The mediating role of cyber victimization in the effect of perceived social support on cyberbullying in university students

Abstract

Background

This study aimed to determine the mediating role of cyber victimization in the effect of perceived social support on cyberbullying among university students.

Methods

This descriptive and cross-sectional study used the STROBE checklist. The population of this cross-sectional study consisted of 864 students studying at the health sciences faculty of a state university. Descriptive information form, Revised Cyberbullying Inventory for University Students and Multidimensional Scale of Perceived Social Support were used as data collection tools.

Results

As the perceived social support level increases, the level of cyber victimization decreases. As the level of cyber victimization of individuals increases, the level of cyberbullying also increases. As the perceived social support level increases, the level of cyberbullying decreases. While perceived social support has a reducing effect on cyberbullying, cyber victimization functions to reduce this effect.

Conclusions

Social support is an important factor in reducing cyberbullying; however, cyber victimization reduces this positive effect. It is recommended to carry out studies that will activate the social support resources of university students, to increase the activities that will support social relations in universities, to support people who have experienced cyber victimization to share their experiences and to receive psychological help when necessary.

Introduction

With the rapidly developing information technology in recent years, people have the opportunity to communicate with each other easily. Applications on technological devices such as tablets, smartphones and computers offer people many opportunities such as video and photo sharing, video chat, and messaging. While these opportunities have positive aspects such as better time management, improved communication, fast access to information and news, and entertainment, cyber technology also has negative individual and social consequences. One of these negative consequences is cyberbullying, which is common especially among young people and individuals exposed to cyberbullying are referred to as cyber victims [1–3].

Cyberbullying is defined as all the voluntary and repeated harming behaviors against individuals or groups through computers, mobile phones and other communication technologies [4–7]. Unlike other forms of bullying, cyberbullying quickly reaches a much wider audience by exceeding the limits of time as well as physical and personal space [8]. Cyberbullies victimize users in various ways in the internet environment [9]. Cyberbullying manifests in multiple forms, including harassment, threats, social exclusion, humiliation, exposure of private or sensitive information, defamation, and even blackmail. Recent research underscores the diversity and severity of these tactics. For instance, Brewer and Kerslake (2020) identify cyber harassment, denigration, and outing as prevalent forms among adolescents [10] Similarly, Kowalski et al. (2021) highlight impersonation and flaming as other common strategies used by perpetrators to inflict psychological harm [11]. In addition, Patchin and Hinduja (2020) emphasize the rise of digital blackmail (also known as sextortion) as a particularly harmful form of cyber aggression, especially among youth [12]. Cyberbullying encompasses a wide array of aggressive online behaviors, including but not limited to direct harassment, social exclusion, public humiliation, impersonation, non-consensual sharing of images or information, defamation, cyberstalking, and sextortion. For example, cyber harassment, defined as the repeated sending of threatening or abusive messages, is one of the most common forms and often occurs on social media platforms [13]. Another prevalent form is doxxing, where private information such as home addresses, phone numbers, or school details is publicly shared without consent [14].

Impersonation—whereby a cyberbully hijacks a victim’s account or creates a fake profile to post harmful content under their name—is recognized as a serious form of cyberbullying that can inflict significant psychological and reputational harm, particularly among young people [15, 16]. Furthermore, image‑based sexual abuse including the non‑consensual creation, distribution, or threat to share intimate images has become a growing concern, with women and younger people reporting more severe psychological impacts than men [17, 18]. In more severe cases, cyberbullying escalates into sextortion, in which perpetrators threaten to release sexual images unless certain demands often sexual or financial are met [19]. In cyber victimization, an individual or group, a private or legal entity, is exposed to harmful behaviors through information and communication technologies in a technical or relational manner and suffers material or moral victimization from these behaviors [20].

Studies on cyberbullying and cyber victimization show that the problem is widespread in the world and in Türkiye. The rate of cyberbullying in the United States ranges from 9.1 to 23.1% while cyber victimization is reported in the range of 5.7%-18.3% [21]. A study conducted with university students in China concluded that 64.3% of the students experienced cyber victimization [22]. Both cyberbullying and victimization rates were reported as 15%-16% in a meta-analysis study [23]. A study conducted with university students in Türkiye found the rate of cyberbullying as 22.5% and the rate of being cyberbullied at least once as 55.3% [24]. The study conducted by Rodop et al. (2022) with university students concluded that 72.3% of the students were exposed to cyberbullying at least once and 64.3% engaged in cyberbullying behavior [25]. Another study found that 57% of students cyberbullied someone at least once in the last 6 months and 68.8% were exposed to cyberbullying at least once [26]. Cyberbullying and cyber-victimization are common during university years, a period of transition from adolescence to young adulthood [27].

There are studies in the literature examining the relationship between cyber bullying and cyber victimization. One study found a linear and significant relationship between cyberbullying and cyber victimization and concluded that cyber victimization is the most important independent variable in explaining cyberbullying [28]. Studies conducted with both high school and university students showed that increases in cyber victimization increased students’ cyberbullying behaviors [25, 29–34].

Both cyberbullies and victims are affected by this bullying and victimization in behavioral, emotional, physical, academic, psychological and interpersonal areas [35, 36]. Both cyberbullying and cyber-victims are reported to experience decreased academic achievement, low self-esteem, intense feelings of loneliness, and psychological problems such as anxiety, depression and substance and alcohol use [37, 38]. The rate of suicide attempts was found to increase in university students experiencing these psychological problems and cyber victimization [39]. Perceived social support is thought to be an important factor in dealing with these hardships.

Perceived social support is defined as the perception that an individual is cared for and valued by his/her family, peers, and teachers. It is stated that social support is a protective factor against the consequences of peer victimization [40]. Perceived social support is a concept that is closely related to both being a cyberbully and being a victim of cyberbullying and has a preventive effect on both cyberbullying and cyber victimization [41].

Studies show that social support is effective on cyberbullying and cyber victimization. In their study with high school students, Eroğlu and Peker (2011) found that perceived social support from the family negatively predicted cyber victimization while cyber victimization also negatively predicted perceived social support from peers [42]. The frequency of cyberbullying decreases for students with high family support [41, 43]. On the other hand, another study found that as cyberbullying and victimization increased, perceived social support from peers and teachers decreased but there was no significant relationship between cyberbullying and victimization and perceived social support from the family [31]. A study conducted with university students concluded that students with weak family relationships displayed more cyberbullying behaviors [27]. Different studies conducted with university students showed the protective role of social support against cyber victimization and reported that both the students experiencing cyber victimization had low perceived social support from their peers and significant others and students engaging in cyberbullying behaviors also had low perceived social support from significant others and their families [44]. This result emphasizes the importance of social support perceived from family members, peers or close friends not only during childhood and adolescence, but also during the university years, regarded as the transition period to adulthood [8]. Perceived social support reduces cyber victimization and depressive symptoms associated with it [45] and reduces psychological problems such as stress, depression, anxiety and suicidal ideation [39]. The protective role of social support in regard to cyberbullying and cyber victimization is evident. From social support sources, individuals will have a sense of value and acceptance and a sense of security. This will also help the person to cope with stressful situations. Family members’ communication skills, listening and speaking skills, and respect play an important role to prevent cyberbullying and victimization. By increasing awareness of social support resources and establishing a good social support system is important [45].

There are studies in literature examining the relationship between cyberbullying and victimization, and the effect of social support on cyberbullying and cyber victimization. However, it is not clear whether cyber victimization mediates the effect of perceived social support on cyberbullying. The effects of cyberbullying can be reduced with social support. However, the negative emotions associated with cyberbullying may cause victims to be unable to express and suppress their experiences. Therefore, this study was planned to examine how cyber victimization mediates the effect of university students’ perceived social support on cyberbullying.

Figure-1 presents the theoretical model and hypotheses of the research based on the causal and mediating relationship between perceived social support, cyber bullying and cyber victimization. The research model addressed whether cyber victimization mediated the effect of perceived social support on cyberbullying. In this direction, the hypotheses put forward in the research were developed based on the aforementioned literature.

Fig. 1.

Research Model and Hypotheses. X: social support, Y: cyberbullying, M: Cyber victimization. In the proposed model, social support (X) is hypothesized to influence cyberbullying (Y), with cyber victimization (M) functioning as a mediating variable

H1

“Perceived Social Support” is effective on “Cyber Victimization”.

H2

“Cyber Victimization” is effective on “Cyber Bullying”.

H3

“Perceived Social Support” is effective on “Cyberbullying”.

H4

“Cyber Victimization” has a mediating role in the relationship between “Perceived Social Support” and “Cyber Bullying”.

Methods

This cross-sectional study was reported using the STROBE checklist. The population of the study consisted of students in the Faculty of Health Sciences of a state university in the 2022–2023 academic year. The study employed a convenience sampling method. The Faculty of Health Sciences has departments of physical therapy and rehabilitation, nursing, health management, speech and language therapy, and audiology with 1426 students in total.

The data were collected between November-December 2022. The data collection process was conducted face-to-face with a paper questionnaire and in the classroom. Data collection forms were distributed to 874 students who agreed to participate in the study, and all were returned. Accordingly, the analysis was conducted with 874 data.

Data were collected through the “Descriptive Information Form”, “Revised Cyberbullying Inventory for University Students (RCIUS)” and “Multidimensional Scale of Perceived Social Support (MSPSS)”.

The descriptive information form consists of 12 questions regarding participants’ demographic characteristics and internet usage characteristics.

The “Revised Cyberbullying Inventory for University Students (RCIUS)” is a measurement tool consisting of 12 items that examines the participants’ cyberbullying and cyber victimization experiences. The first part measures cyberbullying and the second part measures the experiences of being cyberbullied. Both parts of the inventory have the same items. Participants are asked to respond to the items in the first part about cyberbullying behaviors with “I did it” if they were engaged in cyberbullying behaviors in the last six months, and to respond to the items in the second part about being victimized with “It happened to me” if they were cyberbullied in the last six months. The RCIUS is scored on a four-point Likert scale (1 = never, 2 = once, 3 = two or three times, 4 = more than three times). High scores from the inventory indicate more frequent cyberbullying or cyberbullying is cyber victimization. The Cronbach α reliability coefficient for the cyberbullying part of the inventory is .80, and the Cronbach α reliability coefficient for the cyber-victimization part is .73 [46].

The Multidimensional Scale of Perceived Support (MSPSS) was developed as a short and easy-to-use scale by Zimet et al. to evaluate the adequacy of social support received from three different sources [47, 48]. The factor structure and validity-reliability of the MSPSS were conducted in Türkiye, by Eker and Arkar (1995), and the Cronbach α value of the scale was found to be. 85 for university students [49]. It was revised by Eker, Arkar, Yaldız in 2001 [50]. The Multidimensional Scale of Perceived Social Support (MSPSS) is a 12-item scale and the items are scored on a 7-point rating scale ranging from 1 (very strongly disagree) to 7 (very strongly agree) and the lowest score to be obtained from the scale is 12, and the highest score is 84. A high score indicates that the perceived social support is high [50]. The Cronbach α value of the scale was found 0.92 in this study.

Prior to conducting data collection ethics committee approval was obtained from Izmir Bakırçay University Non-Interventional Clinical Research Ethics Committee to conduct the study (Decision no:758, Date: 09.11.2022), institutional permission was obtained from the Dean of the Faculty of Health Sciences of İzmir Bakırçay University (No: E-99838568-000-2200036679, Date: 18.11.2022), for the scales to be used, permission was obtained from the researchers who developed the scales via e-mail and written informed consent was obtained from the participants. Since all participants were over 16 years of age, it was not necessary to obtain permission from their parents.

The Decleration of Helsinki principles were followed throughout the study.

The study data were analyzed with the SPSS 26 package program. In addition, mediator variable analysis was carried out using the “Process Macro” developed by Hayes (2018). Model 4 was used to examine the mediation role. AMOS 23 package program was used for the validity of the measurement tools used in the study [51].

Before the data analysis, kurtosis and skewness values were examined to decide whether the variables had a normal distribution. As a rule of thumb for a normal distribution, skewness and kurtosis values in the range of +/-1.0 or +/-1.5 are considered as a normal distribution. When the relevant values were examined, the skewness and kurtosis values of the variables were found to be within the normal limits. It was also understood that there was no multicollinearity and autocorrelation between the variables in the model. Cronbach’s Alpha values were examined to test the reliability levels of the scales used in the study, and Cronbach α values were found to be sufficient for reliability.

Results

70% of the students were female students. All the students reported using the internet for playing games, watching movies, listening to music, communicating, following social media, researching or sharing content. Of the participants, 5.6% reported spending 1–2 h, 38.4% 3–4 h, 44.6% 5–6 h and 11.5% 7 h or more on the internet (Table 1).

Table 1.

Distribution of students’ characteristics ^*Multiple answers were allowed

Characteristics	n	%
Gender
Female Male	605 259	70 30
Department
Speech and Language Therapy Physiotherapy and Rehabilitation Nursing Audiology Health Management	130 189 341 97 107	15 21.9 39.5 11.2 12.4
Year of study
1st year 2nd year 3rd year 4th year	232 267 207 158	26.9 30.9 24.0 18.3
Daily internet usage time
1–2 h 3–4 h 5–6 h 7 h and more	48 332 385 99	5.6 38.4 44.6 11.5
Total	864	100
Purpose of internet use*
Research/Education	627	72.6
Social media	765	88.5
Communication	620	71.8
Playing games	431	49.9
Listening to music	598	69.2
Watching film	615	71.2

The normal distribution for Revised Cyberbullying Inventory for University Students Cyberbullying Sub-Dimension, Revised Cyberbullying Inventory for University Students Cyber Victimization Sub-Dimension and Multidimensional Perceived Social Support Scale which were used as data collection tools in the study, was checked by examining the kurtosis and skewness values. According to the findings, the data showed normal distribution on both the scales (Table 2).

Table 2.

Normality findings and average values of measurement tools

Measurement tool	Number of items	Skewness	Kurtosis	Cronbach	Min.	Max.	Average	S.D.
Cyberbullying Sub-Dimension	12	0.455	0.965	0.83	12	45	15.1	4.9
Cyber Victimization Sub-Dimension	12	-1.400	1.253	0.83	12	41	17.2	6.1
Multidimensional Perceived Social Support Scale	12	− 0.658	0.379	0.92	12	84	56.2	20.0

S.D.: Standart Deviation

The reliability of the measurement tools was determined by calculating the Cronbach α. The reliability coefficients of the measurement tools used in the study were found to be sufficient. The calculated Cronbach α value was calculated as 0.83 for the “Cyberbullying Sub-Dimension and Cyber Victimization Sub-Dimension”, while it was calculated as 0.92 for the “Multidimensional Scale of Perceived Social Support” (Table 2).

The scores for “Revised Cyberbullying Inventory for University Students-Cyberbullying Sub-Dimension” ranged from 20 to 99 for the sample, and the sample mean was calculated as 15.1 ± 4.9. “Revised Cyberbullying Inventory for University Students-Cyber Victimization Sub-Dimension” scores ranged from 12 to 41, with a sample mean of 17.2 ± 6.1.

The “Multidimensional Scale of Perceived Social Support” scores ranged from 12 to 84 for the sample, and the sample mean was calculated as 56.2 ± 20.0.

Measurement model compatibility of the “Multidimensional Scale of Perceived Social Support”, which consists of 12 items, was tested with second-level multi-factorial DFA. Maximum likelihood calculation method was used due to the normal distribution of the data. The goodness of fit values of the scale were as follows: χ2 /df = 4.03, AGFI = 0.91, CFI = 0.93, GFI = 0.90, RMSA = 0.07, NFI = 0.92. In line with these findings, it was concluded that the fit values were at the desired level, so the measurement model of the original version of the scale was confirmed and the measurement validity was provided for this study.

The model compatibility of the “Revised Cyberbullying Inventory for University Students-Cyberbullying Scale”, which consists of a single factor and 12 items, was tested with the first level single factor DFA. The maximum likelihood calculation method was used since the data had normal distribution. Due to the high level of covariance between the error terms of the items ZY6 – ZY7, the error terms of these items were combined. The goodness of fit values of the scale were as follows: χ2 /df = 4.59, AGFI = 0.94, CFI = 0.94, GFI = 0.96, RMSA = 0.06, NFI = 0.92. In line with these findings, it was concluded that the fit values were at the desired level, thus the original version of the scale was confirmed, and the measurement validity was provided for this study (Fig. 2).

Fig. 2.

Measurement model for the Revised Cyberbullying Inventory for University Students - Cyberbullying Scale (Cyberbullying Scale Measurement Model)

The model compatibility of the “Revised Cyberbullying Inventory for University Students-Cyber Victimization Scale”, which consists of a single factor and 12 items, was tested with the first level single factor DFA. The maximum likelihood calculation method was used since the data had normal distribution. Due to the high level of covariance between the error terms of the items ZU6 – ZU7, the error terms of these items were combined. The goodness of fit values of the scale were as follows after combining the error terms: χ2 /df = 4.35, AGFI = 0.93, CFI = 0.92, GFI = 0.95, RMSA = 0.07, NFI = 0.90. In line with these findings, it was concluded that the fit values were at the desired level, thus the original version of the scale was confirmed, and the measurement validity was provided for this study (Fig. 3).

Fig. 3.

Measurement Model for the Revised Cyberbullying Inventory for University Students – Cyber Victimization Scale (Cyber ​​Victimization Scale Measurement Model)

When the relationship between the measurement tools in the study was examined, it was determined that there was a moderate, negative and statistically significant relationship between “Perceived Social Support” and “Cyber Bullying” (r = − .65; r2 = 0.42; p < .001). According to this finding, as the level of social support increases, the cyberbullying score decreases. When the relationship between “Perceived Social Support” and “Cyber Victimization” was examined, it was determined that there was a low, negative and statistically significant relationship between these two variables (r = − .38; r2 = 0.15; p < .001). According to this finding, as the perceived social support level increases, the cyber victimization score decreases. When the relationship between “Cyber Victimization” and “Cyber Bullying” was examined, it was determined that there was a moderate, positive and statistically significant relationship between these two variables (r = .63; r2 = 0.15; p < .001). According to this finding, as the level of cyberbullying increases, the cyber victimization score also increases.

A regression analysis based on the bootstrap method was conducted to test whether “Cyber Victimization” mediated the effect of “Perceived Social Support” on “Cyber Bullying”. It is argued that the Boostrap method provides more reliable results compared to the traditional method of Baron and Kenny (1986) [52] and the Sobel test [51, 53, 54]. Analyzes were made using Process Macro developed by Hayes [51]. In the analysis, 5000 resampling options were preferred with the bootstrap technique. In the mediation effect analyzes performed with the bootstrap technique, the 95% confidence interval (CI) values obtained as a result of the analysis should not include the zero (0) value so that the research hypothesis can be supported [55]. Table 3; Fig. 1 present the results of the regression analysis performed for this purpose and the summary of the model.

Table 3.

Regression analysis results for the mediation test (n = 864)

	Result Variables
	M (Cyber Victimization)				Y (Cyberbullying)
Predictor Variables		b	S.D.			b	S.D.
X (Perceived Social Support)	a	− 0.115***	0.009		c’	− 0.128***	0.006
M (Cyber Victimization)	-	-	-		b	0.268***	0.126
Constant	İM	23.718***	0.563		İY	17.655***	0.614
		R2 = 0.38				R2 = 0.51
	F(1;862) = 148.721; P < .001				F(2;861) = 444.710; P < .001

***p < .001; S.D.: Standart Deviation. Non-standardized beta coefficients (b) are reported

According to the findings, “Perceived Social Support (X)” affected the mediating variable “Cyber Victimization (M)” at a significant level and negatively (b = − 0.12; t = -12.20; p < .001). As the “Perceived Social Support” score increased, the “Cyber Victimization” score decreased. A one-unit increase in the “Perceived Social Support” score caused a 0.115-unit decrease in the “Cyber Victimization” variable. According to the data obtained, 38% (R2 = 0.38) of the change in the “Cyber Victimization” variable can be explained by “Perceived Social Support”. In line with these findings, the H1 Hypothesis was accepted.

When the effect of the mediator variable “Cyber Victimization” on the outcome variable “Cyberbullying” was examined, it was determined that the path between these variables was statistically positive and significant (b = 0.27; t = 12.61; p < .001). As the “Cyber Victimization” score increased, the “Cyber Bullying” score also increased. A one-unit increase in the “Cyber Victimization” score caused an increase of 0.268 units in the “Cyber Bullying” score. According to the findings, 51% (R2 = 0.51) of the variance in the “Cyber Bullying” variable was due to the “Cyber Victimization” variable. In the light of these findings, the H2 Hypothesis was accepted.

When the total effect of the “Perceived Social Support” variable on the outcome variable “Cyber Bullying” was examined, it was found that this effect was statistically significant and negative (b = − 0.16; t = -24.85; p < .001). According to this finding, as the “Perceived Social Support” score increased, the level of “Cyberbullying” decreased. A one-unit increase in the “Perceived Social Support” variable caused a. 159-unit decrease on the “Cyber Bullying” variable. The direct effect of “Perceived Social Support” in this effect was. 128 units (b = − 0.13; t = -20.12; p < .001). In line with these findings, the H3 Hypothesis was accepted.

According to the findings, the indirect effect of “Perceived Social Support” on “Cyberbullying” was significant, therefore “Cyber Victimization” mediated the relationship between “Perceived Social Support” and “Cyberbullying” (Indirect effect = [b = − 0.03; LLCI = − 0.04; ULCI = − 0.02]). As a result of the bootstrap analysis, corrected bias and accelerated confidence interval values (BCA CI) do not include 0 (zero). The fully standardized effect size of the mediation effect (K2) was 0.13, which can be said to be a high effect size. The indirect effect value (b = − 0.03) was interpreted as a person with a “Perceived Social Support” score one unit higher than the other individual (individuals with high perceived social support have a low level of cyber victimization and those with a high level of cyber victimization have a high level of cyberbullying) having a cyberbullying level that was 0.031 units lower. In other words the mediation analysis indicated a partial complementary mediation effect: perceived social support had a significant direct effect on cyberbullying (β = − 0.128, p < .001) and an additional indirect effect via cyber victimization (a × b = − 0.031, p < .001), with both effects operating in the same (negative) direction. In line with these findings, the H4 Hypothesis was accepted.

Discussion

Model 4 was used in this study to examine the mediating role of cyber victimization in the effect of perceived social support on cyberbullying in 864 university students studying at the Faculty of Health Sciences.

According to the findings of the research, there was a significant and negative relationship between Perceived Social Support and Cyber Victimization and perceived social support had a significant negative effect on cyber victimization (H1).

Studies conducted with both high school and university students showed that as perceived social support increased, cyber victimization decreased. In their study with high school students, Eroğlu and Peker (2011) determined that perceived social support from family negatively predicted cyber victimization [42] and Bingöl and Tanrıkulu (2014) determined that cyber victimization would decrease when perceived social support from friends and teachers increased [31]. A study conducted with university students concluded that higher perceived social support was associated with a 31% lower probability of experiencing cyber victimization. Social support was reported to have a protective effect on cyber victimization [8]. Other studies conducted with university students found a negative significant relationship between social support and cyber victimization [39, 45]. Findings of previous studies support the findings of the present study. Social support is defined as the knowledge that enables the individual to believe that he or she is cared for, loved, valued, and regarded as a part of mutual communication [31]. It can be argued that young people who do not find the feelings of interest, love and worthiness in their relationships may tend to seek them in virtual environments. In this case, it is thought that the time spent on the internet will increase leading to an increase in cyber victimization. The findings show that cyber victims have higher levels of emotional and social loneliness compared to non-victims [8, 45]; the tendency of young people experiencing these feelings to use the internet more [56] and the increase in cyber victimization parallel to the duration of internet use [57] supports these findings.

The research found a moderate, positive and statistically significant relationship between cyber victimization and cyberbullying and determined that as individuals’ cyber victimization levels increased, their cyberbullying levels also increased (H2). In the literature, cyber victimization is stated as a factor that enables the estimation of the level of cyberbullying [30] Studies reported a relationship between cyber victimization and cyber bullying [25, 28, 31, 33] and showed that cyber victimization is an important variable that predicts cyberbullying [28, 32]. A study investigating the causes of cyberbullying connected individuals’ cyberbullying behavior to taking revenge which may explain the relationship between cyber victimization and bullying [58] In addition, cyber victim university students may develop negative emotions such as anger and fear as a result of the emotional and psychological pressure created by bullying and such negative emotions may increase the likelihood of cyber victims to exhibit aggressive behaviors such as cyberbullying [59].

The present study determined that the effect of the perceived social support variable on the cyberbullying variable was statistically significant and negative (H3). According to this finding, as the “Perceived Social Support” score increased, the level of “Cyberbullying” decreased. Studies showed that as family relationships increased, the level of cyberbullying decreased [27, 41, 43]. This finding and the finding that individuals with cyberbullying had low levels of perceived family support this finding of the present study [44].

The research showed the mediating role of “Cyber Victimization” in the relationship between “Perceived Social Support” and “Cyber Bullying”. While perceived social support had a reducing effect on cyberbullying, cyber victimization reduced this effect (H4). Although perceived social support has a reducing effect on individuals’ cyberbullying behavior, the fact that cyber victimization acts as a mediator in this relationship is important in terms of showing that cyber victimization is an important determinant of cyberbullying. Even if the social support of the student experiencing cyber victimization is high, they may engage in cyber bullying with the emotional and psychological burdens of being a cyber victim. These feelings can negatively affect students’ friendship relations and increase the cyberbullying behavior of young people [42] It can be argued that the effect of cyber victimization is important on friendship relations.

Limitations

This study has limitations. This study is based on a local sample drawn from a single faculty of a single university in Türkiye and therefore does not reflect the experiences or characteristics of university students across the country. Also the sample comprised 70% female students, which is consistent with gender distributions commonly found in health sciences programs. For example, a recent study reported that over 90% of both applicants and enrolled nursing students in Southwest England were female [60]. This suggests that gender distribution reflects underlying demographics rather than sampling bias. Nonetheless, because the data were collected from a single faculty at one university, caution is warranted when generalizing these results to a broader university student population that may present different gender compositions. It would be appropriate to conduct studies with universities from different regions with different departments.

Conclusion

Based on the findings of this study, the following conclusions were reached: “Perceived Social Support” is effective on “Cyber Victimization”: As the perceived social support level increases, the level of cyber victimization decreases. “Cyber Victimization” is effective on “Cyber Bullying”: As the level of cyber victimization of individuals increases, the level of cyber bullying also increases. “Perceived Social Support” is effective on “Cyberbullying”: As the perceived social support level increases, the level of cyberbullying decreases. “Cyber Victimization” has a mediating role in the relationship between “Perceived Social Support” and “Cyber Bullying”: While perceived social support has a reducing effect on cyberbullying, cyber victimization functions to reduce this effect. This result reveals the importance of preventing cyberbullying and thus cyber victimization. For this reason, it is thought that it is important to make plans for the prevention of cyberbullying.

In line with these results, it is suggested to conduct studies that will activate the social support sources of young people, to increase the activities to support social relations in universities, to conduct studies examining the effect of cyber victimization on friendship relations, to support people who have experienced cyber victimization to share their experiences and to get psychological help when necessary, and to identify the causes of cyber bullying and cyber victimization with qualitative studies. And also it is suggested that future research should examine different types of social support (e.g. familial, peer) or conduct similar studies across various countries or cultures to provide a broader understanding of the social support framework in the context of cyberbullying. At the institutional level, universities should adopt comprehensive anti-cyberbullying policies. These policies should include the development of confidential reporting systems, procedures for timely intervention, and educational programs on digital citizenship. Institutions should also ensure that these policies are visible, accessible, and actively communicated to students. In addition, the results highlight the importance of providing accessible and youth-sensitive psychological support services for students exposed to cyberbullying. Institutions should collaborate with mental health professionals to develop support mechanisms that are both proactive and responsive to students’ psychological needs. At the national level, accessible support networks such as youth centers, counseling services, and public digital helplines should be established and expanded to promote social support for young people, and these services should be restructured to raise awareness and provide targeted assistance regarding cyberbullying.

Abbreviations

RCIUS

Revised cyberbullying inventory for university students

MSPSS

Multidimensional scale of perceived social support

Author contributions

Pınar Tektaş: Conceptualization, Method, formal analysis, data curation, supervision, writing (reviewing, and editing). Elif Deniz Kaçmaz: Conceptualization, Method, formal analysis, data curation, writing (original draft, reviewing, and editing). The author(s) read and approved the final manuscript.

Funding

No.

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Approval for this study was given by the ethics committee of Izmir Bakırçay University Non-Interventional Clinical Research Ethics Committee to conduct the study (Decision no:758, Date: 09.11.2022), institutional permission was obtained from the Dean of the Faculty of Health Sciences of İzmir Bakırçay University (No: E-99838568-000-2200036679, Date: 18.11.2022), for the scales to be used, permission was obtained from the researchers who developed the scales via e-mail and written informed consent was obtained from the participants. Since all participants were over 16 years of age, it was not necessary to obtain permission from their parents.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.