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Purpose Social media use (SMU) has become an intrinsic part of adolescent life. Negative consequences of SMU for adolescent health could include exposures to online forms of aggression. We explored age, gender, and cross-national differences in adolescents' engagement in SMU, then relationships between SMU and victimization and the perpetration of cyber-bullying. Methods We used data on young people aged 11–15 years (weighted n = 180,919 in 42 countries) who participated in the 2017–2018 Health Behaviour in School-aged Children study to describe engagement in the three types of SMU (intense, problematic, and talking with strangers online) by age and gender and then in the perpetration and victimization of cyber-bullying. Relationships between SMU and cyber-bullying outcomes were estimated using Poisson regression (weighted n = 166,647 from 42 countries). Results Variations in SMU and cyber-bullying follow developmental and gender-based patterns across countries. In pooled analyses, engagement in SMU related to cyber-bullying victimization (adjusted relative risks = 1.14 [95% confidence interval (CI): 1.10–1.19] to 1.48 [95% CI: 1.42–1.55]) and perpetration (adjusted relative risk = 1.31 [95% CI: 1.26–1.36] to 1.84 [95% CI: 1.74–1.95]). These associations were stronger for cyber-perpetration versus cyber-victimization and for girls versus boys. Problematic SMU was most strongly and consistently associated with cyber-bullying, both for victimization and perpetration. Stratified analyses showed that SMU related to cyber-victimization in 19%–45% of countries and to cyber-perpetration in 38%–86% of countries. Conclusions Accessibility to social media and its pervasive use has led to new opportunities for online aggression. The time adolescents spend on social media, engage in problematic use, and talk to strangers online each relate to cyber-bullying and merit public health intervention. Problematic use of social media poses the strongest and most consistent risk.
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Original article
Social Media Use and Cyber-Bullying: A Cross-National Analysis of
Young People in 42 Countries
Wendy Craig, Ph.D.
, Meyran Boniel-Nissim, Ph.D.
, Nathan King, M.Sc.
, Sophie D. Walsh, Ph.D.
Maartje Boer, M.Sc.
, Peter D. Donnelly, M.D.
, Yossi Harel-Fisch, Ph.D.
Marta Malinowska-Cie
slik, Ph.D.
, Margarida Gaspar de Matos, Ph.D.
, Alina Cosma, Ph.D.
Regina Van den Eijnden, Ph.D.
, Alessio Vieno, Ph.D.
, Frank J. Elgar, Ph.D.
, Michal Molcho, Ph.D.
Ylva Bjereld, Ph.D.
, and William Pickett, Ph.D.
Department of Psychology, Queen's University, Kingston, Canada
School of Social Sciences and Humanities, Kinneret Academic College on the Sea of Galilee, Zemach, Israel
Department of Public Health Sciences, Queen's University, Kingston, Canada
Department of Criminology, Bar-Ilan University, Ramat Gan, Israel
Department of Interdisciplinary Social Science, Utrecht University, Utrecht, the Netherlands
Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
The International Research Program on Adolescent Well-Being & Health, School of Education, Bar Ilan University, Ramat Gan, Israel
Department of Environmental Health, Health Sciences Faculty, Jagiellonian University Medical College, Krakow, Poland
Health Promotion and Education Centre, FMH/ISAMB, University of Lisbon, Lisbon, Portugal
Department of Developmental and Social Psychology, University of Padova, Padova, Italy
Institute for Health and Social Policy, McGill University, Montreal, Quebec, Canada
Department of Children Studies, National University of Ireland Galway, Galway, Ireland
Department of Behavioural Sciences and Learning (IBL), Linköping University, Linköping, Sweden
Article history: Received October 4, 2019; Accepted March 2, 2020
Keywords: Adolescent health; Cyber-bullying; Epidemiology; Social media; Violence
Purpose: Social media use (SMU) has become an intrinsic part of adolescent life. Negative con-
sequences of SMU for adolescent health could include exposures to online forms of aggression. We
explored age, gender, and cross-national differences in adolescents' engagement in SMU, then
relationships between SMU and victimization and the perpetration of cyber-bullying.
Methods: We used data on young people aged 11e15 years (weighted n ¼180,919 in 42 countries)
who participated in the 2017e2018 Health Behaviour in School-aged Children study to describe
engagement in the three types of SMU (intense, problematic, and talking with strangers online) by
age and gender and then in the perpetration and victimization of cyber-bullying. Relationships
between SMU and cyber-bullying outcomes were estimated using Poisson regression (weighted
n¼166,647 from 42 countries).
Results: Variations in SMU and cyber-bullying follow developmental and gender-based patterns
across countries. In pooled analyses, engagement in SMU related to cyber-bullying victimization
(adjusted relative risks ¼1.14 [95% condence interval (CI): 1.10e1.19] to 1.48 [95% CI: 1.42e1.55])
This 2017e2018 study of
181 thousand adolescents
from 42 countries exam-
ines social media use dur-
ing adolescence. Intense
use, problematic use, and
frequent online contact
with strangers each are
each independently associ-
ated with cyber-bullying.
The universality of such as-
sociations is explored, and
Conicts of interest: The authors declare they have no conicts of interest.
Disclosure: This supplement was supported by the World Health Organization
European Ofce and the University of Glasgow. The articles have been peer-
reviewed and edited by the editorial staff of the Journal of Adolescent Health.
The opinions or views expressed in this supplement are those of the authors and
do not necessarily represent the ofcial position of the funder.
*Address correspondence to: William Pickett, Ph.D., Department of Public
Health Sciences, Queen's University, Kingston K7L3N6, Canada.
E-mail address: (W. Pickett).
1054-139X/Ó2020 Published by Elsevier Inc. on behalf of Society for Adolescent Health and Medicine. This is an open access article under the CC BY-NC-ND license (http://
Journal of Adolescent Health 66 (2020) S100eS108
and perpetration (adjusted relative risk ¼1.31 [95% CI: 1.26e1.36] to 1.84 [95% CI: 1.74e1.95]).
These associations were stronger for cyber-perpetration versus cyber-victimization and for girls
versus boys. Problematic SMU was most strongly and consistently associated with cyber-bullying,
both for victimization and perpetration. Stratied analyses showed that SMU related to cyber-
victimization in 19%e45% of countries and to cyber-perpetration in 38%e86% of countries.
Conclusions: Accessibility to social media and its pervasive use has led to new opportunities for
online aggression. The time adolescents spend on social media, engage in problematic use, and talk
to strangers online each relate to cyber-bullying and merit public health intervention. Problematic
use of social media poses the strongest and most consistent risk.
Ó2020 Published by Elsevier Inc. on behalf of Societyfor Adolescent Health and Medicine. This is an open
access article under the CC BY-NC-ND license (
high-risk groups for cyber-
perpetration and cyber-
victimizationare identied.
Social media use (SMU) has become a normal part of the lives
of young people [1]. Its benets include the ability to commu-
nicate with friends, quickly access information and gain new
knowledge, and stay in touch with adult mentors including
parents, family members, and teachers [2]. However, emerging
problems associated with SMU include frequent or intense use,
which may detract from opportunities to participate in other
constructive activities, such as extracurricular and community
events [3]. A recent international study concluded that although
statistically signicant, the effects of exposure to digital tech-
nology on adolescent well-being are modest and, in the authors'
opinions, insufcient to invoke the need for policy change[4].
In contrast, others have argued that problematic SMU(indi-
cated by symptoms of addiction to social media) puts adolescents
at risk for problems because it facilitates potential risky online
interaction with strangers with harmful intentions [5] and con-
tributes to addictive behaviors [6], social withdrawal [7], and
impaired social functioning [6].
Intense and problematic SMU exposes adolescents to online
aggression, including cyber-bullying [6,8e11]. Contemporary
social theories provide frameworks to understand these links.
First, more frequent and intense SMU expose adolescents to
aggressive behavior, including the perpetration of cyber-bullying
[12]. Second, Problem Behavior Theory [13,14] posits that certain
risk behaviors are covaried, organized, and clustered, and specic
proles of risk underlie vulnerability. Repeated exposure to on-
line aggression can make the behavior seem more acceptable
through role modeling and reinforcement [8e11]. Witnessing
the social rewards of aggression or cyber-bullying, such as
increased social status, also reinforces the behavior [15,16], as
young people become motivated to conform to group norms in
their social environment [15]. In addition, the lack of face-to-face
cues associated with SMU hides the negative consequences of
online aggression or cyber-bullying. Therefore, without this
critical feedback, the aggressive behavior may be more likely to
recur [16,17]. Furthermore, repeated exposure to online aggres-
sion or bullying may result in the disinhibition effect[16,17].
That is, these aggressive behaviors may become normalized to
youth over time. Such effects are likely to increase the likelihood
of engaging in cyber-bullying or being cyber-victimized. In
addition, those who use electronic communications excessively
may be differentially vulnerable children, who experience psy-
chosocial problems such as loneliness and social anxiety. These
vulnerable children feel positive about being online but may lack
the social resources and skills to prevent being cyber-victimized
at the moment when it is happening [18]. Thus, frequent and
problematic SMU may increase the likelihood of witnessing and
emulating aggressive online behaviors, both as a perpetrator and
as a victim.
Cross-sectional analyses have shown that both intense and
problematic SMU relate to increased cyber-bullying and cyber-
victimization in adolescents [19e25]. Longitudinal research
supports the implied temporality of these links [26,27]. Although
the opportunity to interact with strangers in online environments
also may play an etiological role [17 ], few studies have examined
this as a potential risk factor for online aggressive behaviors.
In light of this background, through a school-based survey of
adolescents in 42 countries and regions [28], we investigated age,
gender, and cross-national differences in adolescents' engage-
ment in SMU, then relationships between SMU and victimization
and the perpetration of cyber-bullying. We hypothesized that
three SMU variablesdfrequent use, problematic (characterized
by addictive-like behaviors), and involvement with strangersd
would represent a continuum of exposures with varying harms in
terms of online aggression. We examined their unique associa-
tions with cyber-bullying and cyber-victimization while simul-
taneously adjusting for the effects of salient covariates, including
mutual control for each indicator of SMU. Our aim was to provide
foundational information that supports policies that support
adolescent health in a contemporary, digital world.
Study population and procedures
The 2017e2018 Health Behaviour in School-aged Children
(HBSC) survey was conducted in 47 countries and regions
throughout Europe and Canada in the 2017e2018 academic year.
National research teams surveyed nationally representative
samples of 11-, 13-, and 15-year-old children according to a
common research protocol [28]. Questionnaires were translated
to suit the language of the participating countries following a
standardized protocol that included translation, back-translation
into English, then centralized verication. Sampling procedures
involved the selection of classes within schools with variations in
sampling criteria suited to country-level circumstances. Some
countries oversampled subpopulations (e.g., by geography and
ethnicity), and standardized weights were created to ensure
Our analysis of SMU and cyber-bullying/victimization used
data from 42 countries and regions (illustrated for two indicators
of SMU and cyber-bullying in illust Figures 1 and 2). According to
World Bank Classications [29], these represented 31 high-in-
comeand 11 low- and middle-incomecountries. Of the
W. Craig et al. / Journal of Adolescent Health 66 (2020) S100eS108 S101
original 47, three countries did not collect information on SMU,
and two others did not submit data by the time of our analysis.
Each country team obtained approval to conduct the survey from
the ethics review board or equivalent regulatory body associated
with the institution conducting each respective national survey.
Participation was voluntary, and consent (explicit or implicit)
was sought from school administrators, parents, and adolescents
as per national human subject requirements.
We used data on 180,919 adolescents in 42 countries in our
prevalence estimation (Table 1). We tested associations be-
tween variables using a subsample of 166,979 adolescents
(weighted n ¼166,647) from the 42 countries that had com-
plete data on cyber-bullying, SMU, age, gender, and socioeco-
nomic class.
Cyber-bullying. Using an item modied from the validated
Olweus bullying scale [16], participants indicated how often that
they had been victimized by cyber-bullying in the past couple of
months: that is, sent mean instant messages,email,or text mes-
sages;wall postings;created a website making fun of someone;
posted unattering or inappropriate pictures online without
permission or shared them with others. Because of the low prev-
alence of cyber-bullying and based on international precedent,
its ve ordinal response categories were dichotomized into a
binary outcome (never vs. at least once in the past couple of
months)[30]. A second question asked, using a similar stem and
response categories, how often they had taken part in the
Figure 1. Adjusted relative risks by country for problematic social media use and perpetration of cyber-bullying. Note that *and^indicate statistical signicance, based
on a 95% condence interval, for boys and girls, respectively.
W. Craig et al. / Journal of Adolescent Health 66 (2020) S100eS108S102
perpetration of cyber-bullying. Response options were similarly
categorized in a binary fashion [30].
Intense SMU. A4-itemadaptedscalefromtheEUKidsOnline
Survey was used to measure SMU [31]. Respondents were
people: close friend(s),friends from a larger friend group,friends
that you got to know through the internet but didntknowbefore,
and other people than friends (e.g., parents,brothers/sisters,
classmates,and teachers). For each of these four items, answer
categories ranged from 1 (never/almost never)to5(almost all
the time throughout the day), and a do not know/doesntapply
option. Intense SMU was dened as having online contact
almost all the time throughout the day on at least one of the
four items.
Problematic SMU. The Social Media Disorder Scale (
¼.89) [32]
measured problematic SMU in 9 dichotomous (yes/no) items
that describe addiction-like symptoms: preoccupation with so-
cial media, dissatisfaction about a lack of time for its use, feeling
bad when not using it, trying but failing to spend less time using
it, neglecting other duties in order to use it, regular arguments
over it, lying to parents or friends about its use, using it to
escape from negative feelings, and having a serious conict with
family over SMU. Endorsement of 6e9 items indicated prob-
lematic SMU, as recommended by the originators of the scale.
Figure 2. Adjusted relative risks by country for intense social media use and perpetration of cyber-bullying. Note that *and^indicate statistical signicance, based on a
95% condence interval, for boys and girls, respectively.
W. Craig et al. / Journal of Adolescent Health 66 (2020) S100eS108 S103
Frequent online contact with strangers. We measured frequent
online contact with strangers using the response of almost all
the time throughout the dayto an item describing the frequency
of online contact with friends that you got to know through the
internet but did not know before. [31].
Other variables. The HBSC questionnaire also collected data on
self-identied gender group (boy, girl, and in some countries
neither term describes me), age group (11, 13, and 15 years),
socioeconomic class (a 6-item measure of material assets in the
home including number of vehicles, bedroom sharing, computer
ownership, bathrooms at home, dishwashers at home, and
family vacations), [33] family support (a 4-item scale describing
the degree of help, emotional support, communication, and
assistance in decision-making perceived to be experienced in
families), and peer support (a 4-item scale describing the degree
of help from friends, ability to count on them, communication of
happy and sad feelings, and communication of problems with
friends) [34].
Statistical analysis
We analyzed the data in SAS 9.4 (SAS Institute, Cary, NC,
2016). Descriptive analyses characterized the international
sample, restricted to participants with complete data for age,
gender, and the cyber-bullying and SMU variables. We then
estimated the prevalence of cyber-bullying (victimization and
perpetration) and then the three types of SMU (intense,prob-
lematic, and online contact with strangers) by age and gender. For
each country, we calculated the prevalence per 100 children and
then summarized these estimates in a pooled analysis using
minimum, median, and maximum values.
In the pooled international sample, we then used Poisson
regression analyses to model cyber-bullying (victimization then
perpetration) as dependent variables with each of the three in-
dicators of SMU as independent variables. We analyzed data on
boys and girls separately and restricted our analyses to records
with complete data on cyber-bullying, SMU, and all covariates
under consideration. Based on a priori consideration of con-
founding, age, family afuence, peer support, and family support
were forced into every adjusted model. Models with victimization
as the dependent variable controlled for perpetration, and vice
versa. Models examining intense SMU were adjusted for prob-
lematic SMU and vice versa, whereas frequent online contact with
strangers was adjusted for problematic SMU. In addition, because
frequent online contact with strangers was one of four items that
also contributed to the intense SMU scale, we performed a
sensitivity analysis that examined the effects of intense SMU, with
and without inclusion of the online contact with strangers item.
All models accounted for the clustered nature of the sampling
scheme via inclusion of school, then country identiers as
random effects. Beta coefcients and standard errors were used
to generate crude and adjusted estimates of relative risk (RR) and
associated 95% condence intervals (CIs).
Following this step, we reran each of the adjusted regression
models at the country level. The numbers of countries where we
identied statistically signicant (p<.05) effects indicated the
consistency of any observed risks or protections across countries.
We also described these effects across the countries graphically
to illustrate the size of effects and their level of consistency,
irrespective of statistical signicance.
Given the large sample sizes involved, the pooled analysis was
90% powered to detect an adjusted RR of 1.06e1.18 in boys and
girls (
¼.05, two sided) for each of the relationships under
study. Detectable effects varied but were generally larger (>1.20)
in the country-specic analyses.
The prevalence of reported victimization by cyber-bullying
and perpetration of cyber-bullying varied by country, gender,
and age group (Table 2). Median estimates of both victimization
and perpetration were generally low and, in the pooled analysis,
remained fairly consistent by age group for victimization by
cyber-bullying in boys (p¼.22) but not girls (p¼.02), but
increased with age for perpetration (boys: p¼.01; girls: p¼.02).
The median prevalence of victimization reported by girls was
higher than boys, especially at age 13 years (p¼.02). Conversely,
the median prevalence of perpetration reported by boys was
higher than girls in all age groups (all comparisons, p<.01).
Country-specic estimates for both victimization and perpetra-
tion are provided in Supplementary Table 1.
Social media use
Based on median estimates, intense SMU positively related to
age, especially among girls (p<.01; Table 3). Girls were less likely
than boys to engage in problematic SMU at age 11 years, but
more likely at ages 13 and 15 years. Frequent online contact with
strangers increased with age and was more prevalent among
boys versus girls. In general, among girls, the prevalence of
problematic SMU was higher than the prevalence of frequent
contacts with strangers. Among boys, the prevalence of frequent
contact with strangers was higher than the prevalence of prob-
lematic SMU.
SMU and cyber-bullying
Table 4 presents the regression analysis of cyber-bullying
victimization and perpetration using the pooled sample. For
cyber-victimization, bivariate models showed modest to strong
relationships (adjusted RRs: 1.14 [95% CI: 1.10e1.19] to 1.48 [95%
CI: 1.42e1.55]) between each of the three indicators of SMU and
being victimized by cyber-bullying. Adjusted models showed
Table 1
Description of international study sample, HBSC study, 2018
Descriptor Number
Number of countries reporting, n 42
Total participants, n 180,919
By country, n
Median 3,715
Minimum 1,446 (Albania)
Maximum 11,155 (Wales)
By gender, n (%)
Boys 86,981 (48.1)
Girls 93,938 (51.9)
By age group, n (%)
11 years 56,219 (31.1)
13 years 62,661 (34.6)
15 years 62,039 (34.3)
All values are weighted.
HBSC ¼Health Behaviour in School-aged Children.
W. Craig et al. / Journal of Adolescent Health 66 (2020) S100eS108S104
that the observed effects for each of the three indicators were
partially explained by known confounders, as the magnitude of
the RRs decreased after adjustment. However, the relation be-
tween each indicator of SMU and cyber-victimization held up to
these added controls. The consistency of such effects across
countries is shown in the number of countries reporting statis-
tically signicant increases in relative risk. There was signicant
variation between countries in the signicance of the association
between intense use and cyber-victimization (there were eight
countries with a signicant association for boys and 25 for girls).
Problematic SMU was most strongly and consistently related to
cyber-victimization. The association was signicant in 20 coun-
tries for boys and in 29 countries for girls. Cyber-victimization
related to frequent contact with strangers in 10 countries for
boys and 19 countries for girls.
For perpetration of cyber-bullying bivariate models showed
stronger effects (adjusted RR: 1.31 [95% CI: 1.26e1.36] to 1.84
[95% CI: 1.74e1.95]) between each of the three indicators of SMU
and being a perpetrator when compared with the relative risks
for victimization. Adjustment for confounders attenuated the
overall relative risks, but the effects remained strong and sta-
tistically signicant in boys and girls. Again, there was variation
across countries. We observed a statistically signicant higher
risk for perpetration when reporting intense (24 and 20 coun-
tries for boys and girls, respectively) and problematic SMU
(22 and 36 countries, boys and girls, respectively) (Figures 1 and
2). We found signicant associations with perpetration and
frequent talking to strangers in only 16 countries for boys but 28
countries for girls (Figures 1 and 2).
Finally, the sensitivity analysis used to examine the effects of
intense SMU on the perpetration then victimization by cyber-
bullying outcomes, with and without inclusion of the frequent
online contact with strangers item as part of the intense SMU
measure is presented in Supplementary Table 2. The ndings
were very similar for models that did and did not include the
contact with strangers item.
The proliferation of SMU among adolescents over the past
decade has led to concerns about its negative consequences for
adolescent health and well-being [1,5e7]. Our study explored
relationships between three types of SMU (intense, problematic,
frequent talking with strangers), and involvement in cyber-
bullying/victimization, and the consistency of these relationships
across gender, age groups and diverse geopolitical contexts. We
note three main ndings. First, we observed more consistent
relationships across countries for each of the three types of SMU
with perpetration of cyber-bullying compared with cyber-
victimization. Second, we observed these relationships in more
countries for girls than boys, for both cyber-bullying and cyber-
victimization. Third, for both boys and girls, problematic media
use related to cyber-bullying and cyber-victimization in the most
countries and estimates indicated the presence of modest to
strong effects that merit public health intervention.
Consistent with an exposure perspective[35], our ndings
suggest that SMU exposes young people to risks for involvement
in cyber-bullying and to more aggressive online behaviors,
Table 2
Reported victimization by and perpetration of cyber-bullying in 42 countries, HBSC study, 2018
Prevalence per 100 children
Within countries by age group and gender
11 years 13 years 15 years
Minimum Median Maximum Minimum Median Maximum Minimum Median Maximum
Victimization by cyber-bullying 4.0 12.5 27.5 2.2 11.9 24.3 3.2 11.3 28.5
Perpetration of cyber-bullying 1.8 7.6 26.7 3.1 10.3 28.8 3.6 11.8 31.4
Victimization by cyber-bullying 3.8 12.7 24.5 6.4 13.9 27.8 5.3 12.7 20.9
Perpetration of cyber-bullying .7 6.1 14.0 2.5 7.2 19.1 1.6 7.5 19.4
All values are weighted.
HBSC ¼Health Behaviour in School-aged Children.
Table 3
Reported engagement in sentinel indicators of electronic media communication within countries, HBSC study, 2018
Prevalence per 100 children
Within countries by age group and gender
11 years 13 years 15 years
Minimum Median Maximum Minimum Median Maximum Minimum Median Maximum
Intense use of social media 14.1 28.9 47.3 17.1 32.4 48.9 18.0 36.8 52.3
Problematic social media use 1.2 5.8 25.3 2.9 6.4 17.8 2.1 6.1 17.5
Frequent social media
contact with strangers
1.2 6.0 14.3 2.4 7.5 12.7 2.7 8.5 13.0
Intense use of social media 12.5 29.3 48.5 16.9 41.8 60.3 21.1 45.6 64.4
Problematic social media use 1.1 4.7 14.5 3.6 8.4 20.1 3.9 8.8 18.7
Frequent social media contact
with strangers
.6 3.4 7.7 2.7 6.2 11.6 2.2 6.2 13.7
All values are weighted. For Slovenia, only 15-year-olds included for problematic social media user.
HBSC ¼Health Behaviour in School-aged Children.
W. Craig et al. / Journal of Adolescent Health 66 (2020) S100eS108 S105
Table 4
Bivariate and adjusted relative risk estimates for victimization byand perpetration of cyber-bullying associated with three indicators of social media use in 42 countries, HBSC study, 2018(weighted n¼166,647 [79,486
boys and 87,161 girls] from 41 countries included in the overall analyses)
Victimization by cyber-bullying Perpetration of cyber-bullying
Overall prevalence Overall relative risk No. countries
Overall prevalence Overall relative risk No. countries
Bivariate Adjusted
Adjusted RR
Bivariate Adjusted
Adjusted RR
n (% yes) RR (95% CI) RR (95% CI) >11 <1 n (% yes) RR (95% CI) RR (95% CI) >11 <1
Intense use
No 54,287 (10.7) 1.00 (ref) 1.00 (ref) 8 34 0 54,287 (9.5) 1.00 (ref) 1.00 (ref) 24 18 0
Yes 25,199 (13.9) 1.29 (1.24e1.35) 1.14 (1.10e1.19) 25,199 (14.2) 1.48 (1.42e1.54) 1.31 (1.26e1.36)
Problematic use
No 74,424 (11.0) 1.00 (ref) 1.00 (ref) 20 22 0 74,424 (10.2) 1.00 (ref) 1.00 (ref) 22 19 1
Yes 5,062 (23.2) 2.12 (2.00e2.24) 1.35 (1.28e1.42) 5,062 (23.2) 2.25 (2.12e2.39) 1.44 (1.37e1.52)
Frequent contact with strangers
No 73,848 (11.2) 1.00 (ref) 1.00 (ref) 10 32 0 73,848 (10.4) 1.00 (ref) 1.00 (ref) 16 26 0
Yes 5,638 (18.6) 1.64 (1.55e1.75) 1.22 (1.16e1.29) 5,638 (19.0) 1.79 (1.69e1.91) 1.34 (1.27e1.41)
Intense use
No 53,628 (11.9) 1.00 (ref) 1.00 (ref) 25 17 0 53,628 (5.6) 1.00 (ref) 1.00 (ref) 20 22 0
Yes 33,533 (17.2) 1.44 (1.39e1.49) 1.30 (1.25e1.34) 33,533 (9.9) 1.72 (1.63e1.80) 1.39 (1.32e1.45)
Problematic use
No 80,211 (12.6) 1.00 (ref) 1.00 (ref) 29 13 0 80,211 (6.2) 1.00 (ref) 1.00 (ref) 36 6 0
Yes 6,950 (28.9) 2.26 (2.16e2.37) 1.48 (1.42e1.55) 6,950 (19.6) 3.10 (2.92e3.28) 1.84 (1.74e1.95)
Frequent contact with strangers
No 82,323 (13.2) 1.00 (ref) 1.00 (ref) 19 23 0 82,323 (6.8) 1.00 (ref) 1.00 (ref) 28 14 0
Yes 4,839 (26.3) 1.96 (1.85e2.07) 1.39 (1.31e1.47) 4,839 (16.0) 2.30 (2.14e2.48) 1.40 (1.30e1.50)
All analyses are weighted.
Armenia is excluded from the overall pooled analysis because of missing data but included in country-specic analyses adjusting for all covariates except socio-economic class.
CI ¼condence interval; HBSC ¼Health Behaviour in School-aged Children; ref ¼reference; RR ¼relative risk.
Based on CIs that do or do not overlap 1.00.
Adjusted for age, county-specic socioeconomic class, family support, and peer support and for clustering at the country and school level. RR estimates for victimization by cyber-bullying are adjusted for
perpetration of cyber-bullying and vice versa. RR estimates for intense user are adjusted for problematic use and vice versa. RRs for frequent contact with strangers are adjusted for problematic use.
W. Craig et al. / Journal of Adolescent Health 66 (2020) S100eS108S106
particularly for boys. Time spent online, especially if SMU is
frequent and/or problematic, replaces opportunities to engage in
constructive and protective in-person social activities that pro-
mote socioemotional and moral development [3]. From a theo-
retical perspective, aggressive tendencies may develop in young
people who grow up in environments that reinforce aggression,
provide aggressive models, frustrate and victimize them, and
teach them that aggression is acceptable and successful(p. 47)
[36]. Intense and problematic SMU may expose adolescents to
peers and social norms that validate and reinforce different
forms of aggression, including cyber-bullying. Associations be-
tween intense and problematic SMU and cyber-bullying may be
exacerbated by cognitive, emotional, and associated social vul-
nerabilities because SMU provides a safe and anonymous way of
expressing frustrations, which could translate into online
aggression among vulnerable youth [37,38].
Exposure theory also explains gender differences in the con-
sistency of ndings across countries. We found more cross-
country consistency for girls than boys in the associations
among problematic use and frequent contact with strangers and
cyber-bullying. Because girls spend more time online and report
more problematic use, they have greater exposure to aggressive
role models, potentially reinforcing the opportunity to engage in
cyber-bullying. Similarly, for girls, intense and problematic use
related to cyber-victimization in most countries but not for boys.
Increased exposure and use of social media among girls may
result in an increased risk of cyber-victimization [39]. Similarly,
the gendered patterns surrounding frequent online contact with
strangers become more pronounced as children age and were
particularly experienced by boys. This may be attributable to
differences in sensation seeking and risk taking, as mediated by
hormonal factors and social norms [40], as well as engagement in
interactive game playing by boys and older adolescents, which by
necessity often involve interaction with strangers [41].
All three of SMU measures independently related to cyber-
bullying and cyber-victimization with modest to strong effect
sizes. Although these associations varied by country, they high-
light the unique contribution of each construct and importance
of broad assessments of SMU that include measures of intense
use, problematic use and talking with strangers. Problematic
SMU related to cyber-bullying and victimization in many coun-
tries. Cross-country variations in associations with cyber-
bullying and cyber-victimization also highlight the importance
of a socioecological approach in understanding these relation-
ships. Cultural, economic, and social factors, such as Internet
access, availability of electronic devices, and social cultural
norms about online behavior, underlie these associations. Future
research should examine the importance of these contextual
differences in explaining cross-national differences found here.
The strengths of our study include large, representative
samples and our use of standardized, validated measures that
differentiated aspects of SMU and cyber-bullying involvement
[31]. The ndings are almost certainly generalizable to contem-
porary populations of young people from high-income countries
and may be generalizable beyond such populations, given that
access to the internet is common, even in low- and middle-
income countries [42].
The limitations of the study include the cross-sectional, self-
report nature of the data collection, which limited the potential for
causal inferences. Second, self-reports of sensitive behaviors,
including perpetration and victimization due to cyber-bullying, are
also subject to bias and misclassication because of the social
stigmas associated with their occurrence [43]. Third, HBSC's reli-
ance on a binary indicator of gender does not reect the experi-
ences of young people whose identity does not match these binary
categories, nor those for whom the sex assigned at birth does not
correspond with that identity. In some circles, the HBSC item is best
considered a measure of sex at birthrather than gender.
There are theoretical and methodological implications of the
ndings. First, research will benet from a socioecological approach
to understand variations in association across countries. Exposure
theory may not sufciently describe cross-country variations in the
pattern of results. Second, research should also assess multiple
aspects of SMU use, given the unique determinants and conse-
quences of its intensity, problematic use, and contact with strangers
online. Third, interventions need to consider a sex-/gender-specic
approach. Frequency of contact with strangers may represent a risk
factor that is particularly important to girls' involvement in cyber-
bullying. The ndings also point to the need for further research
that examines the interplay of gender and age in SMU use and
cyber-bullying. On a more practical level, parents, educators, clini-
cians, and others who care for children should be aware of these
correlates of SMU. Problematic and intense SMU as well as talking
with strangers are not innocuous in terms of their potential links to
cyber-bullying and require both awareness and evidence-based
strategies for prevention.
The developing world of electronic social media technology
and its intensive introduction into the daily lives of adolescents
provide new and alternative social settings in which young
people engage in relationships. Although social media environ-
ments replicate much of what is present in traditional face-to-
face activities, these rapidly evolving environments have
changed the meaning and manifestation of social connectedness
among adolescents. Easy access to social media and its pervasive
use have led to new opportunities for cyber-bullying and pre-
sents new challenges and opportunities for health policy and
practice to protect youth from harm.
Health Behaviour in School-aged Children is an international
study carried out in collaboration with WHO/EURO. The Inter-
national Coordinator was Jo Inchley (University of Glasgow) for
the 2017/2018 survey. The Data Bank Manager was Professor
Oddrun Samdal (University of Bergen). The 2017/2018 survey
included in this study were conducted by the following principal
investigators in the 42 countries and regions: Albania (Gentiana
Qirjako), Armenia (Sergey G. Sargsyan), Austria (Rosemarie
Felder-Puig), Azerbaijan (Gahraman Hagverdiyev), Flemish
Belgium (Bart De Clercq), French Belgium (Katia Castetbon),
Canada (William Pickett, Wendy Craig, and [the late] John
Freeman), Croatia (Ivana Pavic Simetin), Czech Republic (Michal
Kalman), Denmark (Mette Rasmussen), England (Fiona Broks,
Ellen Klemera), Estonia (Leila Oja, Katrin Aasvee), Finland (Jorma
Tynjälä), France (Emmanuelle Godeau), Georgia (Lela Shengelia),
Germany (Matthias Richter), Greece (Anna Kokkevi), Hungary
(Ágnes Németh), Iceland (Arsaell M. Arnarsson), Ireland (Saoirse
Nic Gabhainn), Italy (Franco Cavallo), Kazakhstan (Shynar
Abdrakhmanova and Valikhan Akhmetov), Lithuania (Kastytis
Smigelskas), Latvia (Iveta Padule), Luxembourg (Helmut Wil-
lems), Malta (Charmaine Gauci), the Netherlands (Gonneke Ste-
vens and Saskia van Dorsselaer), Norway (Oddrun Samdal),
Poland (Joanna Mazur and Agnieszka Ma1kowska-Szkutnik),
Portugal (Margarida Gaspar de Matos), Republic of Moldova
W. Craig et al. / Journal of Adolescent Health 66 (2020) S100eS108 S107
(Galina Lesco), Romania (Adriana Baban), Russian Federation
(Anna Matochkina), Scotland (Jo Inchley), Serbia (Jelena Rakic),
Slovakia (Andrea Madarasova Geckova), Slovenia (Helena Jer-
icek), Spain (Carmen Moreno), Sweden (Petra Lofstedt),
Switzerland (Marina Delgrande-Jordan and Hervé Kuendig),
Ukraine (Olga Balakireva), and Wales (Chris Roberts).
Funding Sources
Grant funding for the researchers involved in this cross-na-
tional manuscript was provided by the (1) Public Health Agency
of Canada; (2) Canadian Institutes of Health Research (operating
grant MOP341188); (3) Ministry of Health, Ofce of the Director,
Israel; (4) Ministry of Health, Wellbeing, and Sports, the
Netherlands; (5) Institute of Mother and Child, and Warsaw
University, Poland; (6) Public Health Agency of Sweden; (7)
Italian Ministry of Health/Centre for Disease Prevention and
Control; and (8) Department of Health, Ireland.
Supplementary Data
Supplementary data related to this article can be found at
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... Indeed, adolescents and young people's increased use of the Internet and information and communication technologies (ICT) during the COVID-19 pandemic could have posed a greater risk of being involved in cyberbullying and cybervictimization behaviors, thus exacerbating the likelihood of displaying mental health problems. This is in line with the previous empirical evidence showing that spending much time online and engaging in online activities are crucial risk factors for cyberbullying and cybervictimization [15,16,[46][47][48][49][50]. ...
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In light of the alarming results emerging from some studies and reports on the significant increase in aggressive online behaviors among children and adolescents during the COVID-19 pandemic, the current research aimed at providing a more detailed evaluation of the investigations focusing on the cyberbullying prevalence rates published between 2020 and 2023. To this purpose, systematic searches were conducted on four databases (Web of Science, APA PsycInfo, Scopus and Google Scholar), and following PRISMA guidelines, 16 studies were included and qualitatively reviewed. Although studies were characterized by a large variety in cyberbullying operationalization and measurement, and by different methodologies used for data collection, the prevalence rates of the involvement in cyberbullying and/or cybervictimization generally revealed opposite trends: an increase in many Asian countries and Australia and a decrease in Western countries. The findings were also discussed by considering the effects of the COVID-19 pandemic. Finally, some suggestions were provided to policy makers for promoting prevention and intervention anti-cyberbullying programs in school contexts.
... Adolescent males were reported to engage in cyberbullying perpetration. The study's findings were in line with the study conducted in Australia and America, indicating that adolescent males reported higher cyber perpetration than females (Craig et al., 2020;Jackson & Trompeter, 2020;Navarro, 2016). Perhaps adolescent males increased engagement with masculine attributes was associated with their cyberbullying behaviour. ...
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Cyberbullying perpetration has emerged as a serious problem among adolescents worldwide. Living in Indonesia, a patriarchal country with approximately 260 million people, adolescents are at risk of being committed to cyberbullying. Cyberbullying behaviour and associated variables, including fathering style and peer attachment, were examined to understand the interrelationships among the associated variables. Using exploratory research, this study collected data from four hundred and ten adolescents aged ranged 14–18 years old. Participants were recruited online through schools chosen purposively within Jabodetabek, West Java, Central Java, East Java, Sulawesi, and Sumatra and parental networks on social media. Results found that the Authoritarian fathering style was linked to cyberbullying perpetration. Secure peer attachment was negatively associated with cyberbullying perpetration, while Insecure attachment was significantly related to cyberbullying. Authoritarian style and Insecure attachment by peers were revealed as predictors of cyberbullying, and three forms of cyberbullying (Cyber Verbal bullying, Hiding Identity, and Cyber Forgery). Meanwhile, Secure attachment from peers appeared as a protective factor against cyberbullying perpetration. Interestingly, gender was revealed as a predictor for cyberbullying perpetration in which boys were more likely to commit cyberbullying than girls. This study highlights some key concerns about involving the father and peers in strategies to reduce cyberbullying among adolescents in Indonesia.
... The aforementioned displacement hypothesis (e.g., displacing social interaction in the real world) and upward social comparisons offer two possible explanations for the association between chatting/surfing the Internet and internalizing problems. Some other speculative explanations include that excessive chatting/surfing online may increase dependence on social media/internet (see Lissak, 2018, for a review), that chatting/surfing online may increase the likelihood of negative social media experiences, such as cybervictimization (Craig et al., 2020), and that night-time use of screen media may lead to sleep disturbances (see Lissak, 2018, for a review), all of which may in turn lead to internalizing problems. On the other hand, this finding may be explained by the fact that youth at risk of internalizing problems are more likely to overuse the Internet. ...
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Excessive screen time among adolescents is discussed as a significant public health concern. Identifying adolescent longitudinal patterns of time spent on regularly-used media screens and understanding their young adulthood mental health and behavioral issue correlates may help inform strategies for improving these outcomes. This study aimed to characterize joint developmental patterns of time spent on videogames, surfing/chatting the Internet, and TV/DVDs during adolescence (at ages 11, 13, 15, 17) and their associations with mental health (i.e., depression, anxiety, suicidal ideation, and self-injury) and behavioral issues (i.e., substance use, delinquency, aggression) in early adulthood (at age 20). A parallel-process latent class growth analysis was used to model data from a diverse community-ascertained sample of youth in Zurich, Switzerland (n = 1521; 51.7% males). Results suggested that a five-class model best fitted the data: (1) low-screen use, 37.6%; (2) increasing chatting/surfing, 24.0%; (3) moderate-screen use, 18.6%; (4) early-adolescence screen use, 9.9%; and (5) increasing videogame and chatting/surfing, 9.9%. After adjusting for baseline levels of outcomes (primarily at age 11), the trajectory groups differed in their associations with adulthood outcomes of mental health and behavioral problems, indicating the importance of problematic screen usage patterns in predicting these outcomes. Future research to test the directionality of these associations will be important. These findings suggest which patterns of screen use may be a marker for later mental health and behavioral issues in different domains.
... Despite the high heterogeneity in prevalence estimates, cyberbullying could still be on the rise, especially following the COVID-19 pandemic, perhaps due to students' increased technology use [10], which also has the potential to increase adolescents' risk of experiencing psychological, behavioral, and health problems associated with the involvement in such phenomena [2,[11][12][13]. ...
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Although cyberbullying and cybervictimization prevention programs have proved effective in the short term, their effectiveness remains unclear in the long run. Thus, the present study evaluated the long-term effects of the Tabby Improved Prevention and Intervention Program (TIPIP). Participants were 475 middle and high school students (Mage = 12.38; SD = 1.45; F = 241, 51%), of whom, 167 were in the Experimental Group (EG; Mage = 13.15; SD = 1.52; M = 51.5%), and 308 were in the Control Group (CG; Mage = 13.47; SD = 1.35; M = 47.7%). Students completed measures assessing cyberbullying and cybervictimization at three time points: baseline (T1), immediately after the intervention (6 months, T2), and at 1 year (T3). The results showed no significant effects of the TIPIP in reducing both cyberbullying and cybervictimization over time. Overall, our results confirm the lack of effectiveness of long-term preventive programs and emphasize that different curricula should be implemented in future programs to prevent and manage cyberbullying and cybervictimization, also taking into account psychological mechanisms and processes involved in such behaviors.
... The increased use and integration of technology into daily life introduces new risks and threats to people. Prior work shows these risks include hate speech, harassment, doxing, and bullying [51], [48], [47], [14], [56], [41], [16], [60], [17]. One prevalent issue exacerbated by technology is intimate partner violence (IPV), which is pervasive in the US (and in the world), affecting 1 in 10 men and 1 in 4 women [20]. ...
... While a 2020 UNICEF report found that only one-third of youth globally have internet access at home (UNICEF and International Telecommunication Union, 2020), a 2017 survey found that 71% of youth ages 15-24 were digitally connected through devices such as smartphones (Keeley & Little, 2017). Cyberbullying victimization has unfortunately accompanied the movement of youth communication online; one meta-analysis of cyberbullying research found the prevalence rate to be 15% (Modecki et al., 2014), and a recent, cross-national analysis of cyberbullying research found rates of 11-12% (Craig et al., 2020). Additionally, a previous study analyzing the role of technology in peer harassment suggested that online and in-person harassment incidents tend to overlap, with online victimization rarely occurring in isolation (Mitchell et al., 2016a, b). ...
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Medical social media derivatives such as tweets, online forums or drug reviews, can be subject of interest for medical sentiment analysis. Such data is data published by individuals (not necessarily patients, but their relatives, friends and healthcare professionals). While tweets are restricted in their length and are therefore characterised by a specific style of writing which is very concise and full with abbreviations, data from online communities or review sites can be more comprehensive. This chapter describes the various social media text types that have already been used for medical sentiment analysis along with their linguistic and semantic peculiarities.
O presente artigo buscou analisar a literatura científica acerca do uso excessivo de multitelas por crianças e adolescentes, relacionando tal contexto com o aparecimento de transtornos psíquicos e comportamentais nessa parcela social. Com os avanços tecnológicos das últimas décadas, houve a ampliação do acesso aos meios midiáticos pela população infantil. O desenvolvimento neurocognitivo desse grupo pode ser prejudicado pelo uso desenfreado de dispositivos eletrônicos. Foi observada a vinculação da exposição precoce às mídias com o aparecimento de sintomas depressivos, desequilíbrio emocional, assim como o aumento de transtornos de ansiedade em crianças e adolescentes. O uso excessivo de telas evidencia o aumento da dificuldade de relacionamentos interpessoais, fato esse agravante para problemas psíquicos. Estudos também comprovam associação significativa do uso de multitelas com sintomas de Transtorno do Déficit de Atenção e Hiperatividade (TDAH). A mediação parental para controle do acesso dos menores, em consonância aos incentivos às interações sociais e brincadeiras ativas, se mostrou eficaz na diminuição de distúrbios na população infantil.
Full-text available
Purpose: This study examined time trends in significant child and adolescent internalizing symptoms and explored the association of excessive and problematic social media use with these symptoms. Methods: Time trends in internalizing symptoms were assessed using data from five waves of the international survey of Health Behavior in School-aged Children (HBSC), conducted between 2001 and 2018 (N=1,036,869). The associations of frequent and problematic social media use with significant internalizing symptoms were assessed by hierarchical multinomial logistic regression using data from 2001-2002 and the 2017-2018 survey waves. Causal direction between social media use and internalizing symptoms was assessed using linear non-gaussian acyclic models (LiNGAM). Results: Prevalence of more severe internalizing symptoms increased from 6.7% in 2001-2002 to 10.4% in the 2017-2018 survey waves. The increase was especially large among 15-year old and older girls: from 10.9% to 19.1%. The difference in prevalence of more severe internalizing symptoms across survey waves was fully explained by problematic social media use. LiNGAM analysis confirmed the causal direction of social media use variables with internalizing symptoms. Conclusions: The study findings suggest that widespread use of social media may explain the increased prevalence of internalizing symptoms in adolescents in recent years.
All over the world, teens are constantly engaged on social media: refreshing their Facebook feeds, liking a post on Instagram, sending a Snapchat message to their friends. In the United States, 95% of adolescents now have a smartphone and as mobile-optimized social media platforms like Instagram, Snapchat, TikTok, YouTube, and others continue to grow in popularity, adolescents are spending more of their time navigating a complex virtual world. With this massive increase in virtual social participation comes the benefit of accessing information, gaining knowledge, and connecting with others, and the negative cost of social conflict, primarily in the form of cyberbullying. Studies demonstrate that cyberbullying, or the intentional harm of others through computers, cellphones, and other electronic devices, is becoming increasingly pervasive among youth. This impacts both the victim and the perpetrator. Being a victim of cyberbullying has various negative health implications, including increased rates of depression, suicidality, and substance use in youth.This chapter aims to define cyberbullying in the context of social media and explore the wide-ranging impact that social media cyberbullying has on youth mental health. Recommendations for how parents, schools, and clinicians can help children navigate social media safely and approach cyberbullying are provided.KeywordsCyberbullyingAdolescent digital useBullyingMental health and social media
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The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.
Full-text available
The advance of digital media has created risks that affect the bio-psycho-social well-being of adolescents. Some of these risks are cyberbullying, cyber dating abuse, sexting, online grooming and problematic Internet use. These risks have been studied individually or through associations of some of them but they have not been explored conjointly. The main objective is to determine the comorbidity between the described Internet risks and to identify the profiles of victimized adolescents. An analytical and cross-sectional study with 3212 participants (46.3% males) from 22 Spanish schools was carried out. Mean age was 13.92 ± 1.44 years (range 11–21). Assessment tools with adequate standards of reliability and validity were used. The main results indicate that the most prevalent single risk is cyberbullying victimization (30.27%). The most prevalent two-risk associations are cyberbullying-online grooming (12.61%) and cyberbullying-sexting (5.79%). The three-risk combination of cyberbullying-sexting-grooming (7.12%) is highlighted, while 5.49% of the adolescents present all the risks. In addition, four profiles are distinguished, with the profile Sexualized risk behaviour standing out, with high scores in grooming and sexting and low scores in the rest of the risks. Determining the comorbidity of risks is useful for clinical and educational interventions, as it can provide information about additional risks.
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Is cyberbullying essentially the same as bullying, or is it a qualitatively different activity? The lack of a consensual, nuanced definition has limited the field's ability to examine these issues. Evidence suggests that being a perpetrator of one is related to being a perpetrator of the other; furthermore, strong relationships can also be noted between being a victim of either type of attack. It also seems that both types of social cruelty have a psychological impact, although the effects of being cyberbullied may be worse than those of being bullied in a traditional sense (evidence here is by no means definitive). A complicating factor is that the 3 characteristics that define bullying (intent, repetition, and power imbalance) do not always translate well into digital behaviors. Qualities specific to digital environments often render cyberbullying and bullying different in circumstances, motivations, and outcomes. To make significant progress in addressing cyberbullying, certain key research questions need to be addressed. These are as follows: How can we define, distinguish between, and understand the nature of cyberbullying and other forms of digital conflict and cruelty, including online harassment and sexual harassment? Once we have a functional taxonomy of the different types of digital cruelty, what are the short- and long-term effects of exposure to or participation in these social behaviors? What are the idiosyncratic characteristics of digital communication that users can be taught? Finally, how can we apply this information to develop and evaluate effective prevention programs?
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In China, public concern continues to mount regarding the risks of excessive Internet use among adolescents. This study investigated the factors influencing Internet addiction and adolescent risk behaviors among excessive Internet users. Proposing a conceptual model with a theoretical origin in risk behavior theory and media dependency theory, this study examined the influence of personality traits, online gaming, Internet connectedness (both the overall index and various scopes) and demographics on Internet addiction and risk behaviors (smoking, drinking, gambling, and risky sexual behaviors). Clinical data (N = 467) were retrieved from one of the earliest and largest Internet addiction clinics in China. The findings reveal that certain personality traits are significantly associated with Internet addiction and risk behaviors. Online gaming had a strong impact on both Internet addiction and risk behaviors among excessive Internet users. The study also reveals that various scopes of Internet connectedness, such as site scope, facilitate addictive Internet use and risk behaviors among adolescents. The findings can contribute to the prevention of and intervention into Internet addiction and adolescent risk behaviors.
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Many adolescents are heavily engaged with social media and text messaging (George & Odgers, 2015; Lenhart, 2015), yet few psychologists have studied what digital communication means for adolescents’ relationships and adjustment. This article proposes that psychologists should embrace the careful study of adolescents’ digital communication. We discuss theoretical frameworks for understanding adolescents’ involvement with social media, present less widely recognized perils of intense involvement with social media, and highlight positive features of digital communication. Coconstruction theory suggests that adolescents help to create the content of digital communication that shapes their lives, and that there may be strong continuity between adolescents’ offline and online lives (Subrahmanyam, Smahel, & Greenfield, 2006). However, psychological theories and research methods could further illuminate the power and the pain of adolescents’ digital communication. Psychologists need to understand more about subtle but potentially serious risks that adolescents might face: The agony of victimization by even a single episode of cyberbullying and the pain of social exclusion and comparison resulting from vast amounts of time reading large social media feeds and seeing friends doing things without you and comparing your inner emotional experience to everyone else’s highly groomed depictions of their seemingly marvelous lives. If we seek to understand developmental psychopathology and to help youth at risk, psychologists need to embrace careful study of the content of adolescents’ online communication, parents need to talk with their children about their own online experiences and become familiar with social media themselves, and clinicians need to address adolescents’ online social lives in prevention and treatment programs.
The Dark Tetrad traits (i.e., Machiavellianism, psychopathy, narcissism, sadism) are associated with antisocial online behaviors. However, the mediating role of these behaviors between the Dark Tetrad and problematic social media use (PSMU) is unclear. Among a sample of 761 participants, we investigated direct and indirect associations of the Dark Tetrad traits with PSMU via cyberbullying, cyberstalking, and cybertrolling. Multiple mediation analyses demonstrated cyberbullying and cyberstalking fully mediated the relationship between Machiavellianism and PSMU in the total sample and among men. Narcissism was indirectly associated with PSMU via cyberstalking in the total sample and among women. The relationship between sadism and PSMU was fully explained by cyberbullying and cyberstalking in the total sample. Cybertrolling was associated with sadism, psychopathy, and Machiavellianism, although it was not related to PSMU. We suggest that antisocial online behaviors may provide an explanation for the relationship between dark personality traits and PSMU with different behaviors mediating different traits among men and women.
During adolescence, adolescents move away from their parents in order to establish their place in society. Therefore, there are two arenas that have a significant impact on adolescents; the family and the social one. Adolescents’ intensive internet use leads to concern about Problematic Internet Use (PIU) (Siciliano et al., 2015). Therefore, the goal of this study was to examine if stressful environments such as being a victim to bullying and/or cyberbullying, and poor relationships with parents could be linked directly and indirectly to PIU. Data was collected from a representative sample of 1,000 Israeli adolescents aged 12-17 (53% females, average age 14.19 (SD=1.34)). Measures included demographics, a short problematic internet use test, relationships with parents' questionnaire, cyberbullying scale and, separately, a traditional bullying test. Path analysis model revealed that both poor parent-child communication and being a cyberbullying victim were related to PIU. Correspondingly, Poor parent-child communication had an indirect effect on PIU through bullying and/or cyberbullying victimization. Conversely, both positive mother-child communication and positive father-child communication had an indirect effect on PIU through bullying or cyberbullying victimization, implying that good communication with parents actually can assist reducing bullying victimization and PIU behavior. Limitations, conclusions, and suggestions for further research are discussed.
Social networking (e.g., blogging and social networking website use) frequency among adolescents has increased exponentially in the last decade. An unfortunate by-product of increased communication via the Internet is cyberbullying; however, there is a paucity of longitudinal research exploring the relationships between social network use and cyberbullying in an adolescent sample. The current study used a three-wave longitudinal study of over 3000 (at Wave 1) Singaporean youth to examine whether the relationship between Wave 1 social network use and Wave 3 cyberbullying perpetration was mediated by an increase in Wave 2 development of positive cyberbullying attitudes. Results using structural equation modeling showed support for this hypotheses: Wave 1 social networking use predicted Wave 2 positive cyberbullying attitudes 2 years later. Finally, Wave 2 cyberbullying attitudes predicted Wave 3 cyberbullying perpetration 1 year later. Overall, these results suggest that social networking can be used to harm others through the development of positive cyberbullying attitudes - a link that has received very little empirical attention.
Adolescents tend to go to bed later and sleep less as they grow older, although their need for sleep stays the same throughout adolescence. Poor sleep has negative consequences on personal and interpersonal functioning, including increased aggressive tendencies. With adolescents’ social life increasingly including interactions via digital media, these interactions may also become more aggressive when adolescents’ sleep problems increase. One of the ways in which online aggression may be enacted is through cyberbullying. Although previous research has examined the role of sleep disruptions in offline bullying, the role of sleep in cyberbullying has not yet been addressed. Therefore, this study examines the longitudinal effect of poor sleep quality on later cyberbullying behavior. Thirteen- to fourteen-year-old adolescents completed self-report measures on sleep quality, anger, cyberbullying perpetration, and frequency of digital media use. Because one of the pathways through which sleep is proposed to be linked to aggression is an affective pathway, namely via angry affect, a mediation model of poor sleep quality predicting cyberbullying via feelings of anger was tested. Results from structural equation modeling and a bootstrap test indicated that poor sleep quality was indeed indirectly associated with later cyberbullying behavior through heightened feelings of anger, even when taking the effects of the use of digital media and previous cyberbullying behavior into account. This finding provides support for the proposed affective pathway linking sleep problems to aggression. As sleep problems and anger seem to play a predicting role in cyberbullying behavior, suggestions for cyberbullying intervention and prevention strategies are formulated.
This book, the first in a series of collected works, traces the evolution of Problem Behavior Theory from its inception to its current status as a widely used framework for understanding and addressing risky behavior in youth and young adults. The theory is explored from its beginnings as a study of deviant behavior and alcohol abuse in a tri-ethnic community through its expansion to include psychosocial aspects of development, risk and protective factors, and health behavior in the larger societal context of youth behavior. In its current form, Problem Behavior Theory constitutes an interdisciplinary approach to research personal and societal factors that are involved in both normative and problematic behavior. Chapters highlight the many contributions of the theory to social science and its potential for informing evidence-based intervention and prevention programs for youth and young adults. Topics featured in this book include: • The Tri-Ethnic Community Study. • The Socialization of Problem Behavior in Youth Study. • The Young Adult Follow-up Study. • The problem behavior syndrome. • The cross-national generality of Problem Behavior Theory. • Problem Behavior Theory and adolescent pro-social behavior. The Origins and Development of Problem Behavior Theory is a must-have resource for researchers/professors, clinicians, and related professionals as well as graduate students in social and developmental psychology, criminology/criminal justice, public health, social work, and related disciplines.