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Cyber-bullying: An exploration of bystander behavior and motivation

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While previous research has examined mainly self-reported bystander behavior during cyber-bullying, the current study explored if and how bystanders responded when presented with a cyber-bullying simulation. We hypothesized that individuals high in empathy would supportively intervene (defend the victim) most frequently. College age participants (M = 20.34, SD = 1.26, range 18-27; N = 149), viewed a simulated Facebook conversation in which negative comments were directed towards another student and were provided open-ended opportunities to be involved in the Facebook conversation (i.e., “comment” to the other fictitious characters) and explain their reasoning for their behavior (“motivators”) at two time points in the conversation (Time 1 and Time 2). Using a deductive-inductive process, we categorized participants’ comments and motivators, the frequency of these responses, and their reasons for them. While the majority of participants (91%) asserted that cyber-bullying occurred in the conversation, most participants did not comment (Time 1: 69%, Time 2: 52%). Among those who commented, the most frequently cited motivators were either to defend the victim or mediate the situation. Consistent with our hypothesis, individuals who identified with the victim had higher empathy scores than those who identified with the bullies, although this was true only for the second part of the conversation (Time 2). Empathy scores did not differ by type of response at either time period. Future studies could utilize the categories and motivators established in this study as a framework for more extensive quantitative research to more comprehensively understand the underlying reasons for low intervention rates in cyber-bullying. © 2008 Cyberpsychology: Journal of Psychosocial Research on Cyberspace.
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Shultz, E., Heilman, R., & Hart, K. J. (2014). Cyber-bullying: An exploration of bystander behavior and
motivation. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 8(4), article 3. doi:
10.5817/CP2014-4-3
Cyber-bullying: An exploration of bystander behavior and
motivation
Emily Shultz1, Rebecca Heilman2, Kathleen J. Hart3
1,2,3 Xavier University, Cincinnati, OH, USA
Abstract
While previous research has examined mainly self-reported bystander behavior during cyber-
bullying, the current study explored if and how bystanders responded when presented with a
cyber-bullying simulation. We hypothesized that individuals high in empathy would supportively
intervene (defend the victim) most frequently. College age participants (M = 20.34, SD = 1.26,
range 18-27; N = 149), viewed a simulated Facebook conversation in which negative comments
were directed towards another student and were provided open-ended opportunities to be involved
in the Facebook conversation (i.e., “comment” to the other fictitious characters) and explain their
reasoning for their behavior (“motivators”) at two time points in the conversation (Time 1 and
Time 2). Using a deductive-inductive process, we categorized participants’ comments and
motivators, the frequency of these responses, and their reasons for them. While the majority of
participants (91%) asserted that cyber-bullying occurred in the conversation, most participants did
not comment (Time 1: 69%, Time 2: 52%). Among those who commented, the most frequently
cited motivators were either to defend the victim or mediate the situation. Consistent with our
hypothesis, individuals who identified with the victim had higher empathy scores than those who
identified with the bullies, although this was true only for the second part of the conversation
(Time 2). Empathy scores did not differ by type of response at either time period. Future studies
could utilize the categories and motivators established in this study as a framework for more
extensive quantitative research to more comprehensively understand the underlying reasons for
low intervention rates in cyber-bullying.
Keywords: Cyber-bullying; bystander; intervention; motivation
Introduction
As society continues to embrace communication on social-networking sites, cyber-bullying (CB) has
become a more common occurrence and a substantial concern. Tokunaga (2010) provided an integrative
definition of CB derived from a meta-synthesis of current research and theories, describing it as “any
behavior performed through electronic or digital media by individuals or groups that repeatedly
communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (p. 278).
Previous studies have used similar definitions of CB (Freis & Gurung, 2013; Hinduja & Patchin, 2012,
2013; Li, 2010), but it is not clear that these definitions reflect the views of the general public. Indeed, it
may be difficult to differentiate teasing from CB. Desmet et al. (2012) conducted focus groups in which
they asked participants to describe CB and differentiate it from other forms of communication. Whereas
participants defined ‘teasing’ as a communication among friends that was not intended to be hurtful and
where the object of the teasing may even find the comments funny, they defined CB as communication
that involves intentional harm. Similarly, in a comparison study of CB definitions across six European
countries, Menesini et al. (2012) concluded that among the five definitional criteria for CB (intentionality,
imbalance of power, repetition, anonymity, and public vs. private), the two clearest dimensions across the
countries were imbalance of power and intentionality.
Thus, the definition of CB often relies on the perceptions and judgments of bystanders (observers) to the
interaction to identify when the bully is asserting himself/herself over the victim, and when he or she is
causing intentional harm to the recipient. Clearly, a bystander’s perception may vary by the context of the
interaction and the specific lens through which he or she views the situation; this lens can be influenced
by culture, age, sex, and social relationships with the involved parties (Thornberg et al., 2012).
Although the prevalence of CB depends on the definition utilized and the specific sample under study,
previous research indicates that CB is prevalent in a wide variety of settings. In a meta-analysis of CB,
Tokunaga (2010) found that, on average, approximately 20-40% of youths reported being victimized by a
cyber-bully, whereas Hinduja and Patchin (2012) found that approximately 6-30% of adolescents have
been the victims of some form of CB. Addressing the rate of perpetrators, Hinduja and Patchin (2013)
found that 28% of adolescents self-reported engaging in CB behaviors towards others, with the likelihood
to engage in CB influenced by peers’ engagement in CB. They found that 62% of the students who said
“all” or “most” of their friends had cyber-bullied others in the previous six months reported that they had
also engaged in CB in the last 30 days, whereas only 4% of the students who said “none” of their friends
had cyber-bullied others reported engaging in CB.
Tokunaga’s (2010) meta-synthesis suggests that there may be a curvilinear relationship between age and
frequency of CB victimization, such that the highest frequency of victimization occurs between the ages of
12 and 13, with a lower rate of victimization occurring from ages 10 to 12 and 14 to 20. Although the
majority of prior research has examined CB within elementary and high school settings where CB may
occur most frequently, examining the young adult population should not be neglected because of their
frequent utilization of social networking sites. Previous studies indicated that college students report
spending from 30 minutes (Pempek, Yermolayeva, & Calvert, 2009) to 60 - 120 minutes each day on
Facebook (Kalpidou, Costin, & Morris, 2011). Schenk and Fremouw (2012) reported that approximately
9% of college students were victims of CB and that the victims scored higher than matched controls on
psychological factors such as depression and anxiety, and in suicidal ideation. Considering the negative
mental health impact of CB even in college aged participants, it is important to gain greater understanding
of this phenomenon in this age group.
The social dynamics of CB are distinct from traditional bullying because of factors attributable to the
electronic device, including feelings of anonymity and the ability to communicate without physical face-to-
face interactions (Tokunaga, 2010). As previous scholars have established, these differences indicate that
CB should be examined with a separate theoretical lens (Tokunaga, 2010). In fact, previous research has
suggested that CB may be worse for victims than traditional bullying methods because there is seemingly
no escape and the harmful material is preserved and easily spread (Freis & Gurung, 2013; Li, 2010).
Similarly, Barlinska, Szuster, and Winiewski (2013) postulated that victims may experience more harm
from CB than face-to-face bullying because online contact increases the likelihood for negative bystander
behavior, in that bystanders may spread the CB content to wider audiences by virtue of the platform
(such as a Facebook page viewed by a large number of “friends”), or by forwarding it to other media sites.
Thus, as CB becomes a prominent method of bullying that may cause significant psychological harm to
victims, it is essential to understand the ways in which bystanders respond to CB, and their motives for
doing so.
CB Bystander Intervention Strategies
An analysis of bystander behavior has identified both active and passive reactions to CB from individuals
who witness it. Among the active reactions to CB, bystanders’ distinct intervention strategies are
apparent. Under the assumption that those who witness the act of CB actively choose to intervene in the
CB situation by deleting or forwarding a CB message, Barlinska et al. (2013) operationally defined
negative bystander behavior as choosing to forward a negative instant message rather than delete it from
the system, which spreads the CB message to a wider audience and perpetuates the CB, thus potentially
increasing psychological harm to the victim. In this situation, the bystander becomes a bully by actively
engaging in the negative comments about the victim. Freis and Gurung (2013) utilized a live chat feature
to observe bystander responses of female college students as they interacted with two confederates (one
“victim” and one “bully”) and identified a series of intervention strategies among participants. They
categorized the responses as: say “stop/bully;” foster discussion; try and change the topic; comfort the
victim; attack the bully; pass; and other. Although 91% of their participants chose to intervene using one
or more of the defined strategies during the interaction, 44% of the sample still said “pass” at some time
during the conversation. Furthermore, those who intervened were more likely to utilize indirect forms of
intervention, such as changing or avoiding the topic rather than engaging in a direct intervention (e.g.,
saying “stop/bully” or comforting the victim).
Machackova, Dedkova, Sevcikova, and Cerna (2013) suggested that bystander behavior could be
differentiated into confrontational versus supportive behavior. Whereas confrontational bystander
behavior involves the bystander directly confronting the bully in defense of the victim, supportive
bystander behavior encourages the victim and includes behavior such as telling the victim to ignore it,
trying to comfort the victim, or recommending the victim to tell someone who could help. They found that
76% of bystanders self-reported offering some form of support in a prior CB situation and many
participants reported offering more than one form of support. Specifically, from the least endorsed to the
most frequently endorsed items, their participants reported: telling the victim to ignore it (55%); trying to
comfort the victim (54%); telling the victim it was not worth the worry (53%); telling the victim they
were sorry about what happened (51%); keeping the victim occupied so he/she would not think about the
CB (40%); recommending the victim tell someone who could help (36%), and/or; giving the victim
technical advice about how to make the CB stop (35%). Studies by Machackova et al. (2013) and Fries
and Gurung (2012) mark beginning attempts to identify the types of responses bystanders might employ
when they encounter CB.
CB Bystander Motivations
In order to gain an understanding of bystander response to CB, several studies have attempted to identify
the underlying factors and motivations that prompt bystanders to confront or support, remain silent, or
contribute to the victimization. Previous research has suggested that personality traits, including
empathy, extraversion, and self-efficacy, can increase the likelihood that a bystander will choose to
supportively intervene in CB (Freis & Gurung, 2013; Polyhonen, Juvonen, & Salmivalli, 2010). Freis and
Gurung (2013) found that individuals with high empathy and high extraversion were more likely to
intervene in CB situations than those who were lower in these traits. Using a middle childhood and early
adolescence sample, Polyhonen et al. (2010) found that a strong sense of self-efficacy and greater social
status within the peer group was associated with defending victimized classmates in traditional bullying
situations. Bystander’s behavior may also be influenced by priming effects. For example, Barlinska et al.
(2013) found that when bystanders were exposed to either affective or cognitive empathy stimuli, they
were less likely to forward CB material onto wider audiences and were more likely to simply delete the CB
content than participants who had not been exposed to those stimuli.
Although there are differences between traditional bullying and CB in the method of bullying, a common
element of bullying remains consistent, in that it involves attacking another individual either physically,
verbally or emotionally. Furthermore, bystander’s behavior online may also relate to their behavior to
bullying offline, as suggested by the Co-construction theory (Wright & Li, 2011). This theory is supported
by previous research that demonstrated the positive relationship between prosocial behaviors face-to-face
to online prosocial behaviors (Wright & Li, 2011). Generally, prosocial behavior motivations include
altruism, which is a desire to improve the welfare of others, and reciprocity, which is a desire to be helped
in the future by those whom they helped. These motivations have also been positively correlated with
prosocial behavior online, specifically in anonymous online gaming scenarios (Wang & Wang, 2008).
Furthermore, Machackova et al. (2013) indicated prosocial behavior as the only individual characteristic
that was a significant predictor of supportive bystander behavior in a hierarchal regression model. It is
important to note that, although other individual factors (such as age, sex, self-esteem, and problematic
relationships) yielded no effect on supportive behavior, Machackova et al. found that contextual factors
(such as existing relationships with the victim, upset feelings evoked by witnessing victimization, and
direct requests for help from the victim) were significantly related to supportive bystander b ehavior in the
regression model.
Considering the parallels between offline and online prosocial behavior, bystander motivations in
traditional bullying provide an initial framework for examining bystander motivations in CB. Thornberg et
al. (2012) described a set of motive domains that might influence bystander motivation to intervene in
traditional bullying situations: interpretation of harm, emotional reactions, social evaluating, moral
evaluating, and intervention self-efficacy. According to this model, bystanders intervene if they perceive
that the bullying is causing significant harm to the victim. Researchers emphasized that an interpretation
of no harm could be due to a habituation of bullying behavior. Within the domain of emotional reactions,
empathy encourages bystanders to intervene, whereas fear of being victimized and audience excitement
(desire to watch the bullying because it provides entertainment) demotivates bystanders to intervene. The
social evaluating component explains that friendship with a victim motivates the bystander to intervene,
while friendship with a bully and a highly socially ranked bully demotivates the bystander to intervene.
Bystanders also engage in a moral evaluation of the bullying situation: A bystander’s belief that bullying is
wrong motivates intervention, but moral disengagement (i.e., bystanders believe it is not their moral
responsibility to intervene) demotivates intervention. Additionally, if bystanders blame the victim, they
are less likely to intervene. Finally, bystanders select an intervention strategy based on how effective they
believe their actions would be.
Gaps in the Current CB Research
Although there has been research on CB in recent years, this research has been mainly focused on victim
and bully behavior, and methodological limitations of the studies prevent generalizable, conclusive
findings regarding bystander behavior. Some research has utilized simulations of realistic CB situations
(Barlinska, et al., 2013; Freis & Gurung, 2013); however, other studies have only utilized self-report
measures of prosocial behavior in bullying situations (Machackova et al., 2013; Wright & Li, 2011).
Furthermore, CB has not been examined in the full variety of digital settings. For example, prosocial
behavior has been studied in anonymous digital venues such as online gaming, but there is a lack of
research in identifiable digital venues such as social networking sites (Wang & Wang, 2008). Additionally,
a majority of the research on CB has been conducted with middle and high school students, ignoring an
analysis of bystander behavior of young adults, who are highly active online (Kalpidou et al., 2011;
Pempek et al., 2009) and who report incidents of CB (Schenk & Fremouw, 2012). The most vivid
disparity, however, is the lack of understanding of bystander behavior and the motivation for bystanders’
responses to CB. Although Thornberg et al. (2012) addressed bystander motivations in traditional bullying
and Freis and Gurung (2013) conducted an exploratory study of bystander behavior in an anonymous, live
chat CB scenario, there is a gap in the research regarding bystander motivation in other CB situations,
including those found on frequently used social networking sites and within the young adult population.
Present Study
The current research involved a realistic CB simulation on a social-networking site, utilized a young adult
sample, and categorized bystanders’ open responses in order to examine patterns of their responses.
Primarily, we were interested in understanding young adults’ responses to CB when they are the
bystanders in that situation. Therefore, we presented a conversation in which two “friends” berated a third
friend for behavior at a party. Participants had the opportunity to contribute to the conversation, which
allowed us to categorize the types of comments they made into intervention strategies. We also asked
participants to explain why they responded (or did not respond) at two different time points in the
conversation simulation, which provided information about their motivation for their responses. We then
examined how the broad categories of responses differed by participant’s empathy and social
identification (i.e., whether they identified with the bullies or victim), hypothesizing that individuals high
in empathy would more frequently identify with the CB victim, and respond more frequently using
supportive forms of intervention than individuals who were low in this trait. In an exploratory capacity, we
examined bystanders’ open-responses to an online conversation in order to establish a more
comprehensive understanding of bystanders’ role in CB.
Method
Participants
Students (N = 149) volunteered to participate in this online study to earn research credit for their courses
in the psychology department at a private university in the Midwest region of the United States. The title
of the study as posted on recruitment information indicated it would involve Facebook, but they were not
given specific information about the nature or purpose of the study. The sample was 69% women and
28% men (approximately 3% of our sample was missing this data) with a mean age of 20.34 ( SD = 1.26,
range = 18 to 27 years). The majority of the participants were juniors (40%), followed by sophomores
(21%), seniors or 5+ year students (17%), and first year students (18%). The sample was 84%
Caucasian, 6% Black or African American, 2% Hispanic, and 4% other or mixed racial background. Nine
respondents (6% of the original sample) were excluded from analyses because they identified themselves
as non-Facebook users. Participants were Facebook members for an average of 5.56 years (SD = 1.40
years, range = 2 - 9 years). Members reported logging-in an average of 5.25 times per day (SD = 4.74,
range = 0 - 25 times) and spending 13.93 minutes per log-in (SD = 31.35, range = 0 - 360 minutes).
Measures and Stimuli
Empathy. The Interpersonal Reactivity Index (IRI, Davis, 1980) is a 28-item scale that measures
individual differences in empathy. The scale has been found to have high internal reliability ranging
from .71 to.77) and high test-retest reliability (rs ranging from .62 to .71, Davis, 1980; 1983). The scale
yields four subscales of empathy measuring perspective taking, fantasy, empathetic concern and personal
distress. The items are answered on a 5-point Likert scale ranging from 1 (does not describe me well) to 5
(describes me very well). We used the Total IRI score for our analyses; this score ranges from 0 140
and in our sample, ranged from 66 to 121 (M = 96.17, SD = 11.31, α = .82).
Facebook conversation. Participants viewed a fictitious Facebook conversation created by the
researchers in two parts (Time 1 / Time 2). The conversation involved an online interaction among three
college students; two students (“Sam” and “Chris”) posted comments on the third student’s (“Jordan’s”)
feed in which they made negative comments about that student’s behavior at a recent party (see
Appendix for the conversations). The conversation portrayed several types of CB, including exclusion
(intentionally excluding a person from a group), flaming (sending angry, rude or vulgar messages),
harassment (repeatedly sending offensive messages), denigration (put-downs), and outing and trickery
(posting material that contains sensitive, private, or embarrassing information), as defined and
established in previous research (Li, 2010).
The instructions to participants above the Facebook conversation read, Imagine that these three people
live in your dorm hall, or are close friends from high school and that this conversation has shown up on
your newsfeed. Please read the conversation and click ‘next’ when you are finished to continue to the next
page.” After reading the conversation (Time 1), participants were directed to the following page where
they were asked, Please select the person from the conversation that you feel you most identify with:
Jordan (the status updater), Chris (the first commenter), or Sam (the second commenter).” This one-item
question was used to measure participants’ social identification with a victim (Jordan) or a bully (Sam or
Chris). Participants then were asked an open-answer question, If you joined the conversation you just
read, where would you join and what would your comment be?” Participants typed their response in a text
box. The final question was to assess possible motivators of the bystanders and was also posed in an
open-answer format: “Please explain why you chose to comment or why you chose not to comment on
the conversation.”
Participants continued to another screen where the instructions for the second portion of the conversation
(Time 2) stated, “This is a continuation of the Facebook conversation you read previously.” After they
read the second screen, participants answered the same series of questions that were posed at Time 1.
Lastly, participants were prompted to describe where in the conversation (considering what was presented
in both Time 1 and Time 2) they thought bullying occurred; they were given the option to indicate that no
bullying occurred, and to explain their reasoning for their response. As the conversation continued, the
statements made to the victim gradually increased in vitriol.
Procedure
This study received approval by Institutional Review Board of the university where the study was
conducted. Students were recruited through an online posting of study opportunities or a participant pool
bulletin board that each included the study specific URL. If they chose to participate, students logged onto
any computer with access to the internet and used the URL to access the online study stimulus materials
and measures. Only those with the URL were able to participate.
When participants utilized the study URL, they first agreed to the statement of informed consent which
described the study as a 30 minute task involving several short surveys inquiring about their personal
Facebook use and attitudes, and responses to a brief scenario. They were informed that the purpose of
the study was to examine the relationship between personality traits and Facebook interactions. To
minimize ordering effects, participants were randomly assigned by the survey website to either read the
Facebook conversation and answer questions about it, then complete the randomly ordered personality
measures, or complete the randomly ordered personality measures then read and answer questions about
the Facebook conversation. At the conclusion of the study, participants were given information about the
complete purpose of the study and were notified that all individuals depicted in the Facebook
conversation, and the conversation itself, were fictitious.
Development of categories. Using a deductive-inductive process for coding (Auerbach & Silverstein,
2003), two of the authors coded participants’ responses to the Facebook simulation, including the
participant’s direct comment and their stated reason for commenting or remaining silent. We decided to
code comments and stated reasons separately for Time 1 and Time 2, the two parts of the conversation,
because some participants drastically changed their responses based on the severity of the bullying and
we wanted to capture these changes. We refined coding procedures for CB responses and bystander
motivators while keeping consistent with grounded theory methods, until inter-rater discrepancies were
resolved and consensus was reached. Utilizing previous literature (Freis & Gurung, 2013; Thornberg et al.,
2012) and deductive reasoning, several bystander intervention strategies were established for coding
purposes. We then employed inductive reasoning by altering coding categories in accordance with the
participants’ responses to cyber-bullying. This deductive-inductive procedure led to the agreed upon final
set of definitions for each category addressing bystander intervention strategy and motivation.
Table 1. Intervention Categories and Examples.
Category
Examples of bystander’s comments and behaviors
No comment
“Would not comment”
Supportive intervention (online)
"Cool it guys relax its fine. Everyone's invited and it will be a good
time!"
“Can’t we all just get along?”
“Guys, let's be respectful here. We all mess up and if Jordan doesn't
want people to know about this then we should just drop it.”
“My friends and I are having a movie night Jordan, you can come if
you want”
Supportive intervention (offline)
It is not my place to comment. If I did, I would risk getting made fun of
as well. However I would go to Jordan and ask him to hang out with
me for the night.
If I saw this I would not want to feed the fire and create more
attention. I would rather text Jordan and tell her to come to hang out
with me if she was my friend.
I would stay out of the conversation because social media is not the
place for serious talks about hookup culture and girls' insecurities. But
I might privately text the boys and tell them to back off. I might also
reach out to Jordan with support.
Confrontational intervention
“You guys are jerks. Back off.”
“Get a life. its funny how you pick on someone to make yourself feel
better about your own pathetic life”
“This is awful, who are you to say these things. you have no right, you
may think you are being funny, i am sure people are reading this like
wow didnt know jordan was like that, but wow sam and chris are
awful,!! who says those things, yeah she made a mistake, you are the
ones who are being hated on now!!”
I would most likely tell Chris and Sam that their behavior is
exceedingly immature for the age of a college student. I would also
tell Jordan to stop wasting her time with them.
Doesn’t belong online
"This conversation is very inappropriate for social media"
I would enter at the after the two first posts on Jordan's status telling
them that they shouldn't be posting this kind of material for everyone
to see.
After everything, I would tell her to just delete it and let it go.
Joining the bullies
I would join in anywhere, just to make fun of the person who was
throwing up all over the place
I would join the conversation when Sam and Chris were talking about
what happened at the last party, just to support the argument that
Jordan cannot hold his/her liquor
Note: Participants comments are denoted by quotation marks and participant’s statements to explain other
behavior are left without quotation marks (however, still in their own wording).
We coded participants’ direct comments to the Facebook conversation into several categories of
intervention strategy, initially using pre-determined categories of bullying intervention from Freis and
Gurung (2013): say “stop/bully”, foster discussion, try and change topic, comfort victim, attack bully,
pass, and other. However, since their study involved a live response design and in a different digital
venue (a private conversation), many comments did not fit into any of these categories (try and change
topic; and pass), so, in keeping with grounded theory, we grouped similar comments together and
created themes from the existing data (Allen, 2012; Thornberg et al., 2012). Confrontational and
supportive intervention strategies were established as distinct categories, a distinction suggested by
Machackova et al. (2013), with confrontational intervention indicating that the participant attacked the
bullies (e.g., “you guys are jerks,” “get a life”), and supportive intervention indicating that the participant
defended the victim (e.g., “give her a break,” “leave her alone,” “you guys need to stop”). Each comment
was coded as one distinct intervention strategy, and if a participant utilized a combination of the
strategies, the most prominent strategy was selected. Given the wide diversity of comments offered by
the participants, we ultimately sorted responses into the following additional categories: would not
comment; conversation does not belong on Facebook/online (e.g., “Facebook is not the place for this”);
and joining the bullies (e.g., “lol”). We also created a second set of broader categories in order to take
into account whether the intervention took place online or offline: no mention of intervention; supportive
intervention online, supportive intervention offline, and confrontational intervention online. The final list of
comment categories we used are listed in detail in Table 1, including examples of comments from
participants.
To determine bystander motivators, participants stated reasons as to why they chose to comment or
remain silent, which were initially coded by each rater who independently formed categories by grouping
together similar responses and deriving descriptive themes in a collaborative coding process (Auerbach &
Silverstein, 2003). We sorted participants’ responses to the question “Why did you choose to comment or
not comment?” into three overarching categories: Would not comment; would comment; and,
indecisive/miscellaneous. In each of these broad categories, the motivators were further divided into a
total of 14 subcategories, based on distinct themes that emerged from the responses. We allowed for
responses to be in more than one subcategory because many participants provided multiple reasons for
their response to the simulation. The bystander motivation themes, subcategories, and representative
examples are listed in detail in Table 2. The categories of comments and motivators, although created
with qualitative methods, were used quantitatively to describe trends in responding and to allow for
comparison of response types by identification with the bully vs. victim and by empathy scores.
Results
Identification of Bullying
We first examined whether participants believed that bullying occurred, and where in the conversation
they believed it occurred. The overwhelming majority (91%) responded that the conversation included
bullying. Of these participants, the majority (48%, n = 71) responded that bullying began at the
beginning of the conversation (when the victim was being excluded), 20% (n = 29) responded that
bullying began when both bullies attacked (these elements occurred in Time 1, before the first opportunity
for the participant to respond). In Time 2, 15% (n = 22) responded that bullying began when the victim
said stop and 8% (n = 12) responded that bullying began with name calling. The Appendix presents the
conversation, labels the types of bullying presented at different parts of the conversation, and the
percentage of individuals who identified bullying as beginning in that portion of the conversation.
Bystander comments. By separately categorizing comments at the two parts of the conversation (Time
1/ Time 2), we were able to capture how participant’s behavior may have changed as the bullying became
more severe over the course of the conversation. Although the rate of responding increased from Time 1
(30%) to Time 2 (47%) in the conversation, the majority of participants (52%) did not comment at either
time point in response to the bullying simulation. Among those participants who commented, their
comments were fairly long (Mlength = 26.2 words at Time 1; 27.5 words at Time 2). Figure 1 displays the
frequency of responses within the identified categories (would not comment, confrontational intervention,
supportive intervention, does not belong online, and miscellaneous) at each of the two time points.
Among participants who chose to comment, the most frequent response was to engage in a supportive
intervention (Time 1: 14%, n = 21; Time 2: 28%, n = 41).
Table 2. Bystander Motivator Themes: “Reasons” for Commenting or Not Commenting.
Motivator for not commenting
Theme
Examples
Bystander/ Do not want to get involved
“Avoid drama,” “I would risk getting made fun of as well,” “I prefer to
act like I didn’t see it”
Feel helpless and/or my comment would
cause more trouble
“There is nothing in the status I can say to change the two bullies
minds,” “Adding my opinion would encourage it to continue and
worsen,” “Fighting an uphill battle to defend Jordan”
Think the conversation is stupid,
immature and/ or unnecessary
“The whole topic is unprofessional”, “Petty and unproductive,”
“Arguing on Facebook is a waste of time”
Against alcohol/drinking/partying
“I hate when people post about getting drunk,” “I speak against my
friends using alcohol,” “I have strong opinions about drinking”
Motivator for commenting
Want to defend the victim (Jordan) and
stop the conversation
“I would not want someone to treat my friend like that,” “It needed
to be stopped,” “Leave her alone”
Want to mediate in the conversation
“People make mistakes,” “No one is perfect,” “They shouldn’t pick on
her for just one night”
Convince the victim (Jordan) that the
other commenters are not worth her
time and/or invite her to another event
“I should extend my hand to someone who is lonely’” “Jordan needs
to ignore these childish individuals,” “She deserved better and could
have better friends”
Remind all commenters that this is a
public media site
“I feel like people should be careful what they put online,” “This is
not appropriate for everyone to see,” “It’s probably not that smart
for any of you to be saying this stuff on the internet for anyone to
see”
It’s fun to participate in negative
interaction
“Just to support the argument that Jordan cannot hold his/her
liquor,” “Since its close friends, it’s fun to mess around,” “Its
entertaining”
Indecisive, Other response options, or miscellaneous comments
On the fence
“I cannot be objective without more information on the people
commenting,” “It would completely depend on the person who
made the status, and the people who made the offenses on the
status update,” “I am torn if I would or not”
Intervene offline
“I would text Chris and Sam and tell them to back off and stop being
mean,” “I would private message each of the three individually to
address the issues,” “Do not want to say something online when I
could say it in person”
Facebook is not the right forum
“Facebook is not the place to fight about this stuff,” “It’s drama that
doesn’t belong on the internet,” “It’s a conversation that should be
had privately, not on a wall for all to see”
The victim (Jordan) should delete this
status
“Jordan seems too ignorant and naïve to just delete the status
update,” “I would tell her to just delete it and let it go,” “The lack of
ability for Jordan to just stop the whole thing by deleting it would
probably anger me”
Recognize it is cyberbullying, wrong and/
or mean
“They are bullying Jordan and it is wrong and illegal,” “Jordan is now
getting upset over it and the comments are really harsh,” “There is
never a time or a place to objectify a person and make him or her an
object of laughter”
Figure 1. Intervention categories in response to Facebook cyber-bullying simulation, including only the
analysis of participant’s responses in the comments section of the survey (N= 149).
We also examined participants’ reactions to the conversation by categorizing their stated reason for their
response. Some participants (Time 1: 9%, n = 14; Time 2: 11%, n = 16) who declined to comment
indicated in their reason that they would choose to intervene offline in a supportive way, such as talking
to the victim in person. By including the analysis of participants’ responses in the reason section of the
survey and taking into account whether the intervention would occur online or offline, we created broader
intervention categories (no mention of intervention; supportive intervention online, supportive
intervention offline, and confrontational intervention online); the frequency of responses with these
categories, separated by time point, are depicted in Figure 2. At Time 1, when bullies were excluding the
victim, 38% (n = 57) of participants intervened, and at Time 2, when bullies were name calling, 58% (n =
86) of participants intervened in some way. At both time points, only a small percentage of participants
responded in a way that confronted the bullies directly (Time 1: 7%, n = 10; Time 2: 9%, n = 13).
Figure 2. Broader intervention categories in response to Facebook cyber-bullying simulation, including the
analysis of participant’s responses in the reason section of the survey and the valence of response online
vs. offline (N= 149).
Empathy and social identification. Given the previous research that has suggested that factors such as
empathy and social identification may increase the likelihood of bystander intervention (Freis & Gurung,
2013; Thornberg et al., 2012), we examined the frequency of participants’ identification with the bullies
versus victim at the two response opportunities. At Time 1, the majority of the participants (64%)
reported identifying with the bully (vs. 36% who identified with the victim), but by Time 2, a greater
majority (83%) reported that they now identified with the victim (vs. 17% who reported identification
with bully). Whereas the IRI (empathy) total scores of those who identified with the bully at Time 1 did
not differ from those who identified with the victim at Time 1, t (146) = 1.12, p = .26, there was a
significant difference in the IRI scores of those who identified with the bullies vs. with the victim at Time
2, t (146) = 3.23, p = .001. Table 3 summarizes the means, standard deviations and t-test findings for
the IRI by bully/victim identification at each time period.
Table 3.Comparison of IRI (Empathy) Scores for Participants Who Identified
with the Bully or the Victim at Time 1 and Time 2.
Victim Identification
M (SD)
Bully Identification
M (SD)
t
p
Time 1
97.57 (11.44)
97.55 (10.57)
1.12
.26
Time 2
95.39 (11.22)
89.69 (12.55)
3.33
.001
Note: Higher IRI scores indicate higher levels of empathy.
Secondly, we examined if the frequency of participants’ interventions differed by their identification with
the victim versus one of the bullies. Chi squares analysis utilizing the broader comment categories from
Figure 2 (no mention of intervention, supportive intervention online, supportive intervention offline, and
confrontational intervention online) indicated that individuals who identified with the victim were more
likely to supportively intervene than individuals who identified with one of the bullies, at both Time 1, χ 2
(149, 3) = 22.82; and Time 2, χ2 (149, 3) = 9.72, p < .05. These results are displayed in Table 4.
Table 4. Results of Chi-square Test for Social Identification and Intervention Strategy
at Time 1 and Time 2.
Intervention Strategy
Time point
Social
Identification
No
mention
Supportive
Online
Supportive
Offline
Confrontational
Time 1
Victim
21
(22%)
21
(64%)
5
(36%)
7
(70%)
Bullies
71
(72%)
12
(36%)
9
(64%)
3
(30%)
Time 2
Victim
47
(72%)
49
(88%)
15
(100%)
12
(92%)
Bullies
18
(28%)
7
(13%)
0
(0%)
1
(8%)
Note: Numbers in parentheses indicate percentages within each intervention strategy. p < .05
Time 1, 2 (149, 3) = 22.82 *; Time 2, 2 (149, 3) = 9.72 *
In light of these findings, we examined whether participants’ empathy differed across the comment
categories. Using the comment categories from Figure 2, we conducted ANOVAs to compare the IRI
scores of participants who engaged in the four different types of broad responses at each time period (no
mention of intervention, supportive intervention online, supportive intervention offline, and
confrontational intervention online). However, we did not find differences in empathy scores at Time 1, F
= 1.67, p = .18, or at Time 2, F = 1.62, p = .19.
Bystander motivators. As previously explained and displayed in Table 2, participants’ reasons for
commenting or not commenting were sorted into 14 themes to better understand bystander’s motivators
within three overall categories: motivator for not commenting; motivator for commenting, and; indecisive,
other response options or miscellaneous. The accompanying paragraphs briefly describe the development
of each of the themes to enlighten the reader on how we conceptualized the stated motivations of the
participants.
Motivators for not commenting. Some participants indicated that they “did not want to be involved,” or
they indicated that they purposely avoided commenting or intervening because they did not feel
comfortable responding to the simulated CB situation. This theme encompassed a variety of responses,
including individuals responding that a comment was not their place, was none of their business, they
wanted to avoid drama, were afraid that the bullies would attack them too, and/or found the conversation
uncomfortable or awkward.
The previous theme was distinguished by participants who were demotivated to comment because of fear,
avoidance, and/or uncomfortableness; however, there were also participants who were unwilling to
intervene because they felt “helpless and/ or their comment would cause more trouble.” These individuals
indicated that they disagreed with the way the victim was being treated, but also specifically stated that
they believed their comment would not be effective, or that their comment would cause the conflict to
escalate.
Other participants stated that they found the conversation “stupid, immature and/ or unnecessary.” We
interpreted their stated motivators to convey that a group of participants chose not to comment because
they found the conversation unworthy of their time or pointless.
Lastly, another sub-group of participants indicated that their beliefs “against alcohol/ drinking/ partying”
demotivated them to comment. These individuals further explained that they did not comment because
they disagreed with the topic of conversation and did not want to be associated with those topics.
Motivators for commenting. Some participants commented in order to intervene on behalf of the victim. A
subset of participants “wanted to defend the victim (Jordan) and stop the conversation,” which we
interpreted to communicate that their moral convictions motivated them to intervene. Within this theme,
often times individuals thought about Jordan as one of their friends and indicated that they intervened
because they felt that no one should be treated in such a manner.
Other participants commented because they “wanted to mediate in the conversation” in order to
encourage empathy for the victim; for example, these responses included statements indicating that the
victim should be given a second chance and should not be harshly judged for a single incident. In contrast
to individuals who chose to “defend the victim” who explicitly chose the victim’s side, individuals who
expressed wishes to “mediate” in the conversation did not explicitly choose sides and instead expressed
that their comment was an effort to indirectly stop the bullying in a manner that was neither
confrontational nor overly supportive toward the victim.
There were also some participants that commented on the conversation in a manner that was extremely
supportive, such as those who commented to “convince the victim (Jordan) that the other commenters
are not worth her time and/ or invite her to another event”, in order to reach out with social support to
her. It is interesting that some participants’ comments were neither supportive nor confrontational. Thus,
another theme was created to encompass the participants who commented with statements indicating
they would “remind all commenters that this is a public media site.” Many of them wrote that they wanted
all parties involved to be more conscious about what they were writing online for everyone to read.
In contrast to the other motivators for commenting, a small group of participants explained that they
would comment to join in with the bullies because “it’s fun to participate in negative interactions.” These
participants stated that because it was among a group of friends, the conversation was entertaining.
These participants may have interpreted the interaction as teasing, although the nature of their brief
comments does not clearly establish this possible interpretation.
Indecisive, Other response options, or miscellaneous motivators. Another group of participants stated that
they were still “on the fence” about whether they would comment or not. They explained that they would
consider other factors including their relationships with each of the commenters and if they were at the
party where the events occurred. We felt that this observation was important to distinguish from other
themes because it was an indication that contextual factors were influencing CB and bystander behavior.
Although some participants were still debating about how to react, many participants explicitly
“recognized it was cyberbullying, wrong and/ or mean,” even if they chose not to comment or intervene
on behalf of the victim. Other participants revealed that although they would not comment publicly on the
status, they would prefer to “intervene offline” by texting, messaging, or privately communicating with
the victim or bullies.
Lastly, expressing similar ideas regarding internet privacy, two final themes are: “Facebook is not the
right forum” and “the victim (Jordan) should delete this status.” These participants expressed that the
public nature of Facebook made both the conversation, and any participation in it, inappropriate.
Furthermore, we inferred that some participants might have believed that the victim had some control of
the situation and were attributing some responsibility to her when they stated that “the victim should
delete the status.”
Discussion
As society’s social interactions move increasingly online, CB is an area of growing concern for individuals
who utilize social networking frequently. In our sample of college students, Facebook use appears to
remain prevalent, despite some media claims that it is “dying” among younger users (Bercovici, 2013).
However, whether occurring on Facebook or through other media, such as Twitter, there are now a
variety of means for individuals to engage in CB. Since previous research has mainly focused on
bystander responses in digital venues such as online gaming sites and chat rooms where posting is done
anonymously, it is worthwhile to separately explore bystander responses in distinct digital venues,
including social networking sites, like Facebook, where the poster is identifiable.
The analysis of bystander behavior and motivation to a CB simulation in the current study revealed that
although the majority (91%) of participants believed that bullying had occurred, fewer than half of the
participants (Time 1: 31%; Time 2: 47%) reported that they would use a supportive intervention method
(whether online comments or offline interventions) to offer support to the victim, which reflects a low
bystander response rate to CB on Facebook. This level of response has been found in previous studies. For
example, Li (2010) found that a majority of participants (70%) who claimed to have witnessed CB did not
intervene or actively respond (Li, 2010). However, the bystander response rate in a naturalistic study of
traditional bullying was 19% (Hawkins, Pepler, & Craig, 2001), which is significantly lower than the
response rate in our study, indicating that individuals may be more likely to intervene online than in a
face-to-face encounter. We speculate, with some evidence from previous research, that individuals may
be more likely to intervene online than face-to-face because of the relative anonymity of typing a
response, rather than verbally standing up for a victim (Tokunaga, 2010).
We also found that some bystanders preferred more indirect strategies of intervention (Time 1: 9%; Time
2: 10%), such as intervening offline/ in private or providing support for the victim, rather than directly
defending the victim to the bullies. Although participants expressed that they would intervene offline or in
private, unfortunately, we do not know how often participants would actually take these actions. In a live
discussion study by Freis and Gurung (2013), the majority (90%) of participants tried to intervene in
some way, yet 44% of participants still said “pass” at some point during the conversation, indicating their
unwillingness to respond. Notably, of the posts in the Freis and Gurung study that involved intervention,
only 3% utilized direct language; the majority involved changing the subject, which was not considered an
intervention method in the current study (since we did not utilize a “live” response design, this category
was not applicable). Future studies could build on our findings by including options for offline interventions
and explore more specifically how and when bystanders intervene in this way.
Beyond examining the frequency and type of bystander intervention, we also explored the motivations for
bystander behavior and the relationship between behavior, empathy and social identification with the
bully versus victim. Asking participants to comment at two different points in the conversation allowed us
to document ways in which their likelihood to respond and their view of the situation might shift as the
intensity of the bullies’ comments increased. With regard to social identification, there was a significant
shift from Time 1 to Time 2. Whereas most students (64%) identified with the bullies at Time 1, 83%
identified with the victim at Time 2, when the bullies’ comments were at their meanest. These findings are
notable, at least in part because nearly half (48%) of the participants identified that bullying began when
the bullies were excluding the victim in the initial posts of the feed and by the time the bullies’ posts
involved attacking the victim, an additional 20% of participants felt that bullying had occurred.
Despite this high level of acknowledgement that bullying was occurring, the majority felt that they related
to the bullies, rather than the victim, at Time 1. Perhaps this is an indication of the frequency with which
young adults engage in harmful posting through social media; it may also explain, at least in part, why
only a minority of participants (28%) posted in support of the victim at this time point (Time 1). Analysis
of participants’ empathy scores revealed no difference in empathy scores by social identification at Time
1, but as both social identification with the victim shifted by the end of the conversation (Time 2), so did
the empathy scores of the bully vs. victim identification groups. Those participants who reported
identification with the victim at Time 2 had significantly higher empathy scores than those who identified
with the bullies. Our findings seem to point to a more nuanced understanding of the role of empathy in
CB. In our simulation, which began with less severe bullying (exclusion), our participants’ identification
with the bully could signal their comfort with or perhaps desensitization to that type of online interaction.
As the bullying increased, those participants with greater empathy may have found themselves
experiencing greater pity for the victim’s plight.
The designs of previous studies that have examined empathy and CB have differed from ours in the type
of response that was required, thus making it difficult to compare their findings to ours. For example,
Barlinska et al. (2013) examined whether or not individuals would continue spreading a CB message to
other friends or delete the CB message, and found that when participants were induced with either
cognitive or affective empathy, they were less likely to forward the CB in an aggressive response. Other
studies (Freis & Gurung, 2013; Machackova et al., 2013) have found that individuals who respond with
empathy may be more likely to intervene in a prosocial manner. However, our analysis of empathy by
broad response categories did not find significant differences at either time point.
Our findings serve as a reminder that there are likely a variety of factors that interplay and influence the
likelihood that a bystander will respond in CB. For example, the rationale that some participants gave for
their inaction is characterized by responses that expressed “feeling helpless” or that an additional
comment “would cause more trouble.” Interestingly, these bystander motivator themes overlapped with
previously established themes of moral evaluating and intervention self-efficacy, which were identified in
a qualitative analysis of traditional bullying (Thornberg et al., 2012). While the majority of participants in
the current study identified the simulation as CB (“moral evaluating”), they did not believe that their
intervention would produce a beneficial result (“intervention self-efficacy”). Several participants in our
study openly acknowledged that they were “on the fence” about whether they would intervene or not,
which could indicate that this internal debate was still transpiring. Similarly, Li (2010) determined that,
whereas 45% reported that CB should be stopped, 47% of their respondents thought there was nothing
that can be done about CB. Our results are also consistent with the ideas Hazler (1996) reported about
the reasons that observers remain bystanders, including: they do not know what to do (similar to our
theme of “feeling helpless”), they are fearful of becoming a target for the bullies (similar to several
participants in our study that stated “I don’t want to risk getting made fun of as well”, within the “do not
want to be involved” theme), and they do not want to cause additional problems (similar to our theme of
“my comment would cause more trouble”). Self-efficacy continues to be an important aspect of bystander
motivations, as indicated by multiple studies.
When Gini, Albiero, Benelli, and Altoe (2008) examined whether the constructs of empathy or social self-
efficacy (i.e., the individual’s overall belief in his/ her competence and assertiveness in social situations )
differentiated passive and defending bystander groups in bullying situations, they concluded that empathy
did not differentiate between the two groups, but high social self-efficacy was associated with defending
behavior by bystanders and low social self-efficacy was associated with passive behavior by bystanders.
These findings demonstrate that it may be most important to examine how effective the bystander
believes their intervention will be. Future research could examine ways to improve bystander’s soc ial self-
efficacy online, such as assertiveness training, to enhance response rates in CB.
While some participants expressed their unwillingness to respond because they didn’t think their comment
would be effective, others indicated that they simply “did not want to get involved.” These themes are
similar to the previously established motivations in the literature of moral evaluation (Thornberg et al.,
2013) and evaluation of fairness (Desmet et al., 2012). Bullying may be considered unfair when the bully
attacks factors that are out of the victim’s control (e.g., appearance, handicap, ethnicity), however
bullying may be considered fair when a victim is blamed for his or her own behavior (Desmet et al.,
2012). In our study, several participants explained that they found the conversation is “stupid, immature
or unnecessary,” or that they were “against alcohol/ drinking/ partying.” These themes indicate that the
participants chose not to comment because perhaps they believed it was partly the victim’s fault for
choosing to engage in certain behaviors (i.e., drinking and partying as well as posting the status) and
therefore, perhaps assessed that the behavior made the victim “fair game” for cyber-bullying. A related
theme from our study was that the victim “should delete this status,” implying that the victim has some
control over the situation, and should assume some responsibility for the malicious content of the
conversation.
Previous literature also indicates that bystanders may not have wanted to comment because they did not
want to be involved in what they consider to be “drama” (Allen, 2012). In a qualitative analysis of
aggressive text messaging, Allen (2012) interviewed high school students and “drama” was a central
theme they identified. They explained that “drama” implies that the relevance of the conflict is
exaggerated and that extraneous people become involved in a trivial issue. Participants in the current
study indicated multiple motivations similar to what Allen (2012) labeled as “drama”; some participants
even directly used the term “drama” in their reason for not commenting, including themes where
participants indicated not wanting to get involved (e.g. “avoid drama”); thinking the conversation is
stupid, immature, or unnecessary (e.g. “petty and unproductive”); and believing that the additional
comment would cause more trouble (e.g. “adds more drama”). We also determined bystander motivations
in the middle ground response category related to “drama”, including claiming social media is not the
correct forum for this behavior (e.g. “it's drama that doesn’t belong on the internet”) and indicating
wishes to intervene offline (e.g. “I usually try to deal with things in person and off of the internet and
social media. Posting said things is a recipe for disaster”). Future research should try to better understand
how the notion of “drama” demotivates bystanders to intervene on behalf of the victim.
Other participants in the current study may have misjudged the situation and believed that the bullies
were teasing or being funny, as explained by our motivator theme “It’s fun to participate in negative
interactions”. In previous research, adolescents said teasing was distinct from CB because teasing was
amongst friends and was not intended to cause harm (Desmet et al., 2012). If a participant perceived
that the interaction was amongst friends, this may have motivated them to join in the conversation,
similar to the social evaluating motivation identified by Thornberg et al. (2013). One participant in our
study specifically stated, “since it’s close friends, it’s fun to mess around.” Furthermore, similar to the
emotional reactions motivation identified by Thornberg et al. (2013), one bystander in our study stated,
“it’s entertaining” as a reason for commenting. This may explain why a few individuals (Time 1: 2%, Time
2: 1%) actually “joined the bullies” in their comment. However, our CB simulation was most clearly not
simply a case of teasing, especially when the victim specifically directed the bullies to stop when they
made harassing comments. This finding indicates that although the majority of participants agreed that
the simulation included CB at some point of the conversation, a sub-group of participants did not perceive
it that way. Future efforts to reduce CB could include specific education about the differences between
teasing and CB, and could extend this education to college-aged young adults.
By comparing our specific motivations in bystander behavior to previous studies in this area, we are able
to reinforce specific themes that are consistent, as explained above. However, there are also a few unique
motivator themes that were identified in the current study. One is that bystanders stated that “Facebook
is not the right forum”, which may indicate their discomfort with seeing CB and/ or conflict in a public,
identifiable venue. Similarly, participants stated that they believed that “Jordan (the victim) should delete
this status”, and often even suggested this directly to the victim. Another unique moti vation identified in
the current study was that some bystanders would prefer to “intervene offline.” These motivations revolve
around similar themes of internet behavior and privacy, and reflect that college-aged young adults are
both judging others based on their online behavior and are also thinking about how they themselves are
perceived by others online. Future studies should include these unique motivations in examinations of
bystander behavior in CB as well as compare motivations across the context of different age groups.
Although the current study contributes to the contemporary knowledge about bystander behavior in CB,
there are several limitations that should be considered and expanded upon for future studies. One
limitation is that we were unable to associate bystander’s behavior (commenting or not commenting) with
their motivations (their stated reasons) because of the exploratory nature of the study. Future studies
could utilize the categories and motivations established in this study as a framework for more extensive
quantitative research on the predictors of bystander behavior. Another limitation of our study design is
that the participants did not personally know the victim or perpetrators, but only were asked to imagine
that they did. Previous studies have indicated the bystander’s social relationship to the victim to be an
important factor in their decision to intervene (Desmet et al., 2012).
Although the situation was created to be as realistic as possible, participants may have altered their
behavior to present their ideal selves and ideal behavior, which may have resulted in an inflated response
rate in our study, and therefore, perhaps there would be an even lower percentage of instances of
bystander intervention in an actual CB situation. On the other hand, an alternate interpretation of our
results could be that participants were not motivated to comment on the simulation because it was
artificial and there was no possibility for spontaneous responding, as there would be in a real CB situation.
Future studies could engage in natural observation of a CB scenario between friends or more closely
simulating a live online conversation, and then asking questions about the motivation of the participants
afterwards. Furthermore, the current study focused on multiple types of CB in an identifiable venue. In
order to achieve a broader understanding of CB across its forms, future research could create CB
scenarios that exclusively include only one type of CB (i.e. exclusion, flaming, harassment, cyberstalking,
denigration, masquerade, outing, or trickery) as well as CB on anonymous forums such as Youtube,
discussion boards, or online gaming networks (Li, 2010). A future goal would be to explore the prevention
styles and bystander intervention strategies that work optimally for each type of CB.
Despite the current study’s limitations, it certainly adds to the current literature on bystander behavior by
establishing a low response rate of young adult bystanders to CB on social-networking sites and
identifying some of the possible motivations for their behavior. Whereas there is a multitude of research
conducted with middle school and adolescent populations in the areas of both traditional and cyber-
bullying, few studies have been conducted with young adults who are the most likely age-group to utilize
social networking sites on a near-daily basis (Pempek et al., 2009). Clearly, additional work in this area,
especially if conducted across diverse age groups, will be helpful in understanding how CB is experienced
by both victims and bystanders. Researchers should continue to study and improve prevention and
intervention methods for CB to create better social well-being for all users of the internet and technology.
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Appendix
Figure A1. Fictitious Facebook conversation separated into four parts, and within each part, the
percentage of participants who indicated the bullying began at that section (N = 149).
Correspondence to:
Emily Shultz
Email: emily.shultz(at)nationwidechildrens.org
About author(s)
Emily Shultz, B.S., is a recent graduate of Xavier University where she has
conducted psychological research on topics including emotional intelligence, sports fan
behavior, and social networking. She also volunteered in the Adherence Center at
Cincinnati Children’s Hospital Medical Center. Currently, she is a research assistant in
the Center for Biobehavioral Health at Nationwide Children’s Hospital where she
assists with studies involving pediatric cancer and survivorship.
Rebecca Heilman, B.S., is currently pursuing her Master of Arts with a concentration
in Social Psychology and Evaluation at Claremont Graduate University. She recently
graduated from Xavier University with her Bachelor of Science in Psychology. Her
research focuses on morality, group interaction, human behavior, and motivations.
Kathleen J. Hart, PhD, ABBP is a Board Certified Clinical Child and Adolescent
Psychologist and a Professor in the Department of Psychology, Xavier University,
Cincinnati, OH. She has published in the areas of child and adolescent adjustment and
legal issues of juvenile offenders.
© 2008 Cyberpsychology: Journal of Psychosocial Research on Cyberspace | ISSN: 1802-7962 | Faculty
of Social Studies, Masaryk University | Contact | Editor: David Smahel
... Bezogen auf die erste Stufe des Bystander-Intervention-Modells zeigen sowohl Selbstberichtsstudien (Olenik-Shemesh et al., 2015;Van Cleemput et al., 2014) als auch experimentelle Simulationsstudien (Dillon & Bushman, 2015;Shultz, Heilman & Hart, 2014), dass von denjenigen, die das Bullying beziehungsweise die Cyberaggression bemerkten, die meisten nicht intervenierten. Dies hebt die Bedeutsamkeit der folgenden Schritte hervor. ...
... Eine Abwägung von Kosten oder möglichen negativen Konsequenzen ist auch im Fall von Bullying von Bedeutung. Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). ...
... Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). Graeff (2014) identifizierte anhand von Interviews zu hypothetischen Szenarien verschiedene Typen passiver Bystander, unter anderem solche, die aus Hilflosigkeit oder aus Sorge um sich selbst passiv bleiben. ...
Chapter
Full-text available
Ein bedauerlicher Fehler im Verlag hat dazu geführt, dass auf Seite 34 in Tabelle 1.4 ein Zahlendreher enthalten war. Dies wurde nachträglich durch den Verlag korrigiert.
... Bezogen auf die erste Stufe des Bystander-Intervention-Modells zeigen sowohl Selbstberichtsstudien (Olenik-Shemesh et al., 2015;Van Cleemput et al., 2014) als auch experimentelle Simulationsstudien (Dillon & Bushman, 2015;Shultz, Heilman & Hart, 2014), dass von denjenigen, die das Bullying beziehungsweise die Cyberaggression bemerkten, die meisten nicht intervenierten. Dies hebt die Bedeutsamkeit der folgenden Schritte hervor. ...
... Eine Abwägung von Kosten oder möglichen negativen Konsequenzen ist auch im Fall von Bullying von Bedeutung. Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). ...
... Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). Graeff (2014) identifizierte anhand von Interviews zu hypothetischen Szenarien verschiedene Typen passiver Bystander, unter anderem solche, die aus Hilflosigkeit oder aus Sorge um sich selbst passiv bleiben. ...
Chapter
Full-text available
Zusammenfassung Im ersten Kapitel wird das Phänomen Bullying im Schulkontext sowie im Cyberspace beschrieben, die Bedeutung des Klassenverbands und der Bystander bei der Entstehung von Bullying herausgearbeitet und ein Überblick über die Prävalenz von Bullying gegeben. Dabei werden die verschiedenen Rollen im Bullying-Gefüge (Täter, Pro-Bullying-Bystander, Opfer, Verteidiger und Außenstehende) betrachtet und Geschlechts- und Altersunterschiede beleuchtet. Schließlich wird ein theoretisches Modell zur Erklärung des Bystander-Verhaltens erarbeitet, welches das Bystander-Intervention-Modell der beiden Sozialpsychologen Bibb Latané und John Darley auf Bullying überträgt und relevante sozial-kognitive und affektive Reaktionen einschließt.
... Bezogen auf die erste Stufe des Bystander-Intervention-Modells zeigen sowohl Selbstberichtsstudien (Olenik-Shemesh et al., 2015;Van Cleemput et al., 2014) als auch experimentelle Simulationsstudien (Dillon & Bushman, 2015;Shultz, Heilman & Hart, 2014), dass von denjenigen, die das Bullying beziehungsweise die Cyberaggression bemerkten, die meisten nicht intervenierten. Dies hebt die Bedeutsamkeit der folgenden Schritte hervor. ...
... Eine Abwägung von Kosten oder möglichen negativen Konsequenzen ist auch im Fall von Bullying von Bedeutung. Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). ...
... Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). Graeff (2014) identifizierte anhand von Interviews zu hypothetischen Szenarien verschiedene Typen passiver Bystander, unter anderem solche, die aus Hilflosigkeit oder aus Sorge um sich selbst passiv bleiben. ...
... Bezogen auf die erste Stufe des Bystander-Intervention-Modells zeigen sowohl Selbstberichtsstudien (Olenik-Shemesh et al., 2015;Van Cleemput et al., 2014) als auch experimentelle Simulationsstudien (Dillon & Bushman, 2015;Shultz, Heilman & Hart, 2014), dass von denjenigen, die das Bullying beziehungsweise die Cyberaggression bemerkten, die meisten nicht intervenierten. Dies hebt die Bedeutsamkeit der folgenden Schritte hervor. ...
... Eine Abwägung von Kosten oder möglichen negativen Konsequenzen ist auch im Fall von Bullying von Bedeutung. Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). ...
... Die Angst vor Vergeltung, davor sich selbst in Gefahr oder Schwierigkeiten zu bringen, in Verruf zu kommen oder einfach nur aufzufallen wurde in vielen Studien zum Bystander-Verhalten bei Schul-und Cyberbullying von Heranwachsenden als Grund dafür genannt, dem Opfer nicht zu helfen (Chen et al., 2016;DeSmet et al., 2014;Mishna, Saini & Solomon, 2009;Olenik-Shemesh et al., 2015;Shultz et al., 2014;Slee, 1994;Van Cleemput et al., 2014;Wójcik & Mondry, 2020). Neben dem Argument der eigenen Sicherheit wurde passives Verhalten zum Teil auch mit der Unsicherheit, was zu tun sei, begründet (Bellmore et al., 2012;Van Cleemput et al., 2014), mit mangelnden Erfolgsaussichten (Cappadocia et al., 2012;Shultz et al., 2014) oder der Befürchtung es zu verschlimmern (Shultz et al., 2014). Graeff (2014) identifizierte anhand von Interviews zu hypothetischen Szenarien verschiedene Typen passiver Bystander, unter anderem solche, die aus Hilflosigkeit oder aus Sorge um sich selbst passiv bleiben. ...
Chapter
Full-text available
Zusammenfassung Die zentralen Befunde der Studien werden zusammengefasst und theoretische Schlussfolgerungen gezogen. Die Stärken und Grenzen der Arbeit werden aufgezeigt und ein Forschungsausblick gegeben. Die Diskussion endet mit den Praxisimplikationen und einem abschließenden Fazit.
... Due to the mixed results, researchers have begun to study empathy as a predictor of willingness to intervene on behalf of a bullied peer (Walters & Espelage, 2019) -it is unclear if a relationship between high empathy and the willingness to intervene on behalf of a bullied peer exists. Schultz et al., (2014) stated that this outcome may be because the definition of empathy is too broadly defined. Similar to empathy, emotional intelligence has failed to correlate significantly to bullying or aggressive behavior (Castillo et al., 2013;Kokkinos & Kipritsi, 2012;Mavroveli & Sanchez-Ruiz, 2011). ...
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Full-text available
The purpose of this article is to provide further exploratory validation of a nascent self-report rating scale designed to measure the concept of other-esteem: The Other-Esteem Rating Scale (OthERS). Other-esteem as conceptualized in this article was coined by Philip Hwang and encompasses the following concepts: non-offensiveness; friendliness, kindness, respectfulness, acceptance, valuing, praising, and the promotion of others. An earlier study provided preliminary norms, reliability, and validity for the OthERS with an undergraduate sample. To continue this exploratory line of inquiry with younger adolescents, the OthERS was administered online to a sample of 486 individuals ages 14–18. An exploratory factor analysis resulted in a four-factor model that accounted for 54.1% of the variance with adequate internal consistency estimates. The results indicated that other-esteem evidenced small inverse relationships with aggression and bullying. Other-esteem and self-esteem were not found to be related. Implications for research and practice are discussed.
... It is stated that the way that bystanders react to bullying is effective in maintaining or ending the bullying (Williford et al., 2013). It is stated that when a bystander is aware of the harmful consequences of bullying for the victim, they intervene in the bullying (Shultz et al., 2014). ...
Article
Full-text available
Due to the prevalence of cyberbullying in adolescence and its association with a number of negative psychosocial consequences, there is a need to develop programs to prevent this phenomenon. In this study, the aim was to examine the effect of the Cyberbullying Awareness Program on adolescents’ awareness of cyberbullying and their coping skills. A total of 38 adolescents were included in the study, where 17 adolescents were assigned to the intervention group and 21 to the control group. The mean age of the adolescents was 13.8 (SD = 0.44). The Cyberbullying Awareness Program was administered to the intervention group in 10 sessions. The Cyberbullying Awareness Scale for Adolescents and Coping with Cyberbullying Scale were used as data collection tools in the study. As a result of the study, it was determined that the Cyberbullying Awareness Program was effective in increasing the awareness level of the adolescents in the intervention group about cyberbullying, as well the development of their skills to cope with cyberbullying. In line with the results of the study, suggestions are presented to educators and policy makers. It is recommended that policy makers include cyberbullying prevention programs in their national curriculums in order to increase the awareness of adolescents about cyberbullying and improve their coping skills, and these programs should be implemented by educators to children and adolescents nationwide.
Conference Paper
Most of the communication today takes place online, thereby reflecting a significant part of our lives. Commuting from offline to online contact causes many challenges, misunderstandings, and differences, not knowing how words are interpreted in various terminologies. In terms of individuals virtually harming another individual-bully-victim scenario when online communication goes wrong, there are witnesses or bystanders who passively involved and do nothing. An upstander is a person who actively involved in the cyberbullying incident, opposes any unfair or intolerant acts, and intervenes on behalf the victims or bullies. While the research on programs to address cyberbullying is keep on increasing, only few studies focused on witness terminologies in cyberbullying. Our study aims to deeper understand the terminologies used in existing studies related to bystander behavior in cyberbullying. A systematic literature review approach was used using three databases. A total of 220 articles were extracted using a predefined search string. A specific criterion was applied to the extracted articles. A total of 99 articles were selected for further analysis. Based on the review, the bystander terminologies can be divided into Neutral, Positive and Negative.
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Cyber-aggression is global epidemic affecting citizens of cyberspace, without regards to physical, geographical and time constraints. Recent research has identified the significant role of cyber-bystanders in exacerbating and de-escalating incidents on cyber-aggression they come across. Additionally, frequent exposure to cyber-aggression is found to have been associated with negative effects on participants of cyber-aggression, ranging from self-esteem problems to mental health disorders such as depression and anxiety, and in the worst cases even suicidal behaviors and ideation. Moreover, past research had also identified that negative bystanders could potentially become aggressors themselves. Therefore, the current review is aimed at uncovering the common themes and factors that drive individuals to resort to negative bystander behavior. Hence, a systematic literature review using the PRISMA framework was carried out, involving articles published between January 2012 to March 2022, on online databases such as SCOPUS, Science Direct, SAGE Journals, Web of Science, and Springer Link. Results obtained through the synthesis of 27 selected articles, were grouped into three categories, namely situational factors, personal factors and social influence. Upon further synthesis of the results, it was noted that many of the factors had interacted with each other. Thus, practical suggestion for prevention and future research would include addressing these interactions in preventative methodologies and research interests.
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The documented effects of cyberbullying take a burden on all those involved, but also impact the wider social environment as well. Victims experience difficult emotions: feelings of humiliation and worthlessness, shame, fear, despair, and sadness. In the long run, they may suffer reduced self-esteem and interpersonal problems: difficulties in establishing contacts and a tendency to withdrawal and isolation. The consequences for perpetrators include the consolidation of aggressive patterns of behavior, the lowering of the sense of responsibility for their own actions, the tendency to antisocial behavior, and the easy slide into conflicts with the law. Witnesses of violence, who are not able to effectively oppose it, or who do not try, often keep their feelings of guilt, dissatisfaction, and self-recrimination for years. For some, it will internalize patterns of passivity, helplessness, and unresponsiveness in difficult situations. This being the case, deepening our knowledge about all of the participants involved in cyberbullying and their mutual relations is of crucial importance.
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Over the past two decades, bullying has received a lot of negative attention, with educators, parents, and youths expressing concerns regarding bullying at schools. However, bullying also occurs outside of schools, and the internet provides a platform that allows bullying to extend beyond the traditional school day. Scholars identify this form of bullying as cyberbullying. Research regarding the relationship between gender and cyberbullying remains unclear. Therefore, using an interdisciplinary approach, this chapter examines gender differences in cyberbullying. Merging theoretical insights from criminology, sociology, and gender studies, this chapter explores how male and female youths utilize the internet to engage in cyberbullying. This chapter also considers the implications of gender differences in cyberbullying for future research and policy development.
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