Bystanders’ Behavior in Cyberbullying Episodes

Abstract

The present study explored bystanders’ behavior in cyberbullying (CB) episodes among children and youth, focusing on active and passive behavior patterns. The study examined prevalence and characteristics of bystanders’ behavior following CB episodes, and their active–passive intervention patterns in relation to personal (age, gender) and socio-emotional (self-efficacy, social support, sense of loneliness) factors. Of the 1,094 participants (ages 9-18), 497 (46.4%) reported they were bystanders to CB episodes. Of the bystanders, 55.4% were identified as having a passive pattern of behavior—they did not provide any help to cyber-victims, whereas 44.6% were identified as having an active pattern—helping the cyber-victim. In line with the “bystanders’ effect,” only 35.6% of the bystanders offered direct help to cyber-victims after witnessing CB. When studying the personal–socio-emotional differences between active and passive bystanders, it was found that the “active bystanders” are more often girls, older, have more social support from significant others, and have lower levels of emotional loneliness than bystanders in the passive group. Differences within the passive and active patterns were studied as well. A logistic regression revealed the unique contribution of each predictor to the probability of being an active bystander. It was found that gender and age predicted the probability of being an active bystander: Girls are more likely than boys, and older bystanders are more likely than younger ones, to choose an active pattern and provide help to cyber-victims. In addition, implications for CB prevention and intervention involvement programs to encourage bystanders to help cyber-victims are discussed.

Keywords

bystanders behavior cyberbullying cyber-victims active-passive patterns socio-emotional factors

Introduction

Cyberbullying (CB) is a form of interpersonal aggression that occurs through electronic means, such as texting, online chats, or social media websites (Smith et al., 2008). CB is also defined as the use of electronic media to express deliberate hostile behavior toward peers to harass, humiliate, or injure the other person (Bilic, 2013). CB shares some common characteristics with traditional (face-to-face) bullying and was found to be correlated with traditional bullying (Sticca & Perren, 2012; Ybarra & Mitchell, 2004). Apart from the need for some degree of technological skill, CB has unique characteristics, such as anonymity, distance, rapid communication, easy accessibility to a wide-ranging audience (Beran & Li, 2005; Patchin & Hinduja, 2010), and diverse negative socio-emotional as well as scholastic impacts on the victims, for example, depressive mood, high levels of a sense of loneliness, frustration, sadness, low self-esteem, difficulties in social adjustment and/or concentration in learning at school, and suicidal thoughts (Brighi et al., 2012; McKenna, 2008; Olenik-Shemesh, Heiman, & Eden, 2012; Ybarra & Mitchell, 2004).

A U.S. study that included 4,441 youth between the ages of 10 and 18 found that approximately 20% of the students reported experiencing CB (Hinduja & Patchin, 2013). When comparing the prevalence of cyber-victimization with traditional (face-to-face) victimization, it was found that out of 1,094 youth ages 9 to 17 years, 27% reported on cyber-victimization, whereas 49% reported being victims in traditional bullying episodes (Heiman, Olenik-Shemesh, & Eden, 2014). Traditional bullying still appears to be more common than CB (Smith et al., 2008; Wang, Nansel, & Iannotti, 2011).

As in traditional bullying, three major participants take part in CB episodes: the perpetrator, the victim, and the bystander. Studies on face-to-face violence and bullying have shown that bystanders can play a key role in the episodes in giving immediate assistance to the victim and thus in the prevention of future episodes (Coloroso, 2010; Cowie, 2000; Craig & Pepler, 1998; Denny et al., 2015; Twemlow & Sacco, 2013). To date, most of the studies on CB among children and adolescents have mainly addressed the victims and perpetrators, whereas the aim of the present study was to examine the behaviors of bystanders in CB episodes, with a focus on active and passive behaviors in helping CB victims in the context of socio-emotional and personal aspects, such as age and gender. This knowledge may contribute to developing intervention programs that focus on encouraging and empowering children and youth to become more involved in CB episodes. Such empowerment would help cyber-victims and prevent the spread of the harm resulting from the viral nature of this type of bullying.

Bystanders in Bullying and Violence Episodes

Violence and bullying involve the abuse of power in a social context, namely, the triangle: bully (perpetrator)–victim–bystander (witness). Each component in this triangle influences the dynamics of bullying episodes, yet Twemlow and Sacco (2013) suggest the key element for analyzing this process is understanding the role and influence of the witness in the bullying situation. As Coloroso’s (2010) work reflected, in all types of bullying, the role of the bystander is crucial. Even though bystanders may not appear to be directly involved in the scene, they in fact have considerable influence on the formation of the victim’s experience and the perpetrator’s behavior. Bystanders to bullying and violence episodes may act in various ways. Traditional bullying literature has identified three main behaviors of bystanders to bullying and violence episodes: assisting or reinforcing the perpetrator, remaining an outsider, and defending and supporting the victim (Pepler, Craig, & O’Connell, 2010; Salmivalli, 2010; Salmivalli, Lagerspetz, Bjorkqvist, Osterman, & Kaukiainen, 1996). The bystanders may be the perpetrator’s target audience whom these perpetrators expect to strengthen their actions.

For example, when bystanders choose to support the perpetrator’s bullying acts, they may gain defense and friendship in exchange. This exchange then reinforces and strengthens the perpetrator’s acts. The bystanders may also play a major role in affecting victims’ experience and by providing or not providing help to the victims. When they choose to help, the bystanders provide social support to the victims and may help reduce their feelings of despair and loneliness (Twemlow & Sacco, 2013). The bystander also may call for help, tell an adult, comfort the victim, and try to find solutions to the conflict (Cowie, 2000). In addition, the help a bystander gives the victim may threaten the perpetrator’s status and might even make him or her stop the bullying (Hawkins, Pepler, & Craig, 2001). Salmivalli argues that supporting the victim may buffer the negative effects of bullying victimization (Salmivalli, 2010).

Studies on the role of bystanders in face-to-face bullying have shown that 85% of bullying occurred in front of bystanders and that only 13% of them intervened on behalf of the victims. The main reasons for not intervening in favor of the victims were fear of getting hurt, fear becoming a target for the bullies, self-defense, fear that action would aggravate the situation in a way that could hurt victims even more, embarrassment, and not knowing how to respond or intervene (Barhight, Hubbard, & Hyde, 2013; Craig & Pepler, 1998; Druck & Kaplowitz, 2005; Twemlow, Fonagy, & Sacco, 2004).

Bystanders in CB Episodes

The unique characteristics of CB influence the behavior of bystanders in CB episodes. These characteristics’ roles in CB may be more complex than in most face-to-face bullying. Only a few studies to date have addressed this issue, by focusing on providing help to cyber-victims.

Online social networks are very popular among children and adolescents. Almost 95% of youth spend time on the Internet and using online social networks (Madden, Lenhart, Duggan, Cortesi, & Gasser, 2013). Of this number, 81% use some kind of social media, and 77% use Facebook (Pew Research Internet Project, 2012). Along with other interpersonal types of communication, these sites where perpetrators, victims, and bystanders interact may become the platform for episodes of CB (Lenhart, Madden, Smith, & Macgill, 2007; Livingstone, Haddon, Gorzig, & Olafsson, 2011; Ybarra & Mitchell, 2004). Whereas in face-to-face bullying, bystanders watch a physical act, the bystanders in CB episodes see messages, and the quantity of these messages on the Internet and in social media is much larger than in a physical event (Druck & Kaplowitz, 2005; Twemlow et al., 2004). Studies have shown that bystanders are the largest group using these sites, as compared with perpetrators and victim (Lenhart et al., 2011). Holfeld (2014) argues that the potentially unlimited audience in the online world suggests that the role of bystanders may be particularly important in CB, even though bystanders can be invisible. Willard (2005) argues that as victims do not tend to share the cyber-victimization experience with adults (parents, teachers), often the only ones who know about the victimization online are the bystanders—generally other children and youth who are participating in the online activities. Therefore, their role may be crucial in creating behavioral change among the perpetrators. For instance, by not giving support and legitimacy to the aggressive behavior of perpetrators, for example, by not clicking on “LIKE,” they may have an influence on decreasing the aggressive episode (Davis & Davis, 2007; Willard, 2005).

Bystanders in CB episodes, as in face-to-face bullying, can support the perpetrator, remain passive as outsiders, or support the victim (Bastiaensens et al., 2014). Because of the possible anonymity in CB episodes, however, bystanders may also choose to sometimes be on the perpetrator’s side and sometimes on the victim’s side, without anyone knowing. Moreover, on the Internet, they can choose to respond publicly (e.g., by clicking on LIKE, which everyone can see) or respond in a private message mode so that the response remains discreet (Bastiaensens et al., 2014).

The desire to affiliate with the stronger group might lead bystanders to support the cyber perpetrator, and they may take an active part by forwarding or sharing hurtful messages or by reinforcing what the perpetrators have posted in their own private messages or posts (Salmivalli, Voeten, & Poskiparta, 2011). Unlike face-to-face bullying acts, in CB acts, bystanders can respond “behind the scenes” where nobody knows and judges them for empowering aggressive behavior (Druck & Kaplowitz, 2005).

Barlinska, Szuster, and Winiewski (2013) found that bystanders in CB are more likely to act in favor of the perpetrator, strengthen his or her power, or choose to be passive, perhaps because of the anonymity that cyberspace makes possible, which may reduce the sense of personal responsibility (Barlinska et al., 2013; McKenna, 2008).

When bystanders in CB episodes stay passive and do not react (e.g., by not passing on a message or sharing a post, or not clicking on LIKE on Facebook), this response can be considered positive for the victim because the harmful action does not continue to spread and it might prevent continuation of the harmful action (Barlinska et al., 2013; Spears, Slee, Owens, & Johnson, 2008). Whereas in face-to-face bullying episodes, such an action may sometimes be interpreted as going against the victim, the “silence” may be interpreted as support for the bullying behavior (Menesini, Codecasa, Benelli, & Cowie, 2003).

When bystanders in CB episodes choose an active action pattern, supporting the victim, they may have a strong impact on the dynamics of the act and a key role in preventing further harm. They may influence others, contribute to stopping CB acts and reducing negative effects on the victim, and thus be the significant factor for the victim (Salmivalli et al., 2011).

The unique characteristics of online behavior also affect bystanders’ choice to assist cyber-victims (Salmivalli et al., 2011; Thornberg & Jungert, 2013). For example, in face-to-face bullying, there are factors that do not exist in online bullying, such as facial expression, body language, and tone of voice. Bystanders generally use these factors to assess a situation when deciding whether to help or not to help the victim. These factors, which are critical in helping the bystander interpret the situation and influence his or her actions and reactions, are missing in CB episodes (Barlinska et al., 2013); thus, the decision to help becomes more complex.

Bastiaensens and colleagues (2014) examined certain factors that could affect bystanders’ decisions regarding whether to provide help to cyber-victims on Facebook, such as the severity of the event and the behaviors of other bystanders. They found that the more severe the bystanders evaluated the situation to be, the greater their tendency became to assist the victim online. The results were not conclusive, however, concerning the impact of the behaviors of other bystanders, considered friends (Bastiaensens et al., 2014). In social media, belonging to a group and determining one’s “friends” is more complex than in real life: The circle of “friends” is very broad, and people defined as “friends” on Facebook are often not actual friends (Wilcox & Stephen, 2013). Bastiaensens and colleagues suggest that in CB, the bystanders will choose to respond in a manner similar to their friends, but the degree of closeness to those friends creates a significantly higher and stronger impact. Thus, the bystanders will choose to assist the victim when they see their close friends doing so and will examine other bystander behavior to assess the degree of urgency to assist. If other bystanders react passively, they might interpret the situation not requiring help.

Garcia, Weaver, Moskowitz, and Darley (2002) suggest that bystanders in CB episodes may often not feel as clear a responsibility to help a victim as bystanders in face-to-face incidents. They may also fear getting caught as being responsible for damage caused to the victim. Cappadocia, Pepler, Cummings, and Craig (2012) have found that most bystanders in CB episodes who reported that they helped the victims chose to do so out of a high level of moral judgment and a desire and conviction to stand up for the other.

To date, little is known regarding personal and socio-emotional variables that may be associated with providing help to cyber-victims. The present study concentrated on examining the behavior of bystanders in CB episodes, divided between those who provide help to cyber-victims (active pattern of behavior) and those who do not (passive pattern of behavior), focusing on personal and socio-emotional factors that may be related to providing help to victims in cyberspace.

Factors Related to Bystanders Providing Help to Cyber-Victims

The “bystander effect” online

The “bystander effect,” sometimes called “bystander apathy,” is a social-psychological phenomenon that refers to cases of bullying and violence in which individuals observing the event do not offer help to a victim when other people are present. The probability of giving help is inversely related to the number of bystanders (Latané & Darley, 1970); the more bystanders present, the less likely any one of them will help. When many bystanders are present, everyone assumes the victim will get help from someone else or that the problem has been solved (Garcia et al., 2002). Several variables help explain why the bystander effect occurs: ambiguity, cohesiveness, and diffusion of responsibility.

Whether the bystander effect is more pronounced online is not clearly known. In a study that focused on Internet forums and chats dynamics, researchers concluded that the bystander effect, similar to the pattern described in the literature regarding traditional violent events, exists also in CB acts that take place on Internet forums and chats, and sometimes they may even be more powerful. In other words, as the number of people present in forums and chats grew, it took more time for students to get or give help (Twemlow et al., 2004). Hudson and Bruckman (2004), who discussed the bystander effect in online environments, found clear behavioral changes when discussions moved from a classroom setting to an online environment, and concluded that the “fear of making mistakes is generally reduced in an online environment” (p. 190) and that students feel more comfortable online, where almost no blocking mechanisms exist that “allow for greater disinhibition in online environments” (p. 190). They suggest that the distribution of responsibility in online environments may even be greater, and bystanders will not “hurry” to offer help to victims. Moreover, the bystanders feel freer to act or not “behind the scenes” (Hudson & Bruckman, 2004). This assumption needs further examination.

Personal background factors: gender and age differences

Pozzoli, Gini, and Vieno (2012) found differences among bystanders’ level of support of a victim according to gender: In face-to-face bullying, boys tended to be less supportive to victims than girls. Cappadocia and colleagues (2012) found gender differences in emotional characteristics examined among bystanders to face-to-face bullying. For example, only among girls, they found that a high level of self-efficacy predicted the taking of action to help the victim. Bastiaensens and colleagues (2014) found that in CB episodes, girls tended to act on behalf of the victim, as opposed to boys, who, when they decided to act, usually supported the bully. These findings are not conclusive and require additional examination, because in some cases, the researchers found no differences between the genders in both types of bullying incidents (Barlinska et al., 2013; Li, 2006).

As to age, it was found that as age increased, so did bystanders’ support of the victim in diverse acts of bullying. Barhight and colleagues (2013) argue that in adolescence, for example, the tendency to help victims of bullying is higher than in childhood, especially among girls (Barhight et al., 2013). Only a small number of initial findings exist regarding age differences in bystanders providing help to cyber-victims, and further examination is needed.

Socio-emotional factors

Self-efficacy

One of the central emotional-social variables that research has repeatedly found and that has had an impact on support for victims of traditional violence is self-efficacy, a concept taken from Albert Bandura’s (1986) social learning theory, which relates to the way in which people perceive themselves and believe in their own ability to organize and successfully achieve a desired result (Bandura, 1986, 1997). A high level of self-efficacy will increase the tendency to invest effort in achieving a task; thus, people with high self-efficacy will more highly estimate their own abilities to successfully cope with specific tasks. On the other hand, low self-efficacy will weaken the motivation to make an effort at the time of a task. Individuals who do not believe in their own ability to intervene successfully when they witness a case of bullying might delay intervening or not intervene at all (Thornberg & Jungert, 2013). To assist a victim, the bystanders must believe they are capable of coping with the situation successfully. Thus, assuming that bystanders with higher self-efficacy will try to assist the victim is possible (Barhight et al., 2013). Thornberg and Jungert (2013) found a significant positive relationship between the sense of self-efficacy and behavior supporting the victim; a high sense of self-efficacy and a relatively high level of moral sensitivity characterized bystanders who tended to support and defend victims. Cappadocia and colleagues (2012) found this result only in girls.

Social support

Social support is defined as the individual’s perception of being cared for, valued, and included in his social environment: Family, peers, and other significant people may have an important role as a protective factor in the consequences of peer victimization (Saylor & Leach, 2009). This form of support occurs by transfer of resources from one person to another and has considerable importance for the person’s sense of well-being (Shumaker & Brownell, 1984). Choi and Cho (2013) found that bystanders with high levels of social support tended more to assist victims in face-to face bullying acts. In CB, social support also has a significant impact on the bystanders’ response; thus, examination is required as to whether differences exist in the levels of social support among the bystanders who provide help to cyber-victims compared with those who do not. Bastiaensens and colleagues (2014) argue that peer pressure to behave positively and the receiving of social support for helping the victim may lead the witness to act on behalf of the cyber-victim.

Loneliness

A sense of loneliness reflects a discrepancy between an individual’s expectations of interpersonal relations and the social situation in reality (Peplau & Perlman, 1982). It arises when the fundamental need for belonging and establishing stable social bonds with caring for others is not adequately satisfied (Cacioppo & Patrick, 2008) and may be manifested by difficulty in establishing close intimate relationships, having few friends, and experiencing frustration and dissatisfaction with existing relationships (Nurmi & Salmela-Aro, 1997). A study that examined the relationship between CB and face-to-face bullying and loneliness among 5,863 children and adolescents ages 12 to 16 years (Brighi et al., 2012) indicates that teenagers who were victims of CB reported a greater tendency toward loneliness. In a study that examined the relationships between friendship, behavioral risk, and peer victimization, Cardoos and Hinshaw (2011) found that the lack of social support and a high sense of both social loneliness (the lack of companionship and the failure to establish stable social relationships) and emotional loneliness (the experience of an unfulfilled need for close companionship) may be associated with the bystanders’ behavior in CB acts. A high sense of loneliness may be related to a passive pattern: not helping victims.

To date, few studies have explored the characteristics and patterns of bystanders’ behaviors in CB acts, and little is known about the differences between active and passive behavior patterns regarding assisting the victims in relation to personal (age, gender) and socio-emotional (self-efficacy, social support, sense of loneliness) factors. The unique contribution of each of these variables in predicting the probability of being an active bystander, providing help to cyber-victims, warrants further study.

Study Objectives

To examine the prevalence and characteristics of bystanders’ behaviors in CB episodes among children and adolescents, studying the differences between bystanders and nonbystanders in terms of personal variables: Internet literacy (time and amount of Internet use), age, and gender differences. Furthermore, in line with the “bystander effect,” we hypothesized that only a small percentage of bystanders will provide help to cyber-victims, because the number of the bystanders on the Internet is especially high.

To study the differences between passive and active behavior patterns in assisting cyber-victims in relation to personal variables: gender, age, and socio-emotional resources (self-efficacy, social support, and loneliness). We hypothesized that active bystanders (those who provide help to cyber-victims) will be older, will more likely be girls than boys, and will report a higher level of self-efficacy and social support and a lower level of loneliness. By using a regression model, the personal–socio-emotional variables that might predict the pattern of “active behavior” in assisting cyber-victims would be identified.

Method

Participants

The sample consisted of 1,094 Israeli adolescents ages 9 to 18 years (M = 12.87, SD = 2.09): 527 girls (48.4%) and 561 boys (51.6%). 6 participants did not report their gender. The study took place in 18 schools in the north, center, and south of Israel. Fifty-one percent of the students were enrolled in primary schools, 33% in junior high school, and 16% in high school.

Procedure

After receiving approval from the Research Ethics Board, the Ministry of Education and from the school principals, the classes were randomly selected. A letter was sent to parents to request their approval for their child’s participation in the study. After receiving the parents’ written permission, the students filled out the questionnaires in their classes over a period of an hour in the presence of the researchers. In the classes of the younger students (ages 9-11), the students filled out the questionnaires over an hour and a half. To ensure a similar level of understanding, every class was informed of the defining features, roles involved, and types of behavior that constitute CB. In addition, all students were provided with detailed information on how to fill out the questionnaire. When both the researchers were present, they answered questions and the students were provided with special clarification, if needed. Anonymity was guaranteed and kept.

Measures

CB questionnaire

The CB questionnaire (Smith et al., 2008; adaptation to Hebrew by Olenik-Shemesh, Tarablus, and Heiman) consists of 22 items addressing knowledge of the Internet and exposure to and coping with CB and face-to-face bullying. The questionnaire includes definitions of CB and consists of questions regarding cyber-victimization, cyber-perpetration, and the bystanding to CB episodes. Cronbach’s alpha for bystanding to CB episodes was .86; for cyber-victimization, it was .79; for cyber-preparation, it was .84. Previous studies evidence construct validity (Brighi et al., 2012; Smith et al., 2008; Steffgen, Kowing, Pfetsch, & Melzer, 2011). The questionnaire was adapted and modified for the younger age groups (ages 9-11), by the researchers, and three judges translated it from English into Hebrew and then back into English to check accuracy.

Self-efficacy questionnaire

The self-efficacy questionnaire (Muris, 2001) contains 13 items relating to two areas: social and emotional—on a 5-point answer scale (1 = not at all, 5 = very well), with the higher score reflecting a higher sense of self-efficacy. One example of a question from the self-efficacy social scale is, “Can you maintain social relationships with other students?” (Cronbach’s α = .73). An example from the emotional self-efficacy scale is, “Can you control your feelings?” (Cronbach’s α = .86). Cronbach’s alpha for the entire scale for this sample was .86. The questionnaire was validated and has face, convergent, and construct validity (Muris, 2001; Sudlo & Shaffer, 2007).

Social support questionnaire: Multidimensional Scale for Social Support (MSPSS)

The MSPSS (Zimet, Dahlem, Zimet, & Farley, 1988) consists of 12 items on a 7-point Likert-type scale (1 = not suitable at all, 7 = very suitable). The general score and three subscales describe the individuals’ social support from (a) family (4 items), (b) friends (4 items), and (c) a significant other (4 items). Scores for each of these scales range from 1 to 28; a higher score indicates higher social support. Cronbach’s alpha for the entire scale for this sample was .94; for the family factor, .90; for the friends factor, .92; and for the significant-other factor, .88. The questionnaire was validated and has face, construct, and predictive validity (Zimet et al., 1988).

Loneliness questionnaire

The loneliness questionnaire (Williams & Asher, 1992; adaptation to Hebrew: Margalit, 1991) includes 24 items: 16 loneliness items and 8 fill-in items on a 5-point Likert-type scale (1 = never feel like this, 5 = always feel like this). Higher scores reflect stronger feelings of loneliness. The loneliness scale (Cronbach’s α = .89) is divided into two subscales: social loneliness (Cronbach’s α = .92) and emotional loneliness (Cronbach’s α = .88). The questionnaire was validated and has face, convergent, and construct validity

Results

Prevalence and Characteristics of Bystanders in CB Episodes

Twenty-seven percent of the study participants (n = 1,094) reported being cyber-victims, and 19% reported being cyber-perpetrators. In all, 497 (46.4%) of the participants reported they were bystanders to CB episodes. All the participants reported a high level of Internet literacy: use of computers and Internet, and easy, frequent access to social media sites. A total of 83.4% reported using the Internet at least once a day. On average, adolescents use the Internet 4.38 hr per day (SD = 2.15). Bystanders (n = 497) reported the kind of CB act they witnessed. The distribution of the types of CB acts reported is presented in Figure 1.

Figure 1.

Distribution of CB acts as reported by bystanders.

As shown in Figure 1, 55.5% of the bystanders (n = 351) reported they witnessed hurtful verbal messages, targeted to harass and offend someone, 44.9% (n = 285) witnessed nasty or humiliating rumors being spread, 29.9% (n = 185) witnessed getting pictures or movies that were meant to insult and hurt, and 22.5% (n = 142) witnessed calling for social exclusion of peers on the Internet.

Differences Between Bystanders and Non-bystanders to CB in Internet Literacy, Age, and Gender

Bystanders tend to use the Internet more hours per day (M = 4.65, SD = 2.55) than nonbystanders (those who report they have not witnessed CB; M = 4.18, SD = 2.04), t(1069) = 3.36, p < .001, Cohen’s d = 0.21. No significant differences were found between bystanders and nonbystanders by age, t(1069) = 0.83, ns, Cohen’s d = 0.05, and class groups, χ²(2) = 4.48, ns. At the elementary school level, 47.8% reported being bystanders; in junior high, that number was 47.9%; and in high school, it was 39.1%. No significant differences were found between boys and girls, χ²(1) = 1.29, ns, for the probability of being a bystander to CB acts.

Bystanders’ Passive and Active Behavior Patterns Supporting Cyber-Victims

The bystanders were asked whether they provided help to cyber-victims. In addition to the options described below, there was a place to write in “Other” reasons for not aiding a victim, but the participants did not indicate different reasons there.

The distribution of answers is presented in Figure 2.

Figure 2.

Distribution of bystanders’ answers regarding supporting cyber-victims.

As shown in Figure 2, 192 (38.6%) of the bystanders reported that they did not provide help to cyber-victims because they felt it was not their business. Eighty-four (16.8%) reported that they did not provide help because of fear. In all, 177 (35.6%) reported they provided help to the victim directly, and 44 (9.0%) answered that they helped the victim by reporting the incident to an adult. The findings support the hypothesis that according to the “bystander effect,” most of the bystanders would not offer direct help to the cyber-victim at the time of the act. Only 35.6% (about a third of all bystanders) actually gave assistance to cyber-victims in real time.

In sum, more than half of the bystanders, 55.4%, were passive and did not provide any help to cyber-victims, either because they thought the incident was not their business or because they were afraid to act. The rest of the bystanders, 44.6%, were active and helped the cyber-victim, either through direct help or by telling an adult.

Differences Between Passive and Active Bystanders in Helping Cyber-Victims

To examine the relationships between active and passive patterns of the bystanders’ reactions with their personal and psycho-social indexes, we conducted a series of independent-sample t tests between the two groups of bystanders, active and passive, in relation to the following variables: gender, age, loneliness, social support, and self-efficacy.

Results of χ² tests found gender differences between the active and the passive groups. Girls tend to be more active than boys in providing help to cyber-victims. Out of the 258 girl bystanders, 156 (60.6%) reported actively helping the victim, whereas only 94 out of the 239 (39.4%) boy bystanders reported actively helping cyber-victims, χ2(1) = 12.36, p < .001, ϕ = .16. The means of the groups, standard deviations, and significant differences are shown in Table 1.

Table 1.

Differences Between Active and Passive Bystanders on Age, Self-Efficacy Social Support, and Loneliness.

	Passive Bystanders			Active Bystanders			df	t	Cohen’s d
	M	SD	n	M	SD	n	df	t	Cohen’s d
Age	12.58	1.88	260	13.25	2.10	280	467	3.60***	0.33
Social self-efficacy	4.02	0.85	249	4.03	0.88	201	448	0.85	0.08
Emotional self-efficacy	3.30	0.93	250	3.22	0.86	200	448	0.94	0.09
Family social support	5.81	1.50	253	5.85	1.52	202	453	0.27	0.03
Friend social support	5.36	1.77	253	5.59	1.67	202	453	1.37	0.13
significant other social support	5.72	1.61	253	6.01	1.33	202	453	2.93*	0.28
Social loneliness	13.21	6.87	253	13.05	6.78	200	451	0.25	0.02
Emotional loneliness	18.60	9.38	253	17.11	7.35	200	451	1.84*	0.17

†

p < .1. *p < .05. ***p < .001.

Table 1 shows significant differences between active and passive bystanders based on the age, emotional loneliness, and social support from significant other. Active bystanders, who provided help to victims, are older than passive bystanders, have more social support from a significant other, and feel less emotional loneliness. No differences were found between the bystander groups on self-efficacy.

Differences Within the Active Bystanders (Those Who Help Cyber-Victims) in Personal–Socio-Emotional Resources

As presented above, active bystanders used two ways to provide help to cyber-victims: 80% of the active bystanders reported they provided direct help to the victim, whereas 20% told an adult. To study the differences between these two behaviors in the active group in relation to the personal–socio-emotional measures, a series of independent-sample t tests, was conducted, and the results are presented in Table 2.

Table 2.

Differences Between the Two Active Bystander Behaviors on Age, Self-Efficacy, Social Support, and Loneliness.

	Providing Direct Help to the Victim			Report to an Adult to Help the Victim			df	t	Cohen’s d
	M	SD	n	M	SD	n	df	t	Cohen’s d
Age	13.55	2.09	167	12.07	1.64	42	207	4.24***	0.59
Social self-efficacy	4.07	0.82	162	3.86	1.10	39	199	1.36	0.19
Emotional self-efficacy	3.20	0.86	162	3.35	0.89	38	207	1.46	0.14
Social loneliness	11.95	5.76	160	17.48	8.64	40	198	4.86***	0.69
Emotional loneliness	16.62	6.66	160	19.13	9.46	40	198	1.95*	0.28
Family social support	5.85	1.54	161	5.89	1.46	41	200	0.19	0.03
Friend social support	5.55	1.66	161	5.75	1.76	41	200	0.68	0.10
significant other social support	5.99	1.37	161	6.11	1.24	41	200	0.50	0.07

p < .05. ***p < .001.

As shown in Table 2, among the two groups of active bystanders, the active bystanders who provided direct help to the victim are older, feel less social and emotional loneliness than the active bystanders who told an adult. No differences were found in self-efficacy and social support and no differences were found between boys and girls, χ²(1) = 0.13, ns, ϕ = .03.

Differences Within the Passive Bystanders (Not Helping the Victim) in the Personal–Socio-Emotional Aspects

Passive bystanders who did not provide help to the cyber-victim explained their behavior in two ways: (a) It is not my business (almost 70% of the passive bystanders) and (b) I was afraid to intervene (30% of the passive bystanders). The differences between the two passive groups in the personal–socio-emotional were analyzed through a series of independent-sample t tests and are presented in Table 3.

Table 3.

Differences Between the Two Passive Bystanders Groups in Age, Self-Efficacy, Social Support, and Loneliness.

	It Is Not My Business			I Was Afraid to Intervene			df	t	Cohen’s d
	M	SD	n	M	SD	n	df	t	Cohen’s d
Age	12.80	1.87	181	12.11	1.83	79	258	2.74**	0.34
Social self-efficacy	4.10	0.87	172	3.86	0.82	77	247	2.03*	0.26
Emotional self-efficacy	3.38	0.96	173	3.14	0.83	77	248	1.95*	0.25
Family social support	5.89	152	174	5.65	1.49	79	251	1.18	0.15
Friend social support	5.60	1.70	174	4.86	1.84	79	251	3.10**	0.39
Significant other Social support	5.89	1.59	174	5.38	1.63	79	251	2.32*	0.29
Social loneliness	12.62	6.65	175	14.55	7.21	78	251	2.07*	0.26
Emotional loneliness	18.31	9.17	175	19.25	9.88	78	251	0.73	0.09

p < .05. ***p < .001.

As seen in Table 3, passive bystanders who expressed fear of intervening were younger and had less social support from friends and significant others, less social and emotional self-efficacy, and higher levels of social loneliness than bystanders who argued that intervening and helping was not their business. No significant differences were found in emotional loneliness and family social support. Furthermore, a difference was found between boys and girls in the passive group. More girls (36.5%) than boys (25.3%) reported they were afraid to intervene, χ²(1) = 3.60, p < .06, ϕ = .12.

We ran a logistic regression to evaluate the unique contribution of each predictor to the probability of helping the cyber-victim (active bystander). (Table 4). The dependent variable was the standpoint of the bystander (1 = active bystander, 0 = passive). The independent variables were gender, age, emotional loneliness, and social support of significant others. The regression model was significant, χ²(4) = 25.01, R² = .07. Gender and age predicted the probability of being an active bystander, helping the victim. Girls more than boys, and older bystanders more than younger ones, showed a higher probability of being an active bystander. Emotional loneliness and social support of a significant other did not predict the dependent variable.

Table 4.

Hierarchical Logistic Regression Analysis Explaining the Variance of Active Pattern of Bystanders’ Behavior in CB Episodes.

Variable	B	SE	Wald	Exp(B)
Gender	−.59	.20	8.70**	0.56
Age	.14	.05	7.86**	1.15
Emotional loneliness	−.01	.01	1.30	0.99
Social support (significant other)	.06	.07	0.84	1.06

Note. CB = cyberbullying.

p < .05. ***p < .001.

Discussion

Who Is Likely to Provide Help to Cyber-Victims?

Through their behaviors, bystanders in CB episodes can actually cause the bullying acts to continue and expand, or to diminish and stop. The bystanders’ behaviors can significantly affect the victims’ feelings, the way the victims cope, and their overall sense of well-being. Studies on face-to-face bullying have shown that bystanders play a key role by providing help to victims and preventing further violent episodes (Coloraso, 2010; Cowie, 2000; Denny et al., 2015; Salmivalli, 2010).

In cyberspace, peers who witness harassment can increase and exacerbate the harm to someone’s life, by passing on secrets, violating privacy, or exposing humiliating and embarrassing texts and photos, or they can stop, delete, block, and report cyber assault. Furthermore, by showing compassion and empathy, bystanders can become protectors who are seen by others.

The present study explored the bystanders’ behavior in CB episodes among Israeli children and adolescents, differentiating between active and passive patterns of behavior and revealed specific personal and socio-emotional variables that may be associated with as well as predict providing help to victims in cyberspace.

The findings indicate that a high number of children (46.4% of the 1,094 participants) were bystanders to CB episodes. More than half of the bystanders were passive (55.4%) and did not provide any help to cyber-victims, most of them out of fear and a few because they thought the incident was not their business. It may be explained by a fear to become victims, a fear of reprisal, and sometimes not knowing practically what to do and how to assist (Camodeca & Goossens, 2005). Moreover, CB bystanders do not tend to report incidents to adults (school staff and parents), perhaps out of fear of retaliation and peer rejection for turning on other students (Berkowitz, 2014). They may also be concerned that their devices will be taken from them, or they do not think adults will understand the dynamics of activity on the Internet and are not “digital” enough (Campbell, 2005).

The hypothesis that according to the “bystander effect” (Latané & Darley, 1970), most of the bystanders will not offer direct help to a cyber-victim, when many other people are present in the situation, was assessed. Only one third of all CB bystanders online actually gave assistance to cyber-victims directly, in real time. We hypothesized that in cyberspace, the bystander effect would be significant as a result of its special characteristics, such as anonymity and the huge number of possible peers witnessing the scene, increasing the diffusion of responsibility. The increased impact of the bystander effect online may be further explained by the factors indicated by Hudson and Bruckman (2004), for example, the absence of blocking mechanisms and the tendency of people online to articulate themselves more freely. Therefore, in addition to the diffusion of responsibility derived from the large audience, the bystanders in CB episodes appear to feel even less responsible for providing help as compared with face-to-face bullying episodes. Yet, this initial finding requires further research to examine the question of whether the bystander effect is more pronounced in online environments.

Another main purpose of this study was to investigate the differences between the passive and active behavior patterns in regard to assisting cyber-victims, in relation to personal variables: gender, age, and socio-emotional resources—self-efficacy, social support, and loneliness. Consistent with our expectations, the results indicated that girls were more likely than boys to intervene and assist cyber-victims. Moreover, the current findings indicate that active bystanders, who provided help to cyber-victims, are older than passive bystanders. It may be that with age and the development of moral judgment and a higher sense of responsibility, the willingness to help cyber-victims increase.

Findings also indicate that the socio-emotional aspects that are associated with an active pattern of providing help to cyber-victims are social support from a significant other (as in the case of face-to-face bullying in the research of Salmivalli, 2010, and Batanova, Espelage, and Rao, 2014), and lower levels of emotional loneliness, compared with the bystanders who manifested a passive pattern. Loneliness, as examined in its two dimensions, reflects a discrepancy between an individual’s expectations of interpersonal relations and his or her social situation in reality (Peplau & Perlman, 1982). More specifically, emotional loneliness reflects the individual’s feelings of isolation in social situations, sadness and anxiety, when he or she is in the company of others, or tries to socialize (Parker & Seal, 1996). To the best of our knowledge, the sense of loneliness as well as social support have not been examined to date in the context of bystanders providing/not providing help to cyber-victims. The current findings suggest that a low sense of loneliness and high levels of social support from someone close and valuable are significantly correlated with providing help to cyber-victims. When the level of social support is high and the level of loneliness is low, bystanders seem to feel more secure and confident in providing help to cyber-victims. However, as opposed to expectations and previous studies (Thornberg & Jungert, 2013; Twemlow & Sacco, 2013), no differences were found between the active and the passive groups in the level of self-efficacy. In the world of children and adolescents, where the social environment and peers are highly important, the way one is treated and perceived socially (reflected by social support and sense of loneliness) might be more significant than one’s self-perception and beliefs in his or her own abilities to successfully perform actions and achieve anticipated outcomes (self-efficacy). In addition, on the Internet, the huge size of the potential audience adds to the effect of the social factor. Therefore, when someone is providing help to cyber-victims, it is possible that the social factors are most significant.

The logistic regression model has revealed that gender and age predicted the probability of being an active bystander, helping the victim. Whereas the study of Bastiaenesens and colleagues has indicated girls expressed higher behavioral intentions to comfort victims, the findings of the present study have taken another step in showing that girls actually help cyber-victims more than boys in CB episodes. This kind of knowledge indicates that special attention should be paid to gender and age differences when developing intervention programs for coping with CB.

When studying these differences between the passive and the active patterns of behavior, it was discovered that the active bystanders who provided direct help to cyber-victims were characterized as older, having social support from a significant other, and being less lonely than those who told an adult in order to help the victim. Within the passive pattern group, the bystanders who expressed a fear of intervening were younger and had less social support, less self-efficacy, and higher levels of social loneliness than those who argued that it was not their business to intervene. Furthermore, more girls than boys expressed a fear of intervening. Identifying the personal–socio-emotional resources that are associated with the fear of peers to help is a highly important factor when developing programs to encourage and strengthen peers to assist cyber-victims.

Limitations and Future Research

Some limitations of this study should be noted. The measures used were self-reported, and common method variance may account for some of the observed relationships. It is suggested that future studies provide external information sources that are not only based on self-reporting and that could validate and extend these results. Further research should draw on more external measures as well as qualitative analyses to deepen the understanding of the motivation for bystanders’ behavior in CB episodes. Further study is also required to explore more deeply the gender and age differences that were found as predicting the likelihood of helping cyber-victims. Moreover, for investigating more options for why not assisting a cyber-victim, it is recommended to ask participants accurately and more directly a question regarding more reasons for not helping victims, such as, “Please provide/explain other reason/s for not helping the victim.”

In the present study, passive and active patterns were found in bystanders’ behaviors in relation to specific personal–socio-emotional aspects. Other relevant variables are important to examine in this context; for example, investigating the level of moral judgment, empathy, and/or personality factors (such as those examined in the OCEANIC–Personality Inventory, based on the Big Five Factor Theory of Personality—Openness, Conscientiousness, Extraversion, Agreeableness, Nueortism Index Combined; Roberts, 2001) would deepen the understanding of the factors that might affect a bystander’s willingness to assist, thus to further characterize, in higher resolution, patterns and profiles of bystanders that help cyber-victims. Another limitation is the cross-sectional nature of the data, which makes the direction of causality unclear.

The present results may serve as a basis for a multilevel analyses model that would be able to examine the factors that contribute to the motivation of bystanders to help acts of bullying and violence on the Internet, as well as to evaluate the value of each factor in this model, including processes of mediation. For a broader picture, examining this kind of model in multicultural samples is also important.

Implications for Interventions

Most of the intervention programs to date dealing with CB have focused on reinforcing the victims and instructions of coping strategies to deal with the harassment, sometimes focusing on “at-risk” students. However, in light of the current intensive use of the Internet and social media by children and youth, the results of the present study imply a need to address the larger social context of the peer groups in which the probability of witnessing CB incidents is high. Like face-to-face bullying, CB often occurs in the presence of bystanders (Hawkins et al., 2001). Encouraging peers to support cyber-victims is an important challenge in the design of anti-CB programs. The results of this study can contribute to designing programs that encourage greater involvement of peers in CB incidents as supporters of victims, thus affecting the prevention of the escalation of CB episodes. The findings of the study indicate that girls and older children are the best target groups for being active bystanders supporting cyber-victims, but room remains for special attention to the population of children and younger teenagers.

The implications of this study suggest development of intervention programs that focus on raising the awareness of the role of bystanders in CB, how to recognize CB on the Internet, how to identify cyber-victims, how to identify peers who call for help, and how to assist them. Moreover, the results emphasize the need for a variety of activities for strengthening the social circle supporting the bystanders, strengthening social networks and peer support, lessening their sense of loneliness, and increasing their social confidence. These may lead to a higher probability that more peers will provide more help to cyber-victims in real time online.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Israeli Ministry of Education.

Author Biographies

Dorit Olenik-Shemesh is a researcher at the Open University of Israel, Department of Psychology and Education. Her current research targets the nature of cyberbullying among youth and children, in the context of social and psychological aspects as well as coping strategies; adolescents’ psychology, emotional abilities, and well-being in relation to coping with stress encounters.

Tali Heiman is a senior lecturer in the Department of Education and Psychology, at the Open University of Israel. Her research interests include cyberbullying among youth with and without disabilities; emotional and social coping of adolescents with learning disabilities; parent coping, and adjustment; technology contribution to students with special needs.

Sigal Eden is a lecturer at the special education track in the School of Education at Bar-Ilan University. Her main research interests are cognitive and lingual enhancement of children with special populations via technology, learning environments integrating advanced technology, and digital reading.

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