JMIR FORMATIVE RESEARCH

Ademiluyi et al

Review

Implications and Preventions of Cyberbullying and Social Exclusion
in Social Media: Systematic Review
Adesoji Ademiluyi1, BS; Chuqin Li2, MS; Albert Park2, PhD
1

Department of Bioinformatics and Genomics, University of North Carolina at Charlotte Charlotte, Charlotte, NC, United States

2

Department of Software and Information Systems, University of North Carolina at Charlotte Charlotte, Charlotte, NC, United States

Corresponding Author:
Albert Park, PhD
Department of Software and Information Systems
University of North Carolina at Charlotte Charlotte
Woodward 310H
9201 University City Blvd
Charlotte, NC, 28223
United States
Phone: 1 704 687 8668
Email: apark11@uncc.edu

Abstract
Background: The growth of social networking has created a paradigm in which many forms of personal communication arebeing replaced by internet communication technologies, such as social media. This has led to social issues, such as cyberbullying.
In response, researchers are investigating cyberbullying to determine its implications in various life sectors.
Objective: This manuscript reviews the methods, results, and limitations of the current cyberbullying research and discusses
the physical and mental repercussions of cyberbullying and social exclusion as well as methods of predicting and counteracting
these events. On the basis of the findings, we discuss future research directions.
Methods: Using ScienceDirect, ACM Digital Library, and PubMed, 34 research articles were used in this review. A review
was conducted using the selected articles with the goal of understanding the current landscape of cyberbullying research.
Results: Studies have analyzed correlations between depressive and suicidal ideations in subjects as well as relationships in thesocial, educational, and financial status of the perpetrators. Studies have explored detection methods for monitoring cyberbullying.
Automated detection has yet to become effective and accurate; however, several factors, such as personal background and physicalappearance, have been identified to correlate with the likelihood that a person becomes a survivor or perpetrator of web-based
cybervictimization. Social support is currently common in recovery efforts but may require diversification for specific applications
in web-based incidents.
Conclusions: Relations between social status, age, gender, and behaviors have been discovered that offer new insights into theorigins and likeliness of cyberbullying events. Rehabilitation from such events is possible; however, automatic detection is not
yet a viable solution for prevention of cyberbullying incidents. Effects such as social exclusion and suicidal ideations are closely
tied to incidents of cyberbullying and require further study across various social and demographical populations. New studies
should be conducted to explore the experiences of survivors and perpetrators and identify causal links. The breadth of research
includes demographics from China, Canada, Taiwan, Iran, the United States, and Namibia. Wider ranges of national populations
should be considered in future studies for accurate assessments, given global internet communication technology activity. The
studies emphasize the need for formal classification terminology. With formal classification, researchers will have a more definite
scope, allowing specific research on a single definable topic rather than on general bullying events and symptoms. Of all thestudies, 2 used a longitudinal design for their research methodology. The low number of longitudinal studies leaves gaps between
causation and correlation, and further research is required to understand the effects of cyberbullying. Research addressing ongoing
victimization is required for the various forms of cyberbullying; social support offers the most effective current standard forprevention.
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KEYWORDS
cyberbullying; cybervictimization; cyberaggression; bullying; mental health; social isolation; social media; mobile phone

Introduction
Background
Internet communication technologies (ICTs) include a wide
variety of platforms, ranging from social media, instantmessaging, and chat rooms to email [1]; all of these affect ournormal modes of communication [2]. The use of ICTs isincreasing; meanwhile, negative consequences such as
cybervictimization are being overlooked. Cybervictimization,
colloquially cyberbullying, is a phenomenon proliferating
through rising rates of interaction with social media [1].
Cyberbullying can be best defined as “an aggressive, intentional
act carried out by a group or individual, using electronic formsof contact, repeatedly over time against a victim who cannot
easily defend him or herself” [3]. This could be a distressing
message about a victim’s appearance, delivered by a perpetrator
over several web-based interactions with the purpose ofdelivering emotional or mental harm. Cyberaggression isformally defined as the intentional harm delivered by the useof electronic means to a person or group of people irrespectiveof their age who perceive such as offensive, derogatory, harmful,or unwanted [4]. One example is a mocking tweet regarding
someone’s race or ethnicity, sent during a web-based interaction.
Cyberaggression is inflicted on any individual and is a
description of a singular incident as opposed to being repeated
and targeted. Cyberbullying relates a cyberaggression event toa cybervictim, and correlates to a history of abuse where theopposing parties know each other on a personal level [5]. Thisimplication does not hold for general cyberaggression [5]. As
we are investigating cyberbullying and not cyberaggressions atlarge, it is important to recognize this distinction. Discourse
surrounding cyberbullying is still new and has only seen
consistent studies from 2007 to 2020 [3,5,6]. Researchers fromfields such as sociology and psychology, now studying thephenomenon, struggle to classify it concisely because of thevarious forms it can take and its relation to traditional bullying.
Several cyberbullying studies disagree regarding the overlap ofcyberbullying and traditional bullying and use separate
definitions to discern them from one another [5]. The tension
stems from the assertion that cyberbullying is more soundly
defined within the purview of cyberaggression [7].
Cyberbullying narrows the classification of instances ofcyber-based attacks to those done over length of time; however,many in the field believe this is detrimental to the identificationof events that may happen only once or between strangers [7].
In this review, we define cyberbullying and cyberaggression by
their formal definitions and examine cyberbullying events on
multiple occurrences.
Cybervictimization has a wide reach given the interconnectivityof each user [8]. Youth (ie, aged 11-18 years) are especiallysusceptible to this form of victimization [8] given the influenceof peer interactions on social development in early life andconventional [9] standards. Several cases of suicide andsuicidality and the presence of suicidal ideations and behaviors[10] have been found to have direct correlations to cyberbullying
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health consequences, including depressive symptoms,
particularly among youth and student populations [12]. Issues
with mental health and social strain are also accumulating amongcollege students and young adults, especially those in the agegroup of 18 to 24 years, who have screened 19% positivity forexperiences of psychological distress because of cyberbullying
incidents [10].
Social exclusion is a phenomenon that occurs when someone
is forcibly or voluntarily separated from groups with whom they
perform social interaction on a daily basis [13]. In clinicalstudies, social exclusion is associated with depressive symptoms
and an increased risk of mortality [14]. It is possible that there
are relations between the domains of cyberbullying, internet
communications, and social exclusion, given an attack on acybervictim being received through their preferred avenue ofsocial interaction [1]. The likelihood of cyberbullying events
among youth and adolescents suggests a considerable risk ofsocial isolation to these populations [1]. Correlations betweensocial isolation and workplace bullying have been drawn in
adult studies [15]. These relations should be explored in school
and in web-based environments so that a crisis among young
people can be identified and prevented.

Objectives
We review the current research in cyberbullying and mental
health, its social outcomes, predictive factors, and novel
approaches to management and suppression. We look tocontribute a contextual understanding of cyberbullying unfoundin the domain, make connections to social isolation and othersociological and psychological effects, and investigate methods
to prevent the overall manifestation of these events. This
analysis sheds light on current measures taken and future
opportunities to combat the prevent the spread ofcybervictimization.

Methods
Search Strategy
The primary electronic databases that were used in the review
are Elsevier, ScienceDirect, PubMed, and ACM Digital Library.
Each of these libraries is a globally recognized and reputable
medical or scientific database, and they are ubiquitously usedin medical and scientific research. Indexing terms for
ScienceDirect and ACM Digital Library were used and were
presented through the search as social isolation OR social
exclusion OR social alienation AND cyberbullying AND social
media. MeSH terms were used for PubMed and were presented
through search as social isolation OR social exclusion OR social
alienation AND cyberbullying AND social media. These
searches were conducted in accordance with the 2009 Preferred
Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines. These searches were completed on
February 23, 2020.

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Study Screening
This study’s scope covers cyberbullying factors in their relations
to psychological and sociological disorders, social media, andintervening technologies. For these aims, a noted definition of
cybervictimization sourced from an article by Smith et al [3]describes it as “an aggressive, intentional act carried out by agroup or individual, using electronic forms of contact, repeatedly
over time against a victim who cannot easily defend him or
herself.”
The criteria for inclusion of the articles were defined by certain
expectations. First, the articles were all original research studies
that did not involve secondary reviews of underlying study
categories. It was imperative that all studies involve primarydata for accurate synthesis of the reviewed information. Second,
all articles were required to include some form of cyberbullying
factors and the effects they incur. Several articles only involved
information about social exclusion or only information about

Ademiluyi et alsocial media and were excluded because they were unrelated
to the primary topic. Conversely, it was imperative that all
articles include social exclusion factors, given its relation to
cyberbullying. Social exclusion factors relate specifically to thephenomena of social distance in reaction to a negative social
event. The first screening required all articles to be nonreview,on the topic of bullying, and with factors relating to social
exclusion. The articles were curated under these constraints andthen screened further for 2 additional metrics. In the second
screening, articles were excluded if they did not have significant
social media factors, even if the included references tocyberbullying and social isolation, given that the research
explicitly focuses on the sociological implications. Finally, thefull-text screening involved a complete reading of the articles
to determine the relevance of their findings. All records withinsufficient information or data, such that they did not provide
relevant or citable information, were excluded. The fullscreening is displayed in Figure 1.

Figure 1. Screening flowchart.

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Results
Landscape of Cyberbullying Research
The research surveyed from 2011 to 2020 focuses on thepsychological effects of the phenomenon, with 10 of the 34
articles focusing on factors such as suicidal ideations, social
agency, and depression [1,4,11,13,16-21]. Automated detection
studies are studies that dealt with automated identification of
cyberbullying events; this takes place through processing oftext from logged chat room conversations [22]. A total of 3
articles dealt with methods of automated detection. Text filtering
was a common strategy, using machine learning to perform
sentiment analysis. Risk factor studies focus on characteristics
that put someone at risk of cyberaggression [23]. Furthermore,
5 articles dealt with risk factors, such as age, oversharing
tendencies, financial disposition, educational background, social
media activity, and social status [1,23-26]. Predictive factors
are behaviors that correlate with involvement in cyberbullying
or cybervictimization, and 4 articles dealt with predictive factorsof cybervictimization [17,24,27,28]. Predictive factors included
parental status, access to goods, academic standings, andAdemiluyi et al
personal identity. Several articles focused on relationships
between cyberbullying and traditional bullying, demographics,
motivations, and information security. These articles provided
information regarding the similarities and differences of
web-based and in-person behavior and how they affect attitude
and perception of events.
The articles dealt with several different ICT platforms, including
Facebook, Twitter, Instagram, Tumblr, and many others. Articles
that used a direct social media source aimed to discover
behaviors and trends that could be used to map potential victims
and perpetrators to quantifiable decisions and habits. Facebook
and Twitter were the most common platforms used byresearchers [29].
The research objectives are displayed in Table 1. The widest
range of articles were related to psychological effects. These
effects are often the focal point of research in cyberbullying.
Predictive factors, risk factors, and demographic factors also
have a substantial presence in the body of research. There is asmaller emphasis on technological applications in security
awareness and automation throughout the research.

Table 1. Research objectives investigating cyberbullying.
Research objective

Study

Victimization risk factors[1,23-26]

Perpetration predictive factors[17,24,27,28]

Psychological effects[1,4,11,13,16-21]

Security awareness[1,28,30]

Automated detection[22,31,32]

Cyberbullying demographics[1,9,22,23,25,33-35]

Cyberbullying versus in-person bullying[19,36]

Forms of Cyberbullying
Limitations relating to the establishment of concrete definitions
are an issue in cyberbullying research; however, a new body ofresearch, which began in 2014, created new definitions forcybervictimization and differentiated it from traditional bullying[35]. Cyberbullying events can occur in many ways [34]. The
most prominent methods are flaming, when a person sends
angry or vulgar messages; harassment, when there is a consistentstream of offensive messages; denigration, when damaging
messages about the target are sent to other associates of thetarget; masquerading, when the victim’s identity is stolen to
imitate harmful or damaging messages produced by anotherentity; trickery, when the victim is deceived into providing
personal information; exclusion, when a victim is ostracizedfrom a social group [34]; stalking, when a person hacks or
obtains personal information from a victim’s social media profile
to determine their location and whereabouts; and blackmailing,when anonymous emails, telephone calls, and private messages
are sent to a person to solicit money or actions from the victim[34].

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Demographics
The demographics in this review included children, adults, bothgenders, and various social groups. People of any age or status
can be the subject of a cyberbullying incident [33]. Incidents
present threats in several circles of interpersonal relations.
Employed adults experience work position as a risk factor; 40%of people cyberbullied in the workplace are in some supervisory
position [33]. Furthermore, 10% of working adults are survivorsof cyberbullying [33]; however, adolescents between the agesof 13 and 21 years are more vulnerable to peer victimization[22]. Internet anonymity and the ability to interact with anyone
anywhere pose severe risks [22]. Adolescents are using
technological resources at the record rates [25]. Smartphones
are the most common ICTs used by adolescents and can beaccessed from any location with cellular service [1]. Large
percentages of females are exposed to threatening messages(50%), stalkers (32%), requests for sexual information (27%),
requests for self-sexual images (20%), and cyberbullying (17%)[1]. Adolescent males who had access to social media throughICTs became exposed to threatening messages (26%), requests
for sexual information (15%), cyberbullying (14%), stalkers(14%), and requests for self-sexual images (10%) after use [1].
The variability in risks, such as substance abuse, self-harm,
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cybervictimization, and depressive symptoms, caused by these
forms of exposure on various age groups is still new in academic
study [1].
Studies suggest adolescent females are victimized at the highest
rates [35]. They are more at risk in their teenage years thanmales, facing cyberbullying rates of 18% at the age of 13, 15%
at 14, 24% at 15, and 21% at 16 [1]. Studies among college
students showed that 44% of the female students reported
experiencing some form of cybervictimization [35].
Cyberbullying is experienced by both males and females, yet

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there are many differences in the frequency of victimizationthat requires investigation.
University students reported that 50% of the cyberbullying theyexperienced was from classmates, 57% was from outside
sources, and 43% was from unrelated individuals [34].
Cyberbullying has become a commonplace in higher education,with half of the college age students encountering it in someform. The investigated demographics are listed in Table 2.
Age, gender, and social status provided the most contextual
information in the research. These factors may imply trends incyberbullying.

Table 2. Demographics investigated by researchers.
Investigated demographics

Study

Gender[1,35]

Social status[33,34]Age[1,9,22,23,25,33]

Causes
Predictive factors indicate where cyberbullying may occur. A
factor may provide information about the perpetrator or thesurvivor. The literature identifies several factors that range fromvisual appearance to personal history. Studies have shown[4,23,24,37-39] several initiators of cyberbullying. The leading
factor among cyberbullying perpetrators is the presence of
normative thinking, peer pressure, and involvement innormatively aggressive peer groups [37]. The peer group isoften the most influential group in a person’s social experience,
especially among youth [37]. Low self-control and difficulty
discerning moral identity are factors that allow peer pressure to
inform a person’s actions [4]. When a social event is normalized
in the peer group, the subject views the interaction as agreeable
even when it contradicts pre-existing moral values [37].
An individual’s perspective on their performance affects theirdecisions, this is known as self-concept [19]. Self-concept isformally defined as “the relatively stable schemata of oneselfthat are generalized to the extent that they refer to an individual’sview of him- or herself across different situations” [19]. Social
success is a salient indicator of happiness and a driver ofbehaviors [19]. In a study on psychological outcomes in social
media interactions, a positive correlation was found between
high self-concept and social success in peer groups [19]. An
individual’s view of themselves is affected by environmental
factors, such as household income, parental marriage status,
gender, and their access to social resources and community[18,19,26]. In a study in Iran by Kabiri et al [4], poor
performance in school and growing up in a low-income
household also had positive correlations with perpetration
likelihoods [4]. Another study of male boarding students
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perpetrators and have a higher vulnerability to peer victimization[24], given that early age interactions and social development
may be limited when certain activities are prevented by apayment gap. Weak emotional bonds with parents and highdiscipline levels are common in perpetrators [26]. Both genders
present similar likelihoods of becoming cyberaggressors [38].
Trends in the studies show the social behaviors of participants
who reported receiving negative remarks, unwanted sexualsuggestions or images, negativity from peers, humiliatingtargeted posts or had their accounts hacked [28]. The first major
identifier was the tendency to post indiscreet information andcontent on social platforms without security [28]. This factor
is positively associated with victimization likelihood andaccounts for 18% variance in data [28]. There is a correlation
between security and victimization incidents. Often, those whoare victimized lack security on their social media profiles or are
not equipped with the skills to implement security on their own[28]. A study by Saridakis et al [30] demonstrated that higher
awareness of security risks and an ability to control generated
information actively creates safer and more user-friendly
environments, critical in preventing the likelihood of victimizing
events.
Another contributing factor is facial features. A study on visual
perception found that survivors of social ostracization (ie,exclusion by general consent from social acceptance of a group)
are likely to be those whose faces are perceived as being
incompetent and cold (unfriendly) [39].
Causes of cybervictimization events include factors of theenvironment, individual behavior, and ideations of the self andthe environment. Several initiators complicate prediction andmay need to be addressed individually.
A detailed list of studies related to causes of perpetration andvictimization is shown in Table 3.

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Table 3. Causes of cyberbullying perpetration and victimization events.
CausesStudy

Social pressures: peer grouping, social success[4,19,37]

Web-based behavior: security awareness, social tendencies[28,30]Self-concept: identity development, social status, educational status[19,37]

Public perception: perceived appearance, nonverbal interactions[39]

Familial issues: marital status, home environment, parental relationships[4,23,26]

Effects of Cyberbullying
Effects are a crucial portion of the research because they contain
results related to suicide and depression. Survivors ofcybervictimization present high psychological distress,
depression, and substance abuse [21]. This phenomenon is
distinct from traditional bullying in its psychological effects[40]. Stress posting and oversharing are behaviors that often
have a heavy correlation with cyberbullying [41]. Thesebehaviors also cause the individual to become more likely to
be targeted by a cyberbully [41] and suggest the possibility ofcyclical processes. The scope of negative psychologicaloutcomes of cyberbullying is summarized in Table 4.

Table 4. Negative psychological outcomes of survivors of cyberbullying.
Negative psychological effects

Study

Social exclusion[13,18,19,32,39]

Self-harm[1,10,21]Depressive symptoms[11,12,17,28,40,41]

Substance abuse[10,17,21,26,40]

Social isolation, the experience of a person who has been
ostracized from a social group, is a common effect ofcybervictimization, as exclusion is a main tactic used in
web-based cyberbullying [13]. When a person is ostracized, it
is common for them to lose a sense of agency because ofremoval from the group that facilitated their social mobility[13]. Social agency is the feeling of control over one’s actions
and the effects of those actions [13]. In some cases, bullies may
use others within a social circle to isolate a person without
directly involving themselves [32]. Facial features that fail toevoke feelings of empathy from viewers play a role in thelikelihood of social exclusion [39]. High amounts ofsupplementary web-based communication have been closely
associated with feelings of social isolation [19]. When a person
has been socially ostracized, they find it harder to rally social
support, as being isolated may corrode most of their social
connections [18]. Still, it is uncommon for cyberbullying tospill over into real-world interactions. A study by Pabian et al[36] found cases often stay exclusively on the internet or offline.
Disadvantaged adolescents are often involved with the misuseof ICTs and become tangentially exposed to self-harm, substanceabuse, and suicidal ideations [21]. These youth tend to develop
habits of misusing personal data and neglecting social
relationships and schoolwork, leading to sexual abuse,
blackmail, threats, and, in some cases, the incitation of violence[1]. Events such as social exclusion, self-harm, substance abuse,and depressive symptoms were emphasized heavily in theliterature.

Methods of Prevention and Amelioration
There is limited research on approaches for deterring or
recovering from cybervictimization. Social support is a
generalized approach for social issues brought into the domain
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scarce and have limited effectiveness. We found 3 studies thatinvestigated potential methods for cyberbullying detection[22,31,42]. A study on the social networking site ASKfm
provides research on a machine learning support vector machineclassifier that detects instances of aggressive communication[22]. Experiments showed that 64% was the highest accuracy
achieved by the algorithm [22]. A study by Ptaszynski et al [31]in 2016 found that natural language processing tested a 30%
drop in performance over just a year of testing.
Cybervictimization incidents are embarrassing events, which
can cause repression of experiences and discourage social
support requests [43]. Social support is the tangible andintangible assistance from friends, partners, family members,
and others [11]. Members in a community encourage and affirm
an individual to stabilize their mental health and improve theirself-concept [11]. Social support can occur on the internet orin-person. Web-based intervention is useful in isolation as it
benefits those without in-person social support systems [11].
Assistance through social support occurs before or after anindividual encounters cyberaggression [24]. Social support has
produced reliable results in ameliorating cybervictimization
effects [24]. Adolescents who perceived high levels of social
support from family members were less likely to experiencecyberbullying [24]. Social support actively reduces the effects
of ongoing cyberbullying [11]. On average, males required moresocial support in these instances than females [24]. Multiple
intersections of gender in the literature may prove to be a salient
factor for future research. There were articles on investigating
methods of prevention [22,31,42] and on investigating
amelioration [22,31,42].

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Research Methods of Current Studies
We identified 4 research methods: exploratory, experimental,
longitudinal, and cross-sectional. For this review, exploratory
design is defined as research conducted in domains with little
or no previous study. These studies are often foundational for
future research and promote familiarity with the scope of theresearch. Experimental design is defined as an approach where
the researcher has control of all the variables being manipulated
and observed. The focus of these studies is accurately predicting
and modeling an outcome based on a hypothesis. Longitudinal
design is defined as a study that takes place with a recurring
sample over a fixed length of time. Longitudinal studies focus
on changes and patterns that develop over long time frames.

Ademiluyi et al
Cross-sectional design does not rely on time and focuses on
existing differences between sample members for one-time datacollection. Cross-sectional studies were the most common, given
quick access to information through population surveys. The
study designs are displayed in Table 5.
There are 2 types of data analysis in the studies: qualitative andquantitative analysesQualitative analysis is defined as
descriptions of specific situations; for example, the use ofinterviews, observations, and documents to describe things.
Quantitative analysis is defined as data that are represented innumerical form, such as frequencies and averages: these aremeasurements. In this study, quantitative analysis was the mostprominent. The analysis types by article are listed in Table 6.

Table 5. Designs of the reviewed studies.
DesignStudy

Cross-sectional[1,4,11,12,18,19,23,25-28,30,34,35,37,38,41,42,44,45]

Experimental[13,16,20,31,32,43]

Longitudinal[21,40]Exploratory[22,24,36,39]

Table 6. Methods of analysis in the reviewed studies.
Data type

Study

Quantitative analysis[1,10-12,16,18-21,23-26,28,30-32,34,35,37-41,43,45]

Qualitative analysis[4,16,22,27,36,42]

Throughout the review, important factors across the articles
weredetermined.
Multimedia
Appendix1[1,4,10-13,16,18-28,31-44] summarizes salient information
relating to the research processes used in each study. The
author’s findings and limitations are summarized for reference.
Methods for the deployment of individual studies were recorded
as well as the target age demographics. Information relevant tothe scope of the review discovered was recorded under social
media factors.

review discerned information about the types of people andbehaviors associated with cyberbullying victimization andperpetration. Most of those who are affected are adolescentsand college students as well as children. Cyberbullying
perpetration varies by age, social class, family life, and academic
standing. The resources a person has in their community also
affect the way someone can cope with being victimized.
Cyberbullying is preventable and mitigatable. The mostsuccessful form of prevention of cyberbullying is robust social
support systems, as technology cannot provide solid methods
for counteraction in real time.

Discussion
Principal Findings
Our review examines current articles relating to cyberbullying
and identifies trends in perpetration and victimization. This
contributes to the discussion of cyberbullying prevention
approaches given the lack of sufficient technologies to censor
it from its victims, likely perpetrators and victims, andconsequences of their occurrence. The results provide predictive
information relating to age, social status, and gender as well as
information on types of cyberbullying, where they occur, andtheir effects.
We found various forms of cyberbullying in the literature thatcarry specific psychological effects. Gaps in research design
limit the understanding of these events. Cyberbullying incidents
carry serious mental health effects for victims leading to
psychological disorders and suicidal ideations. New studies
have shown that cyberbullying leads to real-world decisions,such as self-harm, abuse, and substance abuse [1,6,10]. Our
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There is a lack in information on cybervictimization perpetrators
because of the self-report nature of most surveys [23]. First,
many involved in cyberaggression and victimization refuse toparticipate in studies even when anonymity is ensured by thefacilitators [23]. Second, a wide margin of the survey-based
studies used cross-sectional methods making it difficult todiscerncausationofthediscovered
effects[2,10,21,23,24,26,27,38]. It is not possible to make concrete
causal links to behaviors without a long-term process [34]. Of
the studies that used longitudinal methods, causal links, suchas the direct relation to victimization and substance abuse, werediscovered [21]. Third, given the emergent status ofcybervictimization, there are various discoveries throughout
the literature that reveal new factors not previously associatedwith cybervictimization that have value in its scope [33,39].
The impact of technologies on social relationships is likely an
important factor relating to risk factors [33]. Fourth, there are
significant limitations on the basis of demographics surveyed
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in many of the cross-sectional studies [18,42,46].
Cybervictimization is closely linked with social habits that mayvary across different countries and in different social spheres[12,19,28,30,37,41,42]. Most of the literature focuses onparticipants above the age of 17; however, there are several
indications that it may become rampant in children and teenagesocial spheres [1,12,17,23]. Trends in access to technology
allow vulnerable age groups to access ICTs [1]. Research should
focus on these phenomena using longitudinal design to interpretthe unique issues in child cyberbullying. Finally, studies that
included text monitoring systems did not experience acceptablesuccess rates within their testing periods [22,32,42]. High ratesof accuracy are required for filtering methods to be efficient;among the studies, 65% accuracy was the highest rate produced
given the limitations in syntactic nuance [22]. Syntactic analysis
of aggressive interactions is difficult to discern from healthyinteractions given disparate standards of communication acrossweb-based platforms [22]. Research on language in insularinternet communities should be investigated to expand
understandings of web-based communication.

Negative Mental Health OutcomesForms of Cyberbullying

The effects of cyberbullying can be ameliorated by social
support [11]. Web-based social support is effective for those
who lack strong social connections [11]. College students andworking age adults who are away from family environments
for work or school could benefit from these web-based systems.
Children and adolescents whose families are present in
day-to-day life benefit more from in-person social support
intervention, where development of communication and support
practices occur through pre-existing relationships. One challenge
for social support outreach is that social ostracization can cause
victims to lose their sense of agency and steer away from formsof social support [24]. Negative feelings associated withcyberbullying can cause victims to feel incapable of expressing
their experiences and suppress them [43]. During these events
outreach can only be initiated if the victim is willing to divulge
information about the instance. Given the volatility of events
after initiation and the tendency for victims to become isolated,preventive measures are key in protecting the mental stabilityof the victim [13].

Among the types of cyberbullying (flaming, harassment,
denigration, masquerading, trickery, exclusion, stalking, andblackmailing), some give rise to dangerous effects, such associal isolation and suicidal ideations. Information on thefrequency of the various types of cyberbullying events should
be investigated to determine the implications of each form.
Research should be driven on the forms that have the highest
correlation to suicidality and depressive symptoms. Behaviors
that lead to physical harm and death require the quickest
responses.

Demographics
Recent reviews of cyberbullying literature lack detailed
information on salient factors in predicting and preventing its
occurrence in youth and adults. Given that cyberbullying is
beginning to be seen in places such as elementary schools, withas high as 85% rates of web-based messaging use in preteen
populations, research is crucial to assure the mental health ofyounger students. Adults are less vulnerable; yet, more researchis required to fully understand the complex relations in theworkplace surrounding cybervictimization [23]. Gender may
play a role in the likelihood of victimization, as females see
higher rates of exposure to cyberbullying [1,35]. Females receive
more consistent support than males [11]. Peer groups and setting
plays a large role in determining if someone may cyberbully or
be victimized. Developing children are exposed to fluctuationsof these dimensions of their social experience before
understanding the complexities of social interactions [9].
Counseling resources are effective in handling distress incyberbullying event. Those who are likely to be victimized share
information at high frequencies on social media accounts, havehigh engagement levels with social media platforms, and havevery little understanding of the importance of personal
information security [23,28,30]. This emphasizes the need forresearch on young children with access to ICTs.

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Cybervictimization can result in negative mental healthoutcomes, including depressive symptoms, suicidal ideations,and substance abuse, which are prevalent for young adults andadolescents between the ages of 15 and 23 [10]. These
victimization events cause depression and can lead to thedeterioration of self-concept and academic performance ofstudents. Social exclusion, a process where individuals are
excluded from their social circles, can cause them to lose theirperceived agency and expediate other negative mental processes[13]. This can result in unhappiness that drives victims to depend
on web-based interactions for social experience [12]. The
dangers as well as mental abuse, sexual abuse, and drug use areeven more of a threat to younger victims as they will have lessutility to navigate social complexities than their older
counterparts [1]. This is problematic in a time when intentional
studies prove that victimization events on occasion escalate toface-to-face interactions and altercations [1,8].

Effective Cyberbullying Prevention

The current methods of automatic detection [22,31] struggle
with nuance in web-based communications. Ptaszynski et al[31] extracted phrases and categorized them by harmfulness,based on seed words detected in specific phrases. This study
used syntactic positioning of words to determine harmfulness
within the messages [31]. This approach achieved up to 90%
accuracy but dropped in performance because of the limitations
in further data extraction and issues categorizing nonharmfulphrases [31]. Future models should continue optimization andinclude nonharmful entries and neutral phrases. Van Hee et al[22] used ASKfm training data sets for phrase annotation withthe objective of classifying the role of the participants of thecyberbullying event and the type of cyberbullying that occurred.
This approach achieved up to 64% accuracy but lacked context
for concise classification of cyberbullying types and could notaccurately determine participant roles [22]. A new model that
detects sentiments of the victim, rather than incitement fromthe perpetrator should be studied for more accurate
determination of cyberaggression [22]. The language of different
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social groups can take many forms in web-based discourse andcan range a wide spectrum of literal and coded speech that israrely clear cut for unexperienced readers. The model should
identify speech patterns on the basis of a sizable history ofinteraction to make accurate predictions rather than simply
classifying based on detected words and phrases.
Early intervention is a potential approach for prevention.
Research should be headed to investigate the effects of poverty,social stressors, parent marital status, and environment oninternet behaviors and tendencies [4,37]. Cyberbullying can becombated by intervention, with different methods for web-based
spaces. Given additional data, victim prevention and treatment
can be improved. Research on how cyberbullying effects specificgeographic regions, ethnic groups, and age ranges [1,18,21,23]
should also be continued; these factors may often determine thepsychological outcomes of the victim. New research should
offer new perspectives for preventing the proliferation of
cyberbullying and social isolation.
There are many technologies that are in development today that
could be beneficial if applied to the study and prevention of
cyberbullying [22,31,32,39]. Natural language processing is
widely studied today and can be the basis for understanding andpreventing cyberbullying [32,42]. Methods of recovery are also
being studied through social support programs [24]. Recovery
efforts are common for general depression and anxiety andshould be improved to focus on issues specifically related tocyberbullying. An example of this is social support programs
that promote emotional health [11]. Similarly, a method of
deployment of social support programs to isolated individuals
using technology should be further investigated to provide forthe event of social isolation caused by cyberbullying

Limitations
The review was limited to 3 specific databases, therefore
information about surveys and other research studies on othermajor and minor databases is excluded from this review. Thisnarrows the scope of information available for consideration in
the review to the largest and most beneficial database but may
omit potentially useful granular data. The search did not include
any articles that lacked information specific to cyberbullying,
this means that information pertaining to more general bullying
studies that proved pertinent was included. Articles on young
children were limited in data about social phenomena

Ademiluyi et al
influencing behaviors relating to ICTs, making it more difficult
to assess the relationship between young children andcyberbullying trends. More longitudinal studies would assist in
the understanding of perpetrators and the relations of victim
likelihood within adult and child populations [26]. In future
research, longitudinal studies are required [26] for tracking
cyberbullying victims and perpetrators to support acomprehensive evaluation of their behaviors and outcomes.

Conclusions
Cyberbullying is a newly emerging phenomenon that has
proliferated through the global rise of ICTs that began toconverge internationally between 2000 and 2008 [2]. Previous
reviews have highlighted the severity of the phenomenon[6,47,48], yet do not address solutions combating the rapid
advance of cybervictimization in the social media era. To
confront cybervictimization in the social media era, reevaluationof factors in the scope of the current research and longer-term
longitudinal studies for causal links to be ascertained regarding
suicidal or depressive symptoms is required. A larger emphasis
on demographic groups should be taken to make clear
determinations about how cyberbullying effects people ofvarying age, race, gender, and economic class. Ho et al [2] noteda wide range of ICT activity across resource-limited andtransitional countries; however, few countries have been
explicitly studied. Future research should be carried out in moregeographic locations, as it requires holistic representation of
disparate racial and gender populations. Relations between
cyberbullying and predictive factors, such as low socioeconomic
status, gender, and the presence of divorce, were identified inaddition to studies drawing associations between
cybervictimization and mental health outcomes, such as
depressive and suicidal ideations. Ideations have been shownto lead to lower academic performance, retaliatory action, andsuicide. Detection is a relevant method of counteracting theeffects of cyberbullying on youth and adult populations andneeds consistent research to keep pace with the rate of ICT
growth. Detecting instances of cyberaggression is a challenging
process given the nuances of web-based communication andthe self-report nature of events [12]. Discovery of ongoing
victimization incidents is necessary to reach current victims ofcyberbullying, while predictive factors and preventive measures
are required to halt future growth.

Acknowledgments
The project was supported, in part, by funds provided by the University of North Carolina at Charlotte.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Summary of individual studies.
[DOCX File , 30 KB-Multimedia Appendix 1]

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Abbreviations
ICT: internet communication technology

Edited by J Torous; submitted 10.05.21; peer-reviewed by F Shava, R Kowalski; comments to author 02.06.21; revised version received
25.07.21; accepted 27.08.21; published 04.01.22
Please cite as:
Ademiluyi A, Li C, Park A
Implications and Preventions of Cyberbullying and Social Exclusion in Social Media: Systematic Review
JMIR Form Res 2022;6(1):e30286
URL: https://formative.jmir.org/2022/1/e30286
doi: 10.2196/30286
PMID:©Adesoji Ademiluyi, Chuqin Li, Albert Park. Originally published in JMIR Formative Research (https://formative.jmir.org),
04.01.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information,a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

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