Cyberbullying and cyber-victimisation among higher secondary school adolescents in an urban city of Nepal: a cross-sectional study

Abstract

Objective

To assess the prevalence and factors associated with cyberbullying and cyber-victimisation among high school adolescents of Pokhara Metropolitan City, Nepal.

Design

A cross-sectional study.

Setting

Pokhara Metropolitan City, Nepal.

Participants

We used convenient sampling to enrol 450 adolescents aged 16–19 years from four distinct higher secondary schools in Pokhara Metropolitan City.

Outcome measures

We administered the Cyberbullying and an Online Aggression Survey to determine the prevalence of cyberbullying and cyber-victimisation. Univariate and multivariate logistic regression analyses were performed to estimate the ORs and 95% CIs. Data were analysed using STATA V.13.

Results

The 30-day prevalence of cyberbullying and cyber-victimisation was 14.4% and 19.8%, and the over-the-lifetime prevalence was 24.2% and 42.2%, respectively. Posting mean or hurtful comments online was the most common form of both cyberbullying and cyber-victimisation. Compared with females, males were more likely to be involved in cyberbullying (adjusted OR (AOR)=13.52; 95% CI: 6.04 to 30.25; p value <0.001) and cyber-victimised (AOR=2.22; 95% CI: 1.33 to 3.73; p value <0.05). Using the internet almost every day was associated with cyberbullying (AOR=9.44; 95% CI: 1.17 to 75.79; p value <0.05) and cyber-victimisation (AOR=4.96; 95% CI: 1.06 to 23.18; p value <0.05). Students from urban place of residence were associated with both cyberbullying (AOR=2.45; 95% CI: 1.23 to 4.88; p value <0.05) and cyber-victimisation (AOR=1.77; 95% CI: 1.02 to 3.05; p value <0.05).

Conclusion

The study recommends the implementation of cyber-safety educational programmes, and counselling services including the rational use of internet and periodic screening for cyberbullying in educational institutions. The enforcement of strong anti-bullying policies and regulations could be helpful to combat the health-related consequences of cyberbullying.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This study used multivariate logistic regression models, adjusting for potential confounders which helps to eliminate alternative explanations of our findings.

• The cross-sectional design of this study limits inferences regarding the direction of association of several factors with cyberbullying and cyber-victimisation.

• The questionnaire relied on self-reporting for the prevalence of cyberbullying and cyber-victimisation, which may have led to potential information bias.

Introduction

The rapid advances in internet technology and the use of innovative technological tools are pervasive worldwide. The expanding field of internet technology has remarkably helped billions of people connect and communicate in their public and private lives.¹ However, internet penetration rates vary across countries. In the most developed countries, internet penetration is approximately 90.0%. Across developing countries, only an estimated 35.0% of the population has access to the internet.² Among several innovative internet technologies, the use of social media is widespread among all age groups, with children and teenagers under the age of 18 making up about one-third of all internet users globally.³ In search of frequent gratification and peer acceptance, these age groups are at risk of problematic internet use such as cyberbullying and cyber-victimisation.⁴

Cyberbullying is defined as a repeated act when someone repeatedly harasses, harms or mistreats others through internet technologies or devices such as smartphones, email and internet sites.^{5 6} With the surge of information and data-sharing platforms, greater digital socialisation and the popularisation of social media, cyberbullying has become more common than ever. It often occurs when there is little adult oversight.⁷ A nationwide study conducted in the United States of America (USA) reported that at least 37.0% of adolescents aged 12–17 years encountered some form of cyberbullying, including harsh remarks and hurtful comments, rumours and physical threats.⁸ In a similar vein, a research by the United Nations Children’s Fund (UNICEF) found that 19.7% of college students were engaged in cyberbullying at least once in their lifetime, while 54.4% of them reported experiencing it at least once.⁹ Unlike traditional bullying, it is more difficult to identify victims and perpetrators of cyberbullying and to mitigate at homes and schools.⁷ Furthermore, the rising prevalence of cyberbullying has resulted in adverse impacts on adolescents’ physical and mental health and led to poor academic performances globally.¹⁰ Evidence suggests that adolescents who have been victims of cyberbullying are more likely to experience emotional stress, anxiety or depression^11–14 and may even have thoughts of or attempt suicide.^{13 14}

According to the World Telecommunication/Information and Communication Technology (ICT) Indicators Database 2021, about 52.0% of the population has access to the internet in Nepal¹⁵ and the number of mobile users in early 2021 was equivalent to 131.3% of the total population.¹⁶ Furthermore, the social isolation regulation following the COVID-19 pandemic has restricted face-to-face interaction and caused a massive increase in the use of social networking sites and online activity, thereby contributing to the increase in the prevalence of cyberbullying.¹⁷ As a result, there was an increased possibility of encountering cyberbullying making adolescents more vulnerable to cybercrime.¹⁸ So far, the most common forms of cyberbullying in Nepal are related to social media platforms, such as Facebook, followed by YouTube and WhatsApp.^{19 20} To mitigate cyberbullying and cyber-victimisation, the government of Nepal has endorsed the reactive electronic transaction act 2006/2007.²¹ However, because of the unique characteristics of cyberbullying and associated fear and stigma, the perpetrators and victims under-report and hide their identities.^{7 22} Thus, there is interest in exploring how to abate cyberbullying among the most vulnerable adolescents in schools in Nepal.

Although cyberbullying is a growing area of research in public health, there are few reports on its prevalence and contributing factors, especially among Nepalese adolescents. Consequently, this study aims to assess the status and determinants of cyberbullying and cyber-victimisation among adolescents in selected schools of Nepal. We believe that the findings of this study will be beneficial for concerned stakeholders and help commence a meaningful dialogue at the policy level regarding cyberbullying and cyber-victimisation.

Materials and methods

Study design and setting

We conducted a cross-sectional school-based survey among the higher secondary school adolescents (16–19 years) of private and public schools in Pokhara Metropolitan City, Nepal (figure 1). Situated 200 km to the west of the capital Kathmandu, Pokhara serves as Nepal’s urban centre and functions as the capital city of the Gandaki Province. The adolescent age group of 10–19 years, in Gandaki province, makes up 1.6% of the overall population of 6.4 million.²³ The educational landscape in Nepal comprises around 4187 higher secondary schools, encompassing 3628 community (public) schools, 913 institutional (private) schools and 6 religious institutions. In Pokhara, there are a total of 117 higher secondary schools, contributing to a total of 661 642 higher secondary students in Nepal.²⁴ We noted that the use of internet technologies is slightly higher in private schools when compared with the government schools.²⁵ For broad representation and logistical feasibility, we chose two private and public higher secondary schools for the present study.

Figure 1.

Map of Nepal showing the study site.

Sample size and participant recruitment

The sample size for the study participant was calculated using the Cochran formula,²⁶ n=Z²pq/d². Using the formula, the calculated sample size was 394, considering 64.0% prevalence (p) of cyberbullying among school students,¹⁸ a margin of error set at 5%, CI at 95% and considering a non-response rate of 10.0%. Ultimately, we recruited 450 participants who met the inclusion criteria: (a) respondents aged 16–19 years old; (b) having access to the internet; (c) availability of mobile phones and (d) exposure to social network sites (Facebook, Instagram, YouTube, WhatsApp/Twitter, Viber). Participants were selected conveniently in close coordination with the school authorities. We considered the availability of students and ensured participation did not interfere with their regular class schedule. Eligible participants were selected after obtaining the written informed consent and assent forms from their guardians. Individuals who declined to provide consent were excluded from the study. Figure 2 illustrates the number of participants approached for interviews and selection process in the study.

Figure 2.

Flow diagram of study participant recruitment.

Data collection tool

The survey questionnaire was developed using tools from previous studies^{10 27 28} and through expert consultation. Outcome variables were adopted from the standard Cyberbullying and Online Aggression Survey Instrument.²⁷

The questionnaire contained three sections:

1. Sociodemographic characteristics: this section included age (in years), gender (male/female), ethnicity (Brahmin/Chhetri, Gurung/Magar, others), residence (urban, rural), religion (Hindu, others), grade (11 or 12), type of family (nuclear, joint) and school type (private, public). Part of the tools were adapted from the Nepal Demographic Health Survey (NDHS).²⁸

2. Internet-related: this section measured the use of internet in the past month (almost every day, at least once a week), average hours per day spent on social media (less than 1 hour, 1–2 hours, more than 2 hours) and most commonly used social media (WhatsApp/Twitter, Facebook, Instagram and YouTube). The categorisation of these variables was based on NDHS 2016.²⁸

3. Experience with cyberbullying and cyber-victimisation: this section measured experiences with cyberbullying and cyber-victimisation in the last 30 days and over-the-lifetime.This measure was adopted from the Cyberbullying and Online Aggression Survey Instrument.²⁷ The 30-day prevalence for cyberbullying and cyber-victimisation consisted of eight items. The participants provided self-reported responses on a Likert scale, including ‘never [0]’, ‘once [1]’, ‘several times [2]’ and ‘many times [3]’. Further, the reported responses ‘never and once’ were recategorised as ‘0’ and ‘several times and many times’ as ‘1’. For the analysis, we recategorised the response options as binary variables (0,1) where ‘0’ indicated ‘no experience of cyberbullying and cyber-victimisation’ and ‘1’ indicated ‘yes to experience of cyberbullying and cyber-victimisation’. Score of 1 in any of the eight items was considered as yes to cyberbullying and cyber-victimisation. One additional question was added to the tool as ‘Have you ever told anyone about this (cyberbullying or cyber-victimisation)?’, which was adopted from the study conducted by Aye et al.¹⁰

   For the lifetime experience of the cyberbullying, the responses were collected to the question, ‘Over the lifetime, have you ever cyber-bullied someone through mobile phones or internet?’ and for cyber-victimisation, ‘Over the lifetime, have you ever been a cyber-victim through mobile phones or the internet?’.²⁷

Operational definition

In this study, cyberbullying is defined based on participant’s responses to any of the eight items in the Cyberbullying and Online Aggression Survey Instrument.²⁷ Those who answered ‘Yes’ to any of these items were categorised as individuals who have experienced cyberbullying. Similarly, cyber-victimisation is defined as participants who responded affirmatively to any of the eight items in the same survey instrument, indicating that they have been victims of cyberbullying.

Reliability of the tool

To ensure the reliability of the survey instrument, a pre-test was conducted among 30 higher secondary school adolescents in one of the schools in Pokhara Metropolitan City. The pre-test results were later used to refine the questionnaire. Cronbach’s alpha test was conducted, resulting in a coefficient of 0.7 for the tool measuring cyberbullying and 0.8 for the tool measuring cyber-victimisation.

Data collection technique

We employed a self-administered questionnaire to collect information on exposure, outcome and other variables in the classroom setting. Before administering the questionnaire, the principal investigator coordinated with the respective public and private school authorities of Pokhara Metropolitan City and obtained written approval to conduct the study. Information sessions were conducted with school teachers, guardians and study participants to explain the study’s purpose and expectations. Ethical considerations, emphasising participant’s privacy, confidentiality and anonymity during and after the research, were shared. We also explained the voluntary nature of the study and the right to stop participating at any time. Eligible students received an information sheet and informed assent and consent forms before data collection started. Students who provided signed written consent and an assent form from parents/or guardians were enrolled in the study. Data were collected by research assistants with a Bachelor’s degree in Public Health. Research assistants completed a 5-day training on data collection techniques and ethical considerations before the study began. The questionnaire was administered to the students in the classroom setting during non-teaching hours. The research team provided instructions at the beginning and remained available throughout the session to clarify any confusion or queries. Students took about 20–25 min to complete the survey. The data collection was conducted between April and June 2021. A total of 550 students (16–19 years) were identified, out of which 450 eligible participants were enrolled.

Data analysis

Following data collection, the trained research assistants cross-checked the data and the principal investigator verified the data to ensure the questionnaires were filled out correctly. Data were entered into Microsoft Excel and STATA V.13.0 (Stata Corp) was used for cleaning, coding and statistical analysis. Descriptive statistics (mean, frequency and percentage) were calculated to describe the sample characteristics and are presented in the form of figures and tables. The prevalence of cyberbullying and cyber-victimisation is presented for two different duration periods: the last 30 days and over-the-lifetime. Univariate and multivariate logistic regression analyses were conducted to assess the association between dependent (cyberbullying and cyber-victimisation) and independent variables (age, gender, family type, place of residence, class grade and internet-related factors). We reported crude and adjusted OR (AOR) with a 95% CI and p value (<0.05).

Ethical consideration

Official letters and permissions were obtained from concerned school authorities. Written informed consent and assent forms were collected from the study participants and parents or guardians. Data collection commenced only after obtaining written informed assent and consent forms. Confidentiality was maintained at all stages of data handling. The data were anonymised using random identity numbers.

Patient and public involvement

Participants were not involved in the design, conduct, reporting and dissemination plans of our research.

Results

Sociodemographic profile of the participants

The mean age of the participants was 17.5±0.9 years. More than half of the participants were female (54.4%), and about 58.7% of participants resided in urban areas. The majority of the participants followed the Hindu religion (80.0%) whereas the remaining 20.0% were religious minorities (Buddhist, Christian, Kirat and Muslims). About half of the participants were from private higher secondary schools (51.3%) (table 1).

Table 1.

Sociodemographic characteristics of the study participants (n=450)

Characteristics	Frequency	Percentage (%)
Age (in years)	(17.5±0.9)
16	59	13.1
17	178	39.5
18	156	34.7
19	57	12.7
Gender
Female	245	54.4
Male	205	45.6
Ethnicity
Brahmin/Chhetri	201	44.7
Gurung/Magar	164	36.4
Others	85	18.9
Residence
Rural	186	41.3
Urban	264	58.7
Class
Class 11	193	42.9
Class 12	257	57.1
Religion
Hindu	360	80.0
Others*	90	20.0
School type
Private	231	51.3
Public	219	48.7
Type of family
Joint	135	30.0
Nuclear	315	70.0

*Buddhist, Christian, Kirat and Muslims.

Prevalence of cyberbullying and cyber-victimisation

The 30 days and over-the-lifetime prevalences of cyberbullying among participants were 14.4% and 24.2%, respectively. The 30 days and over-the-lifetime prevalences of cyber-victimisation were 19.8% and 42.2%, respectively.

Forms of cyberbullying and cyber-victimisation

The most common form of cyberbullying and cyber-victimisation was posting mean or hurtful comments about someone online, that is, 52.3% and 49.4%, respectively (figure 3).

Figure 3.

Forms of cyberbullying and cyber-victimisation in the past 30 days (n=450).

Risk factors associated with cyberbullying and cyber-victimisation (univariate and multivariate logistic analyses)

Table 2 shows the data on the factors associated with cyberbullying and cyber-victimisation in the last 30 days. Several factors were found significantly associated with cyberbullying and cyber-victimisation in both univariate and multivariate logistic regression analyses. The multivariate logistic regression analysis was performed to adjust for potential confounders (age, gender, place of residence, frequency of internet use, social media use) for outcome variables (cyberbullying and cyber-victimisation). Using the multivariate logistic regression analysis, we found that gender, place of residence, frequency of internet use and social media use were associated with cyberbullying. Compared with females, males were more involved in cyberbullying acts (AOR=13.52; 95% CI: 6.04 to 30.25; p value <0.001). The odds of being involved in cyberbullying were higher among the participants who resided in urban areas as compared with those residing in rural areas (AOR=2.45; 95% CI: 1.23 to 4.88; p value=0.010). Participants who used the internet almost every day were more likely to be involved in cyberbullying as compared with those who used the internet once a week (AOR=9.44; 95% CI: 1.17 to 75.79; p value=0.035). The odds of being involved in cyberbullying were 32.0% lower among Facebook users (95% CI: 0.10 to 0.93; p value=0.037) and 22.0% lower among Instagram users (95% CI: 0.06 to 0.80; p value=0.022) as compared with WhatsApp and Twitter users.

Table 2.

Factors associated with cyberbullying and cyber-victimisation in study participants (n=450)

Variables	Cyberbullying (n=65)					Cyber-victimisation (n=89)
	n (%)	OR (95% CI)	P value	AOR (95% CI)	P value	n (%)	OR (95% CI)	P value	AOR (95% CI)	P value
Age
16 ref.	10 (16.9)					16 (27.1)
17	23 (12.9)	0.72 (0.32 to 1.63)	0.440	0.79 (0.30 to 2.08)	0.639	39 (21.9)	0.75 (0.38 to 1.48)	0.412	0.81 (0.37 to 1.75)	0.596
18	25 (16.0)	0.93 (0.41 to 2.08)	0.870	1.23 (0.40 to 3.74)	0.705	23 (14.7)	0.46 (0.22 to 0.95)	0.038*	0.55 (0.22 to 1.38)	0.211
19	7 (12.3)	0.68 (0.24 to 1.94)	0.479	0.66 (0.16 to 2.65)	0.559	11 (19.3)	0.64 (0.26 to 1.53)	0.321	0.99 (0.33 to 2.97)	0.995
Gender
Female ref.	8 (3.3)					34 (13.9)
Male	57 (47.8)	11.40 (5.29 to 24.59)	<0.001*	13.52 (6.04 to 30.25)	<0.001*	55 (26.8)	2.27 (1.41 to 3.66)	<0.001*	2.22 (1.33 to 3.73)	0.002*
Class
Class 11 ref	32 (16.6)					45 (23.3)
Class 12	33 (12.8)	0.74 (0.43 to 1.25)	0.265	0.54 (0.25 to 1.18)	0.127	44 (17.1)	0.67 (0.42 to 1.08)	0.104	0.62 (0.33 to 1.16)	0.139
School type
Public ref	26 (11.3)					28 (12.1)
Private	39 (17.8)	1.70 (1.00 to 2.91)	0.050	1.10 (0.54 to 2.22)	0.779	61 (27.9)	2.79 (1.70 to 4.58)	<0.001*	1.79 (1.00 to 3.20)	0.047*
Residence
Rural ref	15 (8.1)					27 (14.5)
Urban	50 (18.9)	2.66 (1.44 to 4.90)	0.002*	2.45 (1.23 to 4.88)	0.010*	62 (23.5)	1.80 (1.09 to 2.97)	0.020*	1.77 (1.02 to 3.05)	0.039*
Frequency of internet use
At least once a week ref	1 (2.1)					2 (4.2)
Almost everyday	64 (15.9)	8.89 (1.20 to 65.66)	0.032*	9.44 (1.17 to 75.79)	0.035*	87 (21.6)	6.35 (1.51 to 26.69)	0.012*	4.96 (1.06 to 23.18)	0.041*
Social media
WhatsApp, Twitter ref	9 (29.0)
Facebook	32 (12.6)	0.35 (0.14 to 0.83)	0.018*	0.32 (0.10 to 0.93)	0.037*	45 (17.8)	1.46 (0.48 to 4.38)	0.499	1.79 (0.55 to 5.80)	0.327
Instagram	10 (11.9)	0.33 (0.11 to 0.91)	0.033*	0.22 (0.06 to 0.80)	0.022	22 (26.2)	2.39 (0.75 to 7.61)	0.139	2.19 (0.62 to 7.72)	0.219
You tube	14 (17.1)	0.50 (0.19 to 1.32)	0.163	0.51 (0.14 to 1.83)	0.309	18 (22.0)	1.89 (0.58 to 6.13)	0.284	2.25 (0.62 to 8.17)	0.217
Duration of social media use
Less than 1 hour ref	5 (7.7)
1–2 hours	15 (11.7)	1.59 (0.55 to 4.59)	0.389	1.29 (0.38 to 4.34)	0.677	68 (26.5)	0.80 (0.31 to 2.05)	0.651	0.41 (0.14 to 1.17)	0.098
Greater than 2 hours	45 (17.5)	2.54 (0.96 to 6.70)	0.058	1.99 (0.64 to 6.10)	0.229	8 (12.3)	2.56 (1.16 to 5.64)	0.020*	1.53 (0.63 to 3.74)	0.342
Religion
Hindu ref	52 (14.4)					64 (17.7)
Others†	13 (14.4)	1.00 (0.51 to 1.92)	1.000	1.16 (0.54 to 2.49)	0.699	25 (27.7)	1.77 (1.04 to 3.03)	0.035*	1.66 (0.92 to 3.01)	0.091

*P value <0.05-significant association.

†Buddhist, Christian, Kirat and Muslim.

AOR, adjusted OR.

Similarly, the multivariate logistic regression analysis found that gender, place of residence and frequency of internet use were associated with cyber-victimisation. The odds of being cyber-victimised were 2.22 times higher among males as compared with females (AOR=2.22; 95% CI: 1.33 to 3.73; p value=0.002). Cyber-victimisation was seen more among participants residing in urban areas compared with those residing in rural areas (AOR=1.77; 95% CI: 1.02 to 3.05; p value=0.039). Students in private schools were more cyber-victimised than students in public schools (AOR=1.79; 95% CI: 1.00 to 3.20; p value=0.047). The odds of being cyber-victimised were higher among those who used internet almost every day compared with those who used the internet once a week (AOR=4.96; 95% CI: 1.06 to 23.18; p value=0.041).

Discussion

In this study, we assessed the prevalence of and factors associated with cyberbullying and cyber-victimisation among higher secondary school adolescents. The 30 days and over-the-lifetime prevalences of cyberbullying among higher secondary school adolescents were 14.4% and 24.2%, respectively. For cyber-victimisation, the 30-day and over-the-lifetime prevalences were 19.8% and 42.2%, respectively. Previous studies reported the prevalence ranging from 6.3% to 34.8% for cyberbullying and 14.6% to 56.9% for cyber-victimisation.^29–32 The variation could be explained by differences in the research methods, social-demographic characteristics, perspectives of cyberbullying experiences (victims, perpetrators or both victim and perpetrator), instruments (scales, study-specific questions) and sample size.^{33 34}

Furthermore, the differences in internet penetration rates and access to electronic devices contribute significantly to differing prevalence rates across countries, as evidenced by the higher rates of cyberbullying and cyber-victimisation in the UK,³⁵ USA,³⁶ China,²⁹ South Korea,¹⁷ where there is greater access to internet and electronic devices. Interestingly, in our study, the prevalence of cyber-victimisation was higher than cyberbullying. This was consistent with the studies conducted in Malaysia,³⁷ Canada³⁸ and Croatia.³⁹ This could be due to the complexity and sensitive nature of the problem. Some individuals might be reluctant to admit their involvement in cyberbullying due to concerns about social and cultural consequences.²² The COVID-19 pandemic might also have influenced the prevalence of cyberbullying, with increased internet usage for educational purposes potentially contributing to a rise in cyberbullying.^40–43 These findings underscore the prevalence of cyberbullying among higher secondary school adolescents, emphasising the need for heightened awareness and attention from parents, educators and policymakers to address this significant public health concern.

In our study, males exhibited higher involvement in both cyberbullying and cyber-victimisation. This aligns with findings from a survey conducted among 1917 secondary school adolescents in developed countries, where male adolescents were more prone to cyberbully others and experience cyber-victimisation.^44–51 The higher involvement of males compared with females in cyberbullying and cyber-victimisation may be due to higher levels of exposure to and use of the internet and social platforms, such as YouTube and online gaming sites compared with females.⁵² Lower female involvement in cyberbullying and cyber-victimisation may reflect the culture of upholding family legacy and honour, potentially limiting access to social media and subsequently depriving them of positive digital connections in the context of South East Asia.⁵³ Moreover, cultural and gender norms may play a role, as females perceive a greater risk in online communication and males may be more hesitant to express fear, vulnerability and insecurity related to cyberbullying experiences.⁵⁴ The gendered pattern may also be influenced by cultural stereotypes. For example, males may refrain from reporting victimisation because it might undermine their sense of masculinity.⁵⁵ In addition, females are more likely to view cyberbullying as a serious problem⁵⁶ and hurtful act compared with males,⁵⁷ which might encourage reporting victimisation. The findings emphasise the need for further exploration into the relationship between gender, culture and cyberbullying involvement. Differences in online behaviours, preferred online media and internet access across genders warrant comprehensive examination to deepen our understanding of the relationship between gender and cyberbullying.

Frequency of internet use and exposure to particular social networking sites emerged as significant factors associated with both cyberbullying and cyber-victimisation in our study. We found that Facebook and Instagram users were more frequently involved in cyberbullying which aligns with the findings of studies conducted among school students in Singapore,⁵⁸ Thailand⁵⁹ and Indonesia.⁶⁰ This association may be attributed to Facebook’s popularity among youths⁶¹ in South East countries. The unique features of Facebook, such as allowing users to share detailed information on their profiles, post messages, photos/videos and web links on friends’ ‘walls’, and contribute to enhanced mutual social interaction through features like group chats, applications and fan pages.^{28 62} In contrast to these findings, cyber-victimisation was increasingly reported in WhatsApp groups.⁶³ Unlike Facebook, WhatsApp groups allow for direct interaction within a private space, potentially leading to more targeted or personalised communication. The ubiquity of internet and social media usage in today’s context is undeniable. While the different social media platforms have diverse features, it is imperative to educate adolescents about safe internet practices and equip them with skills to navigate different online platforms responsibly. Technology must be carefully monitored, and efforts should be directed toward promoting responsible technology use. Social media sites should implement control mechanisms to ensure a safe user experience and filter offensive comments or hate speech. Moreover, longitudinal studies are essential to identify specific preventive measures and gain a deeper understanding of the evolving dynamics in the online environment.

Finally, we found that urban place of residence was positively associated with being involved in cyberbullying and cyber-victimisation. Students in urban settings experienced cyberbullying and cyber-victimisation more frequently than those in rural settings. This was consistent with studies in India⁶⁴ and Taiwan.⁶⁵ This could be a result of greater access and exposure to the internet and communication technology in the urban areas compared with rural areas.⁶⁶ The urban–rural differences in cyberbullying and cyber-victimisation might also be due to lack of the parental mediation and internet skills.⁶⁵ Further research should focus on identifying and understanding urban–rural differences in cyberbullying and cyber-victimisation to develop targeted interventions for both contexts.

Strengths/limitations

The study had some limitations. First, it was a cross-sectional study that used a non-probability sampling method. Due to the cross-sectional design, it was not possible to establish the direction of the risk associations. Second, this study was conducted in a specific area within a limited time period. Therefore, the findings may not be generalised to all secondary school students in Nepal. Third, the questionnaire used to assess the prevalence of cyberbullying and cyber-victimisation relied on self-reported data, potentially introducing bias. Furthermore, the tool did not consider the impact of cyberbullying on overall health. Future studies should emphasise capturing the impact of cyberbullying on physical, mental, social and intellectual health to support anti-cyberbullying interventions or plans.

This research is a valuable contribution to the gaps in literature because it provides meaningful insights into cyberbullying and cyber-victimisation among high secondary school adolescents. Besides this, the use of standardised tools and cost-effectiveness nature are strengths of this study.

Conclusion

The widespread use of technology and social media platforms has put children and adolescents at greater risk for cyberbullying. It is arguably high time for policymakers, major stakeholders and researchers to constructively plan for advanced research, evidence-based policy formulations and tailored interventions to prevent cyberbullying and cyber-victimisation. The government can formulate anti-bullying policies for the educational institutions to control cyberbullying issues. Similarly, the government should increase awareness about existing cyber-laws and policies. Integrating education on cyberbullying into school curricula is essential. Curricula should cover topics such as the impact of cyberbullying on mental health, effective coping mechanisms and guidelines for reporting cyberbullying incidents. Periodic screening for cyberbullying, counselling services, cybersafe educational programmes and awareness-raising campaigns are urgently needed for students. By implementing these multifaceted approaches, we can create a safer and healthier online environment for adolescents.

Data availability statement

Data are available upon reasonable request.

Ethics statements

Patient consent for publication

Consent obtained from parent(s)/guardian(s)

Ethics approval

This study involves human participants and was approved. The ethical approval was obtained from the Institutional Review Committee of Kathmandu University School of Medical Sciences (Reference ID: IRC-126/20), Dhulikhel Hospital for conducting the study. Participants gave informed consent to participate in the study before taking part.