cyberbullying research paper questionnaire

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• v.5(3); 2019 Mar

Cyberbullying and its influence on academic, social, and emotional development of undergraduate students

This study investigated the influence of cyberbullying on the academic, social, and emotional development of undergraduate students. It's objective is to provides additional data and understanding of the influence of cyberbullying on various variables affecting undergraduate students. The survey sample consisted of 638 Israeli undergraduate students. The data were collected using the Revised Cyber Bullying Survey, which evaluates the frequency and media used to perpetrate cyberbullying, and the College Adjustment Scales, which evaluate three aspects of development in college students. It was found that 57% of the students had experienced cyberbullying at least once or twice through different types of media. Three variables were found to have significant influences on the research variables: gender, religion and sexual preferences. Correlation analyses were conducted and confirmed significant relationships between cyberbullying, mainly through instant messaging, and the academic, social and emotional development of undergraduate students. Instant messaging (IM) was found to be the most common means of cyberbullying among the students.

The main conclusions are that although cyberbullying existence has been proven, studies of cyberbullying among undergraduate students have not been fully developed. This particular population needs special attention in future research. The results of this study indicate that cyberbullying has an influence on the academic, social, and emotional development of undergraduate students. Additional Implications of the findings are discussed.

1. Introduction

Cyberbullying is defined as the electronic posting of mean-spirited messages about a person (such as a student) often done anonymously ( Merriam-Webster, 2017 ). Most of the investigations of cyberbullying have been conducted with students in elementary, middle and high school who were between 9 and 18 years old. Those studies focused on examining the prevalence and frequency of cyberbullying. Using “cyberbullying” and “higher-education” as key words in Google scholar (January, 2019) (all in title) yields only twenty one articles. In 2009, 2012 and 2013 one article appeared each year, since 2014 each year there were few publications. Of these articles only seven relates to effect of cyberbullying on the students, thus a gap in the literature exists in that it only minimally reports on studies involving undergraduate students. Given their relationship and access to technology, it is likely that cyberbullying occurs frequently among undergraduates. The purpose of this study is to examine the frequency and media used to perpetrate cyberbullying, as well as the relationship that it has with the academic, social and emotional development of undergraduate students.

Undergraduate students use the Internet for a wide variety of purposes. Those purposes include recreation, such as communicating in online groups or playing games; academics, such as doing assignments, researching scholarships or completing online applications; and practical, such as preparing for job interviews by researching companies. Students also use the Internet for social communication with increasing frequency.

The literature suggests that cyberbullied victims generally manifest psychological problems such as depression, loneliness, low self-esteem, school phobias and social anxiety ( Grene, 2003 ; Juvonen et al., 2003 ; Akcil, 2018 ). Moreover, research findings have shown that cyberbullying causes emotional and physiological damage to defenseless victims ( Akbulut and Eristi, 2011 ) as well as psychosocial difficulties including behavior problems ( Ybarra and Mitchell, 2007 ), drinking alcohol ( Selkie et al., 2015 ), smoking, depression, and low commitment to academics ( Ybarra and Mitchell, 2007 ).

Under great emotional stress, victims of cyberbullying are unable to concentrate on their studies, and thus their academic progress is adversely affected ( Akcil, 2018 ). Since the victims are often hurt psychologically, the depressive effect of cyberbullying prevents students from excelling in their studies ( Faryadi, 2011 ). The overall presence of cyberbullying victimization among undergraduate college students was found to be significantly related to the experience of anxiety, depression, substance abuse, low self-esteem, interpersonal problems, family tensions and academic underperformance ( Beebe, 2010 ).

1.1. Cyberbullying and internet

The Internet has been the most useful technology of modern times, which has enabled entirely new forms of social interaction, activities, and organizing. This has been possible thanks to its basic features such as widespread usability and access. However, it also causes undesirable behaviors that are offensive or threatening to others, such as cyberbullying. This is a relatively new phenomenon.

According to Belsey (2006, p.1) , “Cyberbullying involves the use of information and communication technologies such as e-mail, cell-phone and pager text messages, instant messaging, defamatory personal web sites, blogs, online games and defamatory online personal polling web sites, to support deliberate, repeated, and hostile behavior by an individual or group that is intended to harm others.” Characteristics like anonymity, accessibility to electronic communication, and rapid audience spread, result in a limitless number of individuals that can be affected by cyberbullying.

Different studies suggest that undergraduate students' use of the Internet is more significant and frequent than any other demographic group. A 2014 survey of 1006 participants in the U.S. conducted by the Pew Research Center revealed that 97% of young adults aged from 18 to 29 years use the Internet, email, or access the Internet via a mobile device. Among them, 91% were college students.

1.2. Mediums to perpetrate cyberbullying

The most frequent and common media within which cyberbullying can occur are:

Electronic mail (email): a method of exchanging digital messages from an author to one or more recipients.

Instant messaging: a type of online chat that offers real-time text transmission between two parties.

Chat rooms: a real-time online interaction with strangers with a shared interest or other similar connection.

Text messaging (SMS): the act of composing and sending a brief electronic message between two or more mobile phones.

Social networking sites: a platform to build social networks or social relations among people who share interests, activities, backgrounds or real-life connections.

Web sites : a platform that provides service for personal, commercial, or government purpose.

Studies indicate that undergraduate students are cyberbullied most frequently through email, and least often in chat rooms ( Beebe, 2010 ). Other studies suggest that instant messaging is the most common electronic medium used to perpetrate cyberbullying ( Kowalski et al., 2018 ).

1.3. Types of cyberbullying

Watts et al. (2017) Describe 7 types of cyberbullying: flaming, online harassment, cyberstalking, denigration, masquerading, trickery and outing, and exclusion. Flaming involves sending angry, rude, or vulgar messages via text or email about a person either to that person privately or to an online group.

Harassment involves repeatedly sending offensive messages, and cyberstalking moves harassment online, with the offender sending threatening messages to his or her victim. Denigration occurs when the cyberbully sends untrue or hurtful messages about a person to others. Masquerading takes elements of harassment and denigration where the cyberbully pretends to be someone else and sends or posts threatening or harmful information about one person to other people. Trickery and outing occur when the cyberbully tricks an individual into providing embarrassing, private, or sensitive information and posts or sends the information for others to view. Exclusion is deliberately leaving individuals out of an online group, thereby automatically stigmatizing the excluded individuals.

Additional types of cyberbullying are: Fraping - where a person accesses the victim's social media account and impersonates them in an attempt to be funny or to ruin their reputation. Dissing - share or post cruel information online to ruin one's reputation or friendships with others. Trolling - is insulting an individual online to provoke them enough to get a response. Catfishing - steals one's online identity to re-creates social networking profiles for deceptive purposes. Such as signing up for services in the victim's name so that the victim receives emails or other offers for potentially embarrassing things such as gay-rights newsletters or incontinence treatment. Phishing - a tactic that requires tricking, persuading or manipulating the target into revealing personal and/or financial information about themselves and/or their loved ones. Stalking – Online stalking when a person shares her personal information publicly through social networking websites. With this information, stalkers can send them personal messages, send mysterious gifts to someone's home address and more. Blackmail – Anonymous e-mails, phone-calls and private messages are often done to a person who bear secrets. Photographs & video - Threaten to share them publicly unless the victim complies with a particular demand; Distribute them via text or email, making it impossible for the victim to control who sees the picture; Publish the pictures on the Internet for anyone to view. Shunning - persistently avoid, ignore, or reject someone mainly from participating in social networks. Sexting - send sexually explicit photographs or messages via mobile phone.

1.4. Prevalence of cyberbullying

Previous studies have found that cyberbullying incidents among college students can range from 9% to 34% ( Baldasare et al., 2012 ).

Beebe (2010) conducted a study with 202 college students in United States. Results indicated that 50.7% of the undergraduate students represented in the sample reported experiencing cyberbullying victimization once or twice during their time in college. Additionally, 36.3% reported cyberbullying victimization on a monthly basis while in college. According to Dılmaç (2009) , 22.5% of 666 students at Selcuk University in Turkey reported cyberbullying another person at least once and 55.35% reported being a victim of cyberbullying at least once in their lifetimes. In a study of 131 students from seven undergraduate classes in United States, 11% of the respondents indicated having experienced cyberbullying at the university ( Walker et al., 2011 ). Of those, Facebook (64%), cell phones (43%) and instant messaging (43%) were the most frequent technologies used. Students indicated that 50% of the cyberbullies were classmates, 57% were individuals outside of the university, and 43% did not know who was cyberbullying them.

Data from the last two years (2017–18) is similar to the above. A research, of 187 undergraduate students matriculated at a large U.S. Northeastern metropolitan Roman Catholic university ( Webber and Ovedovitz, 2018 ), found that 4.3% indicated that they were victims of cyberbullying at the university level and a total of 7.5% students acknowledged having participated in bullying at that level while A survey (N = 338) at a large midwestern university conducted by Varghese and Pistole (2017) , showed that frequency counts indicated that 15.1% undergraduate students were cyberbully victims during college, and 8.0% were cyberbully offenders during college.

A study of 201 students from sixteen different colleges across the United States found a prevalence rate of 85.2% for college students who reported being victims of cyberbullying out of the total 201 responses recorded. This ranged from only occasional incidents to almost daily experiences with cyberbullying victimization ( Poole, 2017 ).

In A research of international students, 20.7% reported that they have been cyberbullied in the last 30 days once to many times ( Akcil, 2018 ).

1.5. Psychological impact of cyberbullying

Cyberbullying literature suggests that victims generally manifest psychological problems such as depression, anxiety, loneliness, low self-esteem, social exclusion, school phobias and poor academic performance ( DeHue et al., 2008 ; Juvonen and Gross, 2008 ; Kowalski and Limber, 2007 ; Grene, 2003 ; Juvonen et al., 2003 ; Rivituso, 2012 ; Varghese and Pistole, 2017 ; Na, 2014 ; Akcil, 2018 ), low self-esteem, family problems, school violence and delinquent behavior ( Webber and Ovedovitz, 2018 ), which brings them to experience suicidal thoughts as a means of escaping the torture ( Ghadampour et al., 2017 ).

Moreover, research findings have shown that cyberbullying causes emotional and physiological damage to defenseless victims ( Faryadi, 2011 ) as well as psychosocial problems including inappropriate behaviors, drinking alcohol, smoking, depression and low commitment to academics ( Walker et al., 2011 ).

The victims of cyberbullying, under great emotional stress, are unable to concentrate on their studies, and thus their academic progress is adversely affected ( Faryadi, 2011 ). Since the victims are often hurt psychologically, the depressive effect of cyberbullying prevents students from excelling in their studies ( Faryadi, 2011 ).

In a Malaysian university study with 365 first year students, the majority of the participants (85%) interviewed indicated that cyberbullying affected their academic performance, specifically their grades ( Faryadi, 2011 ). Also, 85% of the respondents agreed that bullying caused a devastating impact on students' emotions and equally caused unimaginable psychological problems among the victims. Heiman and Olenik-Shemesh (2018) report that for students with learning disabilities, predictors of cybervictimization were low social support, low self-perception, and being female, whereas for students without learning disabilities, the predictors were low social support, low well-being, and low body perception.

1.6. Academic, social, and emotional development of undergraduate students

The transition to academic institutions is marked by complex challenges in emotional, social, and academic adjustment ( Gerdes and Mallinckrodt, 1994 ; Parker et al., 2004 ).

The adaptation to a new environment is an important factor in academic performance and future achievement. Undergraduate students are not only developing academically and intellectually, they are also establishing and maintaining personal relationships, developing an identity, deciding about a career and lifestyle, and maintaining personal health and wellness. Many students are interacting with people from diverse backgrounds who hold different values and making new friends. Some are also adapting to living away from home for the very first time ( Inkelas et al., 2007 ).

The concept of academic development involves not only academic abilities, but motivational factors, and institutional commitment. Motivation to learn, taking actions to meet academic demands, a clear sense of purpose, and general satisfaction with the academic environment are also important components of the academic field ( Lau, 2003 ).

A second dimension, the social field, may be as important as academic factors. Writers have emphasized integration into the social environment as a crucial element in commitment to a particular academic institution ( Tinto, 1975 ). Becoming integrated into the social life of college, forming a support network, and managing new social freedoms are some important elements of social development. Crises in the social field include conflict in a living situation, starting or maintaining relationships, interpersonal conflicts, family issues, and financial issues ( McGrath, 2005 ), which are manifested as feelings of loneliness ( Clark et al., 2015 ).

In the emotional field, students commonly question their relationships, direction in life, and self-worth ( Rey et al., 2011 ). A balanced personality is one which is emotionally adjusted. Emotional adjustment is essential for creating a sound personality. physical, intellectual mental and esthetical adjustments are possible when emotional adjustment is made ( Ziapour et al., 2018 ). Inner disorders may result from questions about identity and can sometimes lead to personal crises ( Gerdes and Mallinckrodt, 1994 ). Emotional problems may be manifested as global psychological distress, somatic distress, anxiety, low self-esteem, or depression. Impediments to success in emotional development include depression and anxiety, stress, substance abuse, and relationship problems ( Beebe, 2010 ).

The current study is designed to address two research questions: (1) does cyberbullying affect college students' emotional state, as measured by the nine factors of the College Adjustment Scales ( Anton and Reed, 1991 ); (2) which mode of cyberbullying most affects students' emotional state?

2.1. Research settings and participants

The present study is set in Israeli higher education colleges. These, function as: (1) institutions offering undergraduate programs in a limited number of disciplinary fields (mainly the social sciences), (2) centers for training studies (i.e.: teacher training curricula), as well as (3) as creators of access to higher education. The general student population is heterogeneous, coming from the Western Galilee. In this study, 638 Israeli undergraduate students participated. The sample is a representative of the population of the Western galilee in Israel. The sample was 76% female, 70% single, 51% Jewish, 27% Arabs, 7% Druze, and 15% other ethnicity. On the dimension of religiosity, 47% were secular, 37% traditional, 12% religious, 0.5% very religious, and 3.5% other. On the dimension of sexual orientation, 71% were straight women, 23.5% straight men, 4% bisexual, 1% lesbians, and 0.5% gay males (note: according to the Williams Institute, approximately 4% of the population in the US are LGBT, [ Gates, 2011 ], while 6% of the EU population are LGBT, [ Dalia, 2016 ]).

2.2. Instrumentation

Two instruments were used to collect data: The Revised Cyber Bullying Survey (RCBS), with a Cronbach's alpha ranging from .74 to .91 ( Kowalski and Limber, 2007 ), designed to measure incidence, frequency and medium used to perpetrate cyberbullying. The survey is a 32-item questionnaire. The frequency was investigated using a 5-item scale with anchors ranging from ‘it has never happened to me’ to ‘several times a week’. Five different media were explored: email, instant messaging, chat room, text messaging, and social networking sites. Each medium was examined with the same six questions related to cases of cyberbullying (see Table 1 ).

Description of the Revised Cyber Bullying Survey (RCBS) variables.

Note: the theoretical range is between zero to twenty-four.

Table 1 shows the five variables that composed the RCBS questionnaire (all of the variables are composed of 6 statements). The results indicate that the levels of all the variables is very low, which means that the respondents experienced cyberbullying once or twice. The internal consistency reliability estimate based on the current sample suggested that most of the variables have an adequate to high level of reliability, with a Cronbach's alpha of 0.68–0.87.

The College Adjustment Scales (CAS) ( Anton and Reed, 1991 ), evaluated the academic, social, and emotional development of college students. Values were standardized and validated for use with college students. The validity for each subscale ranged from .64 to .80, noting high correlations among scales. Reliability of the scales ranged from .80 to .92, with a mean of .86. The instrument included 128 items, divided into 10 scales: anxiety, depression, suicidal ideation, substance abuse, self-esteem problems, interpersonal problems, family problems, academic problems, career problems, and regular activities (see Table 2 ). Students responded to each item using a four-point scale.

Description of CAS variables.

Anxiety: A measure of clinical anxiety, focusing on common affective, cognitive, and physiological symptoms.

Depression: A measure of clinical depression, focusing on common affective, cognitive, and physiological symptoms.

Suicidal Ideation: A measure of the extent of recent ideation reflecting suicide, including thoughts of suicide, hopelessness, and resignation.

Substance Abuse: A measure of the extent of disruption in interpersonal, social, academic, and vocational functioning as a result of substance use and abuse.

Self-esteem Problems: A measure of global self-esteem which taps negative self-evaluations and dissatisfaction with personal achievement.

Interpersonal Problems: A measure of the extent of problems in relating to others in the campus environment.

Family Problems: A measure of difficulties experienced in relationships with family members.

Academic Problems: A measure of the extent of problems related to academic performance.

Career Problems: A measure of the extent of problems related to career choice.

Participants also responded to a demographic questionnaire that included items on gender, birth year, marital status, ethnicity, and sexual orientation. As sexual orientation is a major cause for bullying ( Pollock, 2006 ; Cahill and Makadon, 2014 ), it was included in the background information.

Convenience sampling and purposive sampling were used for this study. Surveys with written instructions were administered in classrooms, libraries and online via Google Docs at the end of the semester.

The surveys were translated to Hebrew and back translated four times until sufficient translation was achieved. The research was approved by the Western Galilee College Research and Ethic Committee.

A sizeable percentage, 57.4% (366), of the respondents reported being cyber bullied at least once and 3.4% (22) reported being cyber bullied at least once a week. The types of bullies can be seen in Fig. 1 .

Types of bullies.

Three variables were found to have significant influences on the research variables: (1) gender (see Table 3 ); (2) religion (see Table 4 ); and (3) sexual preferences (see Table 5 ).

Results of independent t-tests for research variables by gender.

Note: n male = 127, n female = 510, *p < .05.

Results of independent t-tests for research variables by level of religion.

Note: n religious = 345, n secular = 293, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

Results of independent t-tests for research variables by sexual preference.

Note: n heterosexual = 596, n other = 42, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

Independent t-tests between the CAS variables and gender show significant differences between females and males (see Table 3 ).

Independent t-tests between the CAS variables and level of religiosity show significant differences between secular and religious persons, i.e., observant believers (see Table 4 ).

Independent t-tests between the CAS variables and sexual preference show significant differences between heterosexual individuals and others (see Table 5 ).

The research population was divided into three age groups having five year intervals. One respondent who was 14 years old was removed from the population.

For the variable “career problems” it was found that there was a significant difference between the 26–30 year age group [p < .05, F(2,5815) = 3.49, M = 56.55] and the 31–35 (M = 56.07) as well as the 20–25 (M = 54.58) age groups.

For the variable "depression" it was found that there was a significant difference between the 20–25 year age group [p < .05, F(2,5815) = 3.84, M = 54.56] and the 31–35 (M = 51.61) as well as the 26–30 (M = 52.83) age groups.

For the variable “interpersonal problems” it was found that there was a significant difference between the 20–25 year age group [p < .06, F(2,5815) = 3.84, M = 53.85] and the 31–35 (M = 51.29) as well as the 26–30 (M = 52.19) age groups.

For the variable “suicidal ideation” it was found that there was a significant difference between the 20–25 year age group [p < .06, F(2,5815) = 3.84, M = 55.45] and the 31–35 (M = 49.71) as well as the 26–30 (M = 50.13) age groups (see Table 6 ).

Results of one way Anova for research variables by age.

Note: n 20-25 = 216, n 26-30 = 287, n 31-35 = 82, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

To confirm that there was no effect among the independent variables, a Pearson correlation analysis of cyberbullying with CAS variables was run. As the correlations between the independent variables are weak, no multicollinearity between them was noted (see Table 7 ).

Pearson correlation of cyberbullying with CAS variables.

Note: n = 638, ∼ p < .06, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

Regression analyses on the effect of the cyberbullying variables on the CAS variables (see Fig. 2 ) show that an increase in cyberbullying by social networking and IM increases the academic problems variable. The model explained 6.1% of the variance (F (13,585) = 2.94, p < .001) and shows an increase in the suicidal ideation variable. There is also a marginal effect of cyberbullying by SMS on suicidal ideation, revealing that an increase in cyberbullying by SMS causes a decrease in suicidal ideation. The explained variance of the model is 24.8% (F (11,584) = 14.80, p < .001). Higher cyberbullying by social networking results in an increase in the anxiety variable. The explained variance of the model is 8.8% (F (13,584) = 4.32, p < .001). An increase in cyberbullying by chat and IM shows an increase in the substance abuse variable. The model explains 13% of the variance (F (13,584) = 6.71, p < .001). Increasing cyberbullying by social networking and IM increases the self-esteem problems variable. The explained variance of the model is 9% (F (13,584) = 4.43, p < .001). An increase of cyberbullying by email increases the problems students have with regular activities. The explained variance of the model is 5.2% (F (13,575) = 2.44, p < .01). Heightened cyberbullying by social networking and IM increases students' interpersonal problems. There is also an effect of cyberbullying by IM on suicidal ideation, such that an increase in cyberbullying by IM causes a decrease in interpersonal problems. The explained variance of the model is 8% (F (13,584) = 3.89, p < .001). An increase in cyberbullying by SMS decreases the family problems variable. The explained variance of the model is 11.4% (F (13,584) = 5.76, p < .001). And finally, heightened cyberbullying by IM and social networking decreases the depression variable. The variance explained by the model is 11.9% (F (13,584) = 6.04, p < .001).

The influence of academic cyberbullying variables on the CAS variables.

4. Discussion

The objective of this study was to fill an existing gap in the literature regarding the influence of cyberbullying on the academic, social, and emotional development of undergraduate students.

As has been presented, cyberbullying continues to be a disturbing trend not only among adolescents but also undergraduate students. Cyberbullying exists in colleges and universities, and it has an influence on the development of students. Fifty seven percent of the undergraduate students who participated in this study had experienced cyberbullying at least once during their time in college. As previous studies have found that cyberbullying incidents among college students can range from 9% to 50% ( Baldasare et al., 2012 ; Beebe, 2010 ) it seems that 57% is high. Considering the effect of smartphone abundance on one hand and on the other the increasing use of online services and activities by young-adults can explain that percentage.

Considering the effect of such an encounter on the academic, social and emotional development of undergraduate students, policy makers face a formidable task to address the relevant issues and to take corrective action as Myers and Cowie (2017) point out that due to the fact that universities are in the business of education, it is a fine balancing act between addressing the problem, in this case cyberbullying, and maintaining a duty of care to both the victim and the perpetrator to ensure they get their degrees. There is a clear tension for university authorities between acknowledging that university students are independent young adults, each responsible for his or her own actions, on one hand, and providing supervision and monitoring to ensure students' safety in educational and leisure contexts.

Although there are increasing reports on connections between cyberbullying and social-networks (see: Gahagan et al., 2016 ), sending SMS or MMS messages through Internet gateways ensures anonymity, thus indirectly supporting cyberbullying. A lot of websites require only login or a phone number that can also be made up ( Gálik et al., 2018 ) which can explain the fact that instant-messaging (IM) was found to be the most common means of cyberbullying among undergraduate students with a negative influence on academic, family, and emotional development (depression, anxiety, and suicidal ideation). A possible interpretation of the higher frequency of cyberbullying through IM may be that young adults have a need to be connected.

This medium allows for being online in ‘real time’ with many peers or groups. With the possibility of remaining anonymous (by creating an avatar – a fake profile) and the possibility of exposing private information that remains recorded, students who use instant messaging become easy targets for cyberbullying. IM apps such as WhatsApp are extremely popular as they allow messages, photos, videos, and recordings to be shared and spread widely and in real time.

Students use the Internet as a medium and use it with great frequency in their everyday lives. As more aspects of students' lives and daily affairs are conducted online, coupled with the fact that excessive use may have consequences, it is important for researchers and academic policy makers to study the phenomenon of cyberbullying more deeply.

Sexual orientation is also a significant factor that increases the risk of victimization. Similarly, Rivers (2016) documented the rising incidence of homophobic and transphobic bullying at university and argues strongly for universities to be more active in promoting tolerance and inclusion on campus. It is worth noting that relationships and sexual orientation probably play a huge role in bullying among university students due to their age and the fact that the majority of students are away from home and experiencing different forms of relationships for the first time. Faucher et al. (2014) actually found that same sex cyberbullying was more common at university level than at school. Nonetheless, the research is just not there yet to make firm conclusions.

Finally, cyberbullying is not only an adolescent issue. Although its existence has been proven, studies of cyberbullying among undergraduate students have not been fully developed. This particular population needs special attention in future research.

The results of this study indicate that cyberbullying has an influence on the academic, social, and emotional development of undergraduate students.

In the academic field, findings revealed a statistically significant correlation between cyberbullying perpetrated by email and academic problems. Relationships between academic problems and cyberbullying perpetrated by other media were not found. This suggests that cyberbullying through instant messaging, chat room, text messaging, and social networking sites, have not influenced academic abilities, motivation to learn, and general satisfaction with the academic environment. However, cyberbullying perpetrated by email has an influence on academics, perhaps because of the high use of this medium among undergraduate students.

With regard to career problems, correlations with cyberbullying were not found. This indicates that cyberbullying has no influence on career problems, perhaps because these kinds of problems are related to future career inspirations, and not to the day-to-day aspects of a student's life.

In the social field, it was found that interpersonal problems such as integration into the social environment, forming a support network, and managing new social freedoms, were related to cyberbullying via social networking sites. This finding is consistent with the high use of social networking sites, the purpose of the medium, and the reported episodes of cyberbullying in that medium.

Family problems were also related to cyberbullying perpetrated by all kinds of media. This may indicate that as cyberbullying through the use of email, instant messaging, chat rooms, text messaging, and social networking sites increases, so do family problems. This could be due to the strong influence that cyberbullying generates in all the frameworks of students, including their families.

Finally, in the emotional field, correlations between cyberbullying perpetrated by all kinds of media and substance abuse were found. This may indicate that as cyberbullying through the use of email, instant messaging, chat rooms, text messaging, and social networking sites increases, so does substance abuse. This is important because cyberbullying may be another risk factor for increasing the probability of substance abuse.

Depression and suicidal ideation were significantly related to the same media – email instant messaging and chat cyberbullying – suggesting that depression may lead to a decision of suicide as a solution to the problem. Previous findings support the above that being an undergraduate student – a victim of cyberbullying emerges as an additional risk factor for the development of depressive symptoms ( Myers and Cowie, 2017 ). Also Selkie et al. (2015) reported among 265 female college students, being engaged in cyberbullying as bullies, victims, or both led to higher rates of depression and alcohol use.

Relationships between anxiety and cyberbullying, through all the media, were not found although Schenk and Fremouw (2012) found that college student victims of cyberbullying scored higher than matched controls on measures of depression, anxiety, phobic anxiety, and paranoia. This may be because it was demonstrated that anxiety is one of the most common reported mental health problems in all undergraduate students, cyberbullied or not.

Self-esteem problems were significantly related to cyberbullying via instant messaging, social networking sites, and text messaging. This may suggest that as cyberbullying through instant messaging, social networking sites, and text messaging increases, so do self-esteem problems. This is an important finding, given that these were the media with more reported episodes of cyberbullying.

5. Conclusions

This findings of this study revealed that cyberbullying exists in colleges and universities, and it has an influence on the academic, social, and emotional development of undergraduate students.

It was shown that cyberbullying is perpetrated through multiple electronic media such as email, instant messaging, chat rooms, text messaging, and social networking sites. Also, it was demonstrated that students exposed to cyberbullying experience academic problems, interpersonal problems, family problems, depression, substance abuse, suicidal ideation, and self-esteem problems.

Students have exhibited clear preferences towards using the Internet as a medium and utilize it with great frequency in their everyday lives. As more and more aspects of students' lives are conducted online, and with the knowledge that excessive use may have consequences for them, it is important to study the phenomenon of cyberbullying more deeply.

Because college students are preparing to enter the workforce, and several studies have indicated a trend of cyberbullying behavior and victimization throughout a person's lifetime ( Watts et al., 2017 ), the concern is these young adults are bringing these attitudes into the workplace.

Finally, cyberbullying is not only an adolescent issue. Given that studies of cyberbullying among undergraduate students are not fully developed, although existence of the phenomenon is proven, we conclude that the college and university population needs special attention in future areas of research. As it has been indicated by Peled et al. (2012) that firm policy in regard to academic cheating reduces its occurrence, colleges should draw clear guidelines to deal with the problem of cyberbullying, part of it should be a safe and if needed anonymous report system as well as clear punishing policy for perpetrators.

As there's very little research on the effect of cyberbullying on undergraduates students, especially in light of the availability of hand held devices (mainly smartphones) and the dependence on the internet for basically every and any activity, the additional data provided in this research adds to the understanding of the effect of cyberbullying on the welfare of undergraduate students.

Declarations

Author contribution statement.

Yehuda Peled: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

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ORIGINAL RESEARCH article

Study of the influencing factors of cyberbullying among chinese college students incorporated with digital citizenship: from the perspective of individual students.

• School of Educational Information Technology, South China Normal University, Guangzhou, China

Understanding the influencing factors of cyberbullying is key to effectively curbing cyberbullying. Among the various factors, this study focused on the personal level of individual students and categorized the influencing factors of cyberbullying among college students into five sublevels, i.e., background, Internet use and social network habits, personality, emotion, and literacy related to digital citizenship. Then a questionnaire survey was applied to 947 Chinese college students. The results show that cyberbullying among Chinese college students are generally at a low level. There are many factors influence cyberbullying. Specifically, at the personal background level, gender has a significant impact on cyberbullying and being cyberbullied. In terms of personal Internet use and social network habits, students’ average daily online time has no significant correlation with cyberbullying and being cyberbullied; however, the proportion of online non-learning time has a significantly positive correlation with cyberbullying, and the proportion of online learning/work time has a significant impact on being cyberbullied. At the personality level, the Big Five personality traits have varying degrees of correlation with and influence on cyberbullying and being cyberbullied. At the personal emotions level, students’ life satisfaction has a significantly negative correlation with cyberbullying and being cyberbullied while it only has a significant impact on being cyberbullied; the personal stress and empathetic concern aspects of empathy have a significantly positive correlation with cyberbullying and being cyberbullied among female students. At the literacy related to digital citizenship level, students’ understanding of and compliance with Internet etiquette have significantly negative impacts on cyberbullying; the ability to communicate and collaborate online and Internet addiction have significantly positive impacts on cyberbullying and being cyberbullied; the understanding of and compliance with relevant digital laws and regulations have significantly negative correlations with cyberbullying and being cyberbullied. Overall, college students’ digital citizenship level has a significantly negative correlation with cyberbullying but no significant correlation with being cyberbullied. Finally, analysis and suggestions were provided according to these statistical results and the effects of these factors on cyberbullying and being cyberbullied among college students, so as to help solve this problem and provide a new perspective for research in this field.

Introduction

Currently, the Internet has penetrated into all aspects of people’s lives. While providing various conveniences, the Internet has also caused a series of social problems such as spam, Internet addiction, and Internet crime. In recent years, cyberbullying, as a representative of abnormal Internet behaviors, has been prominent in many countries (e.g., the United States, Japan, and Australia), in which countermeasures and preventive measures against cyberbullying have been formulated. Instagram, a well-known social platform, began developing automated cyberbullying filtering tools in 2019. In his book, Ivester (2011) maintains that social media is evolving into an alternative mechanism of communication and contact among people and is continuously in fashion among students, greatly increasing the likelihood of cyberbullying on college campuses ( Washington, 2015 ). This is especially true for Chinese college students. Statistical results show that Internet users aged 10–19 and 20–29 accounted for 14.8 and 19.9% of the whole population in China ( China Internet Network Information Center, 2020 ), and 87.8% of college students love to use social communication applications ( iiMedia Research, 2018 ). Partly because Chinese college students have much free time and are curious about the outside world, which, coupled with the absence of parental supervision, has led to college students being the major Internet users among the adolescent population. However, negative information is becoming more common in digital society. Being inexperienced and immature emotionally and intellectually, without having established the “Three Views” 1 , college students are more inclined to be inadvertently involved in cyberbullying (as a perpetrator or a victim) and exert adverse influences on others and society as a whole.

Under this circumstance, it is necessary to know the current situation of cyberbullying among Chinese college students and reveal potential influencing factors to help cub it effectively. However, the literature survey of the China National Knowledge Infrastructure (CNKI) indicated that as of July 2020, there has been only 13 publications on “cyberbullying” and “influencing factors,” all published after 2015, accounting for 3.8% of all 337 articles with the subject “cyberbullying.” The lack of studies on the influencing factors of cyberbullying makes relevant prevention strategies and containment mechanisms ineffective and impertinent. Additionally, in terms of research objects, most of the previous studies in China have focused on cyberbullying among youth, with only 32 articles on college students and none on influencing factors. In fact, college life is the most critical time before an individual enters society and thus a critical period for the formation and establishment of personality, morals, and the “Three Views.” Being deeply involved in the Internet and digital society, college students should be guided to keep away from cyberbullying. Therefore, understanding the influencing factors of cyberbullying among them and developing targeted prevention strategies are very important for effectively addressing the problem. In this regard, based on discovering the current situation of college student cyberbullying in China, this paper examined its influencing factors from the perspective of individual students to provide suggestions for the intervention and prevention of cyberbullying.

Literature Review and Hypotheses

Literature review.

Literature review showed that the existing studies mainly focused on individual students, families, schools, society, and the environment. Specifically, in terms of individual students, Li (2007) , Kowalski et al. (2012b) , Topcu and Erdur-Baker (2012) and many other investigators revealed that cyberbullying is gender related. Hsu and Wang (2010) found that personality traits are predictive of cyberbullying, and Gibb and Devereux (2014) and Goodboy and Martin (2015) showed that the dark personality theory can describe the common characteristics of cyberbullies: self-righteous, ruthless, and aggressive. From the psychological perspective, Sun and Deng (2016) found that both perpetrators and victims of cyberbullying have more negative emotions; Liu and Xu (2019) found that the psychological factors related to cyberbullying include empathy, narcissism, self-esteem, depression, and anxiety; Gini and Pozzoli (2009) and Renati et al. (2012) found that cyberbullying is associated with an individual’s empathy; cyberbullying perpetrators often lack empathy and have emotional difficulties ( Weaver and Lewis, 2012 ; Barlińska et al., 2013 ). Zhao and Wang (2019) demonstrated that college students’ perception of well-being is closely correlated with their Internet usage, and Li (2007) , You (2013) , Hayton (2017) , and Nurlita et al. (2018) showed that the frequencies of Internet use and social media use have an important impact on cyberbullying.

In terms of family factors, Ybarra and Mitchell (2004) found that cyberbullying is closely related to the relationship between family members; Wang et al. (2012) , Bayraktar et al. (2015) , and Elsaesser et al. (2017) confirmed the connection between cyberbullying behavior and a lack of parental support; and Pillay (2012) and Park et al. (2014) found that cyberbullying is associated with individuals’ family socioeconomic status to some extent. In addition, some studies revealed that parental supervision is also a factor affecting cyberbullying ( Ybarra and Mitchell, 2004 ; Chen and Astor, 2012 ; Kowalski et al., 2012a ; Low and Espelage, 2013 ).

Regarding school factors, Bevilacqua et al. (2017) showed that the degree of cyberbullying varies with school type and quality, and organizational/management factors within a school affect students’ behavior; Guarini et al. (2012) found that students’ negative relationship with teachers and low recognition of the school are risk factors for cyberbullying; and Calvete et al. (2010) and Souza et al. (2018) found that cyberbullying is related to school atmosphere and environment. Moreover, school culture ( Monks et al., 2016 ), safety ( Bottino et al., 2015 ) and regulatory measures ( Song, 2015 ), sense of belonging ( Baldry et al., 2015 ; Chen et al., 2016 ), and education and training on mental health and cybersecurity ( Gao, 2018 ; Liang, 2019 ) are also important factors affecting cyberbullying.

With respect to social and environmental factors, Huang and Chou (2010) argued that cyberbullying behaviors, in various countries, are highly dependent on the environment and are affected by the education system, school environment, cultural norms, and interpersonal relationships. Markward et al. (2001) found that various factors, such as herd mentality, traditional bullying influence, and cultural background differences, affect cyberbullying behavior. In addition, workplace stress ( Vranjes et al., 2017 ) and peer factors ( Liu and Xu, 2019 ) are also related to the risk of cyberbullying among youth, which is also affected by the characteristics of the Internet ( Kiesler et al., 1985 ; Holland, 2012 ).

In recent years, digital citizenship education has gradually attracted widespread attention from scholars around the world. With the aim of cultivating qualified digital citizens in the information age, digital citizenship education requires digital citizens to acquire global awareness, legal awareness as well as digital citizenship awareness so that technology is used in a safe, responsible, and ethical way ( Yang et al., 2016 ). However, the rise and spread of cyberbullying are inextricably linked to each digital citizen: current Internet users are mostly digital natives who have acquired the ability to use information technology but still lack the corresponding technical ethics and responsibilities. In other words, the occurrence of many cyberbullying incidents is the outcome of weak cyber legal and moral awareness among these digital natives. That’s exactly the core of digital citizenship education ( Ivester, 2011 ; Zheng et al., 2020 ). Therefore, while providing a new perspective for the study of cyberbullying, digital citizenship education is an important means to control cyberbullying ( Lin, 2017 ; Zheng et al., 2020 ). In this regard, digital citizenship, in conjunction with the relevant digital citizenship education content were investigated in this study to conduct an in-depth examination on the influencing factors of cyberbullying at the personal level.

The above literature review and analysis categorizes the influencing factors of cyberbullying into four levels: (1) Personal level, including gender, age, personality traits, well-being, empathy, length or frequency of Internet uses, social behavior type, and digital citizenship; (2) Family level, including relationship between family members, parental support, family socioeconomic status, and parental supervision; (3) School level, including school type and teaching quality, school management, teacher-student relationship, school climate and environment, school culture, school safety and supervision, and education and training on mental health and Internet security; (4) Social and environmental level, including national education system, cultural norms, community influence (herd mentality), cultural differences, interpersonal (peer) relationship, work pressure, and Internet characteristics.

Among the above-described influencing factors, those at students’ personal level have a direct impact on students’ cyberbullying behavior, and are the basis for investigating and analyzing the influencing factors of cyberbullying at other levels. So it sounds reasonable to start from the perspective of individual students. Nevertheless, previous studies have focused on students’ personal variables (e.g., gender, age or grade, and personality traits) and Internet usage (e.g., hours online and frequency per day), without considering students’ literacy related to digital citizenship. Therefore, in this study, personal influencing factors of cyberbullying among college students were categorized into five sublevels, i.e., (1) Background (including gender, age, and time to start using the Internet), (2) Internet use and social network habits (including average daily time online, the proportion of online learning/non-learning time, the number of online social communities joined, and social behavior type), (3) Personality [including five personality traits, i.e., openness, neuroticism, extroversion, agreeableness, and conscientiousness ( Howard et al., 1996 )], (4) Emotion (including subjective well-being and empathy), and (5) Literacy related to digital citizenship [including digital identity and dignity, digital citizenship awareness and accountability, the understanding of and compliance with Internet etiquette, digital communication and collaboration capabilities, degree of Internet addiction, and the understanding of and compliance with relevant laws and regulations ( Ribble, 2015 ; Zheng et al., 2020 )].

In order to explore the impact of personal factors on cyberbullying, this study inspected these variables one by one, as illustrated in the following hypotheses:

Hypothesis 1: The degree of cyberbullying among Chinese college students is affected by students’ personal background. Specifically, college students of different genders and with different ages to start using the Internet have significantly different scores regarding the degree of cyberbullying. This hypothesis corresponds to exploring the influence of individual background (sublevel 1) on cyberbullying.

Hypothesis 2: The degree of cyberbullying among Chinese college students is affected by students’ use of the Internet and social network habits. Specifically, cyberbullying among college students has a significantly positive correlation with students’ length of time online and the proportion of online non-learning time, and students who show different social network habits differ significantly regarding cyberbullying. This hypothesis corresponds to exploring the influence of individual Internet use and social network habits (sublevel 2) on cyberbullying.

Hypothesis 3: The degree of cyberbullying among Chinese college students is affected by students’ personality traits. Specifically, the degree of cyberbullying has a significantly positive correlation with neuroticism and openness but a significantly negative correlation with extroversion, agreeableness, and conscientiousness. This hypothesis corresponds to exploring the influence of individual personality (sublevel 3) on cyberbullying.

Hypothesis 4: The degree of cyberbullying among Chinese college students is affected by students’ emotions. Specifically, the degree of cyberbullying has a significantly negative correlation with their life satisfaction and empathy. This hypothesis corresponds to exploring the influence of individual emotion (sublevel 4) on cyberbullying.

Hypothesis 5: The degree of cyberbullying among Chinese college students is affected by students’ level of digital citizenship and has a significantly positive correlation with their degree of Internet addiction and a significantly negative correlation with their digital identity and dignity, digital citizenship awareness and accountability, understanding of and compliance with Internet etiquette, digital communication and collaboration skills, and understanding of and compliance with relevant laws and regulations. This hypothesis corresponds to exploring the influence of individual literacy related to digital citizenship (sublevel 5) on cyberbullying.

Research Design and Implementation

Research subjects and process.

In this study, through random sampling, college students and graduate students of different cities in China took part in this online survey anonymously. Specifically, a text message and a questionnaire link were first sent to the students of South China Normal University randomly via social communication software (e.g., WeChat groups, QQ groups), then they were asked to forward the message to their classmates or ex-classmates (e.g., their high school classmates but now learning in different universities). Gradually the survey was spread out in a non-linear way. Each student was asked to provide responses to the survey within a specified time. Since ethical review and approval is not required for the study on human participants in accordance with the local legislation and institutional requirements of China, an instruction about the purpose of this survey and how the data will be used later was provided at the beginning of the questionnaire, so that the participants had a total understanding of the survey. Eventually a total of 1,188 online questionnaires were collected, of which 947 were valid, for an effective rate of 79.7%.

Questionnaire Design

The questionnaire consisted of five parts:

(1) Questions regarding students’ personal background, Internet use and social network habits, including students’ gender, age, time to start using the Internet, average daily time online, proportion of online learning/non-learning time, number of online social communities joined, and types of social behavior, in a total of seven items. In China, students mainly use popular social networking platforms such as Sina Microblog, Tencent Microblog, QQ Groups, WeChat Groups, Tianya social community, Zhihu social community, and the like. Of course, some of them may use Facebook, Instagram, Twitter or similar platforms. They will all be considered by default when it comes to statistical analysis of one’s online social networking experience. This instruction was also provided in the questionnaire to make students clearly understand.

(2) A personality questionnaire, i.e., The Big Five Personality Test, compiled by Howard et al. (1996) and used to measure the personality inclination of college students, in a total of 25 items. This questionnaire has been widely used in many studies, with high reliability and validity [0.736 < Cronbach’s α < 0.904 and KMO = 0.806 ( Hee, 2014 )].

(3) Emotion questionnaires to analyze subjective well-being and empathy, measured, respectively, with the Life Satisfaction Scale developed by Diener et al. (1985) and the Interpersonal Reactivity Index scale compiled by Davis (1980) . Both scales have been tested and have good reliability and validity [Cronbach’s α = 0.86 and KMO = 0.84 for the Life Satisfaction Scale ( Silva et al., 2015 ) and Cronbach’s α = 0.75 and KMO = 0.833 for the Interpersonal Reactivity Index Scale ( Zhang et al., 2010 )]. There are totally 27 items in this part.

(4) A digital citizenship questionnaire that measures, using 35 questions answered with a five-point Likert scale, digital identity and dignity, digital citizenship awareness and accountability, the understanding of and compliance with Internet etiquette, digital communication and collaboration capabilities, degree of Internet addiction, and the understanding of and compliance with relevant laws and regulations. Among them, the Internet Addiction Scale was derived from the simplified version of Young’s Internet Addiction Test with high reliability and validity [Cronbach’s α = 0.848 and KMO = 0.924 ( Pawlikowski et al., 2013 )], the scales for the rest variables were modified from or developed based on, respectively, the self-esteem scale for the assessment of adolescents’ self-worth and self-acceptance by Rosenberg (1965) , the digital citizenship scale ( Al-Zahrani, 2015 ), the monograph on digital citizenship education by Ribble (2015) and the content decomposition of digital citizenship by Zheng et al. (2020) . The whole questionnaire in this part was tested in this study and found to have good reliability and validity (Cronbach’s α = 0.789 and KMO = 0.671).

(5) A cyberbullying questionnaire derived from Topcu and Erdur-Baker’s (2010) Cyberbullying Scale that measures the degree to which college students act as perpetrators or victims of cyberbullying. The questionnaire uses 14 items for 14 cyberbullying behaviors, with another 14 for being cyberbullied behaviors. So totally there are 28 items, with high reliability and validity [Cronbach’s α = 0.818 and KMO = 0.873 ( Murwani, 2019 )]. In order to get a better understanding of how personal factors have influence on cyberbullying among college students, the questionnaire limits cyberbullying experience (commit or suffer) to be within the recent one or 2 years. In other words, students will be asked if they have had these experiences (14 cyberbullying behaviors and 14 being cyberbullied behaviors) recently.

Descriptive Statistics

Figure 1 shows the geographical distribution of the respondents. It’s clear that the participants were mostly from big and modern cities of China, such as Guangzhou, Beijing, Zhengzhou, and Shenzhen, where Internet access is easier and faster, and social network application is more popular as well.

Figure 1. Geographical distribution of the respondents.

The respondents’ demographic information, Internet use and social network habits are shown in Table 1 . They were young people with an average age of 20.71 (SD = 2.234). Two-thirds of them were female, indicating that in China girls showed more willingness to help others academically than boys. Over one-half of the respondents (53.9%) started their online experience prior to middle school; on average, 45.2% of the students spent 3–6 h online daily, and one-third of the students spent over 6 h online daily. College students spent an average of 66.63% of time online on social networks and doing other activities unrelated to learning. When using social networks, 54.1% of the students joined at least three online communities while 65.3% did not participate in any online discussions.

Table 1. Statistics for college students’ background information, Internet use and social network habits.

Current Situation of Cyberbullying Among College Students

According to Topcu and Erdur-Baker’s (2010) Cyberbullying Scale, the total score ranges from 14 to 56 points. The higher the score is, the higher the level of cyberbullying or being cyberbullied. As shown in Table 2 , overall, the average cyberbullying score for the 947 college students was 17.14, indicating a low cyberbullying level; the average score for being a victim of cyberbullying was 19.93, which is low but higher than that for cyberbullying. Among the 14 cyberbullying behaviors, “Making fun of comments in online forums” appeared most frequently in both situations ( M = 2.20 and SD = 1.319 for cyberbullying, and M = 1.88 and SD = 1.201 for being cyberbullied), while “Excluding others by blocking or moving their comments” ( M = 1.87 and SD = 1.077) and “Stealing email access (usernames and passwords) and blocking true owner’s access” ( M = 1.84 and SD = 0.999) ranked second in frequently appeared forms of cyberbullying and being cyberbullied, respectively.

Table 2. Statistics for cyberbullying among college students.

According to Brack and Caltabiano (2014) , when committing (suffering) any of the 14 behaviors two or more times, an individual can be deemed as a cyberbullying perpetrator (victim). Those with a dual identity of cyberbullying perpetrator and victim must meet the standards for a cyberbullying perpetrator and victim simultaneously while those who are deemed as non-participants either never committed or experienced any cyberbullying or experienced one incident, at most, of cyberbullying or being cyberbullied. According to these criteria, the proportion of college students who are cyberbullying victims (58.6%) is a bit higher than that of students who are cyberbullying perpetrators (51.2%), and more than 40% of them have a dual identity as both a victim and perpetrator (41.6%); approximately one-third of the students have never experienced cyberbullying (31.8%). Though results show high percentages of cyberbullying and being cyberbullied (over 50%), the most frequent form of both cyberbullying and being cyberbullied is making fun of comments on forums (it’s very common in this era), and the average scores are 17.14 and 19.93 (out of 56), respectively, with SD less than 2. Therefore, it is believed that cyberbullying is generally at a relatively low level among Chinese college students, so is being cyberbullied.

Influencing Factors of Cyberbullying Among College Students

Effect of personal background on cyberbullying.

Gender differences in cyberbullying were examined through the two independent samples non-parametric test. As shown in Table 3 , the progressive significance values are lower than 0.05, indicating that gender differences in cyberbullying is significant. The scores for male students are significantly higher than those for female students, indicating that male students are more likely to cyberbully others or be cyberbullied by others than are female students.

Table 3. Significance tests for gender differences in cyberbullying.

Time to start using the Internet

The relationship between the time to start using the Internet and cyberbullying was examined through the two independent samples non-parametric test. As shown in Table 4 , the progressive significance values are lower than 0.05, indicating that students with different ages to start using the Internet differ significantly regarding cyberbullying.

Table 4. Significance tests for time to start using the Internet in cyberbullying.

Effect of Internet Use and Social Network Habits on Cyberbullying

Internet use.

The correlation between the degree of cyberbullying and daily average time online or daily average non-learning time online was analyzed using the Spearman correlation method. As shown in Table 5 , daily average time online is not significantly correlated to cyberbullying while daily non-learning time online is significantly positively correlated with the degree of cyberbullying but is not significantly correlated with the degree of being cyberbullied.

Table 5. Correlation between Internet use and cyberbullying.

Social network behavior

The effect of social behavior type on the degrees of cyberbullying and being cyberbullied was analyzed through variance analysis. As shown in Table 6 , the significance values are all lower than 0.05, indicating that different social behaviors have significant effects on cyberbullying among college students.

Table 6. Variance analysis results for the effect of social behavior type on cyberbullying.

Effect of Personality Traits on Cyberbullying

The relationship between the personality traits of college students and cyberbullying behavior was examined through the Big Five Personality Test and Spearman correlation analysis. As shown in Table 7 , the degree of cyberbullying is significantly positively correlated with openness and significantly negatively correlated with neuroticism, agreeableness and conscientiousness. The degree of being cyberbullied is significantly positively correlated with openness, and significantly negatively correlated with neuroticism and conscientiousness.

Table 7. Correlation between Big Five personality traits and cyberbullying.

Effect of Emotions on Cyberbullying

Life satisfaction.

The results of the Spearman correlation between life satisfaction and cyberbullying/being cyberbullied are shown in Table 8 , indicating that students’ life satisfaction is negatively correlated with the degree of cyberbullying as well as with the degree of being cyberbullied.

Table 8. Correlation between life satisfaction and cyberbullying.

Given the gender differences in empathy, the samples were grouped based on two genders, and Spearman correlation between empathy and cyberbullying was conducted for the two groups, respectively. As shown in Table 9 , the correlation between each of the empathy variables and cyberbullying (or being cyberbullied) is non-significant in the male student group while the personal distress and empathetic concern variables of empathy are significantly positively correlated with both cyberbullying and being cyberbullied in the female student group.

Table 9. Correlation between empathy and cyberbullying.

Effect of Digital Citizenship on Cyberbullying

The effect of digital citizenship on cyberbullying among college students was examined through the Spearman correlation of cyberbullying with students’ digital identity and dignity, digital citizenship awareness and accountability, understanding of and compliance with Internet etiquette, digital communication and collaboration capabilities, and understanding of and compliance with relevant laws and regulations. As shown in Table 10 , the average scores for all variables related to college students’ digital citizenship (except Internet addiction) are higher than 10; that for students’ understanding of and compliance with relevant laws and regulations is the highest, and that for students’ digital communication and collaboration capabilities is the lowest. The correlation analysis results showed that the degrees of cyberbullying and being cyberbullied are significantly positively correlated with students’ digital communication and collaboration capabilities, and are significantly negatively correlated with students’ understanding of and compliance with relevant laws and regulations; whereas only the degree of cyberbullying is significantly negatively correlated with students’ understanding of and compliance with Internet etiquette. In general, students’ level of digital citizenship is significantly negatively correlated with the degree of cyberbullying but is not significantly correlated with the degree of being cyberbullied.

Table 10. Statistics for students’ digital citizenship and correlations between students’ digital citizenship and cyberbullying.

In order to reveal the relationship between Internet addiction and cyberbullying, the Internet addiction status of Chinese college students was first analyzed, then followed by the correlation between Internet addiction and cyberbullying/being cyberbullied through Pearson correlation analysis. For the Internet Addiction Scale, the higher the score is, the higher the degree of Internet addiction; a score above 40 indicates an Internet addiction. As shown in Tables 11 , 12 , 19.3% of the students are addicted to the Internet, and the students’ Internet addiction is significantly positively correlated with the degree of cyberbullying or being cyberbullied, indicating that the higher the degree of a student’s Internet addiction, the more likely that student is to commit cyberbullying or be cyberbullied.

Table 11. Internet addiction among college students.

Table 12. Correlation between Internet addiction and cyberbullying among college students.

Multivariate Regression Analysis of Influencing Factors of Cyberbullying

To further examine the joint effects of these personal factors on cyberbullying among college students, multivariate regression analyses were conducted using the above variables as independent variables and the degrees of cyberbullying and being cyberbullied as dependent variables; the samples were grouped based on social behavior type, with the socially active group as the reference group and students who do not participate in discussions (accounting for 65.3% of the total sample) as an example in the analysis.

As shown in Table 13 , after excluding several non-significant variables based on the F -test, nine predictors remained in the regression equation for cyberbullying factors, each having a tolerance greater than 0.4 and a VIF value below 5, indicating that these nine predictors retained in the regression equation do not have a multicollinearity problem. The significance of the F value (sig.) is lower than 0.001, indicating that these predictors have a significant linear relationship with the degree of cyberbullying. Specifically, at the personal background level, gender has a significant impact on the degree of cyberbullying. At the Internet use and social network habits level, social behavior type and the number of online communities joined have significant impacts on the degree of cyberbullying. At the personality trait level, only conscientiousness has a significantly positive impact on the degree of cyberbullying, while other traits were eliminated in the stepwise linear regression, indicating that other aspects of the Big Five personality traits have no significant linear relationships with the degree of cyberbullying. At the digital citizenship level, Internet addiction, digital communication and collaboration capabilities, and digital citizenship awareness and accountability have significantly positive impacts on the degree of cyberbullying, while students’ understanding of and compliance with Internet etiquette has a significantly negative impact on the degree of cyberbullying.

Table 13. Results of the multivariate regression analysis of factors influencing the degree of cyberbullying in students who do not participate in online discussions.

In the stepwise multivariate regression equation for factors influencing the degree of being cyberbullied, ten predictors remained in the equation, each having a tolerance greater than 0.4 and a VIF value below 5, showing no multicollinearity problem between the variables. The significance of the F value (sig.) is lower than 0.001, indicating that these predictors have a significant linear relationship with the degree of being cyberbullied. As shown in Table 14 , at the personal background level, gender has a significant impact on the degree of being cyberbullied. At the Internet use and social network habits level, the number of online communities joined and online learning/work time has significant impacts on the degree of being cyberbullied. At the emotion level, life satisfaction has a significantly negative impact on the degree of being cyberbullied. At the personality level, conscientiousness has a significantly positive impact on the degree of being cyberbullied. At the digital citizenship level, the degree of Internet addiction, digital communication and collaboration capabilities, and digital identity and dignity have significantly positive impacts on the degree of being cyberbullied.

Table 14. Results of the multivariate regression analysis of factors influencing the degree of being cyberbullied in students who do not participate in online discussions.

This study randomly selected 947 college students in China as survey subjects to investigate the current situation of cyberbullying and conducted an in-depth analysis on the impact of students’ personal background, Internet use and social network habits, personality traits, emotions and literacy related to digital citizenship on the degrees of cyberbullying and being cyberbullied. Further analysis and discussions are presented as follows.

Effect of Student’s Personal Background on Cyberbullying Among College Students

Regarding gender, the male students’ total scores for cyberbullying and being cyberbullied were significantly higher than those for the female students, indicating that males are more likely to cyberbully others or be cyberbullied by others than are females, which is consistent with the results of some previous studies ( Calvete et al., 2010 ; Huang and Chou, 2010 ; Ozden and Icellioglu, 2014 ; Safaria, 2016 ; Beyazit et al., 2017 ) but contrary to those of others ( Smith et al., 2008 ; Ortega et al., 2009 ; Sourander et al., 2010 ; Giménez-Gualdo et al., 2015 ), likely because in different countries, regions or schools, the understanding and identification of cyberbullying differ, and there are many measurement scales in this field, in which certain behaviors deemed as cyberbullying are controversial. On the other hand, the Internet use awareness and online behavior of different survey subjects vary and are closely related to their education and experience from childhood onward. In addition, the methods for cyberbullying commonly used by male and female students also differ ( Slonje and Smith, 2008 ; Wong et al., 2014 ). Therefore, there are three different conclusions regarding the effect of gender on cyberbullying: more males commit cyberbullying, more females commit cyberbullying, and both genders commit cyberbullying equally ( Hinduja and Patchin, 2008 ; Guarini et al., 2012 ; Pillay, 2012 ; Gibb and Devereux, 2014 ). Therefore, this remains an open question. In regard to the participants in this study, male students had stronger personalities and were more volatile than female students and thus more inclined to have conflicts with others, leading to cyberbullying ( Zhu et al., 2016 ).

In addition, time to start using the Internet is significantly correlated with students’ cyberbullying or being cyberbullied, but the two showed no regression relationship, which is likely related to the students’ Internet awareness, skills and experience. Early exposure to the Internet allows students to have stronger Internet use awareness, more Internet skills and richer Internet experience, making these students more adept to cyberspace and prone to bully newbies intentionally or unintentionally. On the other hand, the participation of college students have been growing in various online forums and communities, which, in the early stage, were relatively open and laden with all kinds of information for which effective supervision and reporting mechanisms lacked; therefore, the longer a student has had access to the Internet (i.e., the earlier the time to start using the Internet), the more cyberbullying the student would have suffered.

These results confirm Hypothesis 1 listed in section “Hypotheses,” suggesting that in cyberbullying intervention and governance processes, it is necessary to pay close attention to the social behavior of male students, especially those with an early age to start using the Internet.

Effect of Students’ Internet Use and Social Network Habits on Cyberbullying

Regarding average daily time online, though daily time online is not correlated with cyberbullying, daily non-learning time online is significantly positively correlated (but no regression relationship) with the degree of cyberbullying, and the proportion of learning/work time online has a significant regression relationship with the degree of being cyberbullied. In other words, the longer the daily non-learning time a student spends online, the more likely he/she is to become a perpetrator of cyberbullying; the longer the daily learning/work time a student spends online, the more likely he/she is to become a cyberbullying victim. In previous studies, time online was not divided into learning and non-learning hours, but cyberbullying usually occurs in non-learning situations, such as social interactions, games, and entertainment; therefore, the conclusions of this study can be considered consistent with those of previous studies ( Hinduja and Patchin, 2008 ; Sticca et al., 2013 ; Zhu et al., 2016 ). This result indicates that students with different purposes and uses for the Internet have different effects on others. Lingering on social network and leisure sites makes these students more susceptible to disinformation or misinformation, prompting them to use offensive and threatening language, send tasteless pictures that violate others’ privacy, or place blame on teammates when playing online games, thereby cyberbullying others.

In terms of social behavior, different types of online behavior are significantly correlated with cyberbullying or being cyberbullied. Regarding average cyberbullying scores, students who are self-expressive and participate in discussions are more inclined to cyberbully others. Students with these two behaviors belong to active social network types and are prone to voice their views and follow suit when participating in debates; when questioned or refuted or when questioning or debating others, these students are liable to have conflict with others and even engage in cyber-stalking and violate the privacy of others, thereby cyberbullying others. Regarding average scores for being cyberbullied, students who are self-expressive had significantly higher scores than those of students with other behaviors, indicating that those who like to voice their opinions and ideas online are more likely to be cyberbullied, especially when their opinions or views are not accepted by others.

These results mostly confirm Hypothesis 2, suggesting that in the cyberbullying intervention and governance processes, it is necessary to strictly control the non-learning/work hours of college students and treat those with different social behaviors differently, so that targeted measures can be taken to prevent cyberbullying.

Effect of College Students’ Personality on Cyberbullying

First, the personality trait “openness” is significantly positively correlated with cyberbullying and being cyberbullied, i.e., college students with a high level of openness are more likely to cyberbully others or be cyberbullied, which is consistent ( Hsu and Wang, 2010 ; You, 2013 ; Peluchette et al., 2015 ) or partially consistent ( Celik et al., 2012 ) with the results reported in other studies, indicating that these students are curious about the outside world, fond of trying new things and thus more prone to be involved in Internet events or comment on others’ opinions, leading to online conflicts. Moreover, students with a high degree of openness have more Internet interactions on a wider range of topics and thus are more prone to be exposed to misinformation or disinformation while fully exposing their own information on the Internet, making them more susceptible to cyberbullying.

Second, neuroticism and conscientiousness are significantly negatively correlated with students’ cyberbullying and being cyberbullied, i.e., college students with strong neuroticism and those who are conscientious are less likely to cyberbully others or be cyberbullied, which is consistent ( Festl and Quandt, 2013 ; You, 2013 ) or partially consistent ( Celik et al., 2012 ) with the results of other studies, indicating that college students who can more effectively balance emotions, such as anxiety and hostility, maintain emotional stability and are more organized, with a greater sense of responsibility and self-control, are less likely to exhibit cyberbullying behaviors and be cyberbullied.

Third, agreeableness is significantly negatively correlated with cyberbullying, i.e., college students with a high level of agreeableness are less likely to cyberbully others, which is consistent with the result of a previous study ( Celik et al., 2012 ). Students with a high level of agreeableness give priority to others, get along with others well and interact with others more harmoniously and thus are popular among others; they are often friendly and considerate and rarely bully others online. However, agreeableness is not significantly correlated with being cyberbullied, which is inconsistent with the findings of other studies ( Celik et al., 2012 ; You, 2013 ; Semerci, 2017 ), likely because students with a high level of agreeableness are always ready to help others and friendly to others; therefore, they are less likely to become a target of bullying by others.

These results partly confirm Hypothesis 3, suggesting that in cyberbullying intervention and governance processes, it is necessary to first determine a student’s personality traits and propose specific measures for college students with different personalities, and if conditions permit, big data and data mining techniques can be employed to determine their personality traits and predict cyberbullying behavior more accurately.

Effect of Students’ Emotions on Cyberbullying

Students’ life satisfaction is significantly negatively correlated with cyberbullying and being cyberbullied and has a significant impact on being cyberbullied, indicating that the higher the level of students’ life satisfaction, the less likely the students will bully others or be bullied, which is consistent with the results of a previous study ( Zhu et al., 2016 ) but different from those of another study ( Pillay, 2012 ); this inconsistency is likely due to the differences between college students in China and other countries when perceiving happiness and the aspects different assessment scales focusing on.

In terms of empathy, personal stress, and empathic concern are significantly positively correlated with cyberbullying and being cyberbullied among female students; however, this correlation is absent among male students, indicating that gender plays a mediating role in the effect of empathy on cyberbullying, which is consistent with the results of some early studies ( Topcu and Erdur-Baker, 2012 ; Baldry et al., 2015 ; Del Rey et al., 2016 ) but contrary to those of other studies ( Renati et al., 2012 ; Brewer and Kerslake, 2015 ; Peterson and Densley, 2017 ). These inconsistent results are likely due to the differences in the active areas of male and female brains regarding displaying empathy ( Schulte-Rüther et al., 2008 ); the emotional awareness of females is stronger, making them more inclined to sympathize and emphasize with others’ stress and perceive and understand others by taking the position of others, ultimately resulting in “being involved too deeply to be able to disengage” and thus being more susceptible to being cyberbullied. They may also turn empathy into vengeance and condemn those who they consider perpetrators through inappropriate ways, such as breeching privacy, verbal abuse and insults, turning a self-righteous act into cyberbullying.

These results mostly confirm Hypothesis 4, suggesting that in cyberbullying intervention and governance processes, it is necessary to pay attention to students’ life satisfaction as well as the emotional stability of female students and integrate Internet supervision mechanism to dynamically display students’ emotional data so that cyberbullying behaviors can be accurately monitored and prevented.

Effect of College Students’ Literacy Related to Digital Citizenship on Cyberbullying

In the first place, students’ understanding of and compliance with Internet etiquette has a significantly negative impact on cyberbullying, indicating that college students’ understanding and recognition of digital ethics, such as Internet etiquette and technical etiquette, actively practicing positive ethics and codes of conduct in the digital space, and regulating their behaviors in digital society through etiquette in real society can allow the vast majority of people to enjoy the convenience and joy brought by digital technology and effectively reduce the probability of cyberbullying. Therefore, it is advisable to fully acknowledge the advantages of school, family and community education, improve college students’ awareness of Internet etiquette, expand the Internet etiquette knowledge base, and cultivate relevant operational skills and norms in all life aspects through supplementation with various lifelong education models, coupled with related online and offline promotion to effectively improve college students’ understanding of and compliance with Internet etiquette, so as to effectively prevent cyberbullying.

In the second place, college students’ digital communication and collaboration capabilities have a significantly positive impact on cyberbullying and being cyberbullied. Cyberbullying mainly manifests as verbal abuse with insulting and offensive language, or privacy disclosures. The results showed that college students who are more able to skillfully select appropriate means of communication and collaboration with others online are more adept at mastering a variety of communication means and skills; once their emotions are out of control, they are prone to voice some inappropriate opinions or disclose the privacy of others, thus resulting in cyberbullying. On the other hand, college students with digital communication and collaboration capabilities are more likely to join more online communities, have richer online social networks or collaboration experience and spend longer amounts of time online, increasing their likelihood of being cyberbullied. Therefore, it is necessary to supervise and control the time and space of communication and collaboration; in particular, schools and families should pay special attention to those students with strong digital communication and collaboration capabilities, and when necessary, administrative and technical means should be used to strictly manage their social networks and collaborations to prevent cyberbullying incidents.

In the third place, college students’ degree of Internet addiction has a significantly positive impact on cyberbullying and being cyberbullied, indicating that students who are more addicted to the Internet are more dependent on the Internet, resulting in higher probabilities of cyberbullying others and being cyberbullied, which is consistent with the results of earlier studies ( Floros et al., 2013 ; Chang et al., 2015 ; Hou, 2017 ). College students are not fully mature mentally, are profoundly affected by emotions and have not yet formed the “Three Views”; when lingering online for too long, they are vulnerable to mental, emotional, and moral erosion through misinformation and disinformation on the Internet and thus develop negative behaviors, intentionally or unintentionally cyberbullying others or being cyberbullied by others. Therefore, it is necessary to pay attention to their digital health and wellness; in schools and families, when necessary, administrative and technical means should be utilized to strictly monitor and control their online time, establish an early warning mechanism for excessive Internet use and take various anti-addiction measures to prevent Internet addiction, encouraging them to find a balance between online and offline life.

In the fourth place, college students’ understanding of and compliance with relevant digital laws and regulations are significantly negatively correlated with cyberbullying and being cyberbullied, indicating that the understanding of and compliance with laws and policies on technology use, especially rules related to Internet ethics, digital rights and responsibilities in the form of legal regulations (e.g., copyright protection for intellectual property), are particularly important for college students’ online behavior. These laws and regulations restrict and regulate the online behaviors, allowing them to clearly know which behaviors are illegal in digital society so that they can strictly abide by them, which helps to significantly reduce the probability of cyberbullying and being cyberbullied. Therefore, it is necessary to strengthen college students’ knowledge and understanding of relevant digital laws and regulations through education at schools, in families and in the community, guiding them to use information technology legally and regulating their words and actions online to avoid cyberbullying and being cyberbullied.

In general, the level of digital citizenship is significantly negatively correlated with the degree of cyberbullying but is not significantly correlated with the degree of being cyberbullied, indicating that improving college students’ digital citizenship level can help significantly reduce their likelihood of cyberbullying others, which mostly confirms Hypothesis 5. Digital citizenship is about the values, necessary qualities, key abilities, and behavior habits for using technology safely, legally, and ethically ( Hao, 2014 ; Zheng et al., 2020 ). Improving college students’ literacy related to digital citizenship will definitely lead to their mastery of knowing how to use technology legally and ethically in daily learning and life, so that the probability of cyberbullying and being cyberbullied among college students can be reduced, and the harm to individuals’ body and mind as well as to society can be avoided, which will ultimately purify cyberspace to a certain extent and prompt the formation of a healthy cyber civilization. Education departments and schools should emphasize and strengthen college students’ digital citizenship education to enhance their digital citizenship in all aspects, thereby ensuring better survival and development in the digital world.

While bringing convenience to people’s interactions, the Internet also causes an obscuration of values and a deficiency in subjectivity ( Hao, 2014 ). It has been well established that cyberbullying has become one of the increasingly serious social problems in the Internet era. Preventing cyberbullying not only relies on means that emphasize “blocking” approaches, such as traditional Internet monitoring, regulations, and legislation, but also requires the adoption of “dredging” approaches to guide youth to correct online behaviors and improve their digital citizenship level, which is also one of the main objectives of digital citizenship education ( Lin, 2017 ; Zheng et al., 2020 ). Incorporated with digital citizenship, this study conducted a questionnaire survey to assess the current situation of cyberbullying among Chinese college students and examined the effect of students’ personal background, Internet use and social network habits, personality traits, emotions, and digital citizenship on cyberbullying from the perspective of individual students. The results showed that cyberbullying among college students is generally at a low level but still requires attention. At the personal background level, gender has a significant impact on college students’ cyberbullying and being cyberbullied, and the time to start using the Internet is significantly correlated to cyberbullying and being cyberbullied but has no significant impact on them. At the personal Internet use and social network habits level, the students’ average daily time online is not significantly correlated with cyberbullying and being cyberbullied; however, the proportion of online non-learning time is significantly positively correlated with cyberbullying, and the proportion of online learning/work time has a significant influence on students’ being cyberbullied. At the personality trait level, different Big Five personality traits have different correlations with and impacts on cyberbullying and being cyberbullied: openness is significantly positively correlated with cyberbullying and being cyberbullied; neuroticism and conscientiousness are significantly negatively correlated with cyberbullying and being cyberbullied; and agreeableness is significantly negatively correlated with cyberbullying. At the personal emotion level, life satisfaction is significantly negatively correlated with cyberbullying and being cyberbullied and has a significant impact on being cyberbullied; the personal stress and empathetic concern aspects of empathy are significantly positively correlated with cyberbullying and being cyberbullied among female students. At the personal digital citizenship level, students’ understanding of and compliance with Internet etiquette has a significant negative impact on cyberbullying, and digital communication and collaboration capabilities and Internet addiction have significantly positive impacts on cyberbullying and being cyberbullied; furthermore, their understanding of and compliance with digital laws and regulations is significantly negatively correlated with cyberbullying and being cyberbullied. Overall, college students’ digital citizenship level is significantly negatively correlated with cyberbullying but is not significantly correlated with being cyberbullied.

In this study, an attempt was made to explore the influencing factors of cyberbullying among college students, not only enriching the theory and practice of cyberbullying among students but also providing a new perspective for research in this field. Limited by several conditions, this paper only surveyed a small group of college students from modern cities in China. In a follow-up study, the sample size should be expanded as much as possible to provide more rational and reliable data support for drawing conclusions with a higher reference value. Furthermore, the effect of other levels such as the family, school, society, and the environment on cyberbullying should be taken into account so that comprehensive measures and governance processes can be developed to effectively curb cyberbullying among college students.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

JZ: literature search, methodology, questionnaire survey, data analysis, and writing–review and editing. YZ: supervision, conceptualization, writing–original draft preparation, and review and editing. XH: literature search, questionnaire survey, and data analysis. DM and JG: literature search and questionnaire survey. ML: questionnaire survey and data analysis. JH: methodology and writing–revision and editing. All authors have read and agreed to the published version of the manuscript.

This research was funded by the Department of Policy and Regulation of the Ministry of Education, grant number Jybzfs2018115, and the National University Student Innovation and Entrepreneurship Training Program, grant number 201910574042.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : cyberbullying, college student, influencing factors, digital citizenship, individual students

Citation: Zhong J, Zheng Y, Huang X, Mo D, Gong J, Li M and Huang J (2021) Study of the Influencing Factors of Cyberbullying Among Chinese College Students Incorporated With Digital Citizenship: From the Perspective of Individual Students. Front. Psychol. 12:621418. doi: 10.3389/fpsyg.2021.621418

Received: 26 October 2020; Accepted: 09 February 2021; Published: 04 March 2021.

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Copyright © 2021 Zhong, Zheng, Huang, Mo, Gong, Li and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yunxiang Zheng, [email protected]

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Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue

• Published: 12 August 2022
• Volume 4 , pages 175–179, ( 2022 )

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• Paul Horton 1 &
• Selma Therese Lyng 2  

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Introduction

School bullying research has a long history, stretching all the way back to a questionnaire study undertaken in the USA in the late 1800s (Burk, 1897 ). However, systematic school bullying research began in earnest in Scandinavia in the early 1970s with the work of Heinemann ( 1972 ) and Olweus ( 1978 ). Highlighting the extent to which research on bullying has grown exponentially since then, Smith et al. ( 2021 ) found that there were only 83 articles with the term “bully” in the title or abstract published in the Web of Science database prior to 1989. The numbers of articles found in the following decades were 458 (1990–1999), 1,996 (2000–2009), and 9,333 (2010–2019). Considering cyberbullying more specifically, Smith and Berkkun ( 2017 , cited in Smith et al., 2021 ) conducted a search of Web of Science with the terms “cyber* and bully*; cyber and victim*; electronic bullying; Internet bullying; and online harassment” until the year 2015 and found that while there were no articles published prior to 2000, 538 articles were published between 2000 and 2015, with the number of articles increasing every year (p. 49).

Numerous authors have pointed out that research into school bullying and cyberbullying has predominantly been conducted using quantitative methods, with much less use of qualitative or mixed methods (Hong & Espelage, 2012 ; Hutson, 2018 ; Maran & Begotti, 2021 ; Smith et al., 2021 ). In their recent analysis of articles published between 1976 and 2019 (in WoS, with the search terms “bully*; victim*; cyberbullying; electronic bullying; internet bullying; and online harassment”), Smith et al. ( 2021 , pp. 50–51) found that of the empirical articles selected, more than three-quarters (76.3%) were based on quantitative data, 15.4% were based on a combination of quantitative and qualitative data, and less than one-tenth (8.4%) were based on qualitative data alone. What is more, they found that the proportion of articles based on qualitative or mixed methods has been decreasing over the past 15 years (Smith et al., 2021 ). While the search criteria excluded certain types of qualitative studies (e.g., those published in books, doctoral theses, and non-English languages), this nonetheless highlights the extent to which qualitative research findings risk being overlooked in the vast sea of quantitative research.

School bullying and cyberbullying are complex phenomena, and a range of methodological approaches is thus needed to understand their complexity (Pellegrini & Bartini, 2000 ; Thornberg, 2011 ). Indeed, over-relying on quantitative methods limits understanding of the contexts and experiences of bullying (Hong & Espelage, 2012 ; Patton et al., 2017 ). Qualitative methods are particularly useful for better understanding the social contexts, processes, interactions, experiences, motivations, and perspectives of those involved (Hutson, 2018 ; Patton et al., 2017 ; Thornberg, 2011 ; Torrance, 2000 ).

Smith et al. ( 2021 ) suggest that the “continued emphasis on quantitative studies may be due to increasingly sophisticated methods such as structural equation modeling … network analysis … time trend analyses … latent profile analyses … and multi-polygenic score approaches” (p. 56). However, the authors make no mention of the range or sophistication of methods used in qualitative studies. Although there are still proportionately few qualitative studies of school bullying and cyberbullying in relation to quantitative studies, and this gap appears to be increasing, qualitative studies have utilized a range of qualitative data collection methods. These methods have included but are not limited to ethnographic fieldwork and participant observations (e.g., Eriksen & Lyng, 2018 ; Gumpel et al., 2014 ; Horton, 2019 ), digital ethnography (e.g., Rachoene & Oyedemi, 2015 ; Sylwander, 2019 ), meta-ethnography (e.g., Dennehy et al., 2020 ; Moretti & Herkovits, 2021 ), focus group interviews (e.g., Odenbring, 2022 ; Oliver & Candappa, 2007 ; Ybarra et al., 2019 ), semi-structured group and individual interviews (e.g., Forsberg & Thornberg, 2016 ; Lyng, 2018 ; Mishna et al., 2005 ; Varjas et al., 2013 ), vignettes (e.g., Jennifer & Cowie, 2012 ; Khanolainen & Semenova, 2020 ; Strindberg et al., 2020 ), memory work (e.g., Johnson et al., 2014 ; Malaby, 2009 ), literature studies (e.g., Lopez-Ropero, 2012 ; Wiseman et al., 2019 ), photo elicitation (e.g., Ganbaatar et al., 2021 ; Newman et al., 2006 ; Walton & Niblett, 2013 ), photostory method (e.g., Skrzypiec et al., 2015 ), and other visual works produced by children and young people (e.g., Bosacki et al., 2006 ; Gillies-Rezo & Bosacki, 2003 ).

This body of research has also included a variety of qualitative data analysis methods, such as grounded theory (e.g., Allen, 2015 ; Bjereld, 2018 ; Thornberg, 2018 ), thematic analysis (e.g., Cunningham et al., 2016 ; Forsberg & Horton, 2022 ), content analysis (e.g., Temko, 2019 ; Wiseman & Jones, 2018 ), conversation analysis (e.g., Evaldsson & Svahn, 2012 ; Tholander, 2019 ), narrative analysis (e.g., Haines-Saah et al., 2018 ), interpretative phenomenological analysis (e.g., Hutchinson, 2012 ; Tholander et al., 2020 ), various forms of discourse analysis (e.g., Ellwood & Davies, 2010 ; Hepburn, 1997 ; Ringrose & Renold, 2010 ), including discursive psychological analysis (e.g., Clarke et al., 2004 ), and critical discourse analysis (e.g., Barrett & Bound, 2015 ; Bethune & Gonick, 2017 ; Horton, 2021 ), as well as theoretically informed analyses from an array of research traditions (e.g., Davies, 2011 ; Jacobson, 2010 ; Søndergaard, 2012 ; Walton, 2005 ).

In light of the growing volume and variety of qualitative studies during the past two decades, we invited researchers to discuss and explore methodological issues related to their qualitative school bullying and cyberbullying research. The articles included in this special issue of the International Journal of Bullying Prevention discuss different qualitative methods, reflect on strengths and limitations — possibilities and challenges, and suggest implications for future qualitative and mixed-methods research.

Included Articles

Qualitative studies — focusing on social, relational, contextual, processual, structural, and/or societal factors and mechanisms — have formed the basis for several contributions during the last two decades that have sought to expand approaches to understanding and theorizing the causes of cyber/bullying. Some have also argued the need for expanding the commonly used definition of bullying, based on Olweus ( 1993 ) (e.g., Allen, 2015 ; Ellwood & Davies, 2010 Goldsmid & Howie, 2014 ; Ringrose & Rawlings,  2015 ; Søndergaard, 2012 ; Walton, 2011 ). In the first article of the special issue, Using qualitative methods to measure and understand key features of adolescent bullying: A call to action , Natalie Spadafora, Anthony Volk, and Andrew Dane instead discuss the usefulness of qualitative methods for improving measures and bettering our understanding of three specific key definitional features of bullying. Focusing on the definition put forward by Volk et al. ( 2014 ), they discuss the definitional features of power imbalance , goal directedness (replacing “intent to harm” in order not to assume conscious awareness, and to include a wide spectrum of goals that are intentionally and strategically pursued by bullies), and harmful impact (replacing “negative actions” in order to focus on the consequences for the victim, as well as circumventing difficult issues related to “repetition” in the traditional definition).

Acknowledging that these three features are challenging to capture using quantitative methods, Spadafora, Volk, and Dane point to existing qualitative studies that shed light on the features of power imbalance, goal directedness and harmful impact in bullying interactions — and put forward suggestions for future qualitative studies. More specifically, the authors argue that qualitative methods, such as focus groups, can be used to investigate the complexity of power relations at not only individual, but also social levels. They also highlight how qualitative methods, such as diaries and autoethnography, may help researchers gain a better understanding of the motives behind bullying behavior; from the perspectives of those engaging in it. Finally, the authors demonstrate how qualitative methods, such as ethnographic fieldwork and semi-structured interviews, can provide important insights into the harmful impact of bullying and how, for example, perceived harmfulness may be connected to perceived intention.

In the second article, Understanding bullying and cyberbullying through an ecological systems framework: The value of qualitative interviewing in a mixed methods approach , Faye Mishna, Arija Birze, and Andrea Greenblatt discuss the ways in which utilizing qualitative interviewing in mixed method approaches can facilitate greater understanding of bullying and cyberbullying. Based on a longitudinal and multi-perspective mixed methods study of cyberbullying, the authors demonstrate not only how qualitative interviewing can augment quantitative findings by examining process, context and meaning for those involved, but also how qualitative interviewing can lead to new insights and new areas of research. They also show how qualitative interviewing can help to capture nuances and complexity by allowing young people to express their perspectives and elaborate on their answers to questions. In line with this, the authors also raise the importance of qualitative interviewing for providing young people with space for self-reflection and learning.

In the third article, Q methodology as an innovative addition to bullying researchers’ methodological repertoire , Adrian Lundberg and Lisa Hellström focus on Q methodology as an inherently mixed methods approach, producing quantitative data from subjective viewpoints, and thus supplementing more mainstream quantitative and qualitative approaches. The authors outline and exemplify Q methodology as a research technique, focusing on the central feature of Q sorting. The authors further discuss the contribution of Q methodology to bullying research, highlighting the potential of Q methodology to address challenges related to gaining the perspectives of hard-to-reach populations who may either be unwilling or unable to share their personal experiences of bullying. As the authors point out, the use of card sorting activities allows participants to put forward their subjective perspectives, in less-intrusive settings for data collection and without disclosing their own personal experiences. The authors also illustrate how the flexibility of Q sorting can facilitate the participation of participants with limited verbal literacy and/or cognitive function through the use of images, objects or symbols. In the final part of the paper, Lundberg and Hellström discuss implications for practice and suggest future directions for using Q methodology in bullying and cyberbullying research, particularly with hard-to-reach populations.

In the fourth article, The importance of being attentive to social processes in school bullying research: Adopting a constructivist grounded theory approach , Camilla Forsberg discusses the use of constructivist grounded theory (CGT) in her research, focusing on social structures, norms, and processes. Forsberg first outlines CGT as a theory-methods package that is well suited to meet the call for more qualitative research on participants’ experiences and the social processes involved in school bullying. Forsberg emphasizes three key focal aspects of CGT, namely focus on participants’ main concerns; focus on meaning, actions, and processes; and focus on symbolic interactionism. She then provides examples and reflections from her own ethnographic and interview-based research, from different stages of the research process. In the last part of the article, Forsberg argues that prioritizing the perspectives of participants is an ethical stance, but one which comes with a number of ethical challenges, and points to ways in which CGT is helpful in dealing with these challenges.

In the fifth article, A qualitative meta-study of youth voice and co-participatory research practices: Informing cyber/bullying research methodologies , Deborah Green, Carmel Taddeo, Deborah Price, Foteini Pasenidou, and Barbara Spears discuss how qualitative meta-studies can be used to inform research methodologies for studying school bullying and cyberbullying. Drawing on the findings of five previous qualitative studies, and with a transdisciplinary and transformative approach, the authors illustrate and exemplify how previous qualitative research can be analyzed to gain a better understanding of the studies’ collective strengths and thus consider the findings and methods beyond the original settings where the research was conducted. In doing so, the authors highlight the progression of youth voice and co-participatory research practices, the centrality of children and young people to the research process and the enabling effect of technology — and discuss challenges related to ethical issues, resource and time demands, the role of gatekeepers, and common limitations of qualitative studies on youth voice and co-participatory research practices.

Taken together, the five articles illustrate the diversity of qualitative methods used to study school bullying and cyberbullying and highlight the need for further qualitative research. We hope that readers will find the collection of articles engaging and that the special issue not only gives impetus to increased qualitative focus on the complex phenomena of school bullying and cyberbullying but also to further discussions on both methodological and analytical approaches.

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Acknowledgements

We would like to thank the authors for sharing their work; Angela Mazzone, James O’Higgins Norman, and Sameer Hinduja for their editorial assistance; and Dorte Marie Søndergaard on the editorial board for suggesting a special issue on qualitative research in the journal.

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Horton, P., Lyng, S.T. Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue. Int Journal of Bullying Prevention 4 , 175–179 (2022). https://doi.org/10.1007/s42380-022-00139-5

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Teens and Video Games Today

85% of u.s. teens say they play video games, and about four-in-ten do so daily. teens see both positive and negative sides of video games – from problem-solving and making friends to harassment and sleep loss, table of contents.

• Who plays video games?
• How often do teens play video games?
• What devices do teens play video games on?
• Social media use among gamers
• Teen views on how much they play video games and efforts to cut back
• Are teens social with others through video games?
• Do teens think video games positively or negatively impact their lives?
• Why do teens play video games?
• Bullying and violence in video games
• Appendix A: Detailed charts
• Acknowledgments
• Methodology

Pew Research Center conducted this analysis to better understand teens’ use of and experiences with video games.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the questions used for this analysis , along with responses, and  its methodology .

There are long-standing debates about the impact of video games on youth. Some credit them for helping young people form friendships and teaching them about teamwork and problem-solving . Others say video games expose teenagers to violent content, negatively impact their sleep and can even lead to addiction.

With this in mind, Pew Research Center surveyed 1,423 U.S. teens ages 13 to 17 about their own video game habits – from how often they play to the friends they’ve made and whether it gets in the way of them doing well in school or getting a good night’s sleep. 1

Key findings from the survey

• Video games as a part of daily teen life: 85% of U.S. teens report playing video games, and 41% say they play them at least once a day. Four-in-ten identify as a gamer.
• Gaming as a social experience: 72% of teens who play video games say that a reason why they play them is to spend time with others. And some have even made a friend online from playing them – 47% of teen video game players say they’ve done this.
• Helpful with problem-solving, less so for sleep: Over half of teens who play video games say it has helped their problem-solving skills, but 41% also say it has hurt their sleep.
• Bullying is a problem: 80% of all teens think harassment over video games is a problem for people their age. And 41% of those who play them say they’ve been called an offensive name when playing.
• Boys’ and girls’ experiences differ: Most teen boys and girls play video games, but larger shares of boys identify as gamers (62% vs. 17%) and play every day (61% vs. 22%). Boys who play them are also more likely to experience positive things from it, like making friends, and more troubling things like harassment.

Jump to read about: Who plays video games | Socializing over video games | Views about video games’ impact | Harassment and violence in video games      

Playing video games is widespread among teens. The vast majority of U.S. teens (85%) say they play them. Just 15% say they never do, according to the survey conducted Sept. 26-Oct. 23, 2023.

In addition to asking whether teens play video games, we also wanted to learn whether they consider themselves gamers. Overall, four-in-ten U.S. teens think of themselves as gamers. Just under half of teens (45%) play video games but do not think of themselves as gamers.

Nearly all boys (97%) say they play video games, compared with about three-quarters of teen girls. There is a substantial gap by gender in whether teens identify as gamers: 62% of teen boys do, compared with 17% of girls. 2

By gender and age

Younger teen girls are more likely than older girls to say they play video games: 81% of girls ages 13 to 14 compared with 67% of those ages 15 to 17. But among boys, nearly all play video games regardless of age. 

Similar shares of teens play video games across different racial and ethnic groups and among those who live in households with different annual incomes. Go to Appendix A for more detail on which teens play video games and which teens identify as gamers.

We also asked teens how often they play video games. About four-in-ten U.S. teens say they play video games daily, including 23% who do so several times a day.

Another 22% say they play several times a week, while 21% play them about once a week or less.

Teen boys are far more likely than girls to say they play video games daily (61% vs. 22%). They are also much more likely to say they play them several times a day (36% vs. 11%).

By whether someone identifies as a gamer

About seven-in-ten teens who identify as gamers (71%) say they play video games daily. This drops to 30% among those who play them but aren’t gamers.

By household income

Roughly half of teens living in households with an annual income of less than $30,000 (53%) say they play video games at least daily. This is higher than those in households with an annual income of $30,000 to $74,999 (42%) and $75,000 or more (39%).

Go to Appendix A to see more details about who plays video games and identifies as a gamer by gender, age, race and ethnicity, and household income.

Most teens play video games on a gaming console or a smartphone. When asked about five devices, most teens report playing video games on a gaming console (73%), such as PlayStation, Switch or Xbox. And 70% do so on a smartphone. Fewer – though still sizable shares – play them on each of the following:

• 49% say they play them on a desktop or laptop computer
• 33% do so on a tablet  
• 24% play them on a virtual reality (VR) headset such as Oculus, Meta Quest or PlayStation VR

Many teens play video games on multiple devices. About a quarter of teens (27%) do so on at least four of the five devices asked about, and about half (49%) play on two or three of them. Just 8% play video games on one device.

Teen boys are more likely than girls to play video games on four of the five devices asked about – all expect tablets. For instance, roughly nine-in-ten teen boys say they ever play video games on a gaming console, compared with 57% of girls. Equal shares of teen boys and girls play them on tablets.  

Teens who consider themselves gamers are more likely than those who play video games but aren’t gamers to play on a gaming console (95% vs. 78%), desktop or laptop computer (72% vs. 45%) or a virtual reality (VR) headset (39% vs. 19%). Similar shares of both groups play them on smartphones and tablets.

One way that teens engage with others about video games is through online platforms. And our survey findings show that teen gamers stand out for their use of two online platforms that are known for their gaming communities – Discord and Twitch :

• 44% of teen gamers say they use Discord, far higher than video game players who don’t identify as gamers or those who use the platform but do not play video games at all. About three-in-ten teens overall (28%) use Discord.
• 30% of teens gamers say they use Twitch. About one-in-ten other teens or fewer say the same; 17% of teens overall use the platform.

Previous Center research shows that U.S. teens use online platforms at high rates .

Teens largely say they spend the right amount of time playing video games. When asked about how much time they spend playing them, the largest share of teens (58%) say they spend the right amount of time. Far fewer feel they spend too much (14%) or too little (13%) time playing them.

Teen boys are more likely than girls to say they spend too much time playing video games (22% vs. 6%).

By race and ethnicity

Black (17%) and Hispanic (18%) teens are about twice as likely than White teens (8%) to say they spend too little time playing video games. 3

A quarter of teens who consider themselves gamers say they spend too much time playing video games, compared with 9% of those who play video games but don’t identify as gamers. Teen gamers are also less likely to think they spend too little time playing them (19% vs. 10%).

Fewer than half of teens have reduced how much they play video games. About four-in-ten (38%) say they have ever chosen to cut back on the amount of time they spend playing them. A majority (61%) report that they have not cut back at all.

This share is on par with findings about whether teenagers have cut back with their screen time – on social media or their smartphone.

Although boys are more likely to say they play video games too much, boys and girls are on par for whether they have ever cut back. About four-in-ten teen boys (39%) and girls (38%) say that they have ever cut back.

And gamers are as likely to say they have cut back as those who play video games but don’t identify as gamers (39% and 41%).

A main goal of our survey was to ask teens about their own experiences playing video games. For this section of the report, we focus on teens who say they play video games.

Socializing with others is a key part of the video game experience. Most teens who play video games do so with others, and some have developed friendships through them.

About nine-in-ten teen video game players (89%) say they play them with other people, in person or online. Far fewer (11%) play them only on their own.

Additionally, about half (47%) report that they have ever made a friend online because of a video game they both play. This equals 40% of all U.S. teens who have made a friend online because of a video game.

These experiences vary by:  

• Gender: Most teen boy and girl video game players play them with others, though it’s more common among boys (94% vs. 82%). Boys who play video games are much more likely to say they have made a friend online because of a video game (56% vs. 35%).
• Race and ethnicity: Black (55%) and Hispanic (53%) teen video game players are more likely than White teen video game players (43%) to say they have made a friend online because of them.
• Whether someone identifies as a gamer: Nearly all teen gamers report playing video games with others (98%). Fewer – though still most – of those who play video games but aren’t gamers (81%) also play them with others. And about seven-in-ten (68%) say they have made a friend online because of a video game, compared with 29% of those who play them but don’t identify as gamers.

Teens who play video games are particularly likely to say video games help their problem-solving skills. More than half of teens who play video games (56%) say this.

Additionally, more think that video games help, rather than hurt, three other parts of their lives that the survey asked about. Among teens who play video games:

• Roughly half (47%) say it has helped their friendships
• 41% say it has helped how they work with others
• 32% say it has helped their mental health

No more than 7% say playing video games has hurt any of these.

More teens who play video games say it hurts, rather than helps, their sleep. Among these teens, 41% say it has hurt how much sleep they get, while just 5% say it helps. And small shares say playing video games has impacted how well they do in school in either a positive or a negative way.

Still, many teens who play video games think playing them doesn’t have much an impact in any of these areas. For instance, at least six-in-ten teens who play video games say it has neither a positive nor a negative impact on their mental health (60%) or their school performance (72%). Fewer (41%) say this of their problem-solving skills.

Teen boys who play video games are more likely than girls to think playing them has helped their problem-solving skills, friendships and ability to work with others. For instance, 55% of teen boys who play video games say this has helped their friendships, compared with 35% of teen girls.

As for ways that it may hurt their lives, boys who play them are more likely than girls to say that it has hurt the amount of sleep they get (45% vs. 37%) and how well they do in school (21% vs. 11%). 

Teens who consider themselves gamers are more likely than those who aren’t gamers but play video games to say video games have helped their friendships (60% vs. 35%), ability to work with others (52% vs. 32%), problem-solving skills (66% vs. 47%) and mental health (41% vs. 24%).

Gamers, though, are somewhat more likely to say playing them hurt their sleep (48% vs. 36%) and how well they do in school (20% vs. 14%).

By whether teens play too much, too little or the right amount

Teens who report playing video games too much stand out for thinking video games have hurt their sleep and school performance. Two-thirds of these teens say it has hurt the amount of sleep they get, and 39% say it hurt their schoolwork. Far fewer of those who say they play the right amount (38%) or too little (32%) say it has hurt their sleep, or say it hurt their schoolwork (12% and 16%).

Teens who play video games say they largely do so to be entertained. And many also play them to be social with and interact with others. Teens who play video games were asked about four reasons why they play video games. Among those who play video games:

• Nearly all say fun or entertainment is a major or minor reason why they play video games – with a large majority (87%) saying it’s a major reason.
• Roughly three-quarters say spending time with others is a reason, and two-thirds say this of competing with others. Roughly three-in-ten say each is a major reason.
• Fewer – 50% – see learning something as a reason, with just 13% saying it’s a major reason.

While entertainment is by far the most common reason given by teens who play video games, differences emerge across groups in why they play video games.

Teens who identify as gamers are particularly likely to say each is major reason, especially when it comes to competing against others. About four-in-ten gamers (43%) say this is a major reason, compared with 13% of those who play video games but aren’t gamers.

Teen boys who play video games are more likely than girls to say competing (36% vs. 15%), spending time with others (36% vs. 27%) and entertainment (90% vs. 83%) are major reasons they play video games.

Black and Hispanic teens who play video games are more likely than White teens to say that learning new things and competing against others are major reasons they play them. For instance, 29% of Black teen video game players say learning something new is a major reason, higher than 17% of Hispanic teen video game players. Both are higher than the 7% of White teen video game players who say the same.

Teens who play video games and live in lower-income households are especially likely to say competing against others and learning new things are major reasons. For instance, four-in-ten teen video game players who live in households with an annual income of less than $30,000 say competing against others is a major reason they play. This is higher than among those in households with annual incomes of $30,000 to $74,999 (29%) and $75,000 or more (23%).

Cyberbullying can happen in many online environments, but many teens encounter this in the video game world.

Our survey finds that name-calling is a relatively common feature of video game life – especially for boys. Roughly four-in-ten teen video game players (43%) say they have been harassed or bullied while playing a video game in one of three ways: 

• 41% have been called an offensive name
• 12% have been physically threatened
• 8% have been sent unwanted sexually explicit things

Teen boys are particularly likely to say they have been called an offensive name. About half of teen boys who play video games (48%) say this has happened while playing them, compared with about a third of girls (32%). And they are somewhat more likely than girls to have been physically threatened (15% vs. 9%).

Teen gamers are more likely than those who play video games but aren’t gamers to say they been called and offensive name (53% vs. 30%), been physically threatened (17% vs. 8%) and sent unwanted sexually explicit things (10% vs. 6%).

Teens – regardless of whether they’ve had these experiences – think bullying is a problem in gaming. Eight-in-ten U.S. teens say that when it comes to video games, harassment and bullying is a problem for people their age. This includes 29% who say it is a major problem.

It’s common for teens to think harassment while playing video games is a problem, but girls are somewhat more likely than boys to say it’s a major problem (33% vs. 25%).

There have also been decades-long debates about how violent video games can influence youth behavior , if at all – such as by encouraging or desensitizing them to violence. We wanted to get a sense of how commonly violence shows up in the video games teens are playing.

Just over half of teens who play video games (56%) say at least some of the games they play contain violence. This includes 16% who say it’s in all or most of the games they play.

Teen boys who play video games are far more likely than girls to say that at least some of the games they play contain violence (69% vs. 37%).

About three-quarters of teen gamers (73%) say that at least some of the games they play contain violence, compared with 40% among video game players who aren’t gamers.   

• Throughout this report, “teens” refers to those ages 13 to 17. ↩
• Previous Center research of U.S. adults shows that men are more likely than women to identify as gamers – especially the youngest adults. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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Star USC scientist faces scrutiny — retracted papers and a paused drug trial

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Late last year, a group of whistleblowers submitted a report to the National Institutes of Health that questioned the integrity of a celebrated USC neuroscientist’s research and the safety of an experimental stroke treatment his company was developing.

NIH has since paused clinical trials for 3K3A-APC, a stroke drug sponsored by ZZ Biotech, a Houston-based company co-founded by Berislav V. Zlokovic , professor and chair of the department of physiology and neuroscience at the Keck School of Medicine of USC.

Three of Zlokovic’s research papers have been retracted by the journal that published them because of problems with their data or images. Journals have issued corrections for seven more papers in which Zlokovic is the only common author, with one receiving a second correction after the new supplied data were found to have problems as well.

For an 11th paper co-authored by Zlokovic the journal Nature Medicine issued an expression of concern , a note journals append to articles when they have reason to believe there may be a problem with the paper but have not conclusively proven so. Since Zlokovic and his co-authors no longer had the original data for one of the questioned figures, the editors wrote, “[r]eaders are therefore alerted to interpret these results with caution.”

“Its quite unusual to see this volume of retractions, corrections and expressions of concern, especially in high-tier influential papers,” said Dr. Matthew Schrag, an assistant professor of neurology at Vanderbilt who co-authored the whistleblower report independently of his work at the university.

Both Zlokovic and representatives for USC declined to comment, citing an ongoing review initiated in the wake of the allegations, which were first reported in the journal Science.

“USC takes any allegations of research integrity very seriously,” the university said in a statement. “Consistent with federal regulations and USC policies, this review must be kept confidential.”

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Zlokovic “remains committed to cooperating with and respecting that process, although it is unfortunately required due to allegations that are based on incorrect information and faulty premises,” his attorney Alfredo X. Jarrin wrote in an email.

Regarding the articles, “corrections and retractions are a normal and necessary part of the scientific post-publication process,” Jarrin wrote.

Authors of the whistleblower report and academic integrity experts challenged that assertion.

“If these are honest errors, then the authors should be able to show the actual original data,” said Elisabeth Bik , a microbiologist and scientific integrity consultant who co-wrote the whistleblower report. “It is totally human to make errors, but there are a lot of errors found in these papers. And some of the findings are suggestive of image manipulation.”

Given the staid pace of academic publishing, publishing this many corrections and retractions only a few months after the initial concerns were raised “is, bizarrely, pretty quick,” said Ivan Oransky, co-founder of Retraction Watch .

The whistleblower report submitted to NIH identified allegedly doctored images and data in 35 research papers in which Zlokovic was the sole common author.

“There had been rumblings about things not being reproducible [in Zlokovic’s research] for quite some time,” Schrag said. “The real motivation to speak publicly is that some of his work reached a stage where it was being used to justify clinical trials. And I think that when you have data that may be unreliable as the foundation for that kind of an experiment, the stakes are just so much higher. You’re talking about patients who are often at the most vulnerable medical moment of their life.”

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Over the years, Zlokovic has created several biotech companies aimed at commercializing his scientific work. In 2007, he co-founded ZZ Biotech , which has been working to gain federal approval of 3K3A-APC.

The drug is intended to minimize the bleeding and subsequent brain damage that can occur after an ischemic stroke, in which a blood clot forms in an artery leading to the brain.

In 2022, USC’s Keck School of Medicine received from NIH the first $4 million of a planned $30-million grant to conduct Phase III trials of the experimental stroke treatment on 1,400 people.

In Phase II of the trial, which was published in 2018 and called Rhapsody, six of the 66 patients who received 3K3A-APC died in the first week after their stroke, compared to one person among the 44 patients who got a placebo. Patients who received the drug also tended to report more disability 90 days after their stroke than those who got the placebo. The differences between the two groups were not statistically significant and could have been due to chance, and the death rate for patients in both groups evened out one month after the initial stroke.

“The statements that there is a risk in this trial is false,” said Patrick Lyden, a USC neurologist and stroke expert who was employed by Cedars-Sinai at the time of the trial. Zlokovic worked with Lyden as a co-investigator on the study.

One correction has been issued to the paper describing the Phase II results, fixing an extra line in a data table that shifted some numbers to the wrong columns. “This mistake is mine. It’s not anybody else’s. I didn’t catch it in multiple readings,” Lyden said, adding that he noticed the error and was already working on the correction when the journal contacted him about it.

He disputed that the trial represented any undue risk to patients.

“I believe it’s safe, especially when you consider that the purpose of Rhapsody was to find a dose — the maximum dose — that was tolerated by the patients without risk, and the Rhapsody trial succeeded in doing that. We did not find any dose that was too high to limit proceeding to Phase III. It’s time to proceed with Phase III.”

Schrag stressed that the whistleblowers did not find evidence of manipulated data in the report from the Phase II trial. But given the errors and alleged data manipulation in Zlokovic’s earlier work, he said, it’s appropriate to scrutinize a clinical trial that would administer the product of his research to people in life-threatening situations.

In the Phase II data, “there’s a coherent pattern of [patient] outcomes trending in the wrong direction. There’s a signal in early mortality … there’s a trend toward worse disability numbers” for patients who received the drug instead of a placebo, he said.

None are “conclusive proof of harm,” he said. But “when you’re seeing a red flag or a trend in the clinical trial, I would tend to give that more weight in the setting of serious ethical concerns around the pre-clinical data.”

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The NIH paused the clinical trial in November, and it remains on hold, said Dr. Pooja Khatr, principal investigator of the NIH StrokeNet National Coordinating Center. Khatr declined to comment on the pause or the trial’s future, referring further questions to USC and NIH.

The NIH Office of Extramural Research declined to discuss Rhapsody or Zlokovic, citing confidentiality regarding grant deliberations.

ZZ Biotech Chief Executive Kent Pryor, who in 2022 called the drug “a potential game-changer,” said he had no comment or information on the halted trial.

Zlokovic is a leading researcher on the blood-brain barrier, with particular interest in its role in stroke and dementia. He received his medical degree and doctorate in physiology at the University of Belgrade and joined the faculty at USC’s Keck School of Medicine after several fellowships in London. A polyglot and amateur opera singer , Zlokovic left USC and spent 11 years at the University of Rochester before returning in 2011 . He was appointed director of USC’s Zilkha Neurogenetic Institute the following year.

A USC spokesperson confirmed that Zlokovic has retained his titles as department chair and director of the Zilkha institute.

About this article

Corinne Purtill is a science and medicine reporter for the Los Angeles Times. Her writing on science and human behavior has appeared in the New Yorker, the New York Times, Time Magazine, the BBC, Quartz and elsewhere. Before joining The Times, she worked as the senior London correspondent for GlobalPost (now PRI) and as a reporter and assignment editor at the Cambodia Daily in Phnom Penh. She is a native of Southern California and a graduate of Stanford University.

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