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Cyberbullying: A review on the causes and consequences
To a product of our virtually-driven epoch
Our modern world relies heavily on technology. Most of daily interactions are now being virtually mediated by information and communication tools such as laptops, phones, the internet and social media. Heightened access to technology has made room for new forms of aggression, such as cyberbullying, to surface. Cyberbullying is broadly defined as bullying that is electronically moderated through emailing, text or instant messaging (Juvenon & Graham, 2014). It is a predominant social issue causing harm to individuals in the world and New Zealand is not immune to its consequences. This paper will review various articles drawing from international and local sources, about explanations for cyberbullying and major debates that exist on the topic. An in-depth analysis of the elements underlying cyberbullying is required to identify the best resources for change against this threat in New Zealand.
Traditional bullying and cyberbullying
Traditional bullying and cyberbullying are two sides of the same coin. Traditional bullying entails targeted humiliation and intimidation and happens when an individual is the prey to any behaviour that is (i) damaging and done with a purpose to hurt (ii) continuous (iii) characterized by an imbalance of power between the bully and the victim (Espelage & Swearer, 2003). The intention to cause harm clearly resonates with one dimension of anti-social behaviour – a topology proposed for anti-social behaviour considered that behaviour directed at people was a main aspect (Home Office Development & Practice Report, 2004). While most definitions have underscored that harmful behaviours must be repeated, some researchers (Juvenon & Graham, 2014) have contested that reoccurrence is not necessary. One traumatic experience can be enough to raise expectations abuse and fear. Bullying behaviour is dichotomized into direct and indirect (Feshback 1969, Lagerpretz et al, 1988). Direct bullying tends to be physical, e.g. physical aggression or name-calling while indirect is relational such as the spreading of rumours or rejection from a group.
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The term cyberbullying (CBB) was first coined by Besley (n.d.). It is the aggressive, intentional act undertaken by a group or individually using electronic tools, against a target who is unable to defend himself or herself (Smith, Mahdavi et al, 2008), and is an extension of traditional bullying. The distinction between bullying and cyberbullying relies on the interaction between the parties involved (Patchin & Hinduja, 2006). Three of its features, i.e. the speed and quick spread of hostile messages and anonymity of bullies also differ from traditional bullying (Juvenon & Graham, 2014, Slonje, Smith & Frisén, 2013). Dooley, Pyzalski and Cross (2009) suggested that these features may aggravate the effect on victims. Contrarily, Law et al (2012) found that such contextual differences allow victims to defend themselves in ways that impossible during face-to-face interaction. Cyberbullying researchers (Hinduja & Patchin, 2009; Wong-Lo & Bullock, 2011) emphasize that, due to its anonymous nature, perpetrators can conceal their identities in the cyber-world, and thus, are liberated from ethical concerns. This reverberates with the “Online Disinhibition Effect” (Suler, 2004) which will be discussed later.
Interestingly, Willard (2006) stated that cyberbullying can either be direct or indirect. Flaming is an indirect form of cyberbullying. It occurs when two people argue by using offensive language. Direct cyberbullying involves harassment and denigration. Empirical research has underlined that a problem exist in conceptualizing cyberbullying. In a study conducted with college student, Baldasare, Bauman, Gold and Robie (2012) found that a large number of participants disagreed with the established definition of cyberbullying due to the shifting nature of technology, allowing an increased amount of behaviour to be defined as CBB. Moreover, they found that connection made between the term and adolescent behaviour was dominant. These issues may have profound repercussions the validity and reliability of studies assessing cyberbullying behaviour.
Predictors of cyberbullying
Age as a potent predictor. A host of research in this area has focused on children and adolescent samples. These age groups thoroughly studied because most bullying behaviours are observed at these developmental stages (Scheithauer, Hayer, Pettermann,& Jugert, 2006). In addition, the most severe consequences, including suicide, have arisen among this population. One suicide case amongst several others was underlined in New Zealand, in Rauskakas’ (2010) study. A New Zealand European 12 year-old girl took her own life at the back of her Putaruru home because of cyberbullying. The harassment began at school until the whole summer. On her suicide note, she accused a group of girls for months of text-bullying and bullying from via email. She died just before school resumed, and cited that death was better than facing the bullies (Hall, 2006). Age trends tend to show a consistent pattern. Victimization and bullying is witnessed in late childhood, culminates at 12 years old when moving to high school and decreases after (Kljakovic, Hunt & Jose, 2015). This trend is compatible with Robin’s (1978) notion that children who partake in anti-social behaviour are more likely to discontinue into adulthood and Moffitt’s (1993) idea of “adolescence-limited” and “life-course persistent” aggressive behaviour.
In an attempt to study the transition period from childhood to adolescence, Pelligrini and Long (2010) used a sample of 11 to 14 years old and asserted that rates of bullying increased during this period but then decreased. Furthermore, six data sets from New Zealand, Canada and the US were used to examine developmental trajectories in youth aggression in one of the most valuable longitudinal studies presented to date (Broidy et al., 2003). The samples comprised of 5000 boys and girls. Databases used consistent measures for aggression with items targeting bullying from middle childhood through adolescence. Increasing and decreasing aggressive trajectories were pervasive across the samples.
Contrarily, other researchers have found either stable or increasing rates for cyberbullying behaviour across the ages of 11 to 18 years (Patchin & Hinduja, 2006; Vandebosch & Van Cleemput, 2009). Beyond this age group, cyberbullying behaviour has been under-researched. Thus, Gibb and Devereux (2014) aimed to stretch the knowledge about this social phenomenon among college samples. They found that approximately 52% of college students took part in cyberbullying. Furthermore, the victims and participants that scored high on the subclinical measure of psychopathy were more likely to admit engaging in cyberbullying. Age was the only variable that negatively correlated with cyberbullying. Interestingly enough, researchers have argued that aggressive behaviour does not diminish over time. Individuals rather engage in other forms of aggressive behaviour according to their abilities Björkqvist, Lagerspetz, and Kaukialnen (1992). A future study drawing the link between this idea and cyberbullying behaviour could be of significant value.
The studies aforementioned demonstrate that cyberbullying behaviours may lessen eventually regress as individuals enter adulthood. However, in New Zealand, further research is required to identify the cyberbullying trend among older samples.
The role of genderin cyberbullying. Gender differences have been found in cyberbullying due to a gendered construction of aggression (Underwood, 2003). There is a consensual belief that traditional bullying is higher in male than female samples (Barboza et al, 2009). This disparity is retained as males are expected to use more physical forms of aggression than females who engage in more relational covert aggression (Craig, 1998). Research focusing gender difference rates in cyberbullying is sparse. However, while some have argued that it is more frequent in males (Li, 2006) others have demonstrated that the deviant behaviour is similar in both genders (Beckman, Hagquist & Hellström, 2013). Kljakovic et al (2015), in a sample of 2174 adolescents from 78 schools across the North Island of New Zealand found no gender difference in the four types of bullying behaviour under investigation, including cyberbullying amongst others.
Gardner, O’Driscoll, Cooper-Thomas, Roche and colleagues (2016) brought an important contribution to the study of bullying in workplace settings in New Zealand. The sample consisted of 58 % of females and 42 % of males. Results showed that 15 % of participants had been bullied and 2.8 % had been cyber-bullied over the last six months. Compared to men, women took more part in workplace bullying than cyberbullying. Nevertheless, such findings are bewildering as cyberbullying has been conceptualized in verbal and relational forms (Raskauska, 2010) – two kinds that has been associated to women. Overall, the above evidence indicate that age, as discussed earlier, is more reliable in predicting cyberbullying than gender.
The influence of dispositional factors. Researchers (Gibb et al., 2014) further declared that personality characteristics can indicate who is likely to participate in cyberbullying. A model of three personality characteristics named the “Dark Triad” (DT) was believed to forecast cyber-bullying behaviour (Paulhus & Williams, 2002). These characteristics are machiavelism, narcissism and psychopathy. They are separate constructs but all entail social hostility. Gibb et al (2014), in their investigation among college students hypothesized that each attributes of the DT could predict acts of cyberbullying among college students. Results have shown that only psychopathic traits anticipated cyberbullying actions. Some scholars have recognized that bullies and cyber-aggressors lack empathy, are calculating and cold (Jolliffe & Farrington, 2004; Gini et al., 2007). They use forceful methods to instill domination. As we will elaborate further, identifying which personality traits prevail in perpetrators may be paramount in designing clear-cut interventions against this dangerous social issue.
Incidence of cyberbullying across cultures
Two distinct features of cyberbullying are the ease and speed at which malevolent messages and photos can travel through social networks, e-mail or text-messages globally. Information technology agencies are constantly enhancing their services and providing increased access to communication tools. Thus, a need to identify patterns of cyberbullyinhg across cultures is required. Byek and Bullock (2014) claimed that, while most studies conducted in different countries have been directed on the prevalence, consequences and strategies to diminish cyberbullying, only a few have attempted to provide a cross-cultural perspective on cyberbullying. Research on cyberbullying has primarily used samples from Western countries such as New Zealand, Australia, Europe and America. Investigations among Non-Western countries, such as China, Korea and Taiwan have only emerged recently (Li, 2008; Tippett & Kwak, 2012). Drawing from samples across the world, Hinduja & Patchin (2009) have synthesized that various students partake in cyberbullying as perpetrators, victims or perpetrators-victims.
In the Western side of the world, Floros et al (2013) in Greece, found that among adolescents between 12 to 19 years old, 14.6 % reported being bullies and 28.3 % were victims. Fenaughty and Harre (2013) in New Zealand examined 1673 students of similar age group and found that 25 % of students had dealt with electronic harassment and approximately 18 % had experienced internet harassment in the previous 12 months. In addition, earlier research in Aotearoa has found that among adolescents 73% aged 15 to 17 have cell phones and about 23% of them have received messages considered offensive, abusive, or threatening (Internet Safety Group, 2005). In Canada, Li (2008) found that out of 157 adolescent participants of 12 to 15 years, 25 % were being victimized and about 15 % were perpetrators.
In Asian communities, Chang et al (2012) unveiled that out of 2882 Taiwanese youth, 5.8 % were cyber-bullies, 18.4 % were cybervictims and 11.2 % were cyber bullies and cyber victims. In Korea, Tippett and Kwak (2012) used a sample of 416 Korean adolescents to study cyberbullying behaviours. Around 6.3 % were cyber-bullied through the internet, 10 % of them were cyber-bullied via mobile phones, and interestingly, 43 % of cyber-bullies had been aggressed via online games in the last 60 days. Online game bullying is an area where research is piecemeal, and they concluded that further research is required to analyse whether this type of bullying is particular to Korean society. Incidence of online game bullying has not been addressed in New Zealand and this could be a revealing area of research.
Cross-cultural studies on cyberbullying have shed the light on its widespread prevalence across countries. Speculations have been made to account for such differences in findings, including different research methods adopted, divergence of selected age groups, differences in periods of analysis, distinct ethnicities under study and contrasting notions on the definition of cyberbullying. Kljakovic et al (2015) found that it was problematic to compare New Zealand to other countries, due to its unique multicultural society. New Zealand’s society comprises of an elevated percentage of immigrants – one in five New Zealanders were born overseas (Department of Labour, 2009). Petrie (2012) advanced that New Zealand has reported some of the highest rates of bulling globally. Yet, the differences in measurements of bullying may unravel the different recorded rates for this aggressive behaviour. Kljakovic et al (2015) found that New Zealand studies on cyberbullying measure any experience of being bullied during the past year, whereas other studies inquire about recent experience of bullying in the questionnaires. This can inevitably result to lower percentage of cyberbullying instances in reports.
Despite the disparities that exist in the literature, each of the article that were reviewed across cultures supported that students belonging to different age groups were involved in cyberbullying as bullies, victims or cyber-bullies. Kljakovic et al (2015) pointed out that it is still unclear if structural factors such as socio-economic status, minority status or other risk factors are causing these differences. In New Zealand, a thorough investigation of these societal factors in relation to cyberbullying could be useful in determining the risk and resilience factors that should be addressed locally.
Theoretical frameworks for cyberbullying involvement
Theorists have attempted to clarify which mental and social mechanisms underlie cyberbulling involvement. Explanatory theories tailored for cyberbullying are yet to be established. However, numerous socio-psychological theories having strong empirical support for aggression and bullying have been fitted to cyberbullying context (Espelage, Rao & Craven, 2012). Some of the theoretical frameworks are reviewed below.
General Strain Theory. General Strain Theory (Agnew, 1992) posits that individuals who have gone through significant strain will later develop frustration and anger in response, putting them at risk to resort to deviant behaviour. This theory is compatible with the frustration-aggression hypothesis (Dollard, Doob, Miller, Mowrer & Sears, 1939) which affirmed that all frustration invariably led to aggression and that, in turn, all aggressive actions were caused by frustration. Thus, Hinduja & Patchin (2010) claimed that youth who have been victimized in schools might later utilize cyberbullying perpetration to release their frustration and anger.
Social Information Processing (SIP) Theory. SIP Deficit model (Dodge & Coie, 1987; Dodge, Petit, McClaskey & Brown, 1986) has been one of the most pivotal model for aggression. It advocates that any forms of aggressive behaviours are due to impairment in social problem solving. This model has been utilized across studies and maintains that aggressive children or adolescents show encoding problems, such as hostile attribution errors, deficits at inferring other people’s mental states and are poor problem-solvers when facing social problems. Cairns & Cairns (1994) revealed that aggressive youth have high, inflated self-views and these unrealistic positive self-views may be due to their information-processing bias. The attributional bias to take all ambiguous situations as peer having hostile intent may explain bullies’ lack of emotional distress.
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Based on the fundamental attribution error (Weiner, 1995) it is stipulated that cyber-bullies keep their positive self-concepts by attacking and blaming others, instead of taking responsibility for their negative actions. Dodge and Schwartz (1997) confirmed that a child’s behaviour is greatly influenced by how situations are processed. Hence, accurate social information processing is likely to encourage positive social behaviours. This knowledge may be useful to policymakers and in school-based interventions aiming at removing cyberbullying behaviours from the school climate.
Online Disinhibition Effect. This phenomenon presented by Suler (2004) states that internal censorship is immensely diminished when communicating in cyberspace. Cyberspace provides a place for individuals to get increased access to their social networks. Contrarily to face-to-face interactions, people can engage in numerous conversations at once, with an array of other individuals. Also, interactions in these places can be very lengthy. Although this creates opportunities for peers to bond with each other, social interactions via technology miss emotional and immediate feedbacks received in physical interactions (Espelage, Craven & Rao, 2012). Suler (2004) identified six factors inherent to technology which produce this effect. Four of them consist of: the invisibility between communicators, a dissociative anonymity which allows one to mentally split online activity from real life, a dissociative imagination, i.e. personas one develops in cyber- environments remain in the online world only and the minimization of authority. Aboujaoude (2011) similarly attested that more aggressive, self-centered and uncivil persona exist in the digital world and that more dangerous e-personality exists compared to our non-digital selves.
Social Identity Theory. Cyberbullying is social in nature. Researchers (for example, Duffy & Nesdale, 2009; Gini, 2006, 2007) have used the Social Identity Theory (SIT) (Tajfel & Turner, 1979) to inform the group-level mechanisms underlying this phenomenon. SIT proposes that individuals’ value, understanding of themselves and their behaviours is highly dependent on the attitudes and beliefs of the social groups to which they belong and identify with (Matsumoto, 2009). Groups are characterised by the attitudes and behaviours to which they adhere as group members. These norms are specific to the groups and differentiate them to other groups (Turner, 1999). Gini (2006) found that children preferred the group to which they were part of, even when this group was labeled being bullies.
Morrison (2006) further found that peer groups possess norms regarding bullying behaviours and are praised for abiding to them or rejected when they fail to follow them. This echoes with the belief that anti-social behaviour may lead to, or be caused by exclusion of the “normal” peer group (Curtis, 2016). Norms have also been identified for interactions in the cyber-world with similar enforcement for adhering to them or sanctions for failing to adhere to them (Espelage, Rao & Craven, 2012). For example, a common norm among adolescents may be the code of silence about bullying behaviour that could lead peers to get in trouble.
Advocates of SIT (Yzerbyt, Dumont, Wigboldus, & Gordijn, 2003) argue that members’ level of identification with their groups influence the strength of reactions to a group-relevant situation. Jones, Manstead & Livinstone (2010) used SIT and intergroup emotions (Mackie, Devos & Smith, 2000) to examine the group processes for intext-message bullying behaviour among pre-adolescents in the UK. Intergroup Emotion Theory (IET) suggests that the degree to which one describes oneself and others as part of a same group determine the intensity and affect felt when something happens to the group. Jones et al (2010) used a sample of students of 10-11 year olds. Their aim was to examine social identity processes and group-based emotions in perceptions and responses to text bullying.
Children were randomly assigned to one of three groups: a perpetrator’s group, a target’s group, or a third party group. They hypothesized that: group membership would affect intensity of group-based emotions felt in relation to a bullying incident, and that this effect would be moderated by identification with the group, the norms of the perpetrator’s group and perceptions of group responsibility for the bullying incident. Findings showed that group membership, norms, and precursors of group-based emotions of pride, shame, and anger influenced the group-based emotions and action tendencies regarding bullying.
The results obtained underline the importance of interpreting the group-based affect involved in cyberbullying to tackle this issue and the importance of explaining which group norms are appropriate in interventions. This knowledge could be applied particularly in strategies for New Zealand.
Psychological and social outcomes of cyberbullying
The pernicious nature of cyberbullying implies that psychological and social distress will prevail in perpetrators and victims. Empirical studies from the world have linked cyberbullying to various emotional and behavioural issues. In New Zealand, peer influences, bullying and cyberbullying have been outlined amongst factors that influence youth suicide (Ministry of Health, Education, Justice and Social Development, 2017).
Behavioural issues. The outcomes of cyberbullying are thought to be higher than traditional bullying. Cyberbullying causes targets to feel angry, sad and frustrated, and online bullies have a high probability of developing behavioural problems (Patchin & Hinduja, 2006; Ybarra & Mitchell, 2007). Sticca, Ruggieri Alsaker, & Perren (2013) divulged that cyberbullying correlates with unruly behaviours such as cheating, stealing, alcohol consumption and destruction of property. Additionally, cyber-victims may resort to anti-social behaviours such as substance abuse and school violence (Hinduja & Patchin, 2007). Online and offline bullying has been related to poor social functioning such as withdrawal from school activities, dropping out of school, fighting and holding weapons at school in adolescents (Willard, 2006; Ericson, 2001; Hinduja & Patchin, 2007, Rigby, 2003). All parties involved in cyberbullying are inevitably prone to emotional distress, regardless of cultural differences as evaluated below.
Emotional and Psychological consequences. In cyber-bullies, Ybarra and Mitchell (2004) uncovered that the compulsive use of the internet may develop into personality disorders and addictions. The urge to use internet networks prevent them from having real life interactions, and increase their likelihood of being exposed additional aggressive behaviour. In Hawaii, Chin (2011) surveyed 211 teenagers and found that both anxiety and depression were more severe in cybervictims than non-victims. Machmutow and colleagues (2012), in Switzerland, found that a robust association between depression and being a cyberbullying victim. They concluded that victimization in the cyber universe could predict increased future depression. Byek & Bullock (2014) added that exhibit restrained coping strategies often leading to depression (Andreou, 2001; Kochenderfer-Ladd & Skinner, 2002) and self-blame attribution (Kingsbury & Espelage, in press).
In New Zealand, Raskauskas (2010) found that adolescents who had been text-bullied reported high depressive symptoms. Victim groups showed depression levels above the cut-off score for mild and moderate symptoms. A significant local concern among adolescents has been the association or causation of bullying and suicide. Adolescence is a developmental stage where emotional and social dependency is shifted from parents to the peers. Thus, peer relationships ostensibly affect thoughts and behaviours and suicidal ideation. As mentioned earlier, New Zealand has had one of the highest reported rates globally (Petrie, 2012). Cyberbullying within this population has been connected to negative outcomes such as high anxiety, lower academic achievement and suicide (Foody, Samara & Carlbring, 2015). Some social groups, for instance the LGBTQI and Māori youths, seem more vulnerable than others (Departmental Science Advisors from Ministry of Health, Education, Justice and Social Development, 2017).
Consequences of cyberbullying have shown to impair adaptability at both individual and community level. Therefore, the strategies and interventions implemented in New Zealand and elsewhere must be axed on alleviating personal and social symptoms resulting from cyberbullying.
Policies, strategies and interventions for cyberbullying
Policy in New Zealand. Cyberbullying is punishable by law in New Zealand. Since 2015, The Harmful Digital Communications Act 2015 has come into force. It consists of several actions taken to reduce or prevent the impact of cyberbullying and other forms of intimidation or harassment (Hundreds helped by Cyberbullying Laws, 2017). For instance, posting material online or sending messages causing harm or inciting someone to commit suicide is regarded as an offence. The Act also created an agency, called “Net Safe” which locates and deals with complaints from victims. It has established a civil court process for repeated destructive digital communications. Cyber-aggressors who are found guilty can be imprisoned for five years and fined up to $50,000 (Cyberbullying, when bullying goes online, n.d.).
Strategies and interventions. Evidence-based cyberbullying interventions are lacking in New Zealand. However, some empirically supported implementations have shown to be efficacious in other countries. These contributions could be put in practice locally in order to reduce the occurrence of cyberbullying. The most successful interventions have used a holistic approach to the problem, by combining strategies to improve the self-concept and environmental climate where cyberbullying takes place. One of these interventions is discussed below.
Espelage, Rao & Craven (2012) held that bullies make use of bullying behaviours to ameliorate their self-concepts. They increase their sense of power, social self-concept and self-esteem by terrifying others. As previously discussed, aspects of the self, i.e. maleficent personality traits, inability to infer other people’s mental states and unrealistic positive self-image have been manifested in cyber-perpetrators. Marsh, Parada, Yeung & Healer (2001) asserted that a low self-concept may further provoke further troublemaking behaviours to boost the self-concept. Thus, placing the self-concept at the heart of interventions has important implications for developing influential intervention plans.
Ortega-Ruiz et al (2012) in Spain have put in practice a European evidence-based programme known as The ConRed to reduce the effects of cyberbullying among students in secondary school. ConRed strands for “Programa Conocer, Construir y Convivir en la Red” that is translated as the Knowing, Building and Living Together on the Internet Program. It aims at (a) improving perceived control over information on the internet (b) reduce the time dedicated to digital device usage (c) prevent and reduce cyberbullying. The program creates a healthy and secure virtual environment for students, forge them in a culture of mutual support by developing empathy for the weakest and help to maintain positive social relationships between students, teachers and families. The objectives of the study were to improve the students’ use of Information and Communications Tools (ICT), to make them aware of the amount of time devoted to ICT and understand the unhealthy nature of cyberbullying and damage caused to victims. Results showed that the ConRed program brought favourable outcomes in all the three objectives.
Despite the difference in context and demographics, the application of programs as the ConRed in New Zealand may be promosing in eliminating this social menace, and protect the young population at risk.
Cyberbullying is a widespread social nuisance, in New Zealand and across the world. It stems from traditional bullying but the contextual features and damages caused differ. Local research on cyberbullying is still evolving but cross-cultural studies have documented its high incidence rates. Debates exist about its nature, predictors and consequences and this may lead to misinterpretation in findings. A significant concern has been the studies that have focused on children and adolescent samples at the cost of providing in-depth analysis for this anti-social behaviour among older individuals. Nevertheless, there is a consensual belief that cyberbullying is inherently deviant and is done to cause harm to victims. Cyberbullying can have detrimental consequences – especially among youths in New Zealand. Combining current frameworks for cyberbullying and evidence-based interventions, such as the Conred, could be beneficial in forming a programme adapted to New Zealand’s needs to eliminate cyberbullying.
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