Cyberbullying in the United Kingdom and Ireland

Abstract

Cyberbullying and cybervictimization are important and growing problems that have undesirable health-related outcomes. They are significantly related to school bullying and school victimization. This chapter reports the results of a systematic review of the prevalence of cyberbullying and cybervictimization in the United Kingdom and Ireland. It also reviews the effects of different methods of data collection and questioning techniques on prevalence estimates. Research suggests that girls are more likely to be cybervictims, while boys are more likely to be cyberbullies. Cyberbullying and cybervictimization are related to cognitive and psychological factors such as low self-esteem and loneliness, and they are more prevalent in deprived schools. The chapter finishes by reviewing policies and programmes that are designed to reduce cyberbullying and cybervictimization.

Full text

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Chapter 5: Cyberbullying in the United Kingdom and Ireland
Hannah Gaffney and David P. Farrington
Institute of Criminology, University of Cambridge
Chapter Published in:
International Perspectives on Cyberbullying: Prevalence, Risk Factors and Interventions
edited by Anna Costanza Baldry, Catherine Blaya and David P. Farrington.
London: Palgrave-Macmillan (2018), pp. 101 - 143

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Introduction
Research interest in cyberbullying has grown significantly, both in the UK and
internationally, in the past decade. Information communication technologies (ICTs) have
become ever more present in our everyday social interactions, with large percentages of
young people being very active online. Findings from European research (the EU Kids
Online survey) has concluded that a growing number of children and adolescents have access
to and are active on the Internet (Livingstone & Haddon, 2009). In the UK, the survey
identified similar trends, with 90% of all children aged 6 to 17 years of age being active
online, and 87% of children aged 6 to 10 years old reporting Internet use. The figures for UK
adolescents aged 11 to 14 (94%) and 15 to 17 years old (95%) were comparable to the figures
reported for Irish adolescents.
In addition, a recent systematic review of children’s rapidly increasing access to ICTs
reported that, in the UK, the use of the internet at home increased with age, from 37% of 3-4
year olds, to 58% of 5-7 year olds, 87% of 8-11 year olds, and 95% of 12-15 year olds
(Livingstone & Smith, 2014, p. 3). The ownership of mobile phones, particularly
smartphones, and other Internet-ready devices such as tablets, music players and games
consoles, is also on the rise, with 62% of 12-15 year olds in 2012 reporting ownership. Thus,
as our interpersonal communications move into the online sphere, it is only to be expected
that these platforms will increasingly be used for aggressive forms of behaviours (Asam &
Samara, 2016).
Cyberbullying has been defined by UK academics as an aggressive, intentional act
carried out by a group or individual, using electronic forms of contact repeatedly and over
time against a victim who cannot easily defend himself or herself (Smith, Mahdavi, Carvalho,
Fisher, Russell, & Tippett, 2008, p. 376). However, the three core elements of the widely
accepted definition of traditional school-bullying (i.e. intention to harm, repetitive nature, and

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clear power imbalance) are not as easily distinguished in cases of cyberbullying.
Furthermore, there are several features that are unique to cyberbullying in comparison to
traditional school-bullying, such as: the ability of the perpetrator to remain relatively
anonymous; the lack of physical and social cues in online communication; the breadth of the
potential audience and the added complexity of the bystanders’ roles in cyberbullying; and
the fact that there is ‘no place to hide’ (Marczak & Coyne, 2015; p. 149). A full discussion of
these issues is beyond the scope of the current chapter; please see Smith, Barrio, and
Tokunaga (2013) for a full and comprehensive overview.
Previous research has found that cyberbullying is associated with several undesirable
psychological, behavioural and health-related outcomes. For example, studies conducted in
Europe have discovered that cyber-victims report higher levels of emotional and social
problems, psychological difficulties, headaches, abdominal pain, and sleeping difficulties
(Sourander et al., 2010). In addition, the cyber-bullies who were identified in this study
reported higher frequencies of conduct problems, hyperactivity, smoking and alcohol use.
Additionally, cyberbullying victimization is correlated with several undesirable mental health
outcomes, such as depression, anxiety, and suicidal ideation (Betts, 2016).
A recent meta-analysis of 80 studies found that, while prevalence rates of
cyberbullying were lower than those for traditional school-bullying, there were significant
correlations between these types of aggressive behaviours (Modecki, Minchin, Harbaugh,
Guerra, & Runions, 2014). Moreover, the perceived impact of cyberbullying has been
frequently reported to be worse than the impact of face-to-face or traditional school-bullying,
but this relationship may vary according to the type of cyberbullying experienced (Smith,
Mahdavi, Carvalho, & Tippett, 2006). A qualitative study conducted with youth in the UK
indicated that children were aware that cyberbullying occurred typically as an extension or
continuation of offline bullying (Betts & Spenser, 2017). Participants in this study reported

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how, in comparison with school bullying that has a clear cut-off point (typically when the
victim goes home from school), cyberbullying experiences had the potential to occur at any
time of the day or night because of constant access to, and engagement with, technology
(Betts & Spenser, 2017, p. 27). In the Republic of Ireland, participants also thought that all
forms of cyberbullying behaviours had more impact than traditional school-bullying, with the
exception of bullying via email (Cotter & McGilloway, 2011).
The complexity of cyberbullying is partially attributable to the significantly large
number of different behaviours that it may encompass (Marczak & Coyne, 2015). For
example, Willard (2006) identified seven potential categories of cyberbullying behaviours:
flaming, online harassment, cyberstalking, denigration, masquerade, outing, and exclusion.
However, these categories, although proposed only 11 years ago, may already be out dated or
incomplete because of the rapid rise and development of social media platforms and sharing
apps that could facilitate cyberbullying. For example, when this typology was suggested, the
vastly popular picture-sharing app Snapchat was not in existence.
More recent research suggests the need to identify a wider array of potential
cyberbullying behaviours. For example, Rivers and Noret (2010) identified ten categories of
behaviours: threat of physical violence, abusive or hate-related, sexual acts, demands or
instructions, threats to damaging existing relationships, threats to family or home, and
menacing chain messages. Moreover, Nuccitelli (2012) proposes over thirty-sex different
behaviours that could be considered cyberbullying. Other studies propose broader categories,
such as: sexting, trolling, and griefing (Slonje, Smith, & Frisén, 2013); or direct and indirect
cyberbullying (Langos, 2012). Direct cyberbullying includes behaviours that occur
exclusively between the perpetrator(s) and the victim(s), for example aggressive content sent
via text/instant messages and/or phone calls, or exclusion from online groups. Indirect
cyberbullying occurs in the public online environment, for example, publicly posting hurtful

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or embarrassing posts and/or pictures about an individual or the creation of public forums
targeting the victim specifically.
Media reports of several cases of teenage suicide, attributed to experiences of
victimization online, have heightened public awareness and concern about cyberbullying. For
example, Felix Alexander, aged 17 from Worcester, tragically committed suicide in 2016
after years of being bullied. Felix’s mother wrote that online bullying had exacerbated the
effect that victimization had on her son, and that in an effort to prevent the online attacks he
had removed himself from multiple social media sites. However in doing so, this increased
his feelings of social isolation (The Guardian, October 5, 2016).
Thus, cyberbullying is an important area for research. Other chapters in this book
review issues surrounding the prevalence of cyberbullying and the associated risk and
protective factors in several international settings. The prevalence rates of reported
cyberbullying behaviours vary greatly between international studies, from 10% to 72%
(Marczak & Coyne, 2015). This chapter aims to address these questions in the context of
adolescents in the United Kingdom and Ireland. When referring to the United Kingdom, it is
worthwhile to note that this term includes England, Scotland, Wales and Northern Ireland.
When discussing cyberbullying in Ireland, we are referring to the Republic of Ireland.
Cyberbullying in the United Kingdom and Ireland
A large-scale international survey conducted by the network company Vodafone Plc,
in collaboration with YouGov Plc, in 2015 of 4,720 adolescents aged 13 to 18 years old,
revealed that 26% of Irish children were victimized online, whilst 85% reported having heard
of someone else being cyberbullied. In the same study, 15% of UK children reported being
cyberbullied themselves and 68% reporting that they had heard of someone else being
cyberbullied. Of the 11 countries surveyed (Czech Republic, Germany, Greece, Ireland, Italy,
Netherlands, New Zealand, South Africa, Spain, UK, and USA), rates of cyber-victimization

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in Ireland were the second highest, while the UK figure was the lowest. Furthermore, this
international survey revealed that 60% of Irish children and 35% of UK children thought that
cyberbullying was worse than face-to-face bullying. The international survey also revealed
that 41% of children reported that cyberbullying victimization made them feel depressed,
26% said they felt completely alone, 18% reported suicidal thoughts, 21% had avoided
school, and 25% had shut down their social media accounts as a result of cyberbullying.
In the UK, the charitable organization Childline reported, for the year 2015-16, that
they had delivered 4,541 counselling sessions relating to cyberbullying, which was a 13%
increase from 2014-15, (National Society for the Prevention of Cruelty to Children, 2015; p.
7) and an overall increase of 88% over the past five years. Young people who had contacted
Childline about online bullying typically reported knowing the identity of the perpetrator and
described how online bullying usually led to offline physical and verbal victimization.
Furthermore, victims reported fear because they felt that the bullies ‘could reach them
anywhere’. Various forms of victimization were reported by callers to Childline, for example
malicious or hurtful messages being posted about them to social media profiles, blogs,
pictures or posts. In some situations, online forums were created as spaces for multiple
perpetrators to specifically post bullying content about the victim(s).
Table 5.1 shows the prevalence of cyberbullying perpetration and/or victimization, as
measured by 25 independent empirical studies employing samples from the United Kingdom
(n = 15; Ackers, 2012; Bevilacqua et al., 2017; Brewer & Kerslake, 2015; Del Rey et al.,
2015; Fletcher, Fitzgerald-Yau, Jones, Allen, Viner, & Bonell, 2014; Genta, Smith, Ortega,
Brighi, Guarini, Thompson, Tippett, Mora-Merchán, & Calmaestra, 2012; Lasher & Baker,
2015; Monks, Robinson, & Worlidge, 2012; Oliver & Candappa, 2003; Pornai & Wood,
2010; Rivers & Noret, 2010; Smith et al., 2008 (Studies 1 and 2); West, 2015; Wolke, Lee, &
Guy, 2017), Northern Ireland (n = 4; Devine & Lloyd, 2012; McClure Watters, 2011;

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McGuckin, Cummins, & Lewis, 2010; Purdy & York, 2016) and the Republic of Ireland (n =
4; Callaghan, Kelly, & Molcho, 2015; Corcoran, Connolly, & O’Moore, 2012; Cotter &
McGilloway, 2011; O’Moore, 2012). One other international study presented results on
cyberbullying in both the United Kingdom and the Republic of Ireland (Livingstone et al.,
2011). In addition, O’Neill and Dinh (2015) present results from the Net Children Go Mobile
survey in the United Kingdom and the Republic of Ireland. However, this study has been
excluded from subsequent exploration, as it reports percentages for offline and online
victimization (UK 21%; Ireland 22%) combined.
These studies were identified during searches of the online academic literature
database Web of Science. Searches were conducted for studies that were published between
the years 2000 and 2017 and employed key terms such as: “cyberbullying”; “cyber
victimization”; “online harassment”; cyber; bully*; victim*; “United Kingdom”; “UK”; and
“Ireland”. The British English spelling of victimization, i.e. victimisation, was also included
as a search term. Additionally, specific searches of key researchers in the UK and Ireland, for
example Professor Peter Smith and Professor Mona O’Moore respectively, were conducted in
order to discover any relevant publications. This chapter will discuss the findings on the
prevalence of cyberbullying perpetration and victimization derived from these studies, and
the factors that potentially influence prevalence.
Prevalence of cyberbullying perpetration and victimization
An early study of cyberbullying prevalence in the UK was conducted in 2002, with
Year 8 students (N = 779; mean age = 12 years old) reporting how often they had received
nasty emails or text messages (Oliver & Candappa, 2003). This seminal study reported that
4% of children reported receiving nasty text messages, and 2% reported receiving nasty
emails. Subsequent research studies identified higher prevalence rates from data collected
between 2002 and 2006. Rivers and Noret (2010) reported the prevalence of ‘receiving nasty

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text messages or emails’ in a sample of British adolescents aged 11 to 14 years old. The
results are presented for this five-year study independently for each year of data collection
and for males and females separately. The results show a steady increase in the rate of
cyberbullying victimization experienced by girls from the first point of data collection in
2002 (14.1%) to 2005 (21.3%). The rates declined slightly in 2006 to 20.8% of girls reporting
receiving nasty text messages or emails. The figures for boys were less consistent, as is
demonstrated in the results shown in Table 5.1.
In 2005, a study conducted with UK adolescents aged 11 to 16 years old (Smith et al.,
2008; Study 1) found that a maximum of 1.1% of children reported cyberbullying others via
phone calls, texts, emails and/or instant messages outside school, and 2.3% reported
cyberbullying others via email inside school more than once or twice. Cyberbullying
victimization varied from 1.1% (via websites outside school), to 3.3% (via phone calls, texts,
emails inside school) and 10.9% reporting bullying victimization via phone calls outside
school more than once or twice in the past couple of months (Smith et al., 2008). This pilot
study was subsequently followed up and revealed higher incidence rates of children reporting
having ‘ever’ cyberbullied someone (from 1% via chat rooms to 5.3% via instant messaging)
or having ever been a victim of cyberbullying (from 2.5% via chatrooms to 9.5% via phone
calls and 9.9% via instant messaging; Smith et al., 2008, p. 379).
The prevalence of cyberbullying perpetration and victimization has varied greatly in
more recent studies, from 2.5% (mobile bullying perpetration), 3% (internet bullying
perpetration), 4.1% (mobile bullying victimization) and 6.6% (internet bullying
victimization) for 2,227 Year 8 to 12 students in 2008 (Genta et al., 2012) to 13.5% of 1,144
Year 8 students reporting engaging in cyberbullying perpetration less than once a week
(Fletcher et al., 2014). Also in 2008, 5% and 20.5% of primary school children in England,
aged 7 to 11 years old, self-identified as cyber-bullies and cyber-victims respectively (Monks

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et al., 2012). Ackers (2012) discovered that 11% of 325 Year 7 to 9 students from one
secondary school in the UK responded that they had been cyberbullied, while 7% of the
sample responded that they had cyberbullied someone else. Research conducted with older
adolescents (90 students aged 16 to 18 years old) found that 13.5% and 16.2% of children
reported cyberbullying perpetration and victimization respectively (Brewer & Kerslake,
2015).
National data collected via the Longitudinal Study of Young People in England (of
Year 10 students in 2014) discovered that 11% of children reported cyberbullying
victimization (Lasher & Baker, 2015). Moreover the international EU Kids Online survey
concluded that 8% of UK children reported cyberbullying victimization (Livingstone et al.,
2011). An exploratory study of cyber-aggression and cyber-victimization found that 31.5%
and 56.2% of 339 Year 7 to 9 students in one UK secondary school reported engaging in and
experiencing cyber-aggression and cyber-victimization respectively (Pornai & Wood, 2010).
A more recent study (Bevilacqua et al., 2017) discovered that, among a sample of Year 7
students from 40 English schools, 1.6% and 6.4% of children reported cyberbullying
perpetration and victimization respectively. Among older children, prevalence rates of
cyberbullying perpetration (1.9%) and victimization (7.9%) were slightly higher (West,
2015).
Other studies have categorized children according to their self-reported involvement
in cyberbullying. Del Rey and colleagues (2015) found that 0.9% of 737 UK students were
categorized as aggressors of cyberbullying, 2.0% were categorized as bully-victims, and
6.4% were victims of cyberbullying. In this large-scale European study, the prevalence of
cyberbullying perpetration among UK adolescents was relatively low in comparison to the
overall sample that included children from countries such as Italy, Greece, Poland, Spain and
Germany. However, the number of children in the UK who reported cyberbullying

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victimization was in line with the mean prevalence reported by the total sample (6.4%
compared with 6.8%). Wolke and colleagues (2017) found that 1.1% of 2,754 UK
adolescents aged 11 to 16 years old were classified as ‘pure cyber-victims’, in other words,
being victimized online only.
In Northern Ireland, a government report concluded that 1.1% and 3.5% of primary
and secondary school students reported cyberbullying perpetration and victimization
respectively (McClure Watters, 2011). Additionally, 3.7% of 425 Year 9 to 11 students from
two secondary schools reported experiencing cyberbullying victimization. Results from a
nationally disseminated survey (Kids Life and Times) showed that 13.8% of Northern Irish
adolescents reported cyberbullying victimization (Devine & Lloyd, 2012). Moreover, among
a sample of nearly 3,500 children, aged 11, attending 217 Northern Irish primary schools,
10.3% reported experiencing cyberbullying victimization (McGuckin et al., 2010).
Seminal research on cyberbullying in the Republic of Ireland in 2011 found that 9%
and 17% of secondary school students reported cyberbullying perpetration and victimization,
respectively (Cotter & McGilloway, 2011). In addition, this study found that the majority of
cyberbullying perpetration and victimization reported by participants was experienced
outside school (see Table 5.1). Furthermore, O’Moore (2012) reported that 4.4% of over
3,000 secondary school students were classified as pure cyber-bullies, 4.1% were categorized
as bully-victims, and 9.8% were categorized as pure cyber-victims. International studies, that
have included Irish children, have found that 4% of adolescents reported cyberbullying
victimization (Livingstone et al., 2011). Similarly, Corcoran and colleagues (2012) reported
that 2.6% of post-primary Irish adolescents reported cyberbullying perpetration, and 6.3%
reported cyberbullying victimization. A recent study conducted in the Republic of Ireland
concluded that 9.8% of Irish adolescents aged 15 to 18 years old (N = 318) had experienced
cyberbullying victimization (Callaghan et al., 2015).

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Measuring Cyberbullying and Cybervictimization
Unfortunately, the measurement instruments used in cyberbullying research are far
from perfect (Patchin & Hinduja, 2015; Gradinger, Strohmeier, & Spiel, 2010), and thus
there are several factors to consider when interpreting the prevalence rates produced. Some
researchers in the field suggest that a precise measure of cyberbullying prevalence may be
impossible to achieve (Sabella, Patchin, & Hinduja, 2013). A recent meta-analysis
investigated the prevalence of cyberbullying perpetration and victimization and the potential
methodological moderator variables that can impact reporting rates (Foody, Samara, &
O’Higgins Norman, 2017). This synthesis of 39 empirical studies, conducted in the Republic
of Ireland and Northern Ireland, found that 5.2% and 3.9% of primary and post-primary
school students, respectively, reported engaging in cyberbullying perpetration. In addition,
13.7% and 9.6% of primary and post-primary school students, respectively, reported
cyberbullying victimization. These aggregate figures were lower than those identified for
traditional school-bullying perpetration (10.1%) and victimization (26.1%).
The observations noted in this chapter are in line with the conclusions of this recent
meta-analysis. In particular, Foody and colleagues (2017) dicovered that a series of
moderator variables also influenced the prevalence rates reported by primary studies. For
example, the time frame during which participants were asked to disclose their experiences of
cyberbullying perpetration and/or victimization was a significant moderator for both
outcomes. Namely, the longer the time, the higher the percentage of cyberbullying
perpetration (Q(df = 2) = 19.53, p < 0.001) and cyberbullying victimization (Q (df = 2) =
22.88; p < 0.001), as measured by 7 studies with post-primary students. Issues relating to
methodology will be investigated in the following sections of this chapter in relation to
cyberbullying research that has been conducted in the UK and Ireland. For example,
prevalence rates of cyberbullying perpetration and victimization may be affected by the

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timeframe specified for experiencing these behaviours, the use of categorical frequency
variables, the use of self-report measures, and the year of data collection.
Timeframe of measurement. The period of time during which children were
required to report cyberbullying victimization and/or perpetration varied between studies
conducted in the UK and Ireland. Of the studies that used a specified timeframe for
experiences of cyberbullying, children were asked about their experiences of cyberbullying
perpetration and/or victimization in the past two (e.g. Genta et al., 2012; Purdy & York,
2016), three (e.g. Bevilacqua et al., 2017), six (e.g. Brewer & Kerslake, 2015; Cotter &
McGilloway, 2011; Pornai & Wood, 2010; Wolke et al., 2017) or twelve (e.g. Lasher &
Baker, 2015) months. Additionally, some studies asked about the frequency of cyberbullying
behaviours in other timeframes, such as ever (e.g. Fletcher et al., 2014; McGuckin et al.,
2010; Rivers & Noret, 2010; West, 2015), the past couple of months (e.g. Callaghan et al.,
2015; O’Moore, 2012; Smith et al., 2008), or the past school term (e.g. Monks et al., 2012).
No definitive pattern could be identified in the prevalence rates according to the
timeframe specified in the measurement instrument. However, in many cases (e.g. Brewer &
Kerslake, 2015; Cotter & McGilloway, 2011; McGuckin et al., 2010; Pornai & Wood, 2010;
Fletcher et al., 2014), prevalence rates of cyberbullying perpetration and victimization were
higher when children were asked to indicate the frequency over longer periods of time (e.g.
six months or ever), in comparison with studies employing shorter periods of time (e.g. two
months, Genta et al., 2012; or three months, Bevilacqua et al., 2017). Surprisingly, other
studies that used longer timeframes for experiencing cyberbullying whilst being a college
student (e.g. West, 2015), or the past couple of months (e.g. Smith et al., 2008), reported
quite low prevalence. This suggests that choosing an appropriate timeframe for measuring
cyberbullying perpetration and victimization may influence the frequency of reporting.
Therefore, this issue should be considered when choosing an adequate measurement

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instrument for empirical research, however, there may be other factors that also influence
reporting rates.
Frequency responses. Cyberbullying perpetration and/or victimization prevalence
rates could also be influenced by how behaviours are categorized. The majority of
measurement tools used in studies reviewed in this chapter employed Likert frequency scales,
asking children to indicate whether they had experienced cyberbullying behaviours never to
once or more a week (e.g. the European Cyberbullying Intervention Project Questionnaire;
Brighi et al., 2012). Typically, most studies found that, when including response categories of
lower frequency, more children reported cyberbullying behaviours. For example, Fletcher
and colleagues discovered that, while only 0.3% of children reported cyberbullying
perpetration several times a week or more, 13.5% of children reported cyberbullying
perpetration less than once a week. Moreover, when the questions specified ever
cyberbullying someone else, or ever being cyberbullied, the prevalence rates were higher in
comparison to results for cyberbullying more than once or twice a week (Smith et al., 2008).
Reports of cyberbullying perpetration and victimization in Northern Ireland suggest
that when lower levels of these behaviours are included, the overall prevalence increases
(McClure Watters, 2011). With respect to cyberbullying victimization, 3.5% of children
experienced cyberbullying two or three times a month (2.1% by mobile phones; 1.3% on the
Internet). A further 16.6% of children reported cyber-victimization via mobile phones or the
Internet that only happened once or twice. In addition, 5.5% reported cyberbullying others
via mobile phones or the Internet ‘only once or twice’. Among the 1.9% and 7.9% of
adolescents aged 16 to 19 years of age who identified as cyber-bullies and cyber-victims,
respectively, the majority of bullies (44.4%) and victims (42.5%) indicated that the
behaviours had only occurred once in the time that they had been a college student (West,
2015).

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Generally, empirical research tends to focus on cyberbullying perpetration and
victimization that occurs more frequently over longer periods of time, which is considered to
have a more severe effect. However, since the issue of repetition is a contentious factor in
defining cyberbullying (Patchin & Hinduja, 2015), it may be desirable to also include
behaviours that occur less frequently. Cotter and McGilloway (2011) asked participants to
indicate the duration of their experiences of cyberbullying. Of 22 cyber-victims identified in
this study, the majority (n = 16) reported that the cyberbullying victimization lasted for one to
two weeks. A minority of participants reported cyberbullying victimization lasting for ‘about
a month’ (n = 2), ‘about six months’ (n = 2) or for several years (n = 2). The experience of
even one incident of cyberbullying victimization may be repeated for the victim and the
negative impact exacerbated because of the permanency of online data and the speed and
extent of distribution (Willard, 2006). Therefore, the restrictions that may be imposed when
measuring cyberbullying prevalence should be carefully considered. For example, only
counting cyberbullying that occurs ‘several times a week or more’ (Fletcher et al., 2014) may
underestimate the extent of the problem.
Self-report measures. Each study included in this review employed self-report
measures of cyberbullying perpetration and/or victimization. Measurement instruments
include the Cyberbullying Questionnaire (Smith et al., 2008), the European Cyberbullying
Intervention Project Questionnaire (Brighi et al., 2012), the Revised Olweus Bully/Victim
Questionnaire (Olweus, 1996) or specific items on cyberbullying from the Bullying and
Friendship Interview (Wolke et al., 2000). However, the use of self-report measures may not
be the best method for establishing true prevalence rates, because of potential biases, such as
social desirability responding. For example, O’Moore (2012) found that between 3.5% and
15.6% of male and female children were categorized as pure cyber-bullies or pure cyber-
victims. However, 39.1% of females and 29.9% of males from the same study reported being

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aware of someone else who had been victimized online, and 28.4% of children indicated that
they were aware of someone who had bullied others online. Moreover, 33% of UK Year 8
students reported knowing someone else who had been cyberbullied, although only 11%
reported being cyberbullied themselves (Ackers, 2012). Social desirability responding may
also potentially explain why cyberbullying victimization is more frequently reported than
cyberbullying perpetration (e.g. Smith et al., 2008; Monks et al., 2012). Thus, the reliability
and validity of self-report measures should be investigated and taken into consideration when
interpreting the reported prevalence of cyberbullying perpetration and/or victimization.
Year of data collection. An additional factor that needs to be considered when
interpreting the rates of cyberbullying perpetration and/or victimization is the year in which
data was collected. The last decade has seen a rapid increase in the prevalence and
accessibility of Internet-ready devices, such as smartphones, tablets, personal computers and
music players. Therefore, the potential to engage in cyberbullying behaviours or to be
cyberbullied is increasingly at our fingertips (Slonje et al., 2013). Analysing the results from
the studies shown in Table 5.1, amongst those that report when data collection occurred, it
appears that cyberbullying prevalence rates may be influenced by the year in which data was
collected.
Genta et al. (2012) reported that 2.1% and 2% of children reported occasional and
severe mobile victimization and 4% and 2.6% reported occasional and severe internet
victimization. Additionally, this study also discovered that 95% of children had a computer
with Internet access in their homes, but only 38.4% reported having access to the Internet in
their own bedrooms. Based on analyses of the overall sample, (i.e. including adolescents
from Italy and Spain) Genta et al. (2012) found that accessibility to the Internet in one’s own
bedroom was a significant predictor of higher levels of both cyberbullying victimization and
perpetration. However, as the data was collected approximately nine years ago, in 2008, these

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figures may not be generalizable to the adolescent population in the UK or Ireland today. For
example, national data collected from UK adolescents in 2014 indicated that 11% of children
reported cyberbullying victimization (Lasher & Baker, 2015). Furthermore, two studies
conducted in Northern Ireland analysed results from the Kids Life and Times survey
administered in 2008 (McGuckin et al., 2010) and 2009 (Devine & Lloyd, 2012). Even
within the space of a year the rate of cyberbullying victimization reported rose from 10.3% to
13.8%.
Another element that may affect the prevalence rates of reported cyberbullying,
relating to the year in which data is collected, is the medium through which the aggression
takes place. For example, Purdy and York (2016) estimated the prevalence of cyberbullying
among a sample of 425 Northern Irish adolescents and also explored their social media use;
90.2% reported using Facebook, 69.9% reported using Snapchat and 55.6% reported using
Twitter. Most students reported spending an average of 1 to 2 hours online per day (31.3%)
and 1 in 5 students reported spending 2 to 3 hours online per day. Sending hurtful or nasty
comments either via text messages or on social networking sites was the most common form
of cyberbullying among all students (Purdy & York, 2016).
In comparison, earlier studies focused solely on cyberbullying occurring via text
messaging or emails (e.g. Oliver & Candappa, 2003; Smith et al., 2008). Moreover, the use of
specific social media sites changes over time too, which could also influence the prevalence
of cyberbullying, as some platforms may be more conducive to this form of aggressive
behaviour. For example, Cotter and McGilloway (2011) specifically refer to the social
networking site ‘Bebo’, but this site is no longer used, so that their prevalence rate for
cyberbullying may not apply to adolescents in 2017.
Risk Factors

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A large-scale review assessed risk factors associated with cyberbullying perpetration
and victimization as measured by 53 studies conducted in various international locations
(Baldry, Farrington, & Sorrentino, 2015). This review categorized factors according to a
socio-ecological framework (Bronfenbrenner, 1979), with risk factors identified at the
individual (e.g. technology use, personality traits, values), peer and family (e.g. pro-social
peers, peer rejection, parental support), and school (e.g. lack of teacher support, negative
school climate) levels. This theoretical framework is commonly used to explain risk factors
associated with cyberbullying (e.g. Cross, Barnes, Papageorgiou, Hadwen, Hearn, Lester,
2015). This chapter will further explore the potential risk factors and predictors that are
associated with cyberbullying perpetration and victimization as measured in UK or Irish
samples. The included studies measured mainly individual-level factors, such as gender,
ethnicity, demographics, traditional bullying perpetration and victimization, and various
psychological and cognitive constructs. In addition, some school-level variables have been
studied. Because of the general lack of longitudinal studies, it is difficult to draw conclusions
about prediction or about causal effects.
Gender. Assessing the prevalence rates reported by studies conducted with samples
in the United Kingdom and Ireland, it appears that girls report, on average, higher rates of
cyber-victimization than boys, and boys report, on average, higher rates of cyber-bullying
perpetration. Bevilacqua and colleagues (2017) concluded that 1.13% of males and 0.45% of
females reported frequent cyberbullying perpetration, and 1.94% of males and 4.48% of
females reported frequent cyberbullying victimization. In Northern Ireland, female
adolescents (15%) reported statistically significant higher rates of cyber-bullying
victimization compared to their male peers (11%; 𝜒2 =18.45, df = 2, p < 0.001; Devine &
Lloyd, 2012, p. 17).

18
Similar results were found by Pornai and Wood (2010), with females (58.8%)
reporting higher rates of cyber-victimization compared to males (53.2%). Of the children
who were categorized as ‘pure cyber-victims’ (i.e. those reporting experiencing bullying
victimization online only) in Wolke and colleagues’ study (2017), 58.1% were female.
Ackers (2012) concluded that there was a significant main effect for gender in self-reported
cyberbullying victimization, with females being more likely to report being victimized. The
frequency of receiving nasty or threatening text messages or emails varied between 10.3%
and 12% for boys, but it was higher for girls, varying between 14.1% and 21.3% (Rivers &
Noret, 2010). An exploratory study of 1,144 year 8 students in UK secondary schools
concluded that males (14.7%) were more likely than females (13.4%) to report engaging in
cyberbullying perpetration (Odds Ratio (OR) = 0.91; CI = 0.64 to 1.28), although this
difference was not statistically significant (Fletcher et al., 2014).
In the Republic of Ireland, however, one study found that boys reported more
cyberbullying victimization than females, 10.3% and 9.2% respectively (Callaghan et al.,
2015). In comparison, employing a sample of Irish adolescents, aged 12 to 16 years old,
O’Moore (2012, p. 213) classified more girls (15.6%) as pure-victims of cyberbullying than
boys (6.9%). This study categorized more boys (4.9%) as pure bullies than girls (3.5%),
however, more girls (4.5%) were classified as bully-victims than boys (3.9%). Similarly,
Pornai and Wood (2010) found that girls were more likely to report cyber-aggression
perpetration than boys (OR = 1.66, p < 0.05). However, some studies found no significant
association between gender and cyberbullying perpetration or victimization (e.g. Monks et
al., 2012, p. 483).
Ethnicity and demographic variables. A few of the studies conducted in the UK
and Ireland considered the impact of several demographic and sociodemographic variables.
Research on ethnicity found that males of mixed ethnicity (4.5%) and females identifying as

19
Black or Black British (0.8%) were more likely to report engaging in frequent cyberbullying
perpetration (Bevilacqua et al., 2017). Both males and females identifying as White Other
(3.4% and 5.2% respectively) were more likely to report frequent cyberbullying
victimization. An analysis of the relationship between ethnicity and cyberbullying
perpetration concluded that, in comparison with children identifying as White British
(11.6%), those of dual heritage (20%; OR = 1.92; CI = 1.09 to 3.40) and other ethnicity
(19.1%; OR = 1.76; CI = 1.03 to 3.00) were more likely to report cyberbullying perpetration
(Fletcher et al., 2014). The differences between children identifying as White British and
those identifying as Asian or Asian British (9.9%; OR = 0.83, CI = 0.47 to 1.48) or as Black
or Black British (17.2%; OR = 1.55, CI = 0.97 to 2.48) were not quite statistically significant.
Fletcher and colleagues (2014) found no differences in cyberbullying perpetration
reported by students according to family structure (i.e. living with two parents, one parent, or
other). Adolescents who reported having unemployed parents (21.1%) were more likely to
engage in cyberbullying perpetration than students with parents in employment (13.8%; OR
= 1.6; CI = 0.98 to 2.6), although this effect was not quite statistically significant.
Traditional school-bullying and victimization. The most common finding by
studies conducted with children in the United Kingdom and Ireland is that there is a
significant relationship between school bullying perpetration and victimization offline and
cyberbullying perpetration and victimization. Previous research has found that there is a
distinct overlap between offline and online victimization, with individuals participating in
both acts as pure offline and pure online bullies and victims, but also various combinations of
online and offline bullies, victims and bully-victims (Schultze-Krumbholz et al., 2015). For
example, in the study conducted by Wolke and colleagues (2017), 8.1% and 5.8% of children
reported experiencing victimization as a result of direct and relationship bullying
respectively, while only 1.1% of children reported experiencing only cyber-victimization. In

20
addition, 5.1% of children reported experiencing direct, relational and online bullying
victimization. Typically, reports of offline bullying perpetration and victimization are higher
than those reported for online perpetration and victimization (e.g. Bevilacqua et al., 2017;
Cotter & McGilloway, 2011; Livingstone et al., 2011; Monks et al., 2012; O’Moore, 2012).
Pornai and Wood (2010, p. 88) concluded that, among a sample of UK adolescents,
high levels of traditional aggression correlated with an increased likelihood of an adolescent
being a cyber-bully (B = 0.24; SE = 0.03; p < 0.001). Similarly, high levels of traditional
victimization correlated with an increased likelihood of being a cyber-victim (B = 0.10; SE =
0.02; p < 0.001), but with a decreased likelihood of being a cyber-bully (B = -0.09; SE =
0.03; p = 0.001). Fletcher et al. (2014) also investigated the relationships between self-
reported aggressive behaviour at school and the frequency of cyberbullying perpetration.
Students who reported higher levels of aggressive behaviour in school were significantly
more likely to also report cyberbullying perpetration (37.1%; OR = 14.35; CI = 7.96 to
25.86), in comparison with those reporting lesser degrees of in-school aggression. In a
sample of primary school children, being a traditional victim was a significant predictor of
being a cyber-victim, but not a cyber-bully. Furthermore, being a traditional bully was a
significant predictor of being a cyber-bully, but not a cyber-victim (Monks et al., 2012; pp.
483 – 484). However, when age is taken into consideration, the relationship between
traditional bullying and cyber-bullying may change. For example, O’Moore (2012, p. 213)
found that 32% of post-primary cyber-bullies reported traditional bullying victimization,
while 28.9% of cyber-victims reported engaging in traditional bullying perpetration.
Cognitive and psychological factors. Five studies reviewed in this chapter (i.e.
Brewer & Kerslake, 2015; Corcoran et al., 2012; Fletcher et al., 2014; Wolke et al., 2017)
investigated the relationship between cyberbullying and different cognitive or psychological
factors. Because of the infrequency of longitudinal studies, it is unclear whether these are risk

21
factors for, or outcomes of, cyberbullying perpetration and victimization, but the results are
important to guide future research.
In an adjusted multi-level regression model, Wolke and colleagues (2017) found that
pure cyber-victimization was significantly related to lower self-esteem (B = -2.19, p = 0.004)
and higher levels of self-reported behavioural difficulties (B = 4.13, p > 0.001). Furthermore,
when effect sizes were adjusted for demographic variables, interesting relationships were
observed between self-reported cyberbullying perpetration of UK adolescents and their
psychological functioning, overall mental wellbeing and several aspects of mental and
physical health (Fletcher et al., 2014
1
). Based on a measure of psychological functioning (the
Strengths and Difficulties Questionnaire; Goodman, 2006), Fletcher et al. (2014) suggested
that children with greater overall difficulties (OR = 2.32; CI = 1.97 to 3.24) and greater
conduct problems (OR = 1.3; CI = 1.08 to 1.55) were more likely to report bullying others
online in comparison with children reporting fewer overall difficulties or fewer conduct
problems. Significant negative relationships were observed between cyberbullying
perpetration and the quality of life (OR = -3.51; CI = -5.7 to -0.1), psychosocial health (OR =
-5.04; CI = -7.26 to -1.6), emotional functioning (OR = -5.6; CI = -9.03 to -0.18) and school
functioning (OR = -7.35, CI = -9.27 to -4.95; see Fletcher et al., 2014).
In Northern Ireland, Devine and Lloyd (2012) observed that adolescents who
experienced cyberbullying victimization reported significantly poorer overall psychological
well-being (t (1, 3382) = 10.77, p < 0.001). In the Republic of Ireland, Corcoran and
colleagues (2012) discovered interesting relationships between aspects of participants’ self-
concepts, measured using the Piers-Harris 2 (Piers & Herzberg, 2002) instrument. Cyber-
victims scored lower on overall general self-concept and the ‘freedom of anxiety’ subscale, in
comparison to non-involved groups. For UK adolescents, Brewer and Kerslake (2015) found
1
Only statistically significant relationships are reported here. For a full overview see Fletcher
et al., 2014, table 3, p. 1396

22
that cyberbullying victimization was significantly and positively correlated with loneliness (r
= 0.8, p < 0.01) and negatively correlated with self-esteem (r = -0.42, p < 0.01). In addition,
cyberbullying perpetration was significantly negatively correlated with loneliness (r = -0.38,
p < 0.01) and self-esteem (r = -0.22, p < 0.01). Based on standard regression models, low
self-esteem was significantly related to cyberbullying victimization. Low levels of empathy
and self-esteem were also significantly related to cyberbullying perpetration (Brewer &
Kerslake, 2015, p. 258).
Pornai and Wood (2010) also conducted exploratory analyses of several individual
cognitive factors and cyber-aggression perpetration and victimization among UK adolescents.
The results indicated that the moral justification facets of moral disengagement were related
to cyber-aggression perpetration (B = 0.20, SE = 0.04; p < 0.001). Moreover, hostile
attribution bias was significantly related to cyber-aggression victimization (B = 0.12, SE =
0.04, p < 0.05). Finally, Corcoran and colleagues (2012) investigated the relationship
between cyberbullying perpetration and victimization and personality, as measured by the
Junior Eysenck Personality Questionnaire (Eysenck & Eysenck, 1975). Significant
differences were observed between groups (i.e. cyber-bullies, cyber-victims, traditional-
bullies, traditional-victims, non-involved) on both psychoticism and neuroticism scores.
Specifically, the cyber-victim group reported significantly higher scores on the neuroticism
scale compared to the non-involved group.
School-level factors. Bevilacqua and colleagues (2017) further investigated the
relationship between several school-level variables and the frequency of self-reported
cyberbullying perpetration and victimization. Effect sizes, adjusted for all individual-level
variables, such as gender and ethnicity, evaluated the relationship between the proportion of
children eligible for free school meals, the Income Deprivation Affecting Children Index
score, and the most recent overall Ofsted rating, and the prevalence of cyberbullying

23
perpetration and victimization. Moreover, school type (e.g. community – funded by local
authorities; voluntary-aided – funded by a charity and partially by local authorities; sponsor-
led academies and foundation schools), size and sex composition were also investigated in
relation to cyberbullying and cyber-victimization. Significant relationships were found for the
impact of the proportion of students eligible for free school meals (adjusted OR = 1.02, CI =
1.002 to 1.05), community schools (adjusted OR = 4.25, CI = 1.54 to 11.71), foundation
schools (adjusted OR = 4.73, CI = 1.83 to 12.26), and the ‘requires improvement’ Ofsted
rating (adjusted OR = 4.01, CI 1.05 to 15.24) versus cyberbullying perpetration. These results
suggested that cyberbullying perpetration was more likely to occur in schools with lower
socioeconomic demographics and poor national ratings. In relation to cyberbullying
victimization, no statistically significant effects were found.
The majority (74.2%) of pure cyber-victims, categorized by Wolke and colleagues
(2017), were from schools that were not eligible for the pupil premium (an indicator of
deprivation and special assistance within schools). This study also investigated the
relationship between cyber-bullying and parental education. The majority of pure cyber-
victims reported that their parents had 12 to 13 years of education; 32.3% reported parental
education of more than 13 years, and 6.5% reported that their parents had spent less than 11
years in full-time education (Wolke et al., 2017). In Northern Ireland, the prevalence of
cyber-victimization was higher among students attending a school in an urban location
(4.3%) compared to those attending a smaller school in a rural location (2.7%; Purdy & York,
2016). These results suggest that adolescents who self-report cyberbullying perpetration are
also more likely to report a wide range of psychological and social problems. This is an
important observation to better inform cyberbullying intervention and prevention
programmes in the UK and Ireland.
Cyberbullying intervention and prevention

24
Legal aspects. In comparison to the United States, there is currently no law in place
in the UK or Ireland that criminalizes cyberbullying behaviours (Marczak & Coyne, 2010).
Some researchers have described cyberbullying as being in a state of legal limbo (Asam &
Samara, 2016, p. 131). However, current legislation in the United Kingdom specifies that all
schools must have a clearly defined anti-bullying policy (Marczak & Coyne, 2010; the
School Standards and Framework Act, 1998). Furthermore, the Education and Inspections
Act (2006) gives teaching professionals powers to regulate students’ behaviour in school,
including the ability to confiscate personal ICTs (Asam & Samara, 2016). As pointed out in
this chapter, there is quite frequently an overlap in experiencing traditional and cyber-
bullying. Therefore, it is pertinent for UK schools to incorporate elements targeting
cyberbullying into these anti-bullying policies. In addition, teachers are key players in
cyberbullying intervention and prevention. By removing ICTs from a student’s possession
they are able to physically stop cyberbullying perpetration from taking place in school.
There are ways in which online aggression, that may amount to cyberbullying, can be
prosecuted in the UK. For example, online hate crimes have recently received media
attention, with sources specifying that the Crown Prosecution Service in the UK will start to
seek harsher penalties for abuse perpetrated online via social media sites such as Twitter
and/or Facebook. The Director of Public Prosecutions stated recently that the criminal justice
system in the United Kingdom must start to handle cases of online hate crimes as seriously as
it handles offences that occur face-to-face (Dodd, 2017). Recent news stories have
highlighted the extreme levels of hate and abuse that those in the public eye receive online.
For example, Olivia Attwood, who appeared on a reality-style dating show aired on ITV2,
received abuse that was so bad that she could not disclose it on live television (BBC, 2017).
Celebrities are not the only ones who are subject to such abuse. Cyber-bullying and general

25
cyber-aggression are becoming increasingly common in our society, as communications
rapidly increase in the online sphere.
School-based intervention and prevention. School-based anti-bullying programs
have been widely researched internationally, with results indicating that they can be effective
in reducing traditional bullying perpetration and victimization (e.g. Farrington & Ttofi,
2009). Thompson and Smith (2012) conducted a large scale review of anti-bullying policies
in UK schools. Their evaluation found that anti-bullying efforts in UK schools occurred at
several different levels, including whole-school, classroom, and playground strategies. As the
current chapter has shown, in the UK and Ireland, traditional and online bullying commonly
overlap, so that it is important that schools in the UK and Ireland integrate cyberbullying into
their existing anti-bullying policies. More recently, a content analysis of anti-bullying
policies in schools in Northern Ireland revealed that the majority of schools incorporate
elements targeting cyberbullying (Purdy & Smith, 2016). Additionally, the ‘Quality Circles’
approach has been employed in schools in order to tackle the problem of cyberbullying (Paul,
Smith, & Blumberg, 2010).
Large numbers of parents in the Republic of Ireland report that they are aware of the
risk posed by cyberbullying, and they are either worried or unsure about whether their
children are exposed (O’Higgins Norman, O’Moore, & McGuire, 2016). Moreover, head
teachers of secondary schools in Northern Ireland and the Republic of Ireland report that
cyberbullying is prevalent in their schools and that they are frustrated with their attempts to
handle this complex problem (Purdy & McGurkin, 2015). Research has investigated the
factors that predict teachers’ intention to intervene in bullying, including cyberbullying,
scenarios. Boulton, Hardcastle, Down, Fowles, and Simmonds (2014) concluded that the
three significant predictors of willingness to intervene were ratings of empathy, coping, and

26
severity of the behaviours. Therefore, the inclusion of parents and teachers in school-based
cyberbullying intervention and prevention efforts is very important.
The effectiveness of several widely disseminated anti-bullying programmes in
reducing cyberbullying perpetration and victimization have been evaluated internationally,
including the KiVa programme in Finland (Williford, Elledge, Boulton, DePaolis, Little, &
Salmivalli, 2013) and the NoTrap! programme in Italy (Menesini, Nocentini, & Palladino,
2012; Palladino, Nocentini, & Menesini, 2016). However, the effectiveness of cyberbullying
intervention and prevention programmes in the UK or Ireland has not yet been evaluated.
Moreover, anti-bullying programmes implemented in the UK have not typically included
cyberbullying-related outcome measures (e.g. the INCLUSIVE program, Bonell et al., 2015;
the Emotional Literacy intervention, Knowler & Frederickson, 2013). Future research should
focus on evaluating the effectiveness of anti-bullying programmes in the UK and Ireland in
reducing cyberbullying perpetration and victimization. It should also be pointed out that,
because research conducted with non-school-aged samples is scarce, this is an important
avenue for future research (Myers & Cowie, 2016).
Conclusions
We conclude that cyberbullying and cyber-victimization are quite prevalent in the UK
and Ireland, although it is difficult to compare conclusions from different studies because of
differences in operational definitions and methods of measuring cyberbullying and cyber-
victimization. Research suggests that girls are more likely to be cyber-victims and boys are
more likely to be cyberbullies. Cyberbullying is closely related to traditional school bullying,
while cyber-victimization is closely related to traditional school victimization. Cyberbullying
and cyber-victimization are related to cognitive and psychological factors such as low self-
esteem, loneliness and behavioural difficulties, but there is a great need for longitudinal
studies to establish causal influences. Cyberbullying and cyber-victimization are more

27
prevalent in deprived schools. Since cyberbullying and cyber-victimization are known to be
related to undesirable psychological and health-related outcomes, it is critical to mount more
intervention programmes in the UK and Ireland and to evaluate them using high quality
methods, in order to reduce cyberbullying and cyber-victimization most effectively.
Acknowledgements: We are very grateful to Mona O’Moore and Peter Smith for their
helpful comments on an earlier version of this chapter.

28
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40
Table 5.1
Descriptions of cyberbullying studies conducted in the United Kingdom and the Republic of Ireland.
Study
Sample
Timeframe/Type of assessment
Prevalence of
cyberbullying (%)
Prevalence of cyber-
victimization (%)
Ackers (2012)
325 Year 7 to 9 students
from one UK secondary
school
13-item structured questionnaire using both open- and
closed-ended questionnaires to gain an understanding
of students’ knowledge and views of cyberbullying,
and also any personal experiences they may have (p.
144)
7%
11%
Bevilacqua et
al. (2017)
6,667 Year 7 students
from a cluster randomized
controlled trial in 40
English schools. 47%
were male and the mean
age was 11.8 years old.
39.4% were White British,
25% Asian/Asian British,
14% Black/Black British,
8.5% White Other, and
5.1% Other ethnicity.
Smith et al. (2008) measure of cyberbullying via
mobile phones or the Internet in the past 3 months.
Responses measured on a 5-point Likert scale.
Authors dichotomized responses to “not/rarely bullied
or bullies” and “bullied or bullies/frequently bullied
or bullies” (p. 3)
Males 1.13%
Females 0.45%
Males 1.94%
Females 4.48%
Brewer &
Kerslake (2015)
90 students aged 16 to 18
years old from further
education colleges in the
North West of England. 51
were female and 39 were
men.
The Revised Cyberbullying Inventory (RCBI; Topcu
& Erdur-Baker, 2010) measures frequency of
cyberbullying perpetration and victimization in the
past six months. Responses are scaled on a 4-point
Likert scale, from never (0) to more than three times
(3).
More than once in
past 6 months
13.54%
More than once in
past 6 months
16.22%
Callaghan et al.
318 secondary school
“How often have you been bullied at school in the
-
Cyber only:

41
(2015)
students aged 15 to 18
years old in Ireland.
past couple of months in the ways listed below?
Someone sent mean instant messages, wall postings,
emails and text messages, or created a web site that
made fun of me? Someone took unflattering or
inappropriate pictures of me without permission and
posted them online? Someone tricked me into sharing
personal information in an email or text message and
forwarded that?”
Responses ranged from: ‘I have not been bullied in
this way in the past couple of months’ to ‘several
times a week’ (p. 200 – 201)
All 9.8%
Boys 10.3%
Girls 9.2%
Traditional & Cyber:
All 9.5%
Boys 6.5%
Girls 13.8%
Corcoran et al.
(2012)
876 adolescents aged 12 to
16 from rural and urban
post-primary schools in
Ireland. 61% were male,
39% female, and the mean
age was 14.22. The
majority of participants
were Irish (90.9%).
Participants completed the Cyberbullying
Questionnaire (Smith et al., 2006) which asks about
the frequency of both cyberbullying and traditional
bullying perpetration and victimization experiences in
the past three months.
2.6%
6.3%
Cotter &
McGilloway
(2011)a
122 students from 2
mixed-gender secondary
schools in Ireland. 64
students were in 1st and 2nd
year (28 male, 36 female,
mean age = 13.08 years)
and 58 students were in 5th
and 6th year (23 male, 35
Participants completed the Cyberbullying
Questionnaire (Smith et al., 2006), which asks
participants to indicate the frequency of
cyberbullying in the past six months. Before
completing the survey, participants are provided with
a definition of both traditional and cyber bullying. (p.
46)
9%
Inside School 36.8%
Outside School 63.2%
17%
Inside School 45.4%
Outside School 54.5%

42
female, mean age = 16.62
years).
Del Rey et al.
(2015)
737 students from 5 UK
secondary schools that
were part of a larger study
in six European countries.
44.6% of the UK sample
were female.
The European Cyberbullying Intervention Project
Questionnaire (Brighi et al., 2012). 22 item Likert
scale with frequency responses for cyberbullying
victimization and aggression from never, to more
than once a week. (p. 143)
UK:
Aggressors 0.94%
Bully/Victims 2.03%
UK:
Victims 6.37%
Devine &
Lloyd (2012)
3,657 adolescents in
Northern Ireland, 54%
were female.
Participants were respondents of the annual Kids Life
and Times (KLT) survey, and data was collected in
2009. Participants were asked to indicate whether
they had been bullied by someone via nasty text
messages or posting bad things on the Internet using
yes/no response categories (p. 13)
-
Total 13.83%
Girls 15%
Boys 11%
Fletcher et al.
(2014)
1,144 Year 8 students
aged 12 to 13 years of age
from 8 mixed-sex
ethnically diverse
secondary schools in the
UK. Mean age was 12.1
years old and 54% were
male. 44% identified as
White British, 18%
Black/Black-British, 16%
Asian/Asian-British, 9%
Dual heritage, and 13%
other.
Item adapted from the Cyberbullying Questionnaire
(Smith et al., 2008) required participants to indicate
the prevalence and frequency of cyberbullying others
via mobile phones and the Internet.
“Have you ever bullied someone else through your
mobile phone or using the Internet?” Participants also
completed ten items from the Edinburgh Study of
Youth Transitions and Crime (McAra & McVie,
2010) concerning school misbehaviour and
delinquency and the Strengths and Difficulties
Questionnaire (SDQ; Goodman, 2006), a measure of
psychological functioning and distress.
(p. 1394)
Less than once a week
Total 13.5%
Males 14%
Females 13%
About once a week
Total 0.3%
Males 0.3%
Females 0.2%
Several times a week
or more
Total 0.3%

43
Genta et al.
(2012)
2,227 students from Years
8, 10 and 12 in 14 English
secondary schools that
took part in a large-scale
European study on
bullying. Data collected in
2008 and the majority of
participants were White
British (1,555). The
remaining identified as
Asian (437), Black (116)
and Mixed race (160).
1,105 were male and
1,114 were female.
“Cyberbullying is a new form of bullying which
involves the use of mobile phones (texts, calls, video
clips) or the Internet (e-mail, instant messaging, chat
rooms, websites) or other forms of information and
communication technology to deliberately harass,
threaten, or intimidate someone” (p. 20). Definition
of traditional bullying was also provided, and
students were given two examples of behaviours that
constitute traditional- and cyber-bullying
perpetration. Participants were asked to indicate the
frequency of experiencing or participating in these
behaviours in the past 2 months. Responses were
measured on a 5-point Likert scale, from never to
several times a week. Due to the lack of respondents
indicating ‘severe cyberbullying’ analysis also
included those indicating ‘occasional cyberbullying’
Mobile bullying:
Occasional 1%
Severe 1.5%
Internet bullying:
Occasional 1.5%
Severe 1.5%
(p. 23)
Mobile victimization:
Occasional 2.1%
Severe 2%
Internet victimization:
Occasional 4%
Severe 2.6%
(p. 23)
Lasher & Baker
(2015)
11,166 adolescents from
the Longitudinal Study of
Young People in England.
Data was collected in
2014 when the sample
were in Year 10, aged 14
to 15 years old.
Past 12 months
-
Total 11%
Males: 7%
Females: 15%
Livingstone et
al. (2011)
Adolescent participants to
the EU Kids Online
survey. Total sample
includes 25,142 9 to 16
Children were asked if they had been treated, or had
treated other people, in a hurtful or nasty way on the
Internet, whether as a single, repeated or persistent
occurrence (p. 24)
-
UK 8%
Ireland 4%

44
year olds from 25
countries.
McClure
Watters (2011)
2,201 primary (Year 6; n =
904) and secondary (Year
9; n = 1,297) students
from rural and urban
schools in Northern
Ireland. 1,222 were
female, and 1,077 were
male.
All participants completed the Revised Olweus
Bully/Victim Questionnaire (OBVQ; Olweus, 1996).
Definition of bullying is provided and time frame for
responses is within the past couple of months. Two
global items asking how often participants have been
victimized or bullied others in the past couple of
months? OBVQ contains some questions concerning
cyberbullying, but supplementary questions were
included in this study (p.24)
Mobile phones 0.77%
Computers 0.34%
Mobile phones 1.34%
Computers 2.12%
McGuckin et al.
(2010)b
3,440 11 year old pupils
from 217 Northern Irish
primary schools
participated.
Participants completed the Kids Life and Times
(KLT) questionnaire in 2008 and were asked to
indicate whether they had ever had been bullied by
someone sending them nasty messages or posting bad
things about them online (p. 87 – 88)
-
10.3%
Monks et al.
(2012)
120 students aged 7 to 11
years old from 5 primary
schools in the southeast of
England. 116 were male
and 104 were female. Data
was collected in 2008.
Modified version of Smith et al. (2008) bullying and
cyberbullying questionnaire. Participants asked to
indicate the frequency of their involvement in
cyberbullying behaviours over the past school term.
5%
20.5%
O’Moore
(2012)
3,004 students from 9
secondary schools in
Ireland. Participants were
aged 12 to 16 years old
and 66.4% were male and
33.6% female. Schools
Measured both traditional bullying (OBVQ; Olweus,
1996) and cyberbullying (Smith et al., 2006).
Definitions of both were provided, cyberbullying was
defined as: “bullying through text messages, pictures
or video clips via mobile phone cameras, phone calls,
e-mail, chat-rooms, Instant Messaging (IM) or
Total:
Pure bullies 4.4%
Bully-victims 4.1%
Boys:
Pure Bullies 4.9%
Total:
Pure victims 9.8%
Boys:
Pure victims 6.9%

45
were selected to reflect
variety of schools in
Ireland: e.g. fee-paying,
single-sex or mixed, urban
and rural etc.
(p.212)
websites (blogs, personal websites, personal polling
sites, or social networking sites). Cyber-bullying can
happen when text messages/pictures/clips/e-
mails/messages etc. are sent to you, but also when
text messages/pictures/clips/e- mails/messages etc.
are sent to others, about you”. Responses were on a
frequency scale from never to several times a week.
Participants were asked: “How often have you been
cyberbullied in the past couple of months?” and
“How often have you cyberbullied others in the past
couple of months?” (p. 212)
Bully-victims 3.9%
Girls:
Pure bullies 3.5%
Bully-victims 4.5%
Girls:
Pure victims 15.6%
(p. 213)
Oliver &
Candappa
(2003)
953 students from Year 5
students from 6 primary
schools (n = 174; mean
age 9 years old) and from
Year 8 students from 6
secondary schools (n =
779; mean age 12 years
old). 49% of the sample
identified as White
British, 20% Black and
Asian, and 23% other
ethnicity. 52% were
female and 47% male.
Mixed methods study, questionnaire element targeted
definitions, perceptions of the prevalence and
experiences of bullying. Data collected in 2002 from
Year 8 students on frequency of receiving nasty text
messages and/or emails. (p. 50)
-
Text messages 4%
Emails 2%
Pornai & Wood
(2010)
339 students in Years 7 to
9 in one UK secondary
school. 159 were male and
26 item scale measuring both offline and online peer
aggression and victimization. 3 items each measured
cyber-aggression and cyber-victimization frequency
Total 31.5%
Males 25.8%
Total 56.2%
Males 53.2%

46
180 female. Mean age was
13.3 years of age and
92.3% were White British.
2.9% identified as mixed
race and 2.4% identified
as other.
measured on a 5-point Likert scale from ‘never’ to
‘very often’ in the past 6 months. Cyber items
included, for example, sending insulting or
threatening messages via email, text or via Internet
chat rooms or forums. Participants also completed a
40-item measure of moral disengagement, hostile
attribution bias and outcome expectancies (p. 84, 85).
Females 36.5%
Females 58.8%
Purdy & York
(2016)
425 students in Years 9,
10, & 11 from 2 post-
primary schools in
Northern Ireland. 47.3%
were male and 52.7% of
respondents were female.
Provided this definition: “Cyberbullying defined as
any behaviour 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”
(Tokunaga, 2010, p. 278).
Structured questionnaire; One question referred to
cyberbullying; Responses measured on a 5-point
(strongly disagree to strongly agree) Likert scale; Past
2 months
-
3.7% (n = 15)
*School A: 2.7%
School B: 4.3%
Rivers & Noret
(2010)
Year 7 to 10 students from
13 schools in the North of
England, aged 11 to 14
years old and
predominantly White
British (98%). Data
collection occurred in
2002, 2003, and 2006.
Approximately 2,500
students were included per
calendar year (p. 651)
“Have you ever received any nasty or threatening text
messages or emails?”
Coded on a six-point Likert scale, from 0 = not
bullied to 6 = frequently, several times a week
(p. 653)
-
2002
Boys: 12%
Girls: 14.1%
2003
Boys: 10.6%
Girls: 14.3%
2004
Boys: 13.8%
Girls: 18.8%
2005
Boys: 11.3%

47
Girls: 21.3%
2006
Boys: 10.3%
Girls: 20.8%
Smith et al.
(2008); Study 1
92 pupils from 14 London
schools. One teacher from
each school randomly
selected one boy and one
girl from Years 7 to 10
(inclusive) to complete the
questionnaire. 43
respondents were male
and 49 were female, aging
from 11 years old to 16
years old. The majority of
students were White
British (n = 54). Other
ethnicities included Afro-
Caribbean (n = 10), Black
African (n = 7), Indian (n
= 7), Chinese (n = 1), and
Mixed race (n = 3).
(p. 378)
Global items: (1) Have you experienced bullying of
any kind in the past couple of months? And (2) Have
you experienced bullying via the seven media in the
past couple of months? Multiple choice questions
were then asked relating specifically to each of the
seven types of media about how often students had
been victimized or cyberbullied others, separately for
inside and outside school. All questions measured on
a 5-point Likert scale, from never, to several times a
week. (p. 377)
‘More than
once/twice’
Outside school:
Phone call 1.1%
Text 1.1 %
Email 1.1%
Picture 0%
Instant messaging
1.1%
Website 0%
Chatroom 0%
Inside school:
Phone call 1.1%
Text 0%
Email 2.2%
Picture 0%
Instant messaging 0%
Website 0%
Chatroom 0%
‘More than
once/twice’
Outside school:
Phone call 10.9%
Text 3.3%
Email 4.4%
Picture 0%
Instant messaging
3.3%
Website 1.1%
Chatroom 0%
Inside school:
Phone call 3.3%
Text 3.3%
Email 3.3%
Picture 0%
Instant messaging 0%
Website 0%
Chatroom 0%
Smith et al.
(2008); Study 2
533 students from 5
schools in several UK
counties from Years 7 to
Global items: (1) Have you experienced bullying of
any kind in the past couple of months? And (2) Have
you experienced bullying via the seven media in the
% Ever cyberbullied
someone else
% Even been
cyberbullied

48
10. 261 were male and
267 were female (5
missing) and the majority
(82.8%) of participants
identified as White
British. (p. 378)
past couple of months? Multiple choice questions
were then asked relating specifically to each of the
seven types of media about how often students had
been victimized or cyberbullied others, separately for
inside and outside school. All questions measured on
a 5-point Likert scale, from never, to several times a
week. (p. 377)
Phone call 4.3%
Text 2.8%
Email 2.4%
Picture 1.8%
Instant messaging
5.3%
Website 2.4%
Chat room 1.0%
(p. 379)
Phone call 9.5%
Text 6.6%
Email 4.7%
Picture 5.0%
Instant messaging
9.9%
Website 3.5%
Chatroom 2.5%
(p. 379)
West (2015)
5,690 adolescents aged 16
to 19 years old from 41
colleges. 42.6% of
respondents were male,
and 57.4% were female.
75.8% of participants
identified as White
British, and the remainder
identified as Asian
(13.3%), White other
(3.9%), Mixed race
(3.1%), Black (2.5%) or
other (1.4%). Data
collected in 2014.
An online questionnaire was distributed comprising
50 items about their experiences of cyberbullying
‘while being a college student’. (p. 103)
Cyber-bullies 1.9%
Once 44.4%
2 – 3 times 25%
4 – 6 times 5.6%
7 – 10 times 1.4%
More than 10 times
23.6%
Cyber-victims 7.9%
Once 42.5%
2 -3 times 32.6%
4 – 6 times 10.9%
7 – 10 times 2.6%
More than 10 times
11.4%
Wolke et al.
(2017)
2,754 students from
schools in the Midlands,
UK. Participants were
Four items on the Bullying and Friendship Interview
(Wolke, Woods, Bloomfield, & Karstadt, 2000)
schedule related to cyberbullying victimization
-
Pure cyber-victims
1.13%

49
aged 11 to 16 years old,
mean age = 13.5 years old.
56.9% were female and
82.5% were White British.
(p. 900)
experienced in the past six months: “Had rumours
spread about you online”, “Had embarrassing pictures
posted online without permission”, “Had private
emails, messages or photos forwarded to someone
else or where others can see it”, and “Got threatening
or aggressive emails, instant messages, text messages,
or tweets”. Frequency was measured on a 5-point
Likert scale. Non-victims were classified as those that
indicated these behaviours never or occasionally
occurring, and victims were categorized as those
responding that these behaviours as occurring often or
frequently.
Offline direct and
cyber victims 0.87%
Offline relational and
cyber victims 0.908%
*School A = small non-selective mixed-gender school in a rural location; School B = large selective mixed-gender school in an urban area.
a. Percentages were estimated by the first author; b. This paper also presents results for data collected in 2009 from the KLT survey,
however as Devine & Lloyd (2012) also utilise this data, we do not include these results here.