Annual Research Review: Harms experienced by child users of online and mobile technologies: the nature, prevalence and management of sexual and aggressive risks in the digital age

There are many types of ‘electronic’ or cyber-aggression, including flaming, online harassment, cyberstalking, denigration (put-downs), masquerade, outing, exclusion, putting up false profiles and distributing personal material against someone's wishes (Pyzalski, 2012; see also Calvete, Orue, Estévez, Villardón & Padilla, 2010). An analysis of abusive text messages and e-mails by Rivers and Noret (2010) found them to contain threats of physical violence, abusive or hate-related, name calling (including homophobia), death threats, ending of platonic relationship(s), sexual acts, demands/instructions, threats to damage existing relationships, threats to home/family and menacing chain messages. The victim may be known to the perpetrator offline (e.g., from school, or former girlfriends/boyfriends), may come from certain groups (e.g., fans of a certain pop group or football team), but may also include celebrities, vulnerable people, school staff or victims known only from the internet (Pyzalski, 2012).

Some researchers have used general terms such as ‘cyber victimization’ (Law, Shapka & Olson, 2010) or ‘online harassment’ (Hinduja & Patchin, 2010), while Vandebosch and van Cleemput (2009) used the term POP (potentially offensive internet and mobile phone practices). However, much research has used the term cyberbullying. Following Olweus' definition of traditional bullying, one common definition of cyberbullying is: ‘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, del Barrio & Tokunaga, 2013; Smith et al.,2008:376). In addition to the important criterion of intent to cause harm, Olweus' definition emphasizes the imbalance of power and repetition, to discriminate bullying from other forms of aggression.

Other uses of the term cyberbullying emphasize repetition over imbalance of power (e.g., ‘when someone repeatedly harasses, mistreats, or makes fun of another person online or while using cell phones or other electronic devices’; Patchin & Hinduja, 2012:15), imbalance of power over repetition (e.g., Wang, Iannotti & Nansel, 2009), or they include neither criterion, as in: ‘intentional behaviour aimed at harming another person or persons through computers, cell phones, and other electronic devices, and perceived as aversive by the victim’ (Schoffstall & Cohen, 2011:588). A six country study by Menesini et al. (2012) found that 11–17 year olds themselves gave most weight to imbalance of power in judging whether a scenario was a case of cyberbullying (except in France, where the term cyberviolence is used), followed by intentionality, and anonymity as a substitute for imbalance of power; repetition or the public/private nature of the context were less important.

Repetition and intentionality become linked when a cyberbullying act ‘snowballs’ out of the perpetrator's control. Slonje, Smith and Frisén (2013) found that, having seen information intended to cyberbully someone else, 9% of pupils forwarded the material to other friends, and 6% showed or forwarded it to the victim to bully him/her further. Thus, a single act by one perpetrator may be repeated by others and thus experienced many times by the victim.

The importance of power imbalance is contested in the online and mobile domain, since neither physical strength nor strength in numbers is needed for cyberbullying. Vandebosch and van Cleemput (2008) argued that a greater knowledge of ICT can create a power imbalance, having found that pupils with more advanced internet skills were more likely to have experience with deviant internet and mobile phone activities (such as impersonating someone else on a website, or bullying in virtual worlds). They also argued that anonymity can contribute to a power imbalance, since if the victim does not know the identity of the person bullying him or her, it is more difficult to respond effectively (Raskauskas, 2010; Slonje & Smith, 2008; Smith et al., 2008). However, if a victim does know the perpetrator, it usually being someone from the same school or vicinity (Smith et al., 2008) then conventional criteria of physical/psychological strength and peer group popularity may come into play (i.e., a victim may fear retaliating against a popular and stronger pupil who can take further revenge offline).

At present, opinion remains divided as to whether cyberbullying can be considered in a similar way to traditional bullying (Olweus, 2012a; Smith et al., 2013), or whether linking it to other forms of cyber aggression is more useful (Bauman, Underwood, & Card, 2013). What is clear is that the term cyberbullying is sometimes used too loosely, without reference to either repetition or power imbalance; for this reason, measurement procedures should be clearly specified.

In fact, not only are the key criteria contested but so is the measurement of cyberbullying. Berne et al.'s (2013) systematic review of 43 instruments found that few reported their reliability or validity. Some studies treat cyberbullying as a single construct (e.g., Study 1 in Law, Shapka, Hymel, Olson & Waterhouse, 2012), while others distinguish types according to the technology or platform used – for example, distinguishing internet from mobile phone bullying (e.g., Ortega, Elipe, Mora-Merchan, Calmaestra & Vega, 2009), although with the advent of smart phones, this is no longer straightforward. Indeed, the tools for cyberbullying are diversifying. Rivers and Noret's (2010) survey in 2002 examined text message and e-mail bullying; but a few years later, Smith et al. (2008) examined bullying by mobile phone calls, text messages, picture/video clip bullying, e-mails, chatrooms, instant messaging and websites. Cyberbullying in online games (Tippett & Kwak, 2012) and virtual worlds (Coyne, Chesney, Logan & Madden, 2009) is also now recognized.

Given differences in definition and measurement, it is not surprising that estimates of the prevalence of cyberbullying vary. The 25-country EU Kids Online survey of 9–16 year olds in 2010 defined bullying as a hurtful or nasty way of acting towards them face-to-face, by mobile phone calls or texts, or on the internet, that can often be quite a few times on different days over a period of time (Livingstone et al., 2011a). Prevalence varied across countries, with 6% reporting being bullied online, 3% by using mobile phone or text and 13% face-to-face. Figures for bullying others were lower – at 3%, 2% and 10%, respectively. Using a definition that also includes power imbalance, Olweus's (2012a) surveys between 2007 and 2010 nevertheless found similar percentages in the United States (at around 4–5%, for 8–19 year olds) and Norway (at around 3–4%, for 9–17 year olds). Also emphasizing repetition, Genta et al. (2012) surveyed 12–15-year olds in Italy, Spain and England in 2008, finding that the prevalence of repeated bullying (whether as bullies or victims) ranged from 1% to 3%, depending on country and type of bullying. Similar findings were obtained by Salmivalli and Pöyhönen (2012) in Finland in 2007/8 and by RSM McClure Watters (2011) in Northern Ireland in 2011.

Some researchers find these low figures difficult to believe. In commenting on Olweus (2012a), Hinduja and Patchin (2012a: 541) stated that ‘Olweus’ findings that 4.1–5.0% of youth have been cyberbullied and 2.5–3.2% of youth have cyberbullied others are simply out of line with the weight of the available evidence'. Their own studies suggest 20% of 11–18 year olds have been a victim of cyberbullying (Hinduja & Patchin, 2012b), and in a review of 35 published articles, they found on average 24% of pupils had been cyberbullied and 17% had cyberbullied others. As another example, a Turkish survey of 8–11 year olds reported that 18% had (ever) cyberbullied others, and 27% had (ever) been cyberbullied (Arslan, Savaser, Hallett & Balci, 2012), while Marsh, McGee, Nada-Raja and Williams (2009) found that 8% of 15 year old boys and 14% of girls in New Zealand had received nasty text messages at school several times that year. Using their broader POP definition, Vandebosch and van Cleemput (2009) found 62% victims and 53% perpetrators at least once over the past 3 months.

Olweus (2012b) responded by emphasizing the importance of the time reference period (in the past month, or year, or ‘ever’) and frequency criterion (just once, monthly, more often, etc.). As an example of this, O'Moore and Minton (2009) gave an Olweus-type questionnaire to 12–16 year old in the Republic of Ireland, finding that frequencies of cyberbullying others and being cyberbullied were 8.7% and 14.2% for those to whom it only happened once or twice, but dropped to 1.6% and 2.8%, respectively, if the standard ‘two or three times a month’ criterion was used (similar differences are noted in Livingstone et al., 2011a).

In sum, a range of factors can be identified that affect prevalence estimates of cyberbullying or cyber aggression, including frequency, time reference period, definitions that do or do not include repetition and/or imbalance of power, as well as the nature and age of the sample, the emphasis on particular media or bullying practices, and the date of survey administration, which is important at a time of social and technological change. In terms of incidence, it seems that occasional or one-off occurrences may be reported by over 20% of young people but serious or recent or repeated incidents are reported by only around 5%, less than for traditional bullying.

As with aggressive risks, estimates of the prevalence of sexual risks vary according to the definitions employed. The term ‘pornography’, for instance, can refer to diverse kinds of sexual content ranging from ‘top shelf’ or partial nudity to graphic depictions of sexual intercourse to violent or illegal images of abuse. The ethical difficulty of asking children exactly what they have seen without introducing them to unfamiliar sexual ideas compounds the challenge of identifying exactly what they have been exposed to.

The question of intentionality further confuses, since it is widely accepted that adolescents may deliberately seek pornography but, equally, social desirability concerns make unlikely that they will fully disclose this. Moreover, the internet ‘pushes’ pornography at those seeking informational or health or other material, resulting in accidental exposure (via pop-ups or misleadingly named sites). Jones, Mitchell and Finkelhor (2012:181) surveyed US 10–17 year olds in 2010 and found that between 15% (10–12 year olds) and 28% (16–17 year olds) had been ‘exposed to pictures of naked people or people having sex without seeking or expecting such pictures’ in the last year. The EU Kids Online project asked 9–16 year olds if, in the past year, they had seen ‘pictures, photos, videos [which were] obviously sexual – for example, showing people naked or people having sex’ (Livingstone, Haddon, Görzig & Ólafsson, 2011b). This found similar results to the United States findings: 14% had seen sexual images online, with older teenagers four times more likely than the youngest to have seen such images.

Defining exposure instead in terms of intention, Peter and Valkenburg (2009:416) asked Dutch adolescents how often ‘they had intentionally looked at’ images or movies with exposed genitals or in which ‘people are having sex’ in the previous 6 months. Based on a sample whose average age was around 17 years, they found that only 9% said they had never encountered such material (although their later survey found that most – 75% – had not seen visible genitals online in the past 6 months; Peter & Valkenburg, 2011a). Also assuming intentional exposure, Brown and L'Engle (2009:138) asked US 12–14 year olds how often, in the past year, ‘did you see X-rated movies?’, how often they ‘read magazines like Playboy, Playgirl, Penthouse or Hustler?’ and ‘How often do you view pictures of naked women or men on your computer or the internet?’; the percentage who had done any of these ranged from 21% (12 year old girls) to 66% (14 year old boys).

While traditional conceptions of pornography refer to professionally produced, mass distributed images, online and mobile technologies make it ever easier for young people to create and circulate sexual images themselves. ‘Sexting’ is the most recent mobile and online risk to gain public and research attention. A range of definitions is in use, from adolescents' own account of ‘sexting’ as the willing exchange of messages between romantic partners (Lenhart, 2009) to definitions that regard it as the digital extension of the long-established coercion of girls by boys to provide sexual services or to conform to particular sexual expectations (Albury, 2013; Ringrose, Gill, Livingstone & Harvey, 2012; Ševčíková, Simon, Daneback & Kvapilik, 2012). As with pornography, teenagers and adults may not agree on where to draw the line between acceptable sexual exploration between peers and inappropriate or abusive messaging. The illegality of sexually explicit images of minors and the consequent intervention by law enforcement has exacerbated the tensions surrounding this phenomenon (Arcabascio, 2010; Salter, Crofts & Lee, 2013).

Definitional diversity has led to wide variation in estimates of prevalence, ranging from 7% (Mitchell, Finkelhor, Jones & Wolak, 2012) to 15% (Lenhart, 2009; Livingstone et al., 2011b; Rice et al., 2012) to as many as 48% (National Campaign to Support Teen & Unplanned Pregnancy, 2008). Lounsbury, Mitchell and Finkelhor (2011) argue for definitional clarity regarding technology (mobile or internet), messaging (images or written exchanges) and participants (under or over 18 years; communicating with known peers or unknown contacts). Their aim is to distinguish potentially criminal from legal activity; it seems also important to distinguish harmful from harmless activity (as they do in a related paper; Wolak & Finkelhor, 2011).

While pornography and ‘sexting’ occasion considerable public concern, so-called ‘stranger danger’ has aroused the greatest anxiety, and news about paedophiles seem rarely out of the headlines (Staksrud, 2013). In relation to online sexual solicitation (whether from strangers or from those known to the child), Jones et al. (2012:180) asked US 10–17 year olds in 2010, ‘In the past year, did anyone on the internet ask you for sexual information about yourself when you did not want to answer such questions? I mean very personal questions, like what your body looks like or sexual things you have done?,’ ‘In the past year, did anyone on the internet ever try to get you to talk online about sex when you did not want to?,’ and ‘In the past year, did anyone on the internet ever ask you to do something sexual that you did not want to do?’. They found the prevalence of online sexual solicitation to vary from 2% among 10–12 year olds rising to 14% among 16–17 year olds (with an average of 9% across the age range).

It is difficult to relate such findings to offline sexual abuse. While reliable figures are hard to come by, the UK's National Society for the Prevention of Cruelty to Children estimated that 5% of UK children suffer contact sexual abuse at some point during childhood, with some 10,000 new victims each year (Harker et al., 2013). The Child Exploitation and Online Protection Centre (2013) receives reports from around 1,000 children each year concerning online victimization by adults, so this suggests that abuse by perpetrators known to the child offline is far more common than grooming by strangers online, although doubtless much goes unreported. Research is now tracing the processes by which offline and online contacts can become intertwined in the actions of both perpetrators and victims (Webster et al.,2012). However, as Jones et al. (2012) found in their 2010 US survey, the majority of unwanted online sexual solicitations are not pursued offline– 3% of 10–17 year olds reported ‘aggressive solicitations’ online in which ‘offline contact was attempted or made’(p.182).

While many unwanted sexual encounters, online or offline, are perpetrated by known adults, public anxiety remains focused on ‘strangers.’ This, in itself, is difficult to determine online since children make many contacts online with people they have not met face to face. The EU Kids Online survey found that 30% of European 9–16 year olds had made contact online in the previous year with someone they did not already know (ranging from 13% of 9–10 year olds to 46% of 15–16 year olds); in all, 9% had gone to a meeting face-to-face with someone that they first met on the internet (again, more teenagers than younger children) (Livingstone et al., 2011a). On one description, then, ‘meeting strangers’ is common, though face-to-face meetings with such contacts are rarer, and most of those are within the friendship circle (i.e., with ‘friends of friends’). Notions of friend and stranger are, it seems, newly in flux because of the ease with which online contacts can be made, challenging safety interventions and policy frameworks as well as the definitions and measures employed by researchers.

Reviewing cyberbullying, Tokunaga (2010) argued that there is a curvilinear relationship with age, with a peak around 13–15 years. In the Czech Republic, Ševčíková and Šmahel (2009) also found 12–15 year olds most involved as cyber aggressors, although the proportion of cyber victims was higher at 16–26 years, and both roles were present across the life span. In the United States, Beran, Rinaldi, Bickham and Rich (2012) found incidence to approximately halve between high school and college, but with some continuity of involvement. For sexual risks, the incidence of exposure to pornography, sexual messaging or stranger contact is generally found to increase over adolescence (Livingstone et al., 2011b), but with higher risks for teenagers than adults (e.g., Baumgartner, Valkenburg & Peter, 2010b).

The area of gender differences in cyberbullying has been described as ‘fraught with inconsistent findings’ (Tokunaga, 2010:280). Some find boys more involved than girls (e.g., Calvete et al., 2010), some find girls more involved than boys (e.g., Rivers & Noret, 2010), and some find few or no significant differences (e.g., Livingstone et al., 2011b; Smith et al., 2008). Insofar as an overall picture can be discerned, it seems that while boys are more engaged in traditional bullying than girls, girls can be as much or even more engaged in cyberbullying, possibly because cyberbullying is more verbal or relational rather than physical, and is now often located on social networking sites (Beckman, Hagquist & Hellström, 2013; Görzig, 2011; Smith, 2012).

Regarding sexual risks, Livingstone, Kalmus and Talves (2013) concluded that boys were a little more likely than girls to have seen sexual images online and to receive sexual messages. This may reflect cultural norms that tend to sanction boys' engagement with pornography more than girls' or it may be because boys engage in more risky online activities such as having a public social networking profile or making new online contacts. On the other hand, a content analysis of personal information posted on social networking sites found that girls included more risky and sexual content than boys (Pujazon-Zazik, Manasse & Orrell-Valente, 2012). As Baumgartner et al. (2010b) add from their survey findings, adolescent girls are more at risk of sexual solicitation online than are boys, but most are highly aware of this risk and do not seek it out or see it as beneficial.

To understand cross-national differences, the EU Kids Online study clustered European countries in terms of measured levels of children's internet use, online opportunities and risks experienced, and preferences for particular strategies of parental mediation of the internet. This resulted in a four-fold classification: ‘supported risky explorers’ (Denmark, Finland, the Netherlands, Norway and Sweden); ‘semi-supported risky gamers’ (Bulgaria, Cyprus, Czech Republic, Estonia, Poland and Romania); ‘unprotected networkers’ (Austria, Hungary, Lithuania and Slovenia) and those ‘protected by restrictions’ (Belgium, France, Germany, Greece, Ireland, Italy, Portugal, Spain, Turkey and the UK) (Helsper, Kalmus, Hasebrink, Sagvari & De Haan, 2013).

How can these and other differences be explained? Levels of socio-economic stratification, regulatory framework (more or less stringent), technological infrastructure (more or less developed) and educational system (number of years, inclusion of educational technology) have all been found to shape children's online risks (Livingstone et al., 2011a). However, the differences among individuals are generally considerable larger than the differences among countries, at least within Europe (Genta et al., 2012; Livingstone, Haddon & Görzig, 2012; Vazsonyi, Machackova, Ševčíková, Šmahel & Cerna, 2012). Looking beyond Europe, initial indications of cultural factors are emerging (Child Exploitation and Online Protection Centre, n.d.; Davidson & Martellozzo, 2010; Family Online Safety Institute, 2011; Gasser et al.,2010), but no strong predictive framework for how culture affects the consequences of mobile and online risk has yet been formulated or tested.

As use of online and mobile technologies has become more widespread, a common perception is that both risks and harms are increasing (Sabella, Patchin & Hinduja, 2013). Is this a matter of increased public awareness, whether fanned by the media or because children are spending more time online? Or does the evidence reveal a genuine rise in risk or harm or both?

The three waves of the nationally representative Youth Internet Safety Survey, conducted among US 10–17 year olds in 2000, 2005 and 2010 suggests that the prevalence of online sexual risks depends on the affordances of the online environment and the degree of policy effort to improve safety (for waves 1 and 2, see Wolak, Mitchell & Finkelhor, 2006; for wave 3, see Jones et al., 2012). Unwanted sexual solicitations declined over the three time points, from 19% to 13% to 9%; a change which they attribute in part to the policy action to close or moderate the chatrooms initially so popular among adolescents (and since largely replaced by social networking sites). Online harassment (which they defined as feeling worried or threatened because someone was bothering or harassing you online or using the internet to threaten or embarrass you by posting or sending messages about you for other people to see) increased from 6% to 9% and then 11%, this being more marked for girls; they suggest this increase could reflect the increase in internet use over the period. Unwanted exposure to pornography first increased from 25% to 34%, but then decreased to 23% by 2010; this may reflect behind-the-scenes industry effort to control pop-ups and spam, much of which was pornographic.

In relation to cyberbullying, Rivers and Noret (2010) found that prevalence, measured by annual surveys of 11–13 year olds in a northern British city, increased from 2002 to 2004 but then levelled off by 2006. Updating this picture, Tippett and Smith (unpublished data) surveyed four secondary schools in England in 2008 and 2011, finding a decrease in traditional bullying (both as perpetrators and victims) but not in cyberbullying by mobile and internet. Moreover, by 2011 more of the cyberbullying was on social networking sites (for 69% of victims, compared with 42% in 2008). In the United States, Olweus (2012a) found a slight upward trend in cyberbullying from 2007 to 2010 (but a slight downward trend in Norway). Ybarra, Mitchell and Korchmaros's (2011) Growing Up with Media survey of 10–15 year olds in 2006, 2007 and 2008 found that ‘most rates of youth violent experiences online were stable over the 36-month observation period’ (p.1379), although there was some increase in perpetration of harassment online.

Bringing research on different risks together, in their longitudinal survey of 10,000 Flemish primary school children (averaging 11 years), Valcke, De Wever, Van Keer and Schellens (2011) calculated an Unsafe Internet Usage Index to capture a range of risk experiences. This revealed a very small increase from 2005 to 2008 but then no change to 2009. However, within this broad picture, particular risks rose and fell on an annual basis. This could reflect variation in children's risky behaviour (which increases with digital skills), the affordances of the internet (or, specific sites and services popular at any one time) and the kinds of parental safety guidance received (which have also increased over time).

In summary, while much remains to be researched in this regard, it is striking that, over the period when access to online and mobile technologies increased dramatically, there was no equivalent evidence of a clear of substantial increase in either risk or harm to children. One possibility is that the internet affords no greatly increased risks to children, than they would have encountered offline. Another is that there is an associated risk of harm, but this has been offset by increased awareness raising efforts and industry controls. Yet further possibilities may be identified in future research.

Victims of cyberbullying express a variety of emotions: anger, sadness, frustration, embarrassment, stressed, fright, loneliness and depression (Didden et al., 2009; Ortega, Elipe & Calmaestra, 2009), although this is not inevitable. Ortega et al. (2012) found that 22% of mobile victims and 32% of internet victims reported being not bothered, especially boys and those victimized less often. Cyber victimization is associated with a range of psychosocial problems, including affective disorders (Topcu, Erdur-Baker & Capa-Aydin, 2008), depression (Olenik-Shemesh, Heiman & Eden, 2012; Perren et al., 2010; Wang, Nansel, & Iannotti, 2011); and behavioural problems including substance use (Hinduja & Patchin, 2008); however, a large number of cross-sectional studies in this area have not established cause-and-effect.

A few studies have assessed longitudinal relationships. Schultze-Krumbholz, Jäkel, Schultze and Scheitauer (2012) found that cyberbullying victimization predicted depressive symptoms 3–6 months later in girls but not boys. Over a 6 month period, both Machmutow, Perren, Sticca and Alsaker (2012) and Gámez-Guadix, Orue, Smith and Calvete (2013) found that cyber victimization predicted increased depression in both boys and girls, and also problematic internet use in the latter study. Gámez-Guadix et al. (2013) also found that higher depressive symptoms and more substance use predicted later cyber victimization, suggesting a vicious cycle unfolds for victims over time. Over a longer time frame, Lester, Cross and Shaw (2012) found that levels of traditional victimization and perpetration at the beginning of secondary school predicted levels of engagement in problem behaviours 2 years later; but although cyberbullying predicted problem behaviours, it was not an independent risk factor over and above traditional victimization and perpetration.

Because offline bullying often involves physical intimidation or pain, it might be assumed that the severity of harm is greater than for cyberbullying; on the other hand, the affordances of online and mobile communication (such as anonymity, wide audience, difficulty of escaping) might make cyberbullying more harmful. Campbell, Spears, Slee, Butler and Kift (2012) did find a greater negative impact of being a cyber victim, but most research shows little difference. Hinduja and Patchin (2010) found that being a victim of cyberbullying was associated with suicidal thoughts but no more than is reported by victims of traditional bullying; as did Hay and Meldrum (2010), and Bauman, Toomey and Walker (2013; although with some gender interactions). Gradinger, Strohmeier and Spiel (2009) found that being a cyber-victim was associated with similar degrees of depressive and somatic symptoms as for traditional victims and Beckman, Hagquist and Hellström (2012) found this for both victims and perpetrators.

Olweus (2012a:534) tentatively concluded that ‘if the student is exposed to both traditional and cyber bullying, the additional impact of cyber bullying seems to be negligible’. Yet most studies find that those children who experience both types of bullying report worse symptoms (Brighi et al., 2012; Campbell et al., 2012) and some also find the consequences worse if children are involved as both cyber victims and cyber perpetrators (Beckman et al., 2012; Gradinger et al., 2009), although not all agree (Campbell et al., 2012).

A well-established finding is substantial overlap between victimization and perpetration online and offline. For example Mishna, Khoury-Kassabri, Gadalla and Daciuk (2012) found 45% of those involved to be cyber bully victims. Based on path analysis (of cross-sectional data), Kowalski, Morgan and Limber (2012: 516) suggested that ‘we would expect very frequent perpetrators of traditional bullying to also begin bullying electronically and to become victims of cyberbullying themselves as their frequency of electronic perpetration increases.’ Longitudinal studies are urgently needed to test such models further.

In relation to sexual risks, research often stops at measuring children's exposure, apparently presuming that exposure to pornography or ‘sexting’ is inevitably harmful (or that children cannot report on any harm that results). In the United States, Jones et al. (2012:181) asked 10–17 year olds who had seen pornography if they were ‘very or extremely upset’; in their 2010 survey, nearly half the 10–12 year olds but only a fifth of the 16–17 year olds described the experience in this way. Of the 14% of 9–16 year olds who had seen online pornography in the EU Kids Online project, around one-third said that they had been bothered or upset by this. For sexting, among 15% of 11–16 year olds who had seen or received a sexual message online, a quarter reported being bothered by this. While these risks were more often encountered by older teenagers, a higher proportion of girls and younger children found them upsetting (Livingstone et al., 2011b; see also Baumgartner et al., 2010b; Ringrose et al., 2012).

Finding that only a minority of children who experience sexual risks are upset by them questions those policy interventions designed to prevent harm by preventing exposure. If children's self-reports are to be relied upon, one might conclude that some efforts to prevent risk are too restrictive, especially if they also prevent teenagers' search for sexual information or their expression of sexual identity and interest. An alternative view holds that children cannot know the harmful effects of such exposure, especially in the long term. Owens, Behun, Manning and Reid's (2012: 116) review of the effects of exposure to pornography finds tentative evidence that ‘youth who consume pornography may develop unrealistic sexual values and beliefs.’ For example, Peter and Valkenburg (2009) found that increased exposure to online pornography led to a stronger belief in women being sex objects (although the reverse effect was also supported), suggesting that policy makers should work to minimize such risks.

Even for online sexual solicitations, the Youth Internet Safety Survey found that only a few of the US 10–17 year olds (more often younger children) said that these were distressing (Jones et al., 2012). The EU Kids Online survey found that, among the 9% of 9–16 year olds who had met an online contact offline, one in nine of these (or 1% of all respondents) reported being bothered in some way by what happened. Yet for this small minority, painful and even tragic experiences can result (Ainsaar & Loof 2012), as analysis of cases reported to the UK children's helpline reveals (ChildLine, 2012). Determining which online contacts pose a threat is a difficult decision for a child or parent to make, especially given the complex technical and social tactics employed by sexual offenders (Quale & Taylor, 2011; Webster et al., 2012).

Conclusions and future directions

In terms of substantive conclusions, we note that the risks of cyberbullying, contact with strangers, sexual messaging (‘sexting’) and pornography generally affect fewer than one in five adolescents, albeit with significant variations depending on risk definitions, target age group and the affordances of widely used technologies or services. Despite the rise in children and young people's use mobile and online technologies, there is little compelling evidence that online risks are increasing commensurately. To those who find it implausible that new technologies have not increased the risk of harm in children's lives, it is worth noting that, over the period when internet and mobile use have risen sharply, long-term measures of harm to children reveal little or no increase over recent years (Madge & Barker, 2007; Maughan, Collishaw, Meltzer & Goodman, 2008), and some reductions in bullying and victimization (Finkelhor, 2013).

It remains possible that technology is displacing older forms of risk (for example, some perpetrators seeking to groom children for sexual abuse may now operate online in preference to offline approaches; or, when children seek pornography they now prefer to access it online rather than offline). It is also possible that technology has become so embedded in children's communicative activities that when their experiences become aggressive or inappropriately sexual, online and mobile communication are increasingly likely to be implicated, along with (rather than instead of) face to face communication. It may also be that some risks are increased by the use of new technologies (for example, exposure to pornography or receipt of hostile messages may be amplified by the convenience or anonymity of activities conducted online) but that, partly as a result of such exposure, and partly because of the parallel increase in policy and practitioner efforts to raise awareness and improve safety measures, children are becoming more resilient and so better able to cope; this too could explain why measures of harm have not risen commensurately.

It is important to recognize that not all online risks result in harm, although since this is generally measured by self-reported upset or other adverse consequences, one cannot be conclusive on this point. Nevertheless, there is evidence of a range of adverse emotional and psychosocial consequences, and these are especially convincing when revealed by longitudinal designs that follow-up with those affected several months after initial exposure to risk. By situating the study of online and mobile risk in the more-established tradition of studying offline risk in children's lives, researchers have begun to examine whether the risk and protective factors already identified play a role also in explaining online risk. Thus far, there is evidence that the following factors are important for risks of harm resulting: personality factors (sensation-seeking, low self-esteem, moral disengagement, psychological difficulties), social factors (lack of parental support, peer norms) and digital factors (online practices, digital skills, the affordances of specific online sites and services).

A general conclusion is that children who are already vulnerable offline are likely also to be vulnerable online. However, the variance explained by traditional risk factors is fairly low, suggesting that further factors are yet to be found to account for online vulnerability, and these may lie in either the offline or online context or the interaction between the two. The affordances of a changing array of popular online sites and services have particular implications for aggressive, sexual and other risks that have long been implicated in children's lives. Thus, research is turning to an exploration of the interaction between young people as users and their socio-technological environment. Peter and Valkenburg (2011) hypothesize that the developmental tasks faced by adolescents dovetail with the specific affordances of the internet (for example, between the need to develop an identity and the opportunity to experiment with self-presentation on social networking sites, or between the need to develop intimate relations and the ease of self-disclosure online compared with face-to-face situations). This opens up possibilities for guiding adolescents, and younger children, in the specific use of social networking or chat sites, or for designing safety into sites in the light of knowledge regarding the practices and needs of young people.

Despite the considerable body of research reviewed in this article, there remain significant challenges for both research and practice. In the coming years, the empirical research agenda regarding online risk should prioritize the need for (a) explicit assessment not only of risk but also of any harm associated with that risk, rather than simply presuming that harm results, (b) longitudinal research designs to determine the developmental pathways of involvement, and whether harmful effects are long-lasting, (c) studies of pre-adolescent children, who are now gaining internet and mobile access often via a personal device (smart phone, tablet or games machine) that their parents may not understand or be easily able to monitor, (d) a systematic analysis of the key risk factors, and protective factors that can help children become more resilient to risk when they encounter it, therefore minimizing harm, (e) evaluations of awareness raising and harm reduction interventions, few of which have yet been conducted and (f) exploratory research to identify new and emerging risks, including for particular groups of children or under particular conditions as the social and technological landscape continues to innovate. Whether research satisfactorily captures children's experiences of online and mobile risks is also contested, and one approach is to make more use of young people themselves as researchers (Spears & Kofoed, 2013).

Also challenging, conceptually and empirically, is the importance of gaining a more subtle yet systematic grasp of the shifting relations between online and offline risks. Often, risk is studied either online or offline, making it difficult to assess whether the advent of online and mobile technologies has extended the risk of harm to children or whether the proportion of children at risk is largely unchanged, with only the means by which it occurs changing. Some progress has been made especially in relation to (cyber)bullying, to ensure that online risks are examined in relation to the related offline risks. However, as children further integrate online and mobile technologies into their daily lives, distinguishing and comparing online and offline risk will become more difficult, demanding that researchers and practitioners recognize how a complex interplay among social norms and technological affordances shapes any particular communicative context. While recent research has progressed in terms of defining and measuring risk, the further embedding of online in offline contexts will increasingly challenge our research methodology.

The present review suggests some key messages for policy makers, safety practitioners, clinicians and other professionals concerned with children's welfare. All such professionals should be trained to recognize how the internet and mobile technologies may be implicated in mediating or exacerbating risk of harm to children (Livingstone, Palmer et al., 2012). When clinicians and other professionals see a child showing signs of harm, they should inquire into the possible online as well as offline context in which the harm occurred, and not assume that understanding the offline circumstances will be sufficient to pinpoint or redress any and every problem. Although the relations among online and offline risks are not fully explored, offline risk, risk-taking or victimization have an online dimension which should be considered when addressing the problem (Mitchell & Wells, 2007; Rice et al., 2012). It is important not to overreact to cases of online risk of harm by simply removing the child's access to the internet or mobile phone. Not only will many children not report the online dimension of a problem for fear of losing their phone or computer but also those same technologies may be a source of private information, social support or other help to the child even as it brings risks.

Finding the balance between treating online risk seriously and yet not overreacting is difficult. A world without risk is undesirable, and developmental psychologists are clear that facing and coping with risk is important, for ‘resilience can only develop through exposure to risk or to stress’ (Coleman & Hagell, 2007:15); it is ‘a dynamic process encompassing positive adaptation within the context of significant adversity’ (Luthar, Cicchetti & Becker, 2000: 543). Thus, a risk-averse society, paradoxically, exacerbates rather than reduces the very vulnerabilities it seeks to protect by undermining the development of resilience (Gill, 2007; Green, Mitchell & Bunton, 2000). Moreover, one would misunderstand child development to suppose that, once adolescents become aware of the risks they will cease to take them.

The more children use the internet, and the more digital skills and confidence they gain, the more deeply and broadly they use it, thus encountering more risks as well as opportunities. One concern is that since children and young people's experiences of opportunities and risks are positively correlated, efforts to reduce the latter may also reduce the former if policy interventions are not carefully designed (Livingstone & Helsper, 2010; Livingstone, Haddon et al., 2012). Another concern is the ambiguity of many mobile and online activities: to upload new content, personal information must be disclosed; to find new friends one must make new contacts (‘strangers’), in exploring sexuality or health, one is likely to encounter pornographic or pro-anorexic sites.

It would require a further article to review the diverse and fast-developing array of policies, strategies and interventions designed to reduce harm by restricting risk or harm or enabling coping and resilience. Risk associated with mobile and online technologies is generally addressed by conducting an evidence-based risk evaluation (see Livingstone, Davidson, Bryce, Millwood Hargrave & Grove-Hills, 2012; O'Neill, Livingstone & McLaughlin, 2011; OECD, 2011; Savirimuthu, 2011; UNICEF, 2011). This is used to establish the legitimacy of certain risk management strategies, such as the development of regulatory institutions, the provision of user tools (Patchin & Hinduja2012; Slonje et al., 2013), peer mentoring (van Kaenel-Platt & Douglas, 2012) or campaigns for awareness-raising. However, while weighing the merits and problems of possible interventions (including domestic filtering, parental mediation and education) is the focus of much public and policy deliberation, we conclude that, at present, the evidence base is too sparse for strong recommendations in favour of one approach over another.

For the most part, interventions have tended to address the youth (or parent) population en masse. What is required next is greater effort to tailor these interventions to particular target groups, drawing on the research reviewed here regarding risk and protective factors. Jones, Mitchell and Walsh (2013) reviewed internet safety education programmes, and point out the need for basing such programs on research findings, tailoring them to developmental needs, and evaluating their effectiveness. Much needed, and still little developed, is a body of research that conducts independent evaluations of existing interventions so as to learn from mistakes and share best practice (Perren et al., 2012). As an example, the KiVa antibullying program in Finland has been evaluated to reduce cyberbullying as well as traditional bullying (Williford et al., 2013). Insofar as the evidence base remains insufficient in pinpointing the conditions for and consequences of risk, the precautionary principle (Klinke & Renn, 2001) will continue to be invoked to justify policy interventions. We hope that this review will inspire researchers to fill the gaps identified above, developing the theoretical and empirical strengths of this emerging body of research to meet the undoubted challenges ahead.

 

 

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General internet safety

Cyberbullying