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DOI: 10.1177/0020764020978434
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E CAMDEN SCHIZOPH
Social media (SM) is defined as ‘Internet-based channels 
that allow users to opportunistically interact and selec-
tively self-present, either in real-time or asynchronously, 
with both broad and narrow audiences who derive value 
from user-generated content and the perception of inter-
action with others’ (Carr & Hayes, 2015, p. 50). Popular 
SM platforms are such as WhatsApp and social network-
ing sites (SNSs; e.g. Facebook). Widespread use of SM 
means that problematic SM use has become a serious 
problem. For example, the Bergen Facebook Addiction 
Scale (Andreassen et al., 2012) conservatively estimated 
that 3.3% of Italian users were addicted to Facebook 
(Biolcati et al., 2018). Vangeel et al. (2016) found that 
7.1% of secondary school students in Belgium were 
addicted to Facebook, with compulsive users spending 
2 hours 38 minutes on a school day and 4 hours and 
35 minutes on a holiday. A French study found a 10% rate 
of Facebook addiction (Chabrol et al., 2017). A study 
found that 18% of Turkish college students were classi-
fied as disordered SM users (Kircaburun, Demetrovics, 
& Tosuntaş, et al., 2019). A rate of 26.2% was found in 
college students in US, 29.4% in Singapore, and 44.5% 
in China (Tang et al., 2017).
Empirical evidence revealed that compulsive social 
networking site (SNS) use was related to physical (Moqbel 
& Kock, 2018) and mental health (Frost & Rickwood, 
2017; Pontes, 2017; Ryan et al., 2014). Researchers have 
paid increasing attention to excessive SM use, and have 
performed meta-analyses (Marino et al., 2018a, 2018b) 
about the relationships of compulsive SM use with mental 
health, psychological distress and well-being. While these 
meta-analyses have improved the understanding of the 
relation between SM addiction and mental health, they do 
not address some salient issues. For example, these meta-
analyses focused narrowly on Facebook addiction. The 
present meta-analysis extends the concern to problematic 
use of all SM platforms.
Problematic SM use
Several terms are used to describe problematic SM use. 
Addiction (Ryan et al., 2014) is one common term. 
Chamberlain et al. (2016) defined the core factors of 
A meta-analysis of the problematic 
social media use and mental health
Chiungjung Huang 
Abstract
Background: Although previous meta-analyses were conducted to quantitatively synthesize the relation between 
problematic social media (SM) use and mental health, they focused on Facebook addiction.
Aims: The purpose of this meta-analysis is to examine this relation by extending the research scope via the inclusion of 
studies examining problematic use of all platforms.
Method: One hundred and thirty-three independent samples (N =244,676) were identified.
Results: As expected, the mean correlations between problematic SM use and well-being are negative, while those 
between problematic SM use and distress are positive. Life satisfaction and self-esteem are commonly used to represent 
well-being, while depression and loneliness are usually used to indicate distress. The mean correlations of problematic 
SM use with life satisfaction and self-esteem are small, whereas those of problematic SM use with depression and 
loneliness are moderate. The moderating effects of publication status, instruments, platforms and mean age are not 
significant.
Conclusions: The magnitude of the correlations between problematic SM use and mental health indicators can 
generalize across most moderator conditions.
Keywords
Mental health, well-being, distress, meta-analysis, problematic social media use
Graduate Institute of Education, National Changhua University of 
Education, Changhua
Corresponding author:
Chiungjung Huang, Graduate Institute of Education, National Changhua 
University of Education, Changhua, 1 Jinde Road, Changhua 50058. 
Email: chiung@cc.ncue.edu.tw
978434 ISP0010.1177/0020764020978434International Journal of Social PsychiatryHuang
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2 International Journal of Social Psychiatry 00(0)
behavioral addiction as inability to control of use, functional 
impairment and continuing involvement in the behavior 
regardless of its negative impacts. Some researchers 
(Caldiroli et al., 2018; Miele et al., 1990) used the term 
‘dependency’ to describe problematic SM use. Dependence 
was defined as an indispensable behavior to achieve goals, 
while addiction refers to failure to control leading to impair-
ment of personal or work lives (Ferris & Hollenbaugh, 
2018). Hence, addiction has an absolutely negative effect, 
while dependence does not necessarily. Other terms, such as 
compulsive use (Aladwani & Almarzouq, 2016; De Cock 
et al., 2014), excessive use (Wang et al., 2016), and disor-
dered use (van den Eijnden et al., 2018), were also used. 
Problematic use was chosen in this study because it is broad 
enough to incorporate different levels of excessive use (Lee 
et al., 2017).
Empirical studies
Previous empirical studies were conducted in various 
research contexts, and have different findings about the 
strength of the relation between problematic SM use and 
mental health. For example, Kircaburun (2016) sampled 
1,130 Turkish secondary school students and found that 
the relation between problematic SNS use and self-esteem 
was r = −.09. Turel and Qahri-Saremi (2016) also found a 
small relation between problematic Facebook use and self-
esteem at r = .01 for a pilot sample of 60 undergraduate 
students, and r = −.05 for 341 Facebook users from a large 
university in North America. A moderate correlation (r = 
−.24) was found in Aladwani and Almarzouq (2016) who 
used a sample of 407 undergraduate students in Kuwait. 
Biolcati et al. (2018) also found support for a moderate 
effect. On the other hand, a large correlation between prob-
lematic Facebook use (r = −.43) and self-esteem was 
found in Baturay and Toker (2017) who sampled 120 col-
lege students in Turkey.
Findings about other mental health indicators were also 
inconsistent. For example, the relation between problem-
atic SM use and depression was from small (r = .13; 
Kircaburun, 2016) to large (r = .45; Błachnio et al., 2015). 
As both empirical studies varied in research situations, and 
research findings were not consistent, moderator effects 
are worth investigating.
Publication status
Publication bias refers to the unrepresentativeness of 
included studies that can be caused by availability and 
accessibility (McShane et al., 2016). For example, confer-
ence papers have more limited availability than journal 
papers. The inaccessibility of relevant studies (e.g. unpub-
lished manuscripts) can lead to unrepresentative data in a 
meta-analysis. To explore this possibility, the mean correla-
tions among publication outlets were examined.
Study country
Caldiroli et al. (2018) suggested that the problem of tech-
nology addiction was especially serious in China, South 
Korea and Taiwan. As the prevalence of technology 
addiction varies with country, the relation between prob-
lematic SM use and mental health may vary with country 
or culture. To examine the possible country effect, Marino 
et al. (2018a) examined the country effect on the relation 
between problematic Facebook use and psychological 
distress, and found that the correlation was likely to be 
higher in studies from Western countries compared to 
that from Asian countries. As Marino et al. (2018a) had a 
small number of effect sizes, and thus low generalizabil-
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https://doi.org/10.1007/s11469-019-00071-8Measures of problematic SM use
Researchers have used several instruments to measure 
problematic SM use. The most popular measure is the 
Bergen Facebook Addiction Scale (Andreassen et al., 
2012), which assesses six key components, namely sali-
ence, mood modification, tolerance, withdrawal, conflict 
and relapse. Each component is initially assessed by three 
items. After the deletion of items with relatively low item-
total correlations, one item for each component is retained. 
The one-factor solution is supported by the confirmatory 
factor analysis. The Bergen Social Media Addiction Scale 
(Andreassen et al., 2017) is a modified version to measure 
problematic SM use in general by replacing ‘Facebook’ 
with ‘social media’ in each item.
The Facebook Intrusion Questionnaire (Elphinston & 
Noller, 2011) is composed of eight items measuring cog-
nitive salience, behavioral salience, interpersonal con-
flict, conflict with other activities, euphoria, loss of 
control, withdrawal and relapse. Each item is assessed on 
a seven-point Likert scale. A unidimensional model was 
supported by exploratory factor analysis (Elphinston & 
Noller, 2011).
The Social Media Disorder Scale (SMDS; van den 
Eijnden et al., 2016) was developed in the Netherlands, 
and is based on the DSM-5 criteria, namely preoccupation, 
tolerance, withdrawal, persistence, displacement, prob-
lem, deception, escape, and conflict. Initially, three items 
are developed for each of the nine components, and the 
item with the highest factor loading within each of the nine 
criteria is selected.
Other researchers (Baturay & Toker, 2017; Hong et al., 
2014) adapted the Internet Addiction Test (IAT, Young, 
1998) to measure problematic Facebook use or general 
problematic SM use. As these problematic SM use meas-
ures assess different components, the relation between 
problematic SM use and mental health may vary as a func-
tion of measures of problematic SM use.
Huang 3
Measures of mental health
Empirical studies have examined several positive indi-
cators of mental health, such as self-esteem (e.g. Choi & 
Lim, 2016), life satisfaction (e.g. Hawi & Samaha, 
2018), well-being (e.g. Verma & Kumari, 2016), happi-
ness (e.g. Satici & Uysal, 2015), and positive affect (e.g. 
Satici, 2018). Self-esteem is the most common indicator 
of well-being. The most popular instrument to measure 
global self-esteem is the Rosenberg Self-Esteem Scale 
(Rosenberg, 1965) which consists of 5 positively-worded 
and 5 negatively-worded items. Due to brevity and easy 
administration, this scale has been adapted into more 
than 50 different languages (Schmitt et al., 2005). A 
shorter instrument to measure global self-worth is the 
Single Item Self-Esteem Scale (SISES; Robins et al., 
2001) comprised of the item, ‘I have high self-esteem’, 
on a 7-point Likert scale.
Life satisfaction is another common indicator of well-
being, and the most popular measure is the Satisfaction 
with Life Scale (Diener et al., 1985), which consists of 5 
items with 7 response categories ranging from 1 to 7. Thus, 
the total score ranges from 5 to 35. Total scores of 5 to 9 
are viewed as ‘extremely dissatisfied’, 15 to 19 as ‘slightly 
dissatisfied’, 21 to 25 ‘slightly satisfied’, and 26 to 30 ‘sat-
isfied’ (Pavot & Diener, 1993).
Negative indicators of mental health can be represented 
by anxiety (e.g. Durak, 2018), depression (e.g. Worsley et al., 
2018), loneliness (e.g. Yu et al., 2016), suicidal ideation (e.g. 
Jasso-Medrano & López-Rosales, 2018), distress (e.g. 
Laconi et al., 2018) and negative affect (e.g. Satici, 2018). 
Depression is the most examined indicator, with common 
measures such as the Patient Health Questionnaire-9 (PHQ-
9; Kroenke et al., 2001), the Center for Epidemiologic 
Studies Depression Scale (CES-D; Radloff, 1977), the 
depression subscale of the Depression Anxiety Stress 
Scales-21 (DASS-21; Lovibond & Lovibond, 1995), the 
depression subscale of Short Depression-Happiness Scale 
(SDHS; Joseph et al., 2004), Hamilton Depression Rating 
Scale (HAM-D; Hamilton, 1960, 1967), Montgomery–
Åsberg Depression Rating Scale (MADRS; Montgomery & 
Åsberg, 1979), and Beck Depression Inventory (BDI; Beck, 
& Steer, 1987). The PHQ-9 assesses 9 depressive symptoms 
in primary care settings over the last two weeks with 4 
response categories, 0 for ‘not at all’, 1 for ‘several days’, 2 
for ‘more than half the days’, and 3 ‘Nearly every day’. Thus, 
the total score of the PHQ-9 ranges from 0 to 27. The cutoff 
scores for mild, moderate, moderately severe and severe 
depression are 5, 10, 15, and 20, respectively (Kroenke et al., 
2001). On the other hand, the CES-D assessing depressive 
symptom for general population over the past week consists 
of 20 items on a 4-response scale, ranging from 0 to 3. The 
total score is from 0 to 60. An arbitrary cutoff score of 16 or 
higher is considered to indicate possible depression requiring 
clinical assessment (Radloff, 1977).
The depression subscale of DASS-21 measures depres-
sive emotional state over the past week, and consists of 7 
items, each on a 4-point scale, ranging from 0 to 3 (Szabó, 
2010) Scores of 10, 14, 21, and 28 indicate mild, moderate, 
severe and extremely severe depression (Lovibond & 
Lovibond, 1995). The depression subscale of the SDHS 
consists of 3 items with 4 response categories. The items 
are, ‘I felt dissatisfied with my life’, ‘I felt cheerless’, and 
‘I felt that life was meaningless’. Cutoff scores for the 
depression subscale are not provided in Joseph et al. (2004).
The HAM-D has 6-, 17-, 21-, and 24-item versions, and 
is often used to measure treatment effect instead of state of 
depression (Santen et al., 2008). The most popular version, 
consisting of 17 items, is on a 3- or 5-point scale. The mul-
tidimensional factor is supported, but the number of factors 
varies across studies (Bagby et al., 2004). Another popular 
measure for assessing change of depressive symptoms in 
clinical trial research is the MADRS, which comprises 10 
items chosen by reliability and validity from an original set 
of 17 items from the Comprehensive Psychopathological 
Rating Scale (Montgomery & Åsberg, 1979). The factor 
structure of the MADRS varied across patient groups 
(Ketharanathan et al., 2016). As both HAM-D and MADRS 
were usually used in clinical research, their scores are 
strongly correlated (Heo et al., 2007).
The BDI consisting of 21 items on a 4-point scale is a 
self-reporting measure to assess depression for adolescents 
and adults (Beck & Steer, 1987). The latest version of the 
BDI, the Beck Depression Inventory–II (BDI–II), is 
derived from DSM-IV (American Psychiatric Association, 
1994), with the items of Body Image Change, Work 
Difficulty, Weight Loss and Somatic Preoccupation in the 
BDI replaced with Agitation, Worthlessness, Loss of 
Energy, and Concentration Difficulty in the BDI–II. The 
items of Changes in Sleeping Pattern and Changes in 
Appetite have seven response categories, and the remain-
ing 19 items have four (Beck et al., 1996).
The most prevalent measure to assess loneliness is the 
UCLA Loneliness Scale (Russell et al., 1980), consisting 
of 10 positive-worded and 10 negative-worded items, each 
item on a 4-point Likert scale. The UCLA Loneliness 
Scale has sound psychometric properties (Hartshore, 
1993), and the 3-factor (isolation, relational connected-
ness, and collective connectedness) structure was sup-
ported (Dussault et al., 2009).
Platform
Some researchers focused on problematic use of SM in 
general. For example, Worsley et al. (2018) examined the 
relation between problematic use in general and depres-
sion for 1029 university students in UK, and found that the 
relation was r = .27. Some researchers focused specifi-
cally on problematic Facebook use. Steggink (2015) used 
a sample of 315 users recruited online with mean age of 
4 International Journal of Social Psychiatry 00(0)
28.74 years, and found that the correlation betweenprob-
lematic Facebook use and depression was r = .10. Whether 
the magnitude of correlation varied with platform was 
unknown, and this meta-analysis addressed this 
possibility.
Participant age
Few longitudinal and cross-sectional studies have been 
conducted to examine the age effect on the relation 
between problematic SM use and mental health. van den 
Eijnden et al. (2018) adopted a three-wave design with a 
one-year interval between adjacent assessments for a 
sample of 543 teenagers with mean 12.9 years at study 
inception. The correlations of problematic use of SNSs 
assessed at time 1 with life satisfaction assessed at times 
2 and 3 for boys were r = −.21 and r = −.11, respec-
tively. The corresponding correlations of time 2 problem-
atic use of SNSs with time 2 and 3 life satisfactions for 
boys were r = −.33 and r = −.09, respectively. For girls, 
the correlation of time 1 problematic use of SNSs with 
time 2 and 3 life satisfaction were r = −.43 and r = −.33, 
respectively and those of time 2 problematic use of SNSs 
with time 2 and 3 life satisfaction were r = −.48 and r = 
−.55, respectively. The age effect seemed to be supported 
in the cross-sectional study. Kanat-Maymon et al. (2018) 
found that the relation between problematic Facebook 
use and self-esteem for an online adult sample with mean 
age of 33.36 years was r = −.52. The corresponding cor-
relation for a sample of 80 undergraduate students was 
only small, at r = −.05.
The age effect on the relations of problematic Facebook 
use with psychological distress and well-being were not 
supported in Marino et al. (2018a). The number of effect 
sizes in that meta-analysis was small and non-significant 
findings can be caused by low statistical power.
Participant gender
Griffiths (2000) revealed that technology addicts are usu-
ally male. Some researchers examined the relation 
between problematic SM use and mental health espe-
cially for males. For example, Kim and Park (2015) 
found that the correlation between problematic SNS use 
and self-esteem among 213 male middle-school students 
was r = −.38. Some researchers examined the moderat-
ing effect of gender on the relation between problematic 
SNS use and mental health. For example, van den Eijnden 
et al. (2018) examined the moderating effect of gender 
for teenagers, and found that the correlation between 
problematic SNS use and life satisfaction was r = −.48 
for girls and r = −.33 for boys. Walburg et al. (2016) 
selected 115 boys and 171 girls, and found that the cor-
relations of problematic Facebook use with depression 
and suicidal ideation were r = .37 and r = .23 for boys, 
and r = .10 and r = .20 for girls.
Marino et al. (2018a) examined the gender effect repre-
sented by the proportion of female users on the relations of 
problematic Facebook use with psychological distress and 
well-being and the gender effects were not significant. 
Re-examination of the gender effect is prominent for three 
reasons. First, the small number of effect size in Marino 
et al. (2018a) could lead to a low statistical power. Second, 
empirical studies rarely address the issue of gender effect 
on the relation between SM addiction and mental health. 
Third, empirical studies mentioned above seemed to dem-
onstrate a potential gender effect.
Previous reviews and meta-analyses
Frost and Rickwood (2017) reviewed the relation between 
Facebook addiction and mental health based on 5 cross-
sectional and 1 longitudinal studies. They concluded that 
Facebook addiction was associated with poor mental 
health. Ryan et al. (2014) identified three studies (Hong 
et al., 2014; Koc & Gulyagci, 2013; Uysal et al., 2013), 
and reported that Facebook addiction was related to 
depression and anxiety. Another review by Keles et al. 
(2019) that identified three articles also supported that 
addiction to SNSs was related to depression.
Marino et al. (2018a) identified 23 samples that 
examined the relations of problematic Facebook use 
with psychological distress and well-being. The mean 
correlation between problematic Facebook use and psy-
chological distress was r = .29, and the correlation cor-
rected for attenuation was ρ = .34. The correlations for 
specific factors were r = .30 and ρ = .35 for depression; 
r = .29 and ρ = .33 for anxiety; r = −.19 and ρ = −.22 
for general well-being; r = −.16 and ρ = −.19 for life 
satisfaction. Marino et al. (2018b) meta-analyzed 8 cor-
relations between problematic Facebook use and self-
esteem, and found that the mean correlation was r = 
−.23. No moderator analyses were conducted for this 
correlation.
As two previous meta-analyses (Marino et al., 2018a, 
2018b) specifically focused on problematic Facebook use, 
their conclusions may not be valid for studies addressing 
problematic use of other SM or general problematic use of 
SM. The current meta-analysis aimed to conduct a com-
prehensive analysis of accumulating empirical evidence 
obtained by studies examining the association between 
problematic SM use and mental health.
Method
Literature search
To identify relevant studies, the ERIC, PsycINFO and 
ProQuest Dissertations and Theses Global databases were 
searched using SM terms (namely, Facebook, Twitter, 
Instagram, MySpace, ‘social media’, ‘online social net-
work*’, and ‘social network* site*’) and problematic use 
Huang 5
terms (addict*, abuse, misuse, overuse, intrusion, ‘prob-
lematic use’, ‘excessive use’, ‘compulsive use’, ‘patho-
logical use’, ‘disordered use’) through July 19, 2020. The 
ERIC, PsycINFO and ProQuest databases yielded 157, 
1,553 and 287 articles, respectively. The reference lists 
for eligible articles and previous meta-analysis (Marino 
et al., 2018a, 2018b) were then examined. The author 
screened each article by reviewing the title and abstract. 
The full texts of studies passing the initial screening were 
then identified to determine eligibility based on three 
inclusion criteria. First, studies should provide sufficient 
statistics to compute the correlation between problematic 
SM use and mental health. Second, studies should report 
the sample size. Lastly, the study should be published in 
English. Four unpublished datasets in Marino et al. 
(2018a) were not available because they did not report 
titles, sources or manuscripts.
Analysis
The Pearson Product-Moment correlation between prob-
lematic SM use and mental health was coded. The distri-
bution of r depends on the population correlation, ρ, and 
sample size. Unless the sample size is sufficiently large, 
the distribution of sample correlation is skewed (Card, 
2012). To normalize the sample correlation, the correla-
tion r was converted to Zr using the Fisher’s transforma-
tion equation. The inverse variance (N-3) was used as a 
weight to compute the mean correlation. Random-effects 
were used.
Results
This study included 123 articles presented in the Appendix. 
Andreassen et al. (2016) and Andreassen et al. (2017) ana-
lyzed the same data, yielding 122 studies. Of these 122 
studies, 111 were published in journals, 5 in doctoral dis-
sertations, 3 in Master theses, 2 in conferences, and 1 in a 
book chapter. Eleven studies each consisted of 2 samples, 
and thus 133 independent samples involving 244,676 par-
ticipants were analyzed in the subsequent analyses. Table 
1 presents the descriptive statistics of the 133 samples. 
When multiple indicators of mental health, multiple 
platforms, or multiple measures of problematic use SM 
were assessed, all relevant effect sizes were coded. The 
summary of all effect sizes were presented in Table 2.
Mean correlation between problematic SM use 
and mental health
The same sample analyzed in Andreassen et al. (2016) and 
Andreassen et al. (2017) had over 23,000 participants, and 
that in Boer et al. (2020) had 154,981 participants. As the 
inverse variance (N-3) was used as a weight to compute 
the mean correlation, these two studies would receive 
extremely large weights. However, the common winsori-zation method, which recodes the sample size at two or 
three standard deviations above the mean (Lipsey & 
Wilson, 2001) was not appropriate, as the standard devia-
tion shown in Table 1 was also extremely large (13,536.57). 
The sample sizes of these two samples were set at 3 times 
the mean (5,519).
Table 3 lists the weighted mean correlations between 
problematic SM use and mental health indicators. Eighty-
five effect sizes were related to well-being. Of these, 4 
were for happiness, 30 for life satisfaction, 3 for positive 
affect, 2 for mental health, 42 for self-esteem, 3 for over-
all well-being and 1 for psychiatric well-being. As 
expected, the correlations between problematic SM use 
and well-being indicators were negative, ranging from 
−.11 to −.30. The mean correlations of problematic SM 
use with happiness, life satisfaction, positive affect and 
self-esteem were significantly different from 0. The 
homogeneity test for the correlation between problematic 
SM use and self-esteem was significant, indicating sig-
nificant between-study variation.
Many studies examined the correlations between 
problematic SM use and distress indicators, and all mean 
correlations were positive. The relation between prob-
lematic SM use and depression attracted most research 
attention. The mean correlation was moderate at r = 
.31, indicating possible moderate detrimental impact of 
problematic SM use. Except for the correlations of prob-
lematic SM use with negative affect and social loneli-
ness, other mean correlations were significantly different 
from 0.
Moderator analyses of the relation between 
problematic SM use and self-esteem
Due to insufficient numbers of effect sizes, moderator 
analyses were conducted for correlations of problematic 
SM use with self-esteem (k = 42), life satisfaction (k = 
30), depression (k = 59), and loneliness (k = 29). The 
mean correlations were computed for categories of mod-
erators with at least 4 effect sizes. Table 4 lists the categor-
ical moderator effects on the relation between problematic 
Table 1. Descriptive statistics of the 132 independent 
samples included in the meta-analysis.
variable k Min Max Mean SD
N 133 55 154981 1839.67 13536.57
age 125 13.02 50.13 21.89 6.22
female 129 0 1 0.59 0.17
ES 133 –0.57 0.59 0.10 0.27
age = mean age of the sample; female = proportion of females in the 
sample.
6 International Journal of Social Psychiatry 00(0)
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la
nd
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20
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3
0.
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lf-
es
te
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 s
at
is
fa
ct
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n
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rg
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0.
16
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3
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er
 e
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al
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s
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1
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ct
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tr
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ar
gr
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 (
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)
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er
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9
22
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2
0.
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pr
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si
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nx
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ty
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SS
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1
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ce
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rg
en
0.
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gr
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0.
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pr
es
si
on
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en
ta
l h
ea
lth
BD
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ka
t 
et
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l. 
(2
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ce
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ok
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rg
en
0.
23
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30
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ai
lo
vs
ka
ia
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ch
ill
ac
k,
 M
ar
gr
af
, 
et
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l. 
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8)
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er
m
an
y
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ty
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ap
pi
ne
ss
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A
SS
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1,
 S
H
S
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ok
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rg
en
0.
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0.
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 e
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al
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)
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er
m
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y
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0
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3
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l h
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lth
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ka
t 
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6)
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ce
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ok
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rg
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27
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ai
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el
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 M
ar
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l. 
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9)
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er
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y
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21
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1
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ok
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rg
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43
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ai
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el
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ar
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f, 
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l. 
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01
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2
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S
90
9
37
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4
0.
48
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A
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ce
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ok
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rg
en
0.
55
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ow
n 
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01
5)
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S
55
19
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0
0.
82
se
lf-
es
te
em
R
os
en
be
rg
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ce
bo
ok
Fa
ce
bo
ok
 C
om
pu
ls
io
n 
In
ve
nt
or
y
−
0.
17
(C
on
tin
ue
d)
Huang 7
St
ud
y
C
ou
nt
ry
N
A
ge
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M
H
M
H
M
ea
s.
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SM
M
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rn
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ut
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r 
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S
25
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25
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1
0.
62
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lf-
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te
em
R
os
en
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rg
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A
l-M
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ay
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 (
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15
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0.
26
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ar
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ll 
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01
9)
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S
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xi
et
y
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A
I
SM
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T
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29
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as
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d 
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or
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5)
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1
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al
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22
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5
0
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ci
al
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ty
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S
SN
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PI
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0.
44
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d 
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or
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1
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ci
al
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ty
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A
S
SN
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S2
0.
22
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ha
br
ol
 e
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al
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20
17
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ce
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6
20
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0
0.
76
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pr
es
si
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oc
ia
l a
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ty
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ES
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A
SA
Fa
ce
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ok
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T
0.
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ha
e 
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l. 
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01
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ut
h 
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or
ea
25
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21
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0
0.
64
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on
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ES
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s
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42
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ha
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d 
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ha
ve
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01
7)
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ili
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15
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0
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s
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C
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ce
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nd
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 (
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13
)
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21
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he
n 
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l. 
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02
0)
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on
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on
g
30
8
23
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5
0.
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 d
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ss
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n
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A
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rg
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0.
21
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nd
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im
 (
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16
)
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h 
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or
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41
9
25
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8
0.
49
se
lf-
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te
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R
os
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rg
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ce
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nd
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ul
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i (
20
13
)
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ro
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el
l (
20
14
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S
38
0/
38
1
33
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6
0.
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se
lf-
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te
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ife
 s
at
is
fa
ct
io
n
R
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rg
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lit
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ife
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nj
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m
en
t 
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d 
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tis
fa
ct
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nn
ai
re
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ho
rt
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or
m
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bo
ok
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rg
en
0.
24
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0.
20
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ud
o 
et
 a
l. 
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02
0)
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la
nd
23
5
21
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9
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3
se
lf-
es
te
em
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ep
re
ss
io
n
R
os
en
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rg
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ce
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ok
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Q
−
0.
05
, 0
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7
da
 V
ei
ga
 e
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al
. (
20
19
)
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rt
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al
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4
21
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5
0.
73
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pr
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si
on
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nx
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ty
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I
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ce
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ok
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rg
en
0.
29
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3
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e 
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oc
k 
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l. 
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01
4)
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lg
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m
10
00
43
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0
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A
se
lf-
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te
em
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on
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de
pr
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on
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os
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rg
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as
ch
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 D
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L
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rg
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0.
29
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2,
 
0.
38
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em
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al
. (
20
19
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S
29
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20
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3
0.
58
lif
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tis
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ct
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ty
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rg
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hi
r 
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l. 
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1
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0.
46
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l a
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ty
Sa
lo
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 e
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al
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), 
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SA
 
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ce
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38
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hi
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l. 
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2
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di
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11
44
14
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8
0.
44
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l a
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ty
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), 
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ce
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rg
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0.
31
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2
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ur
ak
 (
20
18
)
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ur
ke
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45
1
15
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0
0.
48
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ci
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nx
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ty
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SA
SM
SM
D
S
0.
58
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ur
ak
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nd
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ef
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lu
 (
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ur
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58
0
22
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0
0.
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ty
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 U
C
LA
SM
SM
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S
0.
37
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1
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er
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rt
 (
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S
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0
0.
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ct
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rt
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11
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io
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13
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re
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23
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0
0.
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pr
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ue
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S
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26
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uv
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19
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S
18
8
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0.
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lf-
es
te
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ife
 s
at
is
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ct
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0.
16
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0.
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aw
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nd
 S
am
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a 
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01
7)
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ba
no
n
36
4
21
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0
0.
48
se
lf-
es
te
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ife
 s
at
is
fa
ct
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n
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rg
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Q
−
0.
23
, 
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0.
03
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aw
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nd
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am
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a 
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01
9)
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ba
no
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51
2
21
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3
0.
44
se
lf-
es
te
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is
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ct
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n
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os
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rg
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LS
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Q
−
0.
20
, 
−
0.
04
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ol
m
gr
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nd
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ne
 (
20
17
)
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S
44
2
18
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6
0.
52
de
pr
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on
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ES
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C
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PU
M
P
0.
29
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on
g 
et
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l. 
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01
4)
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ai
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24
1
20
.0
0
0.
41
se
lf-
es
te
em
R
os
en
be
rg
Fa
ce
bo
ok
IA
T
−
0.
12
(C
on
tin
ue
d)
T
ab
le
 2
. (
C
on
tin
ue
d)
8 International Journal of Social Psychiatry 00(0)
St
ud
y
C
ou
nt
ry
N
A
ge
FM
M
H
M
H
M
ea
s.
SM
SM
M
ea
s.
ES
H
ou
 e
t 
al
. (
20
18
)
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hi
na
71
4
19
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0
0.
62
lo
ne
lin
es
s
U
C
LA
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ei
bo
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ec
ha
t
M
ic
ro
bl
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xc
es
si
ve
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se
 
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al
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W
eC
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t 
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ce
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iv
e 
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se
 
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al
e
0.
14
, 0
.0
3
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us
sa
in
 a
nd
 G
ri
ffi
th
s 
(2
01
9)
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is
ce
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ne
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s
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8
32
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3
0.
47
de
pr
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si
on
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nx
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ty
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A
SS
-2
1
SM
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rg
en
0.
32
, 0
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8
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us
sa
in
 e
t 
al
. (
20
19
)
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K
69
23
.0
9
.6
8
se
lf-
es
te
em
, d
ep
re
ss
io
n,
 
an
xi
et
y
R
os
en
be
rg
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A
SS
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1
Fa
ce
bo
ok
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rg
en
−
0.
12
, 0
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4,
 
0.
16
Ja
ss
o-
M
ed
ra
no
 a
nd
 L
óp
ez
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os
al
es
 
(2
01
8)
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ex
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37
4
20
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1
0.
59
de
pr
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ui
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da
l i
de
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io
n
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ES
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os
iti
ve
 a
nd
 N
eg
at
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e 
Su
ic
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al
 Id
ea
tio
n 
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ve
nt
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y
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ci
al
 N
et
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k 
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n 
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nn
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re
0.
25
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6
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ri
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ar
 e
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al
. (
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19
)
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ru
21
2
20
0.
45
de
pr
es
si
on
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I
SN
Ss
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A
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46
K
an
at
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m
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. (
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), 
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1
Is
ra
el
33
7
33
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6
0.
55
se
lf-
es
te
em
R
os
en
be
rg
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ce
bo
ok
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rg
en
−
0.
52
K
an
at
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ay
m
on
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al
. (
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), 
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2
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el
80
22
.9
1
0.
91
se
lf-
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te
em
SI
SE
S
Fa
ce
bo
ok
Be
rg
en
−
0.
05
K
ar
ak
os
e 
et
 a
l. 
(2
01
6)
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ur
ke
y
71
2
16
.0
0
0.
58
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ne
lin
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s
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C
LA
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ce
bo
ok
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rg
en
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13
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ha
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al
. (
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17
)
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ki
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20
0
N
A
0.
50
de
pr
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si
on
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I-I
I
Fa
ce
bo
ok
Be
rg
en
0.
24
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im
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nd
 P
ar
k 
(2
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5)
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h 
K
or
ea
21
3
14
.0
0
0
se
lf-
es
te
em
K
an
g 
(2
00
7)
 
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Ss
C
ho
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nd
 S
uh
 (
20
13
)
−
0.
38
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ir
ca
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ru
n 
(2
01
6)
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ur
ke
y
11
30
14
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7
0.
59
de
pr
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si
on
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el
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te
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D
I, 
R
os
en
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rg
SM
A
rs
la
n 
an
d 
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ir
ik
 (
20
13
)
0.
13
, −
0.
09
K
ir
ca
bu
ru
n,
 D
em
et
ro
vi
cs
 e
t 
al
., 
(2
02
0)
T
ur
ke
y
34
4
20
.7
6
0.
82
de
pr
es
si
on
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el
f-
es
te
em
SD
H
S,
 S
IS
ES
SN
Ss
X
an
id
is
 a
nd
 B
ri
gn
el
l (
20
16
)
0.
22
, −
0.
21
K
ir
ca
bu
ru
n 
et
 a
l. 
(2
01
9)
T
ur
ke
y
82
7
20
.3
6
0.
60
se
lf-
es
te
em
SI
SE
S
SM
SM
D
S
−
0.
06
K
ir
ca
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ru
n,
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ri
ffi
th
s,
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ill
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ux
 e
t 
al
. 
(2
01
9)
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ur
ke
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0
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9
0.
60
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pr
es
si
on
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H
S
SM
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rg
en
0.
32
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ir
ca
bu
ru
n 
et
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l. 
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02
0)
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ur
ke
y
46
0
19
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4
0.
61
de
pr
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on
el
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S,
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id
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6
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ır
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bu
ru
n 
et
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l. 
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01
8)
, #
1
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ur
ke
y
80
4
16
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0
0.
48
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pr
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te
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gn
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l (
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)
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0.
15
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ır
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ru
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et
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l. 
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01
8)
, #
2
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ur
ke
y
76
0
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8
0.
60
de
pr
es
si
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el
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te
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22
, −
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11
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oc
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 G
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13
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ur
ke
y
44
7
21
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4
0.
22
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pr
es
si
on
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H
Q
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8
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ce
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ok
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nd
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ul
ya
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13
)
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28
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co
ni
 e
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al
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)
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an
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2
21
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0
0.
55
di
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re
ss
BS
I
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ce
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w
itt
er
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rg
en
0.
33
, 0
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2
La
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os
e 
et
 a
l. 
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01
1)
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S
36
4
17
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6
0.
70
se
lf-
es
te
em
, l
on
el
in
es
s
SI
SE
S,
 U
C
LA
SN
Ss
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ap
la
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(2
01
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 a
nd
 
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er
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et
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l. 
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00
9)
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0.
23
, 0
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6
Le
e-
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on
 e
t 
al
. (
20
15
)
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S
24
3
19
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0
0.
72
so
ci
al
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nx
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ty
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ni
gs
te
in
 e
t 
al
. (
19
75
)
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ce
bo
ok
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nd
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ul
ya
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20
13
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0.
18
Li
 e
t 
al
. (
20
18
)
C
hi
na
46
3
19
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2
0.
53
lif
e 
sa
tis
fa
ct
io
n
SW
LS
W
ec
ha
t
W
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ha
t 
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dd
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tio
n 
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al
e
0.
10
Li
m
 e
t 
al
. (
20
20
)
U
S
12
31
37
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8
0.
54
de
pr
es
si
on
PH
Q
-9
SM
SM
D
S
0.
30
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n 
et
 a
l. 
(2
01
7)
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an
26
76
15
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4
0.
44
de
pr
es
si
on
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nx
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ty
D
A
SS
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1
SM
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rg
en
0.
21
, 0
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7
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n 
et
 a
l. 
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02
0)
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an
17
91
27
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0
0.
70
an
xi
et
y,
 d
ep
re
ss
io
n
H
A
D
S
SM
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rg
en
0.
31
, 0
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3
Li
u 
an
d 
M
a 
(2
01
8)
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hi
na
30
1
26
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2
0.
27
se
lf-
es
te
em
R
os
en
be
rg
SM
N
A
−
0.
16
M
aj
id
 e
t 
al
. (
20
19
)
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ki
st
an
37
8
N
A
0.
70
so
ci
al
 a
nx
ie
ty
SI
A
S
SN
Ss
M
oq
be
l a
nd
 K
oc
k 
(2
01
8)
0.
30
M
al
ik
 a
nd
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ha
n 
(2
01
5)
Pa
ki
st
an
20
0
N
A
0.
50
se
lf-
es
te
em
R
os
en
be
rg
Fa
ce
bo
ok
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rg
en
−
0.
18
M
ar
tin
ez
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ec
in
o 
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d 
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ar
ci
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G
av
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n 
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01
9)
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ai
n
23
3
15
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2
0.
47
se
lf-
es
te
em
R
os
en
be
rg
In
st
ag
ra
m
M
ar
in
o 
et
 a
l. 
(2
01
7)
−
0.
14
a
(C
on
tin
ue
d)
T
ab
le
 2
. (
C
on
tin
ue
d)
Huang 9
St
ud
y
C
ou
nt
ry
N
A
ge
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M
H
M
H
M
ea
s.
SM
SM
M
ea
s.
ES
M
en
ni
ng
 e
t 
al
. (
20
20
)
G
er
m
an
y
70
0
25
.6
0
0.
76
di
st
re
ss
BS
I
SN
Ss
SN
SD
Q
0.
06
M
ilo
še
vi
ć-
Đ
or
đe
vi
ć 
an
d 
Ž
ež
el
j 
(2
01
4)
Se
rb
ia
20
14
N
A
0.
52
se
lf-
es
te
em
R
os
en
be
rg
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Ss
M
ilo
še
vi
ć-
Đ
or
đe
vi
ć 
an
d 
Ž
ež
el
j (
20
14
)
−
0.
35
M
itr
a 
an
d 
R
an
ga
sw
am
y 
(2
01
9)
In
di
a
26
4
21
.5
6
0.
62
de
pr
es
si
on
C
lin
ic
al
ly
 U
se
fu
l D
ep
re
ss
io
n 
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ut
co
m
e 
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al
e
SM
SM
D
S
0.
40
N
da
sa
uk
a 
et
 a
l. 
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01
6)
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K
25
6
21
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0
0.
53
lo
ne
lin
es
s
U
C
LA
T
w
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M
ic
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 E
xc
es
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ve
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se
 
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al
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0.
22
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m
ar
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ub
ra
m
an
ia
n 
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01
3)
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al
ay
si
a
40
0
19
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0
N
A
lo
ne
lin
es
s
U
C
LA
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ce
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ok
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im
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nd
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ar
id
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is
 (
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09
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0.
25
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nn
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am
y 
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l. 
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02
0)
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al
ay
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a
36
4
22
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0
0.
51
lif
e 
sa
tis
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ct
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n,
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ne
lin
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s
SW
LS
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C
LA
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st
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ra
m
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rg
en
0.
59
, 0
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9
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nt
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 (
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17
)
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rt
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al
49
5
13
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2
0.
47
de
pr
es
si
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nx
ie
ty
D
A
SS
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1
Fa
ce
bo
ok
Be
rg
en
0.
33
, 0
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1
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nt
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 e
t 
al
. (
20
18
)
U
K
51
1
27
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0
0.
65
di
st
re
ss
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m
pt
om
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he
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6
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rg
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0.
44
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ze
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ór
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ch
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la
nd
42
6
14
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7
0.
49
de
pr
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si
on
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ES
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Fa
ce
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FI
Q
0.
35
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aj
es
h 
an
d 
R
an
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02
0)
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a
11
4
24
.0
0
0.
32
lo
ne
lin
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s
U
C
LA
Fa
ce
bo
ok
Be
rg
en
0.
38
R
au
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p 
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01
9)
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9
15
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0
0.
53
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pr
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on
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ES
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SM
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rg
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0.
34
R
ob
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n 
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01
9)
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S
50
4
20
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0
0.
82
de
pr
es
si
on
PH
Q
-9
SM
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rg
en
0.
20
Şa
hi
n 
(2
01
7)
T
ur
ke
y
61
2
20
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4
0.
62
lif
e 
sa
tis
fa
ct
io
n
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LS
SM
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hi
n 
an
d 
Y
ağ
cı
 (
20
17
)
−
0.
31
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le
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 e
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al
. (
20
14
)
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ki
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an
60
0
19
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0
0.
50
lo
ne
lin
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s
U
C
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bo
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T
0.
23
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tic
i (
20
19
)
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ur
ke
y
28
0
21
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4
0.
58
po
si
tiv
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af
fe
ct
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eg
at
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fe
ct
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lif
e 
sa
tis
fa
ct
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n,
 lo
ne
lin
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s
PA
N
A
S,
 S
W
LS
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C
LA
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rg
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0.
16
, 0
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6,
 
−
0.
19
 0
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5
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tic
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nd
 U
ys
al
 (
20
15
)
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ur
ke
y
31
1
20
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6
0.
58
lif
e 
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tis
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ct
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n,
 h
ap
pi
ne
ss
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LS
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H
S
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ce
bo
ok
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rg
en
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0.
32
, 
−
0.
32
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vc
i e
t 
al
. (
20
18
)
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ur
ke
y
18
7
N
A
0.
47
po
si
tiv
e 
af
fe
ct
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eg
at
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e 
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ct
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N
A
S
SM
SM
D
S
−
0.
23
, 0
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6
Sh
el
do
n 
et
 a
l. 
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02
0)
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S
33
7
23
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5
.5
8
lif
e 
sa
tis
fa
ct
io
n
Li
fe
 P
os
iti
on
 S
ca
le
Fa
ce
bo
ok
, 
In
st
ag
ra
m
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na
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t
Be
rg
en
−
0.
13
, 
−
0.
15
, 
−
0.
04
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et
ta
r 
et
 a
l. 
(2
01
7)
In
di
a
10
0
27
.5
5
0.
46
lo
ne
lin
es
s
U
C
LA
Fa
ce
bo
ok
Be
rg
en
0.
24
So
ra
ci
 e
t 
al
. (
20
20
)
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al
y
21
7
32
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4
0.
64
an
xi
et
y,
 d
ep
re
ss
io
n,
 s
el
f-
es
te
em
A
du
lt 
PR
O
M
IS
 E
m
ot
io
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l D
is
tr
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s/
A
nx
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ty
-
Sh
or
t 
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rm
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du
lt 
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M
IS
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m
ot
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l 
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tr
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s/
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re
ss
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Sh
or
t 
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rm
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os
en
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rg
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ce
bo
ok
Be
rg
en
0.
47
, 0
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8,
 
0.
23
Sp
ra
gg
in
s 
(2
00
9)
U
S
35
0
20
.0
0
0.
81
so
ci
al
 a
nx
ie
ty
, l
ife
 s
at
is
fa
ct
io
n,
 
lo
ne
lin
es
s,
de
pr
es
si
on
, s
el
f-
es
te
em
, h
ap
pi
ne
ss
So
ci
al
 A
vo
id
an
ce
 a
nd
 D
is
tr
es
s 
Sc
al
e,
 
SW
LS
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C
LA
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ES
-D
, R
os
en
be
rg
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xf
or
d 
H
ap
pi
ne
ss
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ue
st
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nn
ai
re
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Ss
G
PI
U
S
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22
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0.
13
, 
0.
28
, 0
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8,
 
−
0.
30
, 
−
0.
28
St
eg
gi
nk
 (
20
15
)
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et
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ds
31
5
28
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4
0.
43
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ss
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n,
 
so
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al
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nx
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ty
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LS
A
-S
, D
A
SS
-2
1,
Se
lf-
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on
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io
us
ne
ss
 S
ca
le
; 
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re
en
 fo
r 
C
hi
ld
 A
nx
ie
ty
 R
el
at
ed
 E
m
ot
io
na
l 
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is
or
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rs
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ce
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ok
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rg
en
0.
09
, 0
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0,
 
0.
10
St
oc
kd
al
e 
an
d 
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oy
ne
 (
20
20
)
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S
38
5
20
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1
0.
53
lif
e 
sa
tis
fa
ct
io
n,
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nx
ie
ty
, 
de
pr
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si
on
Bl
ai
s 
et
 a
l. 
(1
98
9)
 &
 S
W
LS
, S
pe
nc
e 
C
hi
ld
 
A
nx
ie
ty
 In
ve
nt
or
y,
C
ES
-D
C
SN
Ss
M
er
lo
 e
t 
al
. (
20
13
)
0.
11
, 0
.2
4,
 
0.
28
T
es
i (
20
18
)
It
al
y
58
0
32
.0
0
0.
62
lo
ne
lin
es
s
U
C
LA
SM
Be
rg
en
0.
19
T
ab
le
 2
. (
C
on
tin
ue
d)
(C
on
tin
ue
d)
10 International Journal of Social Psychiatry 00(0)
St
ud
y
C
ou
nt
ry
N
A
ge
FM
M
H
M
H
M
ea
s.
SM
SM
M
ea
s.
ES
T
ur
el
 e
t 
al
. (
20
18
)
Is
ra
el
21
5
26
.9
9
0.
73
w
el
l-b
ei
ng
W
H
O
-5
Fa
ce
bo
ok
Be
rg
en
−
0.
57
T
ur
el
 a
nd
 Q
ah
ri
-S
ar
em
i (
20
16
), 
#
1
U
S
34
1
23
.0
0
0.
52
se
lf-
es
te
em
R
os
en
be
rg
Fa
ce
bo
ok
T
ur
el
 a
nd
 B
ec
ha
ra
 (
20
16
)
−
0.
05
T
ur
el
 a
nd
 Q
ah
ri
-S
ar
em
i (
20
16
), 
#
2
U
S
60
19
.5
0
N
A
se
lf-
es
te
em
R
os
en
be
rg
Fa
ce
bo
ok
T
ur
el
 a
nd
 B
ec
ha
ra
 (
20
16
)
0.
01
U
ys
al
 e
t 
al
. (
20
13
)
T
ur
ke
y
29
7
20
.1
0
0.
53
ha
pp
in
es
s
SH
S
Fa
ce
bo
ok
Be
rg
en
−
0.
37
va
n 
de
n 
Ei
jn
de
n 
et
 a
l. 
(2
01
6)
, #
1
N
et
he
rl
an
ds
72
4
14
.3
6
0.
54
se
lf-
es
te
em
, d
ep
re
ss
io
n
R
os
en
be
rg
, K
ut
ch
er
 A
do
le
sc
en
t 
D
ep
re
ss
io
n 
Sc
al
e
SM
SM
D
S
−
0.
19
, 0
.3
7
va
n 
de
n 
Ei
jn
de
n 
et
 a
l. 
(2
01
6)
, #
2
N
et
he
rl
an
ds
87
3
14
.2
8
0.
48
de
pr
es
si
on
, l
on
el
in
es
s
K
ut
ch
er
 A
do
le
sc
en
t 
D
ep
re
ss
io
n 
Sc
al
e,
 U
C
LA
SM
SM
D
S
0.
29
, 0
.2
4
va
n 
de
n 
Ei
jn
de
n 
et
 a
l. 
(2
01
8)
, #
1
N
et
he
rl
an
ds
27
5
13
.9
01
lif
e 
sa
tis
fa
ct
io
n
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N
A
 =
 n
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 =
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of
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 s
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S 
=
 e
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 s
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a E
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 w
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ed
 fr
om
 t
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rs
.
T
ab
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 2
. (
C
on
tin
ue
d)
Huang 11
SM use and mental health. Regarding publication outlet, 1 
correlation between problematic SM use and self-esteem 
was reported in a book chapter, 1 in a conference, 1 in a 
Master’s thesis, 4 in Doctoral dissertations and 35 in jour-
nals. The effect of publication outlet was not significant 
with QB = 3.43, indicating the absence of a file-drawer 
problem. For the country effect, 17 countries reported the 
relation between problematic SM use and self-esteem, and 
3 of them had at least 4 effect sizes. Again, QB = 1.32 was 
not significant, indicating that the mean correlations of 
US, Poland, and Turkey were comparable.
The majority of studies used the Rosenberg Self-Esteem 
Scale (Rosenberg, 1965) to assess global self-worth, and 
the mean correlation for these studies was r = −.18. 
Seven studies used the Single Item Self-Esteem Scale 
(Robins et al., 2001) and these had a mean correlation of r 
= −.13. The correlation between problematic SM use and 
self-esteem did not vary with self-esteem measure. The 
included studies used 18 measures of problematic SM use. 
Twelve samples used the Bergen Social Media Addiction 
Scale (Andreassen et al., 2016), Bergen Facebook 
Addiction Scale (Andreassen et al., 2012) or their adapta-
tions, and 8 samples used the Facebook Intrusion 
Questionnaire (Elphinston & Noller, 2011) and its adapta-
tions. The mean correlations of these measures were quite 
comparable.
Twenty-two effect sizes were related to problematic use 
in general; 18 were for problematic Facebook use; 1 for 
problematic Twitter use, and 1 for problematic WhatsApp 
use. The platform to which users were addicted did not 
exert a significant effect on the relation between problem-
atic SM use and self-esteem.
Table 5 presents the effects of mean age and gender 
composition of the sample on the correlations between 
problematic SM use and mental health indicators. The 
mean age was reported in 38 samples, and the age effect 
was not significant. The effect of gender composition was 
significant, and the meta-regression model was r = −.38 
+ .36 × proportion of females in the sample. Thus, the 
expected correlation was r = −.38 for all-male samples, 
and r = −.02 for all-female samples.
Moderator analyses the relation between 
problematic SM use and life satisfaction
The effect of publication status was not examined, because 
journal articles were the only outlet having more than 3 
effect sizes. For the country effect, Turkey and US are the 
only two countries with more than 3 effect sizes and the 
mean correlations were moderate and small, respectively 
( r = −.31 and r = −.08, respectively). The country dif-
ference was significant with QB = 13.57. The mean cor-
relations were not compared among different life 
satisfaction measures, as the Satisfaction with Life Scale 
(Diener et al., 1985) was the only measure having more 
than 3 effect sizes. The effect of measure of problematic 
SM use was not significant, and the means for studies 
using the Bergen and FIQ were both small. Similarly, the 
effects of platform and of two continuous moderators were 
not significant.
Table 3. Summary of mean correlations between problematic social media use and mental health.
k r 95% CI Q p
 upper lower 
Well-beinga 85 −0.16 −0.20 −0.13 117.21 0.01**
 Happiness 4 −0.30 −0.38 −0.21 2.96 0.40
 Life satisfaction 30 −0.11 −0.18 −0.03 35.94 0.18
 Positive affect 3 −0.18 −0.34 −0.01 0.73 0.69
 Mental Health 2 −0.29 −0.71 0.28 0.07 0.80
 Self-esteem 42 −0.17 −0.22 −0.13 57.26 0.05*
 Overall well-being 3 −0.29 −0.65 0.17 4.54 0.10
Distress 136 0.27 0.25 0.29 150.37 0.17
 Anxiety 17 0.30 0.25 0.35 17.00 0.39
 Depression 59 0.31 0.28 0.33 46.69 0.86
 Overall distress 4 0.27 0.01 0.49 3.36 0.34
 Loneliness 29 0.21 0.17 0.25 25.34 0.61
 Negative affect 4 0.08 −0.29 0.44 4.55 0.21
 Social Anxiety 17 0.30 0.24 0.35 19.65 0.24
 Social loneliness 2 0.19 −0.76 0.88 1.00 0.32
 Suicidal ideation 3 0.18 0.02 0.34 0.53 0.77
aOne correlation between problematic use of social media andpsychiatric well-being was reported and thus mean correlation was not computed.
*ponline gaming 
addiction. These treatments can be considered and modi-
fied to treat male users at high risk of SM addiction.
The study country was related to the correlation between 
problematic SM use and life satisfaction. The correlation 
was moderate for Turkey, and small for US. Although the 
country effect on the relation between problematic 
Facebook use and psychological distress was significant, 
Marino et al. (2018a) found that Western countries had 
larger effect sizes than Asian countries. The inconsistent 
findings can be caused by the composition of both the 
Western and Asian country categories in Marino et al. 
(2018a). In Marino et al. (2018a), US only contributed one 
effect size in the Western country category, and Asian 
countries included Turkey, Taiwan and Thailand.
Implications, limitations, and future directions
This meta-analysis has important implications for practice 
and research. As the magnitudes of correlations varied by 
mental health indicator, future practitioners and research-
ers should use multiple indicators for a comprehensive 
assessment of the effect of problematic SM use on well-
being and distress. The magnitudes of these correlations 
did not vary with publication status, instruments, SM plat-
forms or mean age. The strength of correlation between 
problematic SM use and mental health was similar in 
most research conditions. Thus, the magnitude of these 
correlations can generalize across most moderator condi-
tions, supporting the stability of correlations between 
problematic SM use and mental health indicators in most 
research conditions. Moreover, the moderating effect of 
study country was investigated to examine possible cul-
tural differences. Turkish studies revealed a relatively 
strong correlation between SM use and life satisfactions. 
Since some countries had few studies, more international 
research should be conducted to examine the possible 
country effect.
This study searched articles published in English. 
Different languages or countries might have different 
14 International Journal of Social Psychiatry 00(0)
dominant SM platforms. Few included studies investi-
gated main SM platforms other than Facebook from differ-
ent countries (e.g. WeChat in China). Therefore, future 
research should examine whether the moderating effect of 
SM platform is different across cultures. As each SM plat-
form has distinctive features, the country effect may be 
confounded with platform effect.
One limitation of this study was that it treated multiple 
effect sizes from a single sample independent. For exam-
ple, correlations between multiple measures of problem-
atic SM use and multiple mental health indicators were all 
coded. Moreover, some moderator effects could not be 
examined, because those moderators did not have enough 
effect sizes to warrant stable estimates of mean correla-
tions. Lastly, systematic reviews tend to use multiple 
reviewers or readers (Buscemi et al., 2005). Only one 
reader performed data extraction and coding, possibly 
leading to errors.
Funding
The author(s) disclosed receipt of the following financial support 
for the research, authorship, and/or publication of this article: 
Funding for this study was provided by Ministry of Science and 
Technology (MOST) of the Republic of China, Taiwan Grant 
No. 108-2511-H-018-026.
ORCID iD
Chiungjung Huang https://orcid.org/0000-0001-9687-8608
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