Cyberbullying and cybervictimization: Does gender matter?

Abstract

The purpose of the present study was to evaluate gender effects on college students’ judgements about a hypothetical episode of cyberbullying on Facebook that resulted in the suicide of a cybervictim. A total of 176 undergraduate students at a midsize public university in the Northeast served as participants. Four one-page versions of a hypothetical trial transcript detailing a cyberbullying case were utilized. Participants were randomly assigned to one of four conditions: male cyberbully/male cybervictim, male cyberbully/female cybervictim, female cyberbully/male cybervictim, and female cyberbully/female cybervictim. After reading one of the trial transcripts, participants rated eight variables, including criminal guilt and intent of the cyberbully, responsibility of entities involved, and appropriate punishment for the cyberbully. Results revealed significant differences in judgments based on the gender of the cyberbully, cybervictim, and participant.

Keywords

Cyberbullying cybervictimization decision-making responsibility gender suicide

Introduction

The advent of affordable, digital technologies has allowed many individuals to virtually expose themselves and become vulnerable targets within a cyber landscape. According to Smith et al. (2008), cyberbullying is a modern form of aggression that includes three main criteria: intent to harm, repetition, and power imbalance with the victim. This imbalance of power may not relate to the difference between a cyberbully and cybervictim’s physical size, but rather the level of technological expertise (Kowalski et al., 2018). Unlike traditional bullying, cyberbullying has the potential to be anonymous, reach a much larger audience that is available 24 hours a day, and be less frequently curtailed (Sticca & Perren, 2012). Furthermore, cyberbullies may conceal their identity behind electronic devices and use phony email addresses as well as fictitious names and accounts on social media (Sticca & Perren, 2012). Smith et al. (2008) defined cyberbullying as “an aggressive, intentional act carried out by a group or individual, using electronic forms of contact, repeatedly and over time, against a victim who cannot easily defend him or herself” (p. 376). Additionally, the cyberbully’s motivation may come from the anonymous nature of cyberbullying. As a result, cyberbullies become oblivious to the consequences of their actions (Wong et al., 2017). Cyberbullying can be committed anywhere at any time, and comments may be added and shared multiple times. Previous studies have found that traditional bullying that occurs in public is perceived more negatively than private bullying. Cyberbullying, by its very nature, is often public. However, in cyberbullying cases, when the cyberbully is anonymous, the effects are perceived as more damaging than when the cyberbully is known (Chen & Cheng, 2017; Sticca & Perren, 2012). As most of the prior research works have focused on the prevalence of cyberbullying in childhood and adolescence (Williams & Guerra, 2007), it is important to bring into sharper focus the impact of cyberbullying on young adults.

Cyberbullying is becoming increasingly common among both adolescents and young adults. An online survey was sent to 1378 adolescents under the age of 18 (M = 14.8) from December 2004 to January 2005 (Hinduja & Patchin, 2008). The survey was linked to several websites visited by adolescents. The findings revealed that 18% of boys and 16% of girls reported harassing others online, and older youth were more likely to report offending. Walker et al. (2011) surveyed 120 undergraduate students in three departments at a university in northeastern Pennsylvania, and over half (54%) of the students in total and 100% of the male students claimed to know someone who had been cyberbullied. Furthermore, Marcum et al. (2014) discovered that both male and female college students at a large southeastern university in the United States with a history of being cyberbullied were subsequently more likely to cyberbully others.

Differences in the perceived severity of cyberbullying based on the actual form it takes have been reported. A recent study asked 707 Taiwanese students in grades 5 to 12 to evaluate several types of cyberbullying behaviors (Chen & Cheng, 2017). The students rated impersonating someone else online to do bad things, posting victimized pictures online, and posting fake information and phone numbers online as most potentially damaging. Additionally, more females than males rated cyberbullying behaviors as very severe. In a meta-analysis including 18 longitudinal studies of cyberbullying of Australian students in grades 6 to 12, Campbell et al. (2012) found that almost 60% of self-reported victims rated cyberbullying as being harsh or very harsh, and almost 30% of cybervictims believed that cyberbullying had a great impact on their lives. Additional research is clearly needed to evaluate the impact of factors, such as impersonating someone on social media and posting damaging information, on the subsequent psychological health and well-being of cybervictims. Furthermore, additional research should focus on the intentions of the cyberbullies to cause actual harm to their targets.

While most research on cyberbullying has focused on children and adolescents, some studies have investigated cyberbullying and cybervictimization in college populations. Marcum et al. (2014) found that female college students were somewhat more likely to perpetrate cyberbullying on Facebook, while Kokkinos et al. (2016) and Sun et al. (2016) found that male college students were more likely to engage in such behavior. Moreover, Slonje and Smith (2008) reported that three times as many cybervictims reported being victimized by one boy than one girl. Other studies found no significant gender differences for cyberbullying among both college students and adolescents when personal experiences with the internet were controlled (Choi & Lee, 2017; Gibb & Devereux, 2014; Hinduja & Patchin, 2008; Williams & Guerra, 2007).

Delineating factors, including gender, that are associated with cyberbullying is important to understand its perpetuation. Research has shown that factors such as social connectedness (McLoughlin et al., 2019) and social competence (Romera et al., 2017) are associated with cyberbully perpetration. Cyberbullying has also been shown to be associated with mental health problems. For example, previous research has shown that cyberbullying is a predictor of depression and anxiety (Rose & Tynes, 2015), reduced self-esteem, and suicidal ideation (Hinduja & Patchin, 2010).

Understanding the factors associated with cybervictimization has also been the focus of research over the past decade or so. As with cyberbullying, researchers have focused on gender as a predictor of cybervictimization. In 2009, a self-report survey that involved 3112 school age children in Australia found that more girls than boys reported that they were cybervictims (Campbell et al., 2012). Alvarez-Garcia et al. (2015) and Hinduja and Patchin (2008) found no significant gender differences in victimization among adolescents using a variety of social media platforms (chat rooms, IM, email, cell phone text messages, and other social media), while other studies indicated that more adolescent females reported themselves as cybervictims (Perren et al., 2010; Smith et al., 2008). Examining the role of gender in cybervictimization among university students, Wong et al. (2017) found that males are more likely to be victims of cyberbullying than females. Conversely, Paullet and Pinchot (2014) found that 57% of female undergraduate students indicated that they have been cybervictimized compared to 43% of male undergraduate students. Likewise, Choi and Lee (2017) conducted a self-report study and found that female college students were more likely than male college students to become cybervictims. It is apparent that both males and females have been exposed to cyberbullying; however, further research is needed to evaluate who is more likely to be cybervictimized.

Previous research involving adolescents and young adults has highlighted the potentially devastating consequences of cyberbullying. Young et al. (2017) conducted a content analysis of 184 newspapers in the United States on death by suicide as a result of cyberbullying. This meta-analysis revealed that cybervictims were described as instigators (e.g., sending sexually explicit pictures) which resulted in cyberbullying attacks. Since the blame was assigned to cybervictims as a result of these actions, cyberbullies and bystanders were more likely to place the responsibility on the cybervictim for his or her death. Moreover, Hinduja and Patchin (2010) surveyed 1963 middle school students in the United States who were between 10 and 16 years old to determine if experience with cyberbullying, as an offender or a victim, was correlated with suicidal thoughts and attempts. Findings showed that 20% of the students seriously thought about committing suicide, while 19% reported attempting suicide. Cybervictims were almost twice as likely to have reported attempting suicide compared to those who were not cybervictims. Consistent with earlier research on mental health problems associated with cyberbullying (e.g., Rose & Tynes, 2015), an online survey of 225 German university students revealed that suicide ideation/behavior was positively associated with cyberbullying behavior (Brailovskaia et al., 2018). However, this correlational study does not tell us whether suicidal thoughts motivated the cyberbullying behavior or if cyberbullying brought on suicidal thoughts. In 2016, the second leading cause of death for individuals between the ages of 10 and 34 was suicide (Centers for Disease Control and Prevention, 2018). While experimental investigations of the effects of cyberbullying on both victims and perpetrators are not possible, utilization of mock scenarios using an experimental design evaluating how others might view such behaviors would be potentially instructive.

An increasing number of cyberbullying cases are surfacing in media outlets; however, very few have been tried in criminal court. Pettalia and Pozzulo (2015) found that the majority of potential jurors in a case involving a cyberbully in the suicide death of a cybervictim would agree to a guilty manslaughter verdict and deliver a punitive sentence to the cyberbully. In 2017, a “suicide by texting” case caught the media’s attention. In the case of Commonwealth v. Carter, Michelle Carter was found guilty of involuntary manslaughter in a Massachusetts juvenile court after she encouraged her boyfriend to commit suicide through text messages and phone calls (LaPalme, 2018). In a similar case in 2009, United States v. Drew, a woman created a fake Myspace account and impersonated a 16-year-old boy to convince a young girl who had been spreading rumors about her daughter to commit suicide. The woman was only charged with a misdemeanor, and the case was later vacated from federal district court (LaPalme, 2018). Case law that addresses the encouragement of suicide by electronic communication is lacking at present, and there are currently no federal laws dealing with cyberbullying on social media platforms. As technology has become more prevalent and mobile, and lawmakers are beginning to recognize the devastating consequences of virtual crimes, it would be valuable to employ a hypothetical cyberbullying case where the cybervictim commits suicide in order to evaluate perceptions of the severity of punishment recommended for the cyberbullying behavior.

When using vignettes to investigate perceptions of cyberbullying rather than asking individuals about their personal experiences, few have examined specific characteristics of research participants in judgments about hypothetical cases. Pettalia and Pozzulo (2015) evaluated mock jurors’ decisions in a case, where a cyberbully was charged with the suicide death of a victim. Undergraduates from Canada, who were on average 21 years old, read one of eight versions of a trial transcript. Almost 81% of the participants believed that the defendant was guilty of a crime. More women than men believed the defendant was guilty, and the most frequent punishment was imprisonment. Most of the participants believed the defendant wanted to hurt the victim and that the victim was responsible for his or her own death.

Another recent report by Leduc et al. (2018) also utilized vignettes to examine youths’ justifications of perpetrator and bystander behaviors when they were presented with cyberbullying scenarios. One hundred children, mainly from Canadian families between the ages of 8 and 16, rated four different cyberbullying vignettes as well as their own cyber-aggression behaviors. Results showed that the students displayed a neutral reaction to bystander’s behavior. The researchers proposed that cyberbullying either occurs too often or they may have previously been a bystander.

The present study

While cyberbullying has been a subject of considerable research interest, most of the research has focused on children and young adolescents. Studies that have investigated cyberbullying and cybervictimization among older adolescents and young adults in college environments have reported rates of cybervictimation in excess of 50% (e.g., Walker et al., 2011). Additionally, studies of gender differences in young adults with regard to the prevalence of cybervictimization have yielded inconsistent results, with some reporting higher rates among males (Wong et al., 2017) and some reporting higher rates among females (Choi & Lee, 2017). Research has also been mixed with regard to whether young adult males or females are more likely to be perpetrators of cyberbullying (e.g., Marcum et al., 2014; Sun et al., 2016, respectively).

In light of these inconsistent findings involving young adults as well as a relative dearth of information about how such behaviors are evaluated by others, the present online study was undertaken. We utilized vignettes to describe a hypothetical case of cyberbullying on Facebook in order to evaluate the effects of gender of a cyberbully and cybervictim on judgments of perceived guilt, punishment, intent, and responsibility of a cyberbully for the suicide death of a cybervictim by undergraduate students. The extent to which the cybervictim and other parties, including Facebook and Facebook friends (potential bystanders), were blamed was also evaluated. We also sought to replicate and extend the research of Pettalia and Pozzulo (2015), who utilized vignettes, a somewhat novel approach to studying cyberbullying and cybervictimization, in order to evaluate the effects of a cyberbully’s behavior on perceived guilt and responsibility of various entities and individuals by others. We also sought to build upon Chen and Cheng’s (2017) work assessing the impact of cyberbullying when the cyberbully impersonates the victim and is thereby anonymous. Finally, we investigated the role that gender of the participant played as a potential factor influencing attributions of blame and responsibility. No research to date has evaluated the role of participant gender in this context.

We expected that assignment of blame and responsibility to a cyberbully, cybervictim, Facebook, and Facebook friends would differ based on the gender of the cyberbully as well as the cybervictim. We hypothesized that blame and responsibility assigned to male cyberbullies would be higher and that punishment recommended would be harsher than assigned to female cyberbullies, consistent with findings reported by Pettalia and Pozzulo (2015). We also expected that male and female cybervictims would be assigned different levels of blame and responsibility. However, previous research has not specifically evaluated gender-based differences in cybervictim blaming by others. Additionally, we predicted that participant gender would influence these judgments. Specifically, we expected to find that male and female participants would differ in assignment of criminal guilt and the type of punishment recommended for the cyberbully and would attribute different levels of intent to harm the cybervictim, including to cause suicide. We also expected that male and female participants would differ in the amount of blame and responsibility assigned to male and female cybervictims.

Method

Participants

A convenience sample of 181 undergraduate students who were currently enrolled at a midsize public university in the Northeast were recruited for the study. Five participants who described themselves as nonbinary were excluded from statistical analyses. Our final sample consisted of 176 binary participants (N = 176, 52.3% female, 47.7% male). Participants were required to be at least 18 years of age, and the majority were between the ages of 18 and 25 (90.9%). The majority of participants were White (65.3%), followed by Asian/Pacific Islander (17.0%), Hispanic/Latino (6.8%), Other (5.7%), Black/African American (4.0%), and Native American/American Indian (1.1%). Participants were pursuing a variety of different majors and were about equally distributed among academic class levels. Four participants had previously served on a jury, and the majority of participants had a current Facebook account (86.9%). Fifteen percent of the participants indicated that they had themselves engaged in cyberbullying, and 63% stated that they had been victims of cyberbullying. Participants were offered the opportunity to enter a raffle to win one of four Amazon gift cards worth $25 dollars each in exchange for their participation.

Materials and measures

Materials included four versions of a hypothetical trial transcript that varied the gender of the cyberbully as well as the gender of the cybervictim: male cyberbully/male cybervictim, male cyberbully/female cybervictim, female cyberbully/male cybervictim, and female cyberbully/female cybervictim. The four transcripts consisted of the same description of a cyberbullying case that resulted in the suicide of a bullied cybervictim. The four arguments presented by the prosecution and four arguments presented by the defense were the same in all four cases, with gender of the cyberbully and cybervictim varied across the conditions. Testimonies for the prosecution were provided by the victim’s lawyer, victim’s mother, a childhood friend, and a computer analyst. Testimonies for the defense were provided by the defendant, defendant’s lawyer, employer, and a friend. Cyberbullying and cybervictimization were characterized based on Patchin and Hinduja (2006) and Tynes et al. (2010) descriptions, and definitions were provided before any questions were presented. According to those researchers, cybervictims experience harm through language and behavior involving electronic devices, as a result of the repeated and deliberate use of language and behavior that inflicts harm through the use of electronic devices, such as computers and cell phones by the cyberbully. An example of one of the vignettes is provided in Appendix 1.

The first set of questions regarding the hypothetical crime asked participants how responsible the cybervictim, defendant, Facebook, and Facebook friends were in the cybervictim’s death by checking the level of responsibility they felt was appropriate on a Likert scale (1 = not responsible and 7 = extremely responsible). Participants were then asked how intentional the cyberbully’s desire to harm and have the cybervictim commit suicide was by checking the number that described their opinion on a Likert scale (1 = not intentional and 7 = extremely intentional). Participants were asked to express their opinion about the defendant’s criminal guilt or innocence by checking yes, for agreement, and no, for disagreement, and were then asked to indicate an appropriate level of punishment for the defendant, which varied in severity: (1) no punishment, (2) fine/probation, (3) imprisonment up to 20 years, (4) life imprisonment with the possibility of parole, (5) life imprisonment without the possibility of parole, and (6) death penalty. We also asked whether participants had ever cyberbullied someone else or had ever been the victim of cyberbullying by answering either “Yes” or “No.” A final set of questions asked participants to indicate their gender, age, whether they had served on a jury, ethnicity/race, and if they had a Facebook account at the time of the study.

Design

A 2 × 2 × 2 between-subjects factorial design was employed that included cyberbully gender (male vs. female) and cybervictim gender (male vs. female) as manipulated independent variables and participant gender (male vs. female) as a nonmanipulated independent variable. This design approach was utilized because we wanted to evaluate whether changes in gender of the cyberbully and cybervictim influenced perceptions of blame and responsibility as well as harshness of punishment deemed appropriate for the cyberbully. Participants were randomly assigned to one of the four treatment conditions: (1) male cyberbully, male cybervictim (n = 41); (2) male cyberbully, female cybervictim (n = 41); (3) female cyberbully, male cybervictim (n = 45); and (4) female cyberbully, female cybervictim (n = 49). The major dependent variables were measures of perceived guilt and level of punishment recommended for the cyberbully, responsibility of the cybervictim, defendant, Facebook, and Facebook friends, and cyberbully intent to cause harm and to cause the cybervictim to commit suicide.

Procedure

Undergraduate students were recruited through an email sent out by the Office of Student Affairs, which provided a link to the online research survey in Qualtrics, a robust, secure online survey tool designed for human subjects research. Upon logging into the study, participants were informed that the study was anonymous and that no unique identifying information would be collected. Participants were also informed that the purpose of the research study was to examine judgments of guilt, punishment, intent, and responsibility of a cyberbully for the suicide death of a hypothetical cybervictim. Participants were made aware that the study was voluntary with no more risk or discomfort than might be experienced in everyday life but that the topics of suicide and cybervictimization could potentially cause mild discomfort or anxiety for some individuals. The University’s counseling center information was provided. Participants were informed that they had the right to withdraw at any time and were assured privacy and confidentiality. After indicating their willingness to continue the study, participants were presented with one of the four crime scenarios and were asked questions about the events and about themselves, all of which have been described previously. Finally, participants were asked if they would like to enter a raffle to win one of four $25 Amazon gift cards. If they chose yes, they were redirected to a second survey where they were asked for their email address. Participants were informed that their information would not be connected in any way to the responses they had provided in the survey and would only be used for the raffle. The entire procedure required approximately 30 minutes.

Results

Ninety-six percent of participants believed that the cyberbully was guilty of a crime. A Fisher’s Exact Test revealed that the proportion of male and female participants who judged the cyberbully guilty of a crime was not significantly different.

Multivariate tests of cyberbully gender, cybervictim gender, and participant gender effects

All response variables were measured on a six- or seven-point Likert scale. While the scales utilized were ordinal, the underlying dimensions were arguably continuous, justifying the use of multivariate analysis of variance (MANOVA) for analysis of these variables. A cyberbully gender × cybervictim gender × participant gender MANOVA was used to assess participants’ evaluations of responsibility, intent, and punishment variables. Pillai’s trace was used to evaluate all multivariate effects. Significant multivariate differences were observed for cyberbully gender (p = .021), participant gender (p = .010), and for the cyberbully gender × cybervictim gender × participant gender interaction (p = .034). Means and standard deviations for the seven response variables associated with responsibility, intent, and punishment are included in Table 1.

Table 1.

Descriptive statistics for response variables.

Response variable	Gender of cyberbully	Gender of cybervictim	Participant gender	M	SD	N
Responsibility of the victim	Male	Male	Male	3.40	2.14	20
			Female	3.00	1.64	21
		Female	Male	3.59	2.09	17
			Female	3.26	2.32	23
	Female	Male	Male	3.96	2.51	25
			Female	3.45	1.96	20
		Female	Male	4.32	2.28	22
			Female	2.56	2.14	27
Responsibility of defendant	Male	Male	Male	6.45	1.36	20
			Female	6.19	1.37	21
		Female	Male	6.35	1.42	37
			Female	6.41	1.04	44
	Female	Male	Male	6.04	1.77	25
			Female	6.00	1.49	20
		Female	Male	6.27	1.39	22
			Female	6.74	1.16	27
Responsibility of Facebook	Male	Male	Male	4.50	1.85	20
			Female	5.57	.978	21
		Female	Male	4.18	1.85	17
			Female	4.91	1.73	23
	Female	Male	Male	4.24	2.09	25
			Female	4.00	1.78	20
		Female	Male	3.64	1.73	22
			Female	4.93	1.57	27
Responsibility of Facebook friends	Male	Male	Male	3.40	1.90	20
			Female	4.19	1.50	21
		Female	Male	3.24	2.02	17
			Female	4.09	1.81	23
	Female	Male	Male	4.00	1.76	25
			Female	4.20	1.32	20
		Female	Male	3.86	1.73	22
			Female	4.44	1.55	27
Intent to harm	Male	Male	Male	6.65	.671	20
			Female	6.38	.740	21
		Female	Male	6.00	1.70	17
			Female	6.91	.417	23
	Female	Male	Male	6.40	1.29	25
			Female	6.65	.587	20
		Female	Male	6.59	.590	22
			Female	6.56	1.22	27
Intent to cause suicide	Male	Male	Male	4.30	1.90	20
			Female	4.76	1.90	20
		Female	Male	4.18	1.91	17
			Female	5.13	1.71	23
	Female	Male	Male	4.32	2.19	25
			Female	4.35	1.53	20
		Female	Male	3.68	2.01	22
			Female	4.93	1.98	27
Cyberbully punishment	Male	Male	Male	3.65	1.27	20
			Female	3.14	.854	21
		Female	Male	2.59	.939	17
			Female	3.61	1.08	23
	Female	Male	Male	3.24	1.11	25
			Female	3.45	.945	20
		Female	Male	2.59	1.01	22
			Female	3.41	1.01	27

Univariate tests of cyberbully and cybervictim gender effect

Univariate tests revealed a significant difference in participants’ ratings for the responsibility of Facebook variable only based on the gender of the cyberbully, F(1, 167) = 5.03, p = .026, $η_{p}^{2}$ = .029, 95% CI [4.41, 5.17], with more responsibility assigned to Facebook when the cyberbully was male versus female. A significant main effect of victim’s gender was not observed in the omnibus MANOVA and was not evaluated further.

Univariate tests of participant gender effects

Univariate tests revealed significant differences between male and female participants on ratings of responsibility of the cybervictim, responsibility of Facebook, responsibility of Facebook friends, intent to cause suicide, and cyberbully punishment. Male participants assigned more responsibility to cybervictims than females, F(1, 167) = 5.17, p = .024, $η_{p}^{2}$ = .030, 95% CI [3.35, 4.29]. Female participants assigned more responsibility to Facebook than males, F(1, 167) = 7.38, p = .007, $η_{p}^{2}$ = .042, 95% CI [4.49, 5.21]. Female participants assigned more responsibility to Facebook friends than males, F(1, 167) = 5.45, p = .021, $η_{p}^{2}$ = 032, 95% CI [3.88, 4.59]. Female participants expressed a stronger belief that the cyberbully’s intention was for the cybervictim to commit suicide, F(1, 167) = 5.45, p = .021, $η_{p}^{2}$ = .032, 95% CI [4.40, 5.19]. Harsher punishment was recommended for the cyberbully when the participants were female as opposed to male, F(1, 167) = 5.84, p = .017, $η_{p}^{2}$ = .034, 95% CI [3.19, 3.62].

Univariate tests of participant gender × cyberbully gender × cybervictim gender interaction

The three-way interaction was significant for only the responsibility of Facebook variable. To evaluate the three-way interaction, two univariate ANOVAs tested the significance of the two-way interaction between cyberbully gender and cybervictim gender for male and female participants separately. A significant interaction was observed for females only. Female participants assigned significantly more responsibility to Facebook for the suicide of a male cybervictim if the cyberbully’s gender was also male, F(1, 39) = 12.47, p = .010, $η^{2}$ = .24, 95% CI [4.83, 6.31]. The amount of responsibility attributed to Facebook did not differ for female cybervictims regardless of the gender of the cyberbully. Figures 1 and 2 display results for male and female participants, respectively.

Figure 1.

Male participants’ assignment of responsibility to Facebook for the suicide of a male or female cybervictim when the gender of the cyberbully was male versus female.

Figure 2.

Female participants’ assignment of responsibility to Facebook for the suicide of a male or female cybervictim when the gender of the cyberbully was male versus female.

Discussion

The current study evaluated college students’ perceptions of a hypothetical episode of cyberbullying on Facebook leading to the death of a cybervictim. Consistent with our research hypothesis and with Pettalia and Pozzulo’s (2015) findings, the vast majority of participants (96%) believed that the cyberbully had committed a crime. However, contrary to our research hypothesis, male and female participants did not differ significantly on their assignment of criminal guilt to the cyberbully. With regard to gender of the cyberbully, assignment of blame and responsibility for the cybervictim’s suicide was higher when the cyberbully was male and was consistent with our research hypothesis. The tendency to assign more blame when a cyberbully is male has been reported by Pettalia and Pozzulo (2015). This difference was, however, significant for Facebook’s responsibility only. Considering gender of the cybervictim, we failed to find significant differences on any of the measures of blame or responsibility. Failure to find a difference based on the cybervictim’s gender may have been attributable to the fact that the study was likely underpowered.

We were also interested in the role that participant gender played in attributions of blame and responsibility. Female participants expressed a stronger belief that the cyberbully’s intention was for the cybervictim to commit suicide than did male participants and recommended harsher punishment for the cyberbully. Responsibility of a cybervictim for their suicide was also different for male and female participants, with male participants tending to assign more blame to a cybervictim than female participants. These findings were consistent with our expectation that there would be gender differences in the evaluations of blame and responsibility assigned to a cyberbully and a cybervictim. Interestingly, we found that female participants assigned higher levels of blame to Facebook than male participants. Blame and responsibility ascribed to Facebook for the death of the cybervictim did, in fact, differ for male and female participants and depended on the genders of both the cyberbully and the cybervictim. More specifically, female participants assigned particularly high levels of responsibility to Facebook when both the cybervictim and cyberbully were male. No differences were observed for male participants.

This was one of the first studies to evaluate the assignment of blame and responsibility to a cyberbully, cybervictim, and other entities such as Facebook based on the gender of both the cyberbully and cybervictim as well as gender of the participants making those judgments. The fact that the vast majority of our participants viewed the cyberbully’s behavior as a crime may have important implications for policy decisions. Interestingly, female participants in the current study perceived cyberbullying as more harmful than male participants, as they recommended harsher punishment and were more certain that the cyberbully’s intent was to cause the suicide of the cybervictim. Such differences in perceptions of male and female participants may stem from our understanding of traditional bullying, where males are more likely than females to be bullies and to be physically bullied themselves whereas females participate in more non-confrontational, indirect forms of bullying (Griezel et al., 2012; Marcum et al., 2014). Unlike research on traditional bullying which has shown clear gender differences, differences in rates of cyberbullying based on gender have been inconsistent in the cyberbullying literature (Choi & Lee, 2017; Gibb & Devereux, 2014; Hinduja & Patchin, 2008; Williams & Guerra, 2007).

Our study has several important implications for social service providers, educators, and groups providing web-based platforms for young adults. Our findings revealed that both male and female college students placed responsibility for the suicide death of the cybervictim on parties other than the cyberbully, specifically on the social media platform, Facebook. This displacement of responsibility is something about which social service practitioners, educators, and others should be well-informed. Second, observed gender differences in perception of cyberbullying clearly indicate the need for development of specific, gender-focused interventions. For example, colleges and universities should direct attention to educating young adult males about the potential harmful effects of cyberbullying, as they tend to inappropriately blame female cybervictims for cyberbullying behavior.

Future studies should critically examine differences in usage of social media platforms by young adult men and women. A better understanding of social media usage patterns may assist in the development of targeted interventions and educational awareness programs. For instance, online surveys could be utilized by colleges and universities to identify those who have been cybervictims and on which forms of social media they have been targeted. Such information would be invaluable in providing assistance programs to cybervictims as well as to the development of strategies to deter further cyberbullying. Other strategies to reduce cyberbullying might include having an anonymous reporting system in place for college students and those in the workplace to report cyberbullying incidents.

A few limitations of the current study should be noted. One was related to the composition of the study sample. The sample consisted exclusively of undergraduate students, mostly between the ages of 18 and 25. Students’ judgments were perhaps more homogenous than non-students and older adults, which limits generalizability of our findings. Participants in our study were raised in a digital environment and were keenly aware of the potential for social media platforms such as Facebook to serve as a vehicle for cyberbullying. Therefore, future studies should include older adults to see if their perceptions of cyberbullying and cybervictimization differ in significant ways from those of young adults. The current study involved a hypothetical trial transcript, which may be criticized as having poor external validity. Participants knew that they were not judging or convicting a real person and may not have taken the study seriously. Utilizing videotaped cyberbullying scenarios instead of text-based vignettes would provide conditions that more closely resemble what one might actually observe or experience in real life. Finally, Facebook was the only social media platform used to present our hypothetical cases of cyberbullying. Future research should include additional platforms, such as Twitter and Instagram, to evaluate the effects of cyberbullying and cybervictimization on additional social media platforms.

Despite these limitations, our research provides a better understanding of how college students perceive instances of cyberbullying and cybervictimization on a social media platform. Understanding how such behaviors are perceived and evaluated is an important first step in the development of educational programs, interventions, and even public policy surrounding this important social problem.

Footnotes

Article Notes

ORCID iD

Mary N. Duell

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