Presentation on Facebook and risk of cyberbullying victimisation
Introduction
The use of social networking sites (SNS) such as Facebook, Twitter and Instagram is prolific amongst young people (Duggan and Smith, 2004, Madden et al., 2013). Self-presentation is a central feature of SNS because their interface is based around the creation of visible personal profiles that display a friends list, personal information, and photos. Unfortunately, SNS have also become environments in which users can target and harass other users. This phenomenon is typically called cyberbullying (Smith et al., 2008). Consequently, the associations between the ways in which young SNS users manage their online self-presentation and risk of cyberbullying, has recently begun to attract the interest of researchers.
Cyberbullying has been defined in the research literature 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 him or herself” (Smith et al., 2008, p. 376). Published reports of cyberbullying prevalence rates in teens (generally below 18 years of age) have ranged from 6% to 30% (Sabella, Patchin, & Hinduja, 2013) and victimisation experiences have been associated with multiple emotional, cognitive and behavioural impacts such as social anxiety (Dempsey, Sulkowski, Nichols, & Storch, 2009), poor concentration (Beran & Li, 2005), suicidal thoughts and behaviours (Hinduja & Patchin, 2010), and lower school grades and poor school attendance (Price & Dalgleish, 2010).
Considering the associated negative outcomes, it is important to identify factors that influence the risk of cyberbullying victimisation. Victimisation has been defined as an individual’s “self-perception of having been exposed, either momentarily or repeatedly, to aggressive actions emanating from one or more other persons” (Aquino & Bradfield, 2000, p. 172). There are multiple factors that may influence the risk of victimisation. Victimologists have suggested that these may include perpetrator characteristics, environmental factors, or victim behaviour (Elias, 1986). Identifying the role that victim behaviour may play in the likelihood of being targeted by others, as suggested by the victim precipitation model (Timmer & Norman, 1984), is as important as focusing on perpetrator and environmental factors. According to the victim precipitation model, victim behaviour may, whether intentionally or unintentionally, elicit a response in perpetrators that leads to victimisation (Kim & Glomb, 2010). It is important to note that this perspective does not blame the victim for the victimisation; rather the model identifies behavioural factors that are related to an increased risk of being targeted. The victim precipitation model has been used extensively within the criminal victimology literature (Aquino & Byron, 2002) and has been applied empirically in studies investigating the role of personality characteristics (Coyne, Seigne, & Randall, 2000), conflict management style (Aquino & Bradfield, 2000), and other organisational variables (Aquino & Thau, 2009) on risk of workplace victimisation. Therefore the victim precipitation model may also provide a framework for the study of victim-specific risk factors that increase the likelihood of being cyberbullied.
To date, research investigating factors that influence the risk of cyberbullying victimisation has focused on individual differences of young information and communications technology (ICT) users. Conflicting results regarding the role of gender as a predictor of victimisation have been reported. While some studies have found no significant difference between males and females (e.g., Patchin and Hinduja, 2006, Slonje and Smith, 2008), other studies have found that females are more at risk than males (e.g., Li, 2007, Wang et al., 2009). Conflicting results have also been found regarding the relationship between age and victimisation with some studies finding no relationship (e.g., Patchin and Hinduja, 2006, Smith et al., 2008), and others a positive (Kowalski & Limber, 2007) or negative relationship (Slonje & Smith, 2008). Research has also focused on the relationship between the risk of cyberbullying victimisation in young people and the extent and nature of internet and computer use. For example, time spent online and computer proficiency were significant positive predictors of victimisation among participants under 18 years of age (Hinduja & Patchin, 2008). It has also been shown that likelihood of being a cyberbullying victim was higher for those who (1) were more dependent on the internet (e.g., would surf on the internet at the expense of other activities; Vandebosch & Cleemput, 2008), (2) were more likely to chat with older online acquaintances (Walrave & Heirman, 2011), or (3) who gave passwords to others and shared personal information on a blog (Walrave & Heirman, 2011).
Other studies have found a relationship between being a cyberbullying victim and being a traditional bullying victim or perpetrator in samples of young people. Cyberbullying victims (12–18 years old) have been found to be more than six and a half times more likely to have been a cyberbullying perpetrator (Walrave & Heirman, 2011) and more than two and a half times more likely to be a traditional bullying victim (under 18 years; Hinduja & Patchin, 2008). Results from other studies have confirmed the strong relationship between both cyber and traditional bullying victimisation in children and adolescent samples (e.g., Juvonen and Gross, 2008, Li, 2007, Twyman et al., 2010, Vandebosch and Cleemput, 2009). One issue regarding previous research on cyberbullying victimisation risk factors is that samples have been recruited from different populations (e.g., under 18 years old, 12–15 years, middle school students only) which makes cross study comparisons of risk factors and prevalence rates difficult.
More recently, the role of ‘risky SNS practices’ in online risk was investigated in 9–16 year olds (Staksrud, Olafsson, & Livingstone, 2013). Participants were asked to report the time they spent online daily, how much they knew about the internet (digital competence), whether their SNS profile was set to public/private, whether they had more than 100 SNS contacts, and whether they included specific personal information on their profiles (e.g., last name, address, phone number, school, and correct age). Cyberbullying was measured dichotomously (yes/no) in the last 12 months. Results showed that overall, 8% of participants who use SNS had experienced cyberbullying, while 10% of participants who use SNS and have more than 100 friends had experienced cyberbullying. Those with public SNS profiles and those who displayed their mobile phone number or address on SNS were also more likely to be cyberbullied. However, these differences were not statistically significant. These results support the victim precipitation model in that self-presentation behaviours account for some degree of the risk in cyberbullying victimisation. While the results are interesting, this study relied on participants’ self-report of SNS behaviours, which is subject to potential memory and self-presentation biases. In an effort to avoid these problems, researchers who investigate self-presentation behaviour in SNS directly view and code users’ profile pages. Numerous studies have implemented this approach (e.g., Boyle and Johnson, 2010, Mehdizadeh, 2010, Zhao et al., 2008).
The current study extended the Staksrud et al. (2013) study that investigated the role of only a small selection of self-presentation behaviours in SNS as predictors of cyberbullying victimisation, by coding each profile page feature and the content of specific features. This study also focused on risk in adolescence as this period is considered to be critical in the development of a personal, individuated identity (Erikson, 1968). Furthermore, how adolescents choose to present in SNS may be a key part of identity development (Gonzales & Hancock, 2011). The current study was exploratory due to a lack of previous related research. The main objective was to understand the victim related factors that increase the risk of cyberbullying victimisation so that successful interventions for the prevention of cyberbullying victimisation can be developed and safer SNS environments can be constructed. More specifically, this study aimed to determine the frequency that cyberbullying victimisation occurred in Facebook in the preceding 6 months and what specific features of a Facebook profile page, that when used or used in a certain way, were associated with an increased risk of cyberbullying victimisation in adolescents.
Section snippets
Participants
As part of a larger study, 316 15–24 year old participants completed a battery of online questionnaires. Of these, 147 agreed to provide the researchers with access to their Facebook profile pages for coding purposes. Of these 147 participants, 124 (18–24 year olds) were recruited from the Melbourne campus of Australian Catholic University (ACU), a public university in Australia, and 23 (15–17 year olds) were recruited from two secondary schools in Melbourne. Overall, 28 (19%) participants were
Frequency of cyberbullying victimisation
In the preceding 6 months, 51% reported having experienced more than one of the 14 behaviours (M = 2.54, SD = 2.89), 25.9% reported having experienced one of the 14 behaviours, and 23.1% of participants reported that they had not experienced any of the SNS victimisation behaviours. Table 2 shows the observed frequencies of each target behaviour. The most prevalent reported behaviour was deliberate blocking of participants (“defriending”) from a social networking site.
Participants also indicated the
Discussion
The current study utilised the victim precipitation model to investigate the relationship between victims’ behaviour and the risk of victimisation. More specifically, we aimed to determine the frequency of cyberbullying victimisation in Facebook in the preceding 6 months and to explore whether specific online self-presentation behaviours in SNS and associated constructs were related to the likelihood of cyberbullying victimisation.
Acknowledgements
The authors acknowledge the assistance of the Victorian Department of Education and Early Childhood Development with participant recruitment and also participants’ valuable contribution to the study.
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