Cyberbullying and its risk factors among Chinese high school students

Abstract

Cyberbullying has become a common occurrence among adolescents worldwide; however, it has yet to receive adequate scholarly attention in China, especially in the mainland. The present study investigated the epidemiological characteristics and risk factors of cyberbullying, utilizing a sample of 1,438 high school students from central China. Findings revealed that cyberbullying among high school students in the heartland of central China is relatively common with 34.84% (N = 501) of participants reported having bullied someone and 56.88% (N = 818) reported having been bullied by online. Significant gender differences were found, suggesting that boys are more likely to be involved in cyberbullying both as perpetrators and victims. Students with lower academic achievement were more likely to be perpetrators online than were students with better academic achievement. Students who spend more time on online, have access to the internet in their bedrooms, have themselves experienced traditional bullying as victims, and are frequently involved in instant-messaging and other forms of online entertainment are more likely to experience cyberbullying. Increased parent and teacher supervision reduced students’ involvement in cyberbullying. Implications for intervention are explored.

Keywords

Chinese high school students cyberbullying intervention risk factors

With the popularization of the internet and development of information and communication technology (ICT), online communication has become a common mode of communication. With this change, social phenomena existing off-line have begun to appear online. Cyberbullying is an example of this trend, which has received increasing attention because of its potentially serious consequences and increasing prevalence. However, relatively little is known about cyberbullying (O’Keeffe, Clarke-Pearson, & Council on Communication and Media, 2011) especially in non-Western settings. Although there remains confusion about a standard definition for cyberbullying, most researchers agree that cyberbullying is an intentional, repeated, and aggressive act or behavior carried out by a group or individual instrumentally employing information and communication technology (ICT) (von Marees & Petermann, 2012). Studies show that the prevalence of cyberbullying is high in China; for example, research suggests that 34.9% of Chinese Taiwanese adolescents have been cyberbullied, and 20.4% had cyberbullied others (Huang & Chou, 2010; see Chen & Cheng [2013] for traditional bullying prevalence in a Taiwanese sample). Adolescents who have been bullied online are more likely to experience psychological problems (e.g. anxiety, depression) and engage in problem behaviors (e.g. skipping school, drug and alcohol use) (Beran & Li, 2005; Fosse & Holen, 2006; Juvonen & Gross, 2008; Mitchell, Ybarra, & Finkelhor, 2007; Wolak, Mitchell, & Finkelhor, 2006; Ybarra, Diener-West, & Leaf, 2007; Ybarra & Mitchell, 2007). In extreme cases, cybervictimization has been linked with suicide (Hinduja & Patchin, 2010). These factors have caused cyberbullying to attract attention as an important public health problem.

The majority of previous studies have focused on prevalence and types of cyberbullying. Studies that have systematically examined the risk factors for cyberbullying are rare and recent (e.g. Huang & Chou, 2010; Mishna, Khoury-Kassabri, Gadalla, & Daciuk, 2012; von Marees & Petermann, 2012). To date, most studies investigating risk factors for cyberbullying have focused on demographic variables (Li, 2006; Ortega, Elipe, Mora-Merchán, Calmaestra, & Vega, 2009; Wade & Beran, 2011; Wang, Iannotti, & Nansel, 2009), internet usage (Aricak et al., 2008; Erdur-Baker, 2010; Mesch, 2009; Navarro, Serna, Martínez, & Ruiz-Oliva, 2012; Smith et al., 2008; Topçu, Erdur-Baker, & Capa-Aydin, 2008; Twyman, Saylor, Taylor, & Comeaux, 2010), and experiences of traditional bullying (Hinduja & Patchin, 2010; Kowalski, Morgan, & Limber, 2012; Li, 2008; Tokunaga, 2010; Wang, Iannotti, & Luk, 2012). There have been many contradictory findings, suggesting that more information is needed to clarify the relationship between these factors and cyberbullying. For example, some studies determined that boys are more likely to be involved in cyberbullying as perpetrators, whereas girls are more likely to be cybervictims (Li, 2006; Ortega et al., 2009; Wade & Beran, 2011; Wang, Iannotti, & Nansal, 2009). However, other studies show that boys are more likely to be involved in cyberbullying as both bullies and victims (Aricak et al., 2008; Nansel et al., 2001). In addition, Li (2007) found that students with lower academic status were likely to become cyberbullies, whereas Ma (2001) argued that these students were likely to become victims. It is essential to have a thorough understanding of the risk factors to inform intervention and prevention strategies.

To date, very little is known about cyberbullying among adolescents from the Mainland of China, as the majority of existing studies were conducted in Chinese Taipei (the capital of Taiwan, as it is known in the West) (e.g. Hokoda, Lu, & Angeles, 2006; Huang & Chou, 2010; Wei, Jonson-Reid, & Tsao, 2007). The China Internet Network Information Center (CINIC, 2012) reported that the total number of Chinese netizens (or ‘internet citizens’) reached 537.6 million in June 2012, with adolescents (10- to 19-years-old) accounting for 25.4%. The popularity of internet use among Chinese adolescents makes cyberbullying a social phenomenon deserving of attention.

Culture is a strong predictor for both cyberbullying and cybervictimization (Li, 2007, 2008). Huang and Chou (2010) found that Chinese Taiwanese students usually took no action after being victimized online because of a cultural imperative to avoid conflict so as to maintain group harmony; further, that study found no relationship between cyberbullying and academic achievement. Cultural differences between the West and general Chinese culture may account for some of the differences observed in the empirical literature (e.g. frequency, attitude, setting, motive). Therefore, conclusions about cyberbullying from studies utilizing samples influenced by Western cultures may not be generalizable to Chinese culture. These findings are a reminder that it is essential to examine cultural factors related to the development, presentation, and intervention of cyberbullying.

Due to the limited number of studies on cyberbullying in mainland China, the present study aimed to clarify epidemiological characteristics and risk factors among Chinese mainland high school students with an emphasis on cultural differences between China and Western countries. We also sought to explore effective prevention and intervention measures of cyberbullying.

Methods

Participants

Participants were 1,438 students (42.56% female, 57.44% male, mean age = 15.91, SD = 1.02) from central China. The grade level of participants ranged from grade 10 to grade 12 (43.91%, 10th grade; 39.81%, 11th grade; 16.28%, 12th grade).

Measures

Students anonymously completed a survey which included questions on: Demographics; internet usage; Cyberbullying Inventory (CBI); traditional bullying scale; motivation for cyberbullying; and parents’ and teachers’ supervision of internet usage. With regard to internet usage, participants were asked how long they had been internet users; how many hours they spend online per week; what device they use to access the internet; and where they access the internet. They were also asked how often they engaged in each of the following internet activities: Instant messaging; visiting social networking sites; emailing; phone messaging; online entertainment (e.g. listening to streamed music, watching video); searching for information; playing online games; and online shopping. Answers were provided on a five-point scale ranging from 1 = Never to 5 = Always (where 1 to 3 was coded as low frequency; 4 to 5, high frequency). Finally, students were asked to indicate whether or not they had a mobile-phone and whether or not it could access the internet.

Students’ experiences of cyberbullying were assessed by a Chinese-language questionnaire based on the Cyberbullying Inventory (CBI) (Erdur & Kavsut, 2007) which included two forms: Cyberbullying (CB) and cybervictimization (CV). Both forms consisted of 18 items that described different forms of CB or CV. Two items were added to the cybervictimization form so that both forms would have an equal number of items. Items described experiences such as sending/receiving hurtful emails and making/receiving threats in a chat room. In this study, internal consistency of the CB and CV scales was 0.88 and 0.90, respectively.

The traditional bullying and victimization scale was developed by Li, Zhang, and Yu (2012). This scale consists of six items including, ‘people make-up cruel nicknames about me’, ‘people scold me’, or ‘people tease and mock me’, ‘students hit, kick, punch or threaten me’, and ‘students spread rumors about me and try to make others not like me’. The inverse of each of these questions was asked to assess bullying behavior (i.e. ‘I/we make-up cruel nicknames for other people’, etc.). Each item was assessed using a five-point likert scale. In this study, internal consistency of traditional bullying and victimization was 0.80 and 0.69, respectively.

Eight major motives for cyberbullying were investigated, which had been identified from prior studies. Motives included ‘for fun’, ‘to vent’, ‘because I dislike someone’, ‘for revenge’, ‘out of boredom’, ‘because it looks cool’, ‘to attract someone’s attention’, and ‘for other benefits’. Only participants who had already endorsed the statement that they had bullied others online were asked to answer this portion of the questionnaire. Participants could choose more than one motive based on their own experiences. Victims’ reactions to cyberbullying were also collected using multiple-choice questions with several options, such as ‘ignore/don’t care’, ‘talk about the experience with someone for help’, and ‘seek revenge on people who hurt me’.

In order to examine the relationship between academic achievement and cyberbullying, we collected information on participants’ academic achievement. Due to the difficulty involved in obtaining official academic records for every participant, academic achievement data was obtained only through self-report (a method that has been used by other researchers of Chinese cyberbullying; e.g. Huang & Chou, 2010). Students reported their academic achievement on a three-point scale (1 = ‘above average’, 2 = ‘average’, 3 = ‘below average’).

To assess parental supervision and restriction of internet usage, three questions were asked: ‘Do your parents supervise your online activities?’, ‘Do your parents control your online activities?’, and ‘Do your parents control your use of your mobile-phone at home?’. The same three questions were asked in reference to teachers’ supervision. Additionally, two additional multiple-choice questions were asked to identify parents’ methods for controlling children’s online activities (e.g. install software to prevent access to some websites, install software to monitor online activities, check history of visited sites, control/limit time online).

Procedure

Data were collected during Summer 2012 in classroom-settings by trained graduate students and teachers. Information about the purposes of the survey and confidentiality of responses was clearly explained and assent was obtained. Students were informed that there was no right or wrong answer. Extra support was provided for students who had difficulty completing the surveys.

Results

Prevalence rate of cyberbullying

In this sample, 34.84% (N = 501) of participants reported that they had bullied someone online and 56.88% (N = 818) reported that they had been bullied by someone online within the last semester (one semester continues for about five months in China); in addition, 26.84% (N = 386) reported being both cyberbullies and cybervictims. A total of 37.34% (N = 537) of participants reported that someone in their class had cyberbullied, and 40.33% (N = 580) reported that someone in their class had been cybervictimized.

Our study mirrored previous studies that found varying prevalence rates among different types of cyberbullying. Specifically, ‘to kick out someone from a chat room’ (17.94%), ‘to insult someone in a chat room’ (12.80%), and ‘to spread private information discussed by instant messaging tools (e.g. QQ/MSN)’ (7.79%) were the three most frequently reported cyberbullying behaviors in our study. ‘Someone stole my passwords of my ICM so that I cannot use them anymore’ (24.13%), ‘someone kicked me out from a chat room’ (22.81%), and ‘someone stole my network account and accessed my personal information’ (15.09%) were the three most frequently reported forms of cybervictimization.

Gender differences

Our study found that boys (39.59%) were more likely to report that they were involved in cyberbullying as perpetrators than girls (28.43%), χ² (1, N = 1438) = 19.28, p < 0.0001; boys (59.81%) were also more likely to be cybervictims than girls (52.94%), χ² (1, N = 1438) = 6.76, p < 0.01. Regarding the relationship between gender and types of cyberbullying or cybervictimization, more boys than girls reported cyberbullying someone using each type of bullying behavior measured with the exception of five types (e.g. ‘To spread the information without permission’, ‘To kick out someone from a chat room’, and ‘To harm someone you know from the internet’) (See Table 1). However, male-cybervictims reported experiencing only about half of all 18 types; these results suggest that the gender differences are weaker among cybervictims. In addition, we also found that ‘to kick someone from a chat room’ was the most frequent bullying behavior perpetrated by both boys and girls online, and ‘someone stole my passwords of my ICM so that I cannot use them anymore’ was the most frequent type of victimization experienced by both boys and girls online.

Table 1.

Comparisons of the gender differences on different forms of cyberbullying.

Behavior	As a perpetrator		χ² df = 1	As a victim
Behavior	Boys N = 826	Girls N = 612	χ² df = 1	Boys N = 826	Girls N = 612	χ² df = 1
Send hurtful e-mails	3.1%	1.0%	7.59**	12.7%	9.6%	3.28
Hiding the name via sending SMS	2.2%	0.8%	4.14	6.8%	5.1%	1.82
Take embarrassing photos	9.1%	4.2%	12.57***	14.6%	8.0%	14.88***
Threaten in chat room	3.5%	1.0%	9.48**	6.3%	4.2%	2.87
Spread rumors	4.2%	1.8%	6.76**	13.3%	9.6%	4.58*
Spread information on SMS	8.0%	7.5%	0.11	10.5%	10.3%	0.02
Kick out from chat room	18.9%	16.7%	1.18	25.5%	19.1%	8.25**
Humiliate by using fake photos	6.7%	1.6%	20.56***	6.9%	3.1%	10.12**
Send hurtful SMS	5.2%	2.6%	6.00*	6.4%	4.7%	1.84
Insult in chat room	17.1%	7.0%	31.78***	16.5%	6.7%	31.06***
Get information without permission	2.4%	0.7%	6.69**	13.6%	12.4%	0.40
Block access by stealing password	2.4%	0.3%	10.24**	11.4%	6.9%	8.38**
Reach messages by stealing passwords	1.5%	0.3%	4.62*	10.0%	6.9%	4.50*
Block from using SMS	2.3%	0.3%	9.51**	26.0%	21.6%	3.82
Use other’s username without permission	1.6%	0.2%	7.25**	18.8%	10.1%	20.45***
Violate privacy via Webcam	1.1%	0.3%	2.70	3.4%	2.1%	2.03
Humiliate by creating Web pages	1.1%	0.2%	4.37	2.7%	0.5%	9.72*
Harm someone known from Internet	5.3%	2.0%	10.64**	7.7%	6.7%	0.57

Note: ***p < 0.001; **p < 0.01; *p < 0.05.

Academic achievement differences

The relationship between academic achievement difference and participants’ experience of cyberbullying was examined using a one-way ANOVA analysis. As presented in Table 2, there were significant differences found between the three academic achievement groups (F = 3.89, p < 0.05). Multiple comparisons indicate that compared to students who perform better academically, the students with lower academic achievement were more likely to be online perpetrators. However, there were no differences found between the three academic-achievement groups and experiences of cybervictimization. Therefore, lower academic achievement maybe a potential risk factor for those engaging in cyberbullying as perpetrators.

Table 2.

Cyberbullying experience scores of the three academic-achievement groups.

Above average N = 756		Average N = 383		Below average N = 253
Cyberbullying experience	Mean	SD	Mean	SD	Mean	SD	F
Cybervictims	1.12	0.23	1.16	0.35	1.14	0.25	2.66
Cyberbullies	1.03	0.12	1.07	0.20	1.07	0.23	3.89*

Note: *p < 0.05.

Internet usage and cyberbullying

Descriptive data of internet usage was collected. The mean age of when the participants originally gained access to the internet was 5.60 years (SD = 2.58). Students reported spending time accessing the online world between one and two days each week (1.87 days; SD = 1.77), with 2.13 (SD = 2.06) hours per day, on average. This means that on average, students spend about 4.82 hours per week on the internet. Correlation analysis showed that both cyberbullying and cybervictimization were significantly related to total time spent online each week (r = 0.22. p < 0.01; r = 0.19, p < 0.01). Time spent online appears to be an important factor for predicting cyberbullying.

For technology use, 71.42% of participants (N = 1027) reported having a mobile-phone, and among them 76.73% (N = 788) reported that they could access the internet by phone. More than four-in-five of the youth (84.77%) reported having access to the internet by computer, 58.97% used a mobile-phone, and 16.41% accessed via tablet/pad computer. Additionally, 83.80% of participants reported having access to the internet at home; 17.45% reported accessing the internet at a commercial setting (e.g. ‘coffee-shop’), while 13.70% reported access at school and 9.39% at the home of friends. (As participants could choose more than one option, the totals reported here do not equal 100%).

When asked how often they were involved in any of several major online activities using a five-point scale (from 1 = never to 5 = always), results revealed that participants often spent their time on searching for information (M = 3.84), communicating via instant messages (M = 3.79), and online entertainment (M = 3.61). They were less frequently involved in social networking sites (M = 3.13), phone messaging (M = 2.93), and online game playing (M = 2.55). They were rarely involved in sending emails (M = 1.95) or shopping online (M = 1.74). Further analyses were run in order to gain a deeper understanding of the relationship between the three online activities most frequently endorsed by participants and their experience of cyberbullying. Students were classified into two groups: Students who endorsed ‘often’ and ‘always’ on these items were classified as Group 1 (often involved in), and all others were classified as Group 2(rarely involved in). The results of t-test showed that students who extensively used instant messaging were more likely to be experienced both as cyberbullies and cybervictims (t = 3.46, p < 0.001; t = 3.73, p < 0.0001) and that students who were often involved in online entertainment were more likely to experience cybervictimization (t = 2.21, p < 0.05).

Parents and cyberbullying

With regard to parental supervision, 45.83% of the participants reported having their internet usage monitored by parents. The extent of parental restriction was measured on a five-point scale, showing that only 8.41% of parents fail to restrict their child’s internet usage, and 73.50% of parents restrict children’s internet usage at least a moderate level. T-tests examined the differences in parental restriction between perpetrators and non-perpetrators, as well as victims and non-victims. Non-perpetrators reported that their parents were more restrictive of their internet usage than were perpetrators’ parents (t = 2.65, p < 0.05). No significant differences in parental restriction were found between victims and non-victims (t = 0.71, p > 0.05).

With regard to specific restriction strategies, 71.77% of youth reported that their parents implemented rules to limit the amount of time they are allowed to spend online, 7.79% reported their parents had installed filtering software to block specific web sites, 6.61% reported that their parents check their browsing history, and 2.36% reported that their parents had installed monitoring software to record online activities.

Participants were asked about the location where they accessed the internet at home: 544 participants (37.83%) reported having access to the internet in their own bedroom and 42.91% of youth reported only having internet access in common areas of their house. Chi square analyses showed that both cyberbullies (χ² (1, N = 544) = 21.44; p < 0.0001) and cybervictims (χ² (1, N = 544) = 36.03, p < 0.0001) were more likely to have access to the internet in a private space at home than were non-cyberbullies and non-cybervictims.

Educators and cyberbullying

This survey also showed that 70.45% of participants reported being monitored by teachers when using the internet at school—suggesting that Chinese teachers have a cautious attitude about students’ online activities. The results of t-tests indicate that both perpetrators and victims reported less restriction by teachers while engaging in online activities at school than non-perpetrators and non-victims (t = −2.31, p < 0.05; t = −2.06, p < 0.05).

There are limited opportunities for high school students in China to access the internet using a computer while at school; however, as 58.97% of the participants reported having internet access using their mobile-phones, it can be speculated that students use their phones to access the internet while at school. Therefore, we further examined the relationship between teachers’ restriction on students’ phone usage and students’ experiences of cyberbullying. Results indicated that perpetrators reported their mobile-phone use as being less restricted by teachers than non-perpetrators (t = −2.03, p < 0.05). However, there was no significant difference in teachers’ restrictions of phone use between victims and non-victims.

Traditional bullying and cyberbullying

A significant positive correlation was found between traditional bullying and cyberbullying (see Table 3). To further examine the relationship between traditional and cyberbullying forms, we analysed the overlap between traditional and cyberbullying using the method utilized by Kowalski, Morgan, and Limber (2012). Participants were classified into four groups: Victims only; bullies only; bully/victims; and neither bullies nor victims. Separate categories were created for cyberbullying and traditional bullying. Table 4 presents the overlap between involvement in traditional bullying and involvement in cyberbullying. Participants who were cyberbullies/victims were likely to be involved in traditional bullying as both perpetrators (53.11%) and as victims (68.13%). Similarly, individuals who were bully/victims in traditional bullying were likely to be both bullies (50.49%) and victims (76.83%) online. Individuals who were cybervictims were also likely to be victims of traditional bullying (53.94%). Both bullies (56.67%) and victims (61.72%) of traditional bullying were themselves likely to be victims online. This provides supporting evidence for the predictive effects of traditional bullying on cyberbullying.

Table 3.

Correlation matrix between traditional and cyberbullying.

Traditional victimization	Traditional perpetration	Cyber victimization	Cyberper petration
Traditional victimization	1
Traditional perpetration	0.48**	1
Cyber-victimization	0.35**	0.35**	1
Cyber-perpetration	0.29**	0.44**	0.58**	1
Mean	1.22	1.13	1.13	1.06
SD	0.36	0.31	0.27	0.17

Note: **p < 0.01.

Table 4.

Overlap between traditional and cyberbullying.

Cyberbullying status	Traditional victims	Traditional bullies	All participants
Victim	233 (53.94%)	146 (33.80%)	432 (30.04%)
Bully	47 (40.87%)	28 (24.35%)	115 (8.00%)
Bully/Victim	263 (68.13%)	205 (53.11%)	386 (26.84%)
Neither	160 (32.32%)	95 (19.19%)	495 (34.42%)
Traditional status	Cyber victims	Cyber bullies	All participants
Victim	179 (61.72%)	101 (34.83%)	290 (20.17%)
Bully	34 (56.67%)	24 (40.00%)	60 (4.17%)
Bully/Victim	315 (76.83%)	207 (50.49%)	410 (28.51%)
Neither	258 (41.41%)	150 (24.08%)	623 (43.32%)

Motives for cyberbullying

We also explored motives for cyberbullying. Based on the previous literature, several types of motives were investigated (Kowalski, Limber, & Agatston, 2012). A frequency distribution showed the following motives as most commonly reported by cyberbullies (N = 501): ‘I dislike someone’ (29.14%); ‘for fun’ (23.95%); ‘out of boredom’ (19.16%); ‘to vent’ (15.77%); ‘to get revenge’ (7.58%);‘to conform/fit in’ (5.59%), ‘to attract his/her attention’ (3.19%); ‘it looks cool’ (1.60%); and ‘to get some other benefit’ (1.60%).

Students’ reactions to cybervictimization

Students may react to cybervictimization in different ways. This survey showed that the most frequent reaction of participants who had experienced cybervictimization was to ‘ignore/not react’ (45.84%) followed by ‘talking about the experience with someone for help’ (35.57%). Other outcomes less frequently cited were ‘delete the materials which may hurt me’ (32.27%), ‘change my online account’ (24.69%), and ‘seek revenge on people who hurt me online’ (11.86%). Participants who reported ‘talking about the experience with someone for help’ most frequently wanted to talk with classmates/friends (65.64%), followed by parents (28.87%) and siblings (27.84%). Only 2.75% reported they would talk with their teachers for help.

Discussion and Implications

There are few studies examining the prevalence of cyberbullying in China, especially in the mainland. Li (2008) utilized a study of 197 Chinese students and found that 33% were cybervictims and 7% were cyberbullies. Another study from Chinese Taipei showed that 34.9% of 545 participants had been cyberbullied, 20.4% had cyberbullied others, and 63.4% reported having witnessed or being aware of cyberbullying (Huang & Chou, 2010). As the majority of the students who participated in these studies were from middle schools, the prevalence rate of cyberbullying among Chinese high school students remains unknown. The results of the present study provide preliminary data indicating that high school students in mainland China are frequently involved in cyberbullying. Studies from Western countries have examined the prevalence rate of cyberbullying among high school students with varying results (von Marees & Petermann, 2012). Based on his review of 14 studies conducted on cyberbullying in Australia, the USA, the UK, and Canada, Kraft (2006) summarized that reported levels of cybervictimization varied between 10% and 42%, and that rates of cyberbullying varied from 6% to 33% (with 11.5% recently confirmed in Australia by Sakellariou, Carroll, & Houghton, 2012).

Many researchers have argued that these discrepancies may be due to differing definitions of cyberbullying utilized by assessment instruments, the age-range of participants, and the timeframe of participants’ response (e.g. Kowalski, Limber, & Agatston, 2012). Some researchers have posited that involvement in cyberbullying as perpetrators or victims increases from the age 10- to 16-years-old (von Marees & Petermann, 2012). In the present study, the mean age of participants is 15.93 (SD = 1.02), which is very close to 16-years-old. In accordance with the age trend mentioned above, our participants may be more likely to be involved in cyberbullying. Nevertheless, the prevalence rates of cyberbullying revealed in our research are higher than the upper limits summarized by Kraft (2006). Therefore, this suggests that cyberbullying among high school students in China is relatively common and should attract the attention of parents, educators, and public society.

Consistent with the findings of previous studies from Western countries, the most common venues for victimization by cyberbully perpetrators were chat rooms (18.0%) and instant messaging (8.5%) (Hinduja & Patchin, 2008; Kowalski & Limber, 2007; Patchin & Hinduja, 2006). Within these venues, 23.9% of the respondents reported having their ICM passwords stolen and 22.8% reported having been kicked out from chat rooms. This study also found that instant messaging (e.g. QQ/MSN) was one of the most common activities that students participated in online. Among those who were bullied online (N = 818), 41.08% claimed that they were aware of the bully’s identity. More than one-half (55.06%) of these students reported that the perpetrators were their classmates. This finding may provide support for existing studies that have investigated the anonymity of cyberbullying (Huang & Chou, 2010; Juvonen & Gross, 2008). The proportion of classmates being perpetrators should attract educators’ attention in framing prevention and intervention activities.

Risk factors for cyberbullying among high school students

With regard to gender differences, we found that boys were significantly more likely to be cyberbullies or cybervictims. Although these results are consistent with findings from the traditional bullying literature, they are not aligned with results from early research on cyberbullying. Previous studies have found that girls were more likely to be cybervictims (Smith et al., 2008; Wang et al., 2009) or have found no significant gender differences between cybervictims (Hinduja & Patchin, 2008; Slonje & Smith, 2008; Williams & Guerra, 2007). Our results are supported by the work of Olweus (2003). In a sample of Chinese adolescents from Taipei, Huang and Chou’s (2010) findings mirror our own, with male students reporting greater levels of both perpetration and victimization experiences than females. This suggests that these findings may be due to cultural differences. In traditional Chinese culture, girls are raised to be gentle, polite and kind, while boys are encouraged to be active, brave, and independent. Boys are told that it is not brave to be aggressive towards or even bully girls. This may lead to fewer girls being involved in bullying, whether offline or online.

Our results confirmed that the frequency of internet access may be another risk factor for cyberbullying. Our results are consistent with finings from previous that youth who use the internet more frequently and spend more time online per day may be more likely to become involved in cyberbullying as perpetrators or victims (Mishna et al., 2012; Navarro et al., 2012; Wolak, Mitchell, & Finkelhor, 2007). Chi square analyses showed that having internet access by phone or accessing the internet at a commercial location increased student’s risk of involvement in cyberbullying. Descriptive results showed that almost three-quarters of the youth had a phone, and nearly one-fifth of the youth had access to the internet commercially. This implies that youth are more likely to become involved in cyberbullying in unsupervised spaces. As more youth have access to the internet using mobile-phones, additional attention is warranted regarding youths’ phone usage. Our results indicate that some types of online activities increase the odds of involvement in cyberbullying. Specifically, the more often students are involved in instant messaging, online entertainment, and information searches, the more likely they are to become involved in cyberbullying. Instant messaging may be an activity in which Chinese high school students are the most likely to experience cyberbullying (Huang & Chou, 2010). In the USA, social-network sites and chat rooms have served as fertile ground for cyberbullying (Mesch, 2009). Although social networking sites are not yet as popular in China as in Western countries, nearly half of Chinese youth (42.98%) report frequently visiting social networking sites. This number is expected to increase which may expose youth to additional risks.

Our study revealed a relationship between traditional bullying and cyberbullying which mirrors the findings of previous studies (Hinduja & Patchin, 2010; Kowaski, Morgan, & Limber, 2012; Li, 2007). Students who report involvement in traditional bullying were found to be at a greater risk for involvement with cyberbullying. We speculate three possible reasons for this result. First, previous studies have suggested traditional bullying and victimization are related to personal traits. Specifically, traditional bullies are typically emotionally impulsive, irritable, and lacking in self-control; in contrast, traditional victims are typically introverted, sensitive, and easily restrained (Zhang, Gu, & Ju, 2001). We speculate that students with these personal traits are also likely to experience bullying online. Second, because bullies and victims often know each other in real life, the social interaction that usually occurs at school may be extended online, along with their status of bully or victim (Kowaski, Morgan, & Limber, 2012). Third, because of the power imbalance between perpetrators and victims, victims may be unable to fight back or transform his/her role in traditional bullying. However, the anonymity of the internet makes this transformation easier and may give traditional victims the courage to counterattack. Therefore, the victims of traditional bullying may become cyberbullies (40.85% in our study); consequently, traditional bullies transition into cybervictims (61.80% in our study). This finding sheds light on the similarities between traditional and cyberbullying while highlighting one of the key differences. Therefore, we contend that there should be a different approach to prevention and intervention strategies for traditional bullying and cyberbullying.

The present study revealed that a relatively high proportion of Chinese parents (73.50%) place at least a moderate level of restriction on their children’s internet usage. This suggests that Chinese parents are broadly aware of the risk involved in their children’s online activities. Consistent with previous studies (Mesch, 2009; Navarro et al., 2012), our study found that parental restriction reduces the risk of children’s involvement in cyberbullying. However, parental restriction was not found to be significantly related to children’s experience of cybervictimization. This suggests that although parental restriction of children’s internet usage may effectively reduce the risk of children perpetrating online, it may not protect children from being bullied online. This may be due to the unique feature of cyberbullying—that is, unlike traditional bullying, cyberbullying can occur anytime, anywhere (Li, 2008; Tokunaga, 2010). Thus, even though someone may rarely access the internet, he/she may still be vulnerable to online victimization.

The most important agenda for high school students in China is to prepare for their college entrance examinations. In order to achieve a high score in this critical watershed, they must spend almost all of their spare time studying, leaving little time for leisure activities. It is typical for Chinese high school students to spend more than ten hours daily in study. Going online while at school is virtually impossible; our survey revealed that 70.45% of participants reported that their online activities were closely monitored by teachers at school. It is logical to conclude that teacher-supervision and restrictions on students’ online behaviors can reduce cyberbullying. While restricting students’ computer use at school may be relatively easy, restricting their access to the internet using portable devices may is more complicated. We conclude that teachers’ interventions for cyberbullying should include targeted measures to guide students’ usage of mobile devices.

Implications for students

As adolescents are the direct participants of cyberbullying, prevention, and intervention efforts should help them better understand cyberbullying. Our survey indicated that more than two-thirds (69.14%) participants believe that the harm brought to others through cybervictimization is only ‘moderate’ or ‘minimal’. Further, the two most common motives for cyberbullying were ‘for fun’ and ‘out of boredom’, indicating that adolescents know little about the seriousness of their online behaviors. Therefore, it is not surprising that the prevalence of perpetrating reported by the participants was relatively high (34.84%). We speculate that if adolescents better understood the potential consequences of their online behaviors, the prevalence of perpetration would decrease.

Implications for parents

Our results indicate that while three-quarters of parents limit students’ time online, relatively few use technological strategies such as installing online-filters or monitoring-software. Though this strategy did have some positive effects preventing cyberbullying among Chinese high school students, it was rudimentary and limited by comparison to Western countries. For example, Mesch (2009) investigated 935 US youth and found that 56% reported that their parents had installed ﬁltering software and had rules on the type of information they were allowed to share over the internet. In addition to direct restriction on internet usage, other studies describe group interventions which involve educating students and creating rules about what personal information is appropriate to share (Navarro et al., 2012). In our study, 83.80% of participants reported having internet access at home. Further, youth who had internet access in the private space of their bedrooms were more likely to be both cyberbullies and cybervictims. In addition to controlling children’s time online, Chinese parents may more effective if they intervened by placing the computer in relatively public spaces.

Chinese parents need to better understand cyberbullying; as they gain understanding they can help their children become more aware of the possible negative consequences of online activities. Our study indicated that one-in-three cyberbullied students were willing to talk about their experiences with parents—suggesting that parents could be important supports for children who experience cybervictimization; parents should know how to recognize the signs of cyberbullying and what to do when they suspect cyberbullying.

Implications for educators

Supervision and restriction on students’ online behaviors in school can effectively reduce cyberbullying (Cassidy, Brown, & Jackson, 2012); however, as mobile devices with internet access become increasingly available, supervision and restriction on students’ online access may not be sufficient. The internet is an important part of modern everyday lives; even without computers, youth can go online by using portable devices. The most effective intervention for cyberbullying may be proper guidance. Specifically, schools should re-evaluate their methods of supervision and equip educators to better understand cyberbullying. Although only 4.90% of the cybervictims reported they would talk to their teachers about their experiences, this does not excuse educators from the role they should play to intervene in cyberbullying.

Footnotes

Author biographies

Zongkui Zhou is a Professor at the Central China Normal University (CCNU) where he is Dean of the School of Psychology; he serves as the Director of the Key Laboratory of Adolescent CyberPsychology and Behavior for the National Ministry of Education, China. His research interests involve children and adolescents’ cyberbehavior, peer relationships, ‘left-behind children’ in rural China, mental health education and school practice, and internet psychology. With over 100 publications, including eight books; he is an author and editor of Children’ Socialization (1989, Hubei Child and Adolescent Publishing House), Children’s Social Skill (2003, CCNU Press), Adolescents’ Psychological Development and Education (2007, Higher Education Press).

Tang Hanying is a Lecturer at the School of Psychology, Central China Normal University. His research interests involve cyberbullying, counterproductive work-behavior, and work/family balance. He serves as a Researcher of the Key Laboratory of Adolescent CyberPsychology and Behavior (CCNU), Ministry of Education, China.

Yuan Tian is a Lecturer at the Central China Normal University and is a Researcher at the Key Laboratory of Adolescent CyberPsychology and Behavior, National Ministry of Education, China. She has work published on students at-risk for behavioral disorders and internet addiction tendency. Her research interests involve social withdrawal, peer relationships, and adolescent cyberpsychology and behavior.

Hua Wei is a Doctoral Student in the School of Psychology, Central China Normal University. His research interests focus on adolescents’ internet use and cyberpsychology.

Fengjuan Zhang is a Doctoral Student in School of Psychology, Central China Normal University, China. Her research interests focus on peer relationships and adjustment at school.

Chelsey M. Morrison, MA, is a Graduate Student at Wheaton College in Wheaton, Illinois pursuing her doctorate to practice with children and adolescents, and their families. Her research interests include developmental psychopathology, positive psychology, peer victimization, and international populations.

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