.M.; Dank, M.; Yahner, J.; Lachman, P. The rate of cyber dating abuse among teens and how it relates to other forms of teen dating violence. J. Youth Adolesc. 2013, 42, 1063–1077. [Google Scholar] [CrossRef]
  31. Rodríguez-deArriba, M.L.; Nocentini, A.; Menesini, E.; Sánchez-Jiménez, V. Dimensions and measures of cyber dating violence in adolescents: A systematic review. Aggress. Violent Behav. 2021, 58, 101613. [Google Scholar] [CrossRef]
  32. Flach, R.M.D.; Deslandes, S.F. Cyber dating abuse in affective and sexual relationships: A literature review. Cad. Saúde Pública 2017, 33, e00138516. [Google Scholar] [PubMed]
  33. Galende, N.; Ozamiz-Etxebarria, N.; Jaureguizar, J.; Redondo, I. Cyber dating violence prevention programs in universal populations: A systematic review. Psychol. Res. Behav. Manag. 2020, 13, 1089–1099. [Google Scholar] [CrossRef] [PubMed]
  34. Patchin, J.W.; Hinduja, S. Sextortion among adolescents: Results from a national survey of US youth. Sex. Abus. 2020, 32, 30–54. [Google Scholar] [CrossRef] [PubMed]
  35. Smith, K.; Cénat, J.M.; Lapierre, A.; Dion, J.; Hébert, M.; Côté, K. Cyber dating violence: Prevalence and correlates among high school students from small urban areas in Quebec. J. Affect. Disord. 2018, 234, 220–223. [Google Scholar] [CrossRef] [PubMed]
  36. Van Ouytsel, J.; Ponnet, K.; Walrave, M. Cyber dating abuse: Investigating digital monitoring behaviors among adolescents from a social learning perspective. J. Interpers. Violence 2017, 35, 5157–5178. [Google Scholar] [CrossRef]
  37. Capaldi, D.M.; Knoble, N.B.; Shortt, J.W.; Kim, H.K. A systematic review of risk factors for intimate partner violence. Partn. Abus. 2012, 3, 231–280. [Google Scholar] [CrossRef] [PubMed]
  38. Arnett, J.J. Emerging adulthood: A theory of development from the late teens through the twenties. Am. Psychol. 2000, 55, 469–480. [Google Scholar] [CrossRef] [PubMed]
  39. Kohut, A.; Keeter, S.; Doherty, C.; Dimock, M.; Christian, L. Assessing the Representativeness of Public Opinion Surveys; Pew Research Internet Project; Pew Research Center: Washington, DC, USA, 2012. [Google Scholar]
  40. Leisring, P.A.; Giumetti, G.W. Sticks and stones may break my bones, but abusive text messages also hurt: Development and validation of the Cyber Psychological Abuse scale. Partn. Abus. 2014, 5, 323–341. [Google Scholar] [CrossRef]
  41. Cava, M.J.; Martínez-Ferrer, 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, 111, 106449. [Google Scholar] [CrossRef]
  42. Hancock, K.; Keast, H.; Ellis, W. The impact of cyber dating abuse on self-esteem: The mediating role of emotional distress. Cyberpsychol. J. Psychosoc. Res. Cyberspace 2017, 11, 2. [Google Scholar] [CrossRef]
  43. Taquette, S.R.; Monteiro, D.L.M. Causes and consequences of adolescent dating violence: A systematic review. J. Inj. Violence Res. 2019, 11, 137. [Google Scholar] [CrossRef]
  44. Aghtaie, N.; Larkins, C.; Barte, C.; Stanley, N.; Wood, M.; Øverlien, C. Interpersonal violence and abuse in young people’s relationships in five European countries: Online and offline normalisation of heteronormativity. J. Gend.-Based Violence 2018, 2, 293–310. [Google Scholar] [CrossRef]
  45. Stonard, K.E. The prevalence and overlap of technology-assisted and offline adolescent dating violence. Curr. Psychol. 2021, 40, 1056–1070. [Google Scholar] [CrossRef]
  46. Morelli, M.; Bianchi, D.; Cattelino, E.; Nappa, M.R.; Baiocco, R.; Chirumbolo, A. Quando il Sexting diventa una forma di violenza? Motivazioni al sexting e dating violence nei giovani adulti. Maltrattam. Abus. Infanz. 2017, 19, 49–68. [Google Scholar] [CrossRef]
  47. Temple, J.R.; Choi, H.J.; Brem, M.; Wolford-Clevenger, C.; Stuart, G.L.; Peskin, M.F.; Elmquist, J. The temporal association between traditional and cyber dating abuse among adolescents. J. Youth Adolesc. 2016, 45, 340–349. [Google Scholar] [CrossRef] [PubMed]
  48. Young, A.M.; King, L.; Abbey, A.; Boyd, C.J. Adolescent peer-on-peer sexual aggression: Characteristics of aggressors of alcohol and non-alcohol related assault. J. Stud. Alcohol. Drugs 2009, 70, 700–703. [Google Scholar] [CrossRef][Green Version]
  49. 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]
  50. Reed, L.A.; Ward, L.M.; Tolman, R.M.; Lippman, J.R.; Seabrook, R.C. The association between stereotypical gender and dating beliefs and digital dating abuse perpetration in adolescent dating relationships. J. Interpers. Violence 2021, 36, NP5561–NP5585. [Google Scholar] [CrossRef]
  51. Van Ouytsel, J.; Ponnet, K.; Walrave, M. Cyber dating abuse victimization among secondary school students from a lifestyle-routine activities theory perspective. J. Interpers. Violence 2018, 33, 2767–2776. [Google Scholar] [CrossRef]
  52. Machado, B.; Caridade, S.; Araújo, I.; Lobato, P. Mapping the cyber interpersonal violence among young populations: A scoping review. Soc. Sci. 2022, 11, 207. [Google Scholar] [CrossRef]
  53. Dank, P.; Lachman, P.; Zweig, J.M.; Yahner, J. Dating violence experiences of lesbian, gay, bisexual, and transgender youth. J. Youth Adolesc. 2014, 43, 846–857. [Google Scholar] [CrossRef]
  54. Llorent, V.J.; Ortega-Ruiz, R.; Zych, I. Bullying and cyberbullying in minorities: Are they more vulnerable than the majority group? Front. Psychol. 2016, 7, 1507. [Google Scholar] [CrossRef]
  55. Martin-Storey, A. Prevalence of dating violence among sexual minority youth: Variation across gender, sexual minority identity and gender of sexual partners. J. Youth Adolesc. 2015, 44, 211–224. [Google Scholar] [CrossRef]
  56. Reisner, S.L.; Greytak, E.A.; Parsons, J.T.; Ybarra, M.L. Gender minority social stress in adolescence: Disparities in adolescent bullying and substance use by gender identity. J. Sex. Res. 2015, 52, 243–256. [Google Scholar] [CrossRef] [PubMed]
  57. Birkett, M.; Espelage, D.L.; Koenig, B. LGB and questioning students in schools: The moderating effects of homophobic bullying and school climate on negative outcomes. J. Youth Adolesc. 2009, 38, 989–1000. [Google Scholar] [CrossRef] [PubMed]
  58. Elhai, J.D.; Yang, H.; Montag, C. Fear of missing out (FOMO): Overview, theoretical underpinnings, and literature review on relations with severity of negative affectivity and problematic technology use. Braz. J. Psychiatry 2020, 43, 203–209. [Google Scholar] [CrossRef]
  59. Przybylski, A.K.; Murayama, K.; DeHaan, C.R.; Gladwell, V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput. Hum. Behav. 2013, 29, 1841–1848. [Google Scholar] [CrossRef]
  60. Hussain, S.; Raza, A.; Haider, A.; Ishaq, M.I. Fear of missing out and compulsive buying behavior: The moderating role of mindfulness. J. Retail. Consum. Serv. 2023, 75, 103512. [Google Scholar] [CrossRef]
  61. Tandon, A.; Dhir, A.; Talwar, S.; Kaur, P.; Mäntymäki, M. Dark consequences of social media-induced fear of missing out (FoMO): Social media stalking, comparisons, and fatigue. Technol. Forecast. Soc. Change 2021, 171, 120931. [Google Scholar] [CrossRef]
  62. Geng, J.; Bao, L.; Wang, H.; Wang, J.; Wei, X.; Lei, L. The relationship between childhood maltreatment and adolescents’ cyberbullying victimization: The new phenomenon of a “cycle of victimization”. Child. Abuse Negl. 2022, 134, 105888. [Google Scholar] [CrossRef] [PubMed]
  63. Morelli, M.; Rosati, F.; Chirumbolo, A.; Baiocco, R.; Nappa, M.R.; Cattelino, E. Fear of Missing Out (FoMO) and sexting motivations among Italian young adults: Investigating the impact of age, gender, and sexual orientation. J. Soc. Pers. Relationsh. 2025, 42, 633–654. [Google Scholar] [CrossRef]
  64. Sha, P.; Sariyska, R.; Riedl, R.; Lachmann, B.; Montag, C. Linking internet communication and smartphone use disorder by taking a closer look at the Facebook and WhatsApp applications. Addict. Behav. Rep. 2019, 9, 100148. [Google Scholar] [CrossRef]
  65. Benzi, I.M.A.; Fontana, A.; Lingiardi, V.; Parolin, L.; Carone, N. “Don’t Leave me Behind!” Problematic Internet Use and Fear of Missing Out Through the Lens of Epistemic Trust in Emerging Adulthood. Curr. Psychol. 2024, 43, 13775–13784. [Google Scholar] [CrossRef]
  66. Lai, C.; Altavilla, D.; Ronconi, A.; Aceto, P. Fear of missing out (FOMO) is associated with activation of the right middle temporal gyrus during inclusion social cue. Comput. Hum. Behav. 2016, 61, 516–521. [Google Scholar] [CrossRef]
  67. Eisenberger, N.I.; Lieberman, M.D. Why rejection hurts: A common neural alarm system for physical and social pain. Trends Cogn. Sci. 2004, 8, 294–300. [Google Scholar] [CrossRef] [PubMed]
  68. Lam, L.; Liu, Y. The identity-based explanation of affective commitment. J. Manag. Psychol. 2014, 29, 321–340. [Google Scholar] [CrossRef]
  69. Abrams, D. Social identity, self as structure and self as process. In Social Groups and Identities: Developing the Legacy of Henri Tajfel; Robinson, W.P., Ed.; Butterworth-Heinemann: Oxford, UK, 1996; pp. 143–167. [Google Scholar]
  70. Shahzadi, N.; Asim, S.; Toor, M.A.; Arshad, M. Five Factor Model of Personality as a Predicator of Callous-Unemotional Traits among Students. Pak. J. Psychol. Res. 2024, 39, 663–675. [Google Scholar] [CrossRef]
  71. Abramsky, T.; Watts, C.H.; Garcia-Moreno, C.; Devries, K.; Kiss, L.; Ellsberg, M.; Jansen, H.A.; Heise, L. What factors are associated with recent intimate partner violence? Findings from the WHO multi-country study on women’s health and domestic violence. BMC Public. Health 2011, 11, 109. [Google Scholar] [CrossRef] [PubMed]
  72. Breiding, M.J.; Black, M.C.; Ryan, G.W. Prevalence and risk factors of intimate partner violence in eighteen US states/territories, 2005. Am. J. Prev. Med. 2008, 34, 112–118. [Google Scholar] [CrossRef]
  73. Xu, X.; Zhu, F.; O’Campo, P.; Koenig, M.A.; Mock, V.; Campbell, J. Prevalence of and risk factors for intimate partner violence in China. Am. J. Public. Health 2005, 95, 78–85. [Google Scholar] [CrossRef]
  74. Cheung, N.W.; Yao, W. Assessing the Victim–Perpetrator Overlap in Adolescent Dating Violence in China: A Latent Class Analysis. J. Interpers. Violence 2024. [Google Scholar] [CrossRef]
  75. Park, S.; Kim, S.H. Who are the victims and who are the perpetrators in dating violence? Sharing the role of victim and perpetrator. Trauma. Violence Abus. 2019, 20, 732–741. [Google Scholar] [CrossRef]
  76. Reingle, J.M.; Staras, S.A.; Jennings, W.G.; Branchini, J.; Maldonado-Molina, M.M. The relationship between marijuana use and intimate partner violence in a nationally representative, longitudinal sample. J. Interpers. Violence 2012, 27, 1562–1578. [Google Scholar] [CrossRef] [PubMed]
  77. Reingle, J.M. Victim–offender overlap. In Encyclopedia of Theoretical Criminology; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2014; Volume 1, pp. 1–3. [Google Scholar] [CrossRef]
  78. Edwards, K.M. Incidence and outcomes of dating violence victimization among high school youth: The role of gender and sexual orientation. J. Interpers. Violence 2018, 33, 1472–1490. [Google Scholar] [CrossRef]
  79. Morelli, M.; Bianchi, D.; Baiocco, R.; Pezzuti, L.; Chirumbolo, A. Sexting, psychological distress and dating violence among adolescents and young adults. Psicothema 2016, 28, 137–142. [Google Scholar] [CrossRef]
  80. Li, J.; Ran, G.; Zhang, Q.; He, X. The prevalence of cyber dating abuse among adolescents and emerging adults: A meta-analysis. Comput. Hum. Behav. 2023, 144, 107726. [Google Scholar] [CrossRef]
  81. Kinsey, A.C.; Pomeroy, W.R.; Martin, C.E. Sexual behavior in the human male. Am. J. Public Health 2003, 93, 894–898. [Google Scholar] [CrossRef] [PubMed]
  82. Nappa, M.R.; Morelli, M.; Bianchi, D.; Baiocco, R.; Cattelino, E.; Chirumbolo, A. The dark side of homophobic bullying: The moderating role of dark triad traits in the relationship between victim and perpetrator. Rass. Psicol. 2019, 36, 17–32. [Google Scholar] [CrossRef]
  83. Casale, S.; Fioravanti, G. Factor structure and psychometric properties of the Italian version of the fear of missing out scale in emerging adults and adolescents. Addict. Behav. 2020, 102, 106179. [Google Scholar] [CrossRef]
  84. Hilbe, J.M. Negative Binomial Regression; Cambridge University Press: Cambridge, UK, 2011. [Google Scholar] [CrossRef]
  85. Jamovi, version 2.3; Computer Software; The Jamovi Project: Sydney, Australia, 2023.
  86. Gallucci, M. GAMLj: General Analyses for the Linear Model in Jamovi. Available online: https://gamlj.github.io/ (accessed on 20 January 2025).
  87. Alfasi, Y. Attachment insecurity and social media fear of missing out: The mediating role of intolerance of uncertainty. Digit. Psychol. 2021, 2, 11–18. [Google Scholar] [CrossRef]
  88. Durao, M.; Etchezahar, E.; Albalá Genol, M.Á.; Muller, M. Fear of missing out, emotional intelligence and attachment in older adults in Argentina. J. Intell. 2023, 11, 22. [Google Scholar] [CrossRef]
  89. Wang, L.; Yan, S.; Wang, Y.; Qiu, J.; Zhang, Y. Do mobile social media undermine our romantic relationships? The influence of fear-of-missing-out on young people’s romantic relationships. In Proceedings of the Information Behaviour Conference (ISIC), Berlin, Germany, 26–29 September 2022. [Google Scholar]
  90. Varchetta, M.; Fraschetti, A.; Mari, E.; Giannini, A.M. Social Media Addiction, Fear of Missing Out (FoMO) and online vulnerability in university students. Rev. Digit. Investig. Docencia Univ. 2020, 14, e1187. [Google Scholar] [CrossRef]
  91. Soriano-Sánchez, J.G. Factores psicológicos y consecuencias del Síndrome Fear of Missing Out: Una Revisión Sistemática. J. Psychol. Educ./Rev. De Psicol. Y Educ. 2022, 17, 1. [Google Scholar] [CrossRef]
  92. Brinker, V.; Dewald-Kaufmann, J.; Padberg, F.; Reinhard, M.A. Aggressive intentions after social exclusion and their association with loneliness. Eur. Arch. Psychiatry Clin. Neurosci. 2023, 273, 1023–1028. [Google Scholar] [CrossRef]
  93. Shuai, J.; Cai, G.; Wei, X. Social exclusion and social anxiety among Chinese undergraduate students: Fear of negative evaluation and resilience as mediators. J. Psychol. Afr. 2024, 34, 44–51. [Google Scholar] [CrossRef]
  94. Twenge, J.M.; Baumeister, R.F. Social exclusion increases aggression and self-defeating behavior while reducing intelligent thought and prosocial behavior. In Social Psychology of Inclusion and Exclusion; Psychology Press: Hove, UK, 2004; pp. 45–64. [Google Scholar]
  95. Rozgonjuk, D.; Sindermann, C.; Elhai, J.D.; Montag, C. Fear of Missing Out (FoMO) and social media’s impact on daily-life and productivity at work: Do WhatsApp, Facebook, Instagram, and Snapchat Use Disorders mediate that association? Addict. Behav. 2020, 110, 106487. [Google Scholar] [CrossRef]
  96. Santana-Vega, L.E.; Gómez-Muñoz, A.M.; Feliciano-García, L. Adolescents Problematic Mobile Phone Use, Fear of Missing out and Family Communication. Comun. Media Educ. Res. J. 2019, 27, 39–47. [Google Scholar] [CrossRef]
  97. Morelli, M.; Urbini, F.; Bianchi, D.; Baiocco, R.; Cattelino, E.; Laghi, F.; Chirumbolo, A. The relationship between dark triad personality traits and sexting behaviors among adolescents and young adults across 11 countries. Int. J. Environ. Res. Public. Health 2021, 18, 2526. [Google Scholar] [CrossRef] [PubMed]
  98. Morelli, M.; Plata, M.G.; Isolani, S.; Zabala, M.E.Z.; Hoyos, K.P.C.; Tirado, L.M.U.; Baiocco, R. Sexting behaviors before and during COVID-19 in Italian and Colombian young adults. Sex. Res. Soc. Policy 2023, 20, 1515–1527. [Google Scholar] [CrossRef] [PubMed]
Societies 15 00258 g001
Figure 1. Slope Analysis for CDV perpetration and FoMO in function of gender. Gender (0 = men; 1 = women). The gray shadow indicates the confidence interval (CI) at each point of the slope.
Figure 1. Slope Analysis for CDV perpetration and FoMO in function of gender. Gender (0 = men; 1 = women). The gray shadow indicates the confidence interval (CI) at each point of the slope.
Societies 15 00258 g001
Societies 15 00258 g002
Figure 2. Slope Analysis for CDV perpetration and FoMO in function of sexual orientation. Sexual orientation (0 = exclusively heterosexual; 1= sexual minorities). The gray shadow indicates the confidence interval (CI) at each point of the slope.
Figure 2. Slope Analysis for CDV perpetration and FoMO in function of sexual orientation. Sexual orientation (0 = exclusively heterosexual; 1= sexual minorities). The gray shadow indicates the confidence interval (CI) at each point of the slope.
Societies 15 00258 g002
Table 1. Descriptive statistics and correlations among variables.
Table 1. Descriptive statistics and correlations among variables.
123456Mean (SD)
1.CDV perpetration1 3.41 (4.20)
2.CDV victimization0.78 ***1 3.73 (4.91)
3.FoMO0.13 ***0.13 ***1 24.3 (8.31)
4. Age−0.07 *−0.09 **−0.21 ***1 22.3 (2.57)
5.Gender0.71 *−0.030.92 **−0.061 /
6.Sexual orientation0.030.020.13 ***−0.020.14 ***1/
Note. * p < 0.05, ** p < 0.01, *** p < 0.001. CDV = Cyber Dating Violence; FoMO = Fear of Missing Out; Gender coded 0 for men and 1 for women; Sexual orientation coded 0 for heterosexual participants and 1 for sexual minorities.
Table 2. Negative Binomial Regression Analysis for CDV dimensions.
Table 2. Negative Binomial Regression Analysis for CDV dimensions.
Cyber Dating Violence PerpetrationCyber Dating Violence Victimization
PredictorsBSEExp(B)LCIUCIpBSEExp(B)LCIUCIp
Age−0.010.020.980.951.020.41−0.040.020.960.931.000.05
Gender−0.130.100.880.721.070.200.160.111.170.951.450.13
SO0.020.101.020.841.250.800.040.101.040.851.280.68
FoMO0.030.011.031.021.04<0.0010.020.011.021.011.04<0.001
FoMO*Age0.000.001.001.001.010.380.000.001.001.001.050.85
FoMO*Gender0.030.011.031.001.060.020.010.011.020.991.040.22
FoMO*SO0.020.011.021.001.050.030.010.011.010.991.030.40
Note. Bold formatting is used to highlight significant results. FoMO = Fear of Missing Out; SO = Sexual orientation; LCI = Low confidence interval; UCI = Upper confidence interval. Gender was classified as 0 for men and 1 for women; Regarding Sexual orientation, 0 was used for heterosexual participants and 1 for sexual minorities.
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Ragona, A.; Morelli, M.; Ruggeri, L.; Cattelino, E.; Chirumbolo, A. FoMO as a Predictor of Cyber Dating Violence Among Young Adults: Understanding Digital Risk Factors in Romantic Relationships. Societies 2025, 15, 258. https://doi.org/10.3390/soc15090258

AMA Style

Ragona A, Morelli M, Ruggeri L, Cattelino E, Chirumbolo A. FoMO as a Predictor of Cyber Dating Violence Among Young Adults: Understanding Digital Risk Factors in Romantic Relationships. Societies. 2025; 15(9):258. https://doi.org/10.3390/soc15090258

Chicago/Turabian Style

Ragona, Alessandra, Mara Morelli, Lia Ruggeri, Elena Cattelino, and Antonio Chirumbolo. 2025. "FoMO as a Predictor of Cyber Dating Violence Among Young Adults: Understanding Digital Risk Factors in Romantic Relationships" Societies 15, no. 9: 258. https://doi.org/10.3390/soc15090258

APA Style

Ragona, A., Morelli, M., Ruggeri, L., Cattelino, E., & Chirumbolo, A. (2025). FoMO as a Predictor of Cyber Dating Violence Among Young Adults: Understanding Digital Risk Factors in Romantic Relationships. Societies, 15(9), 258. https://doi.org/10.3390/soc15090258

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