The Texas Medical Center Library The Texas Medical Center Library
DigitalCommons@TMC DigitalCommons@TMC
Dissertations & Theses (Open Access) School of Public Health
Summer 5-2019
The Relationship Between Social Media Use And Depression In The Relationship Between Social Media Use And Depression In
College Students: A Scoping Review College Students: A Scoping Review
Susan Wu
UTHealth School of Public Health
Follow this and additional works at: https://digitalcommons.library.tmc.edu/uthsph_dissertsopen
Part of the Community Psychology Commons, Health Psychology Commons, and the Public Health
Commons
Recommended Citation Recommended Citation
Wu, Susan, "The Relationship Between Social Media Use And Depression In College Students: A Scoping
Review" (2019).
Dissertations & Theses (Open Access)
. 100.
https://digitalcommons.library.tmc.edu/uthsph_dissertsopen/100
This is brought to you for free and open access by the
School of Public Health at DigitalCommons@TMC. It has
been accepted for inclusion in Dissertations & Theses
(Open Access) by an authorized administrator of
DigitalCommons@TMC. For more information, please
contact digcommons@library.tmc.edu.
THE RELATIONSHIP BETWEEN SOCIAL MEDIA USE AND DEPRESSION
IN COLLEGE STUDENTS: A SCOPING REVIEW
by
SUSAN WU, BS
APPROVED:
ACADEMIC ADVISOR AND CULMINATING
EXPERIENCE SUPERVISOR
ADRIANA PEREZ, MS, PhD
CHRISTINE MARKHAM, PhD
Copyright
by
Susan Wu, BS, MPH
2019
THE RELATIONSHIP BETWEEN SOCIAL MEDIA USE AND DEPRESSION
IN COLLEGE STUDENTS: A SCOPING REVIEW
by
SUSAN WU
ECOLOGY AND EVOLUTIONARY BIOLOGY BS,
UNIVERSITY OF CALIFORNIA Santa Cruz, 2012
Presented to the Faculty of The University of Texas
School of Public Health
in Partial Fulfillment
of the Requirements
for the Degree of
MASTER OF PUBLIC HEALTH
THE UNIVERSITY OF TEXAS
SCHOOL OF PUBLIC HEALTH
Houston, Texas
May, 2019
THE RELATIONSHIP BETWEEN SOCIAL MEDIA USE AND DEPRESSION
IN COLLEGE STUDENTS: A SCOPING REVIEW
Susan Wu, BS, MPH
The University of Texas
School of Public Health, 2019
Thesis Chair: Adriana Pérez, MS, PhD
Background: In 2017, 17.3 million adults in the U.S. (7.1% of all U.S. adults) have had at least
one major depressive episode, with individuals ages 18-25 having the highest prevalence of
major depression at 13.1%. Studies have looked at the association between social media (SM)
use and depression with mixed findings. The goal of this research is to conduct a scoping review
of existing evidence for the relationship between SM use and depression among college students.
Methods: Searches for articles published in the scientific literature were performed on PubMed,
Embase and Scopus. Articles selected followed an inclusion criteria: studied SM use and
depression in college students over 18 years, were written in English, were published between
October 2013 and October 2018, studied time spent on SM, addiction to SM and/or different SM
behaviors and were cross-sectional, observational, longitudinal or intervention studies. Articles
were screened and imported into citation manager Refworks for duplicate removal. Full-text
articles were found through Pubmed, Embase, Scopus or Google Scholar for eligibility
screening. Articles needing to be purchased were requested through interlibrary loan or
requested from authors.
Results: 14 articles met inclusion criteria; 92.9% were cross-sectional. Half of the articles
measured time spent on SM (including daily hours and frequency), with mixed findings; 42.9%
of articles studied SM addiction, with all studies presenting a significantly positive association
with depression. Over sixty percent (64%) of the articles studied specific SM behaviors, with
mixed findings. Included articles presented a larger correlation between individual
characteristics (e.g. neuroticism, loneliness, suicidal ideation, self-esteem and academic
stressors) and SM addiction and depression than time spent on SM and SM behaviors. The odds
of SM addiction and depression were 40% higher in college students in China than in the U.S
and the risk of SM addiction and depression in college students was found to be 28% higher in
Hong Kong (HK)/Macau and 12% higher in Japan than in the U.S. Only one randomized
controlled trial was identified in this scoping review. After 2 weeks, statistically significant
reductions were observed in both SM addiction and depression through mean rank comparisons
of before and after treatment.
Conclusion: This review reiterated the need for longitudinal studies to access directionality and
the need to standardize measures used to measure social media, depression in college students.
Future studies could continue to focus on the relationship between individual characteristics (i.e.
loneliness, neuroticism and self-esteem), SM addiction, SM comparison and depression and
further study RCTs utilizing treatment periods longer than 2-weeks.
TABLE OF CONTENTS
List of Tables ............................................................................................................................. i
List of Figures ........................................................................................................................... ii
Background ............................................................................................................................... i
Literature Review ................................................................................................................1
Public Health Significance ...................................................................................................4
Hypothesis, Research Question, Specific Aims or Objectives ............................................5
Methods......................................................................................................................................5
Search Strategy ....................................................................................................................5
Eligibility Criteria ................................................................................................................6
Documentation of Results ....................................................................................................8
Results ........................................................................................................................................9
Discussion ................................................................................................................................16
Conclusion ...............................................................................................................................20
References ................................................................................................................................34
i
LIST OF TABLES
Table 1: Search terms used for the scoping review by main key concepts………………………..6
Table 2: Study inclusion/exclusion criteria of articles for this scoping review on social media and
depression in college students……………………………………………………………………..7
Table 3: Characteristics of Articles Included in this Scoping Review…………………………..23
Table 4: Measurements used in Included Articles to Access Social Media Use and Depression.25
Table 5: Statistics of Social Media Use and Depression/Mental Health/Well-being in College
Students from Scoping Review…………………………………………………………………..27
Table 6: Association between Time on SM, SM Addiction and SM Behaviors and Depression
and Significance………………………………………………………………………………….33
ii
LIST OF FIGURES
Figure 1: Flowchart of Scoping Review Process of Social Media and Depression in College
Students…………………………………………………………………………………….22
1
BACKGROUND
Literature Review
Depression
In the United States, between 2009 and 2012, 7.6% of people ages 12 and older had self-reported
depression, with depression defined as a serious medical illness with mood, cognitive, and
physical symptoms (Depression in the U.S. Household Population, 2014). According to the
National Institute of Mental Health, major depression is defined as one of the most common
mental disorders in the United States, and for some individuals, can result in severe impairments
that interfere with or limit one’s ability to carry out major life activities (Major Depression, n.d.).
In 2017, 17.3 million adults (7.1% of all U.S. adults) have had at least one major depressive
episode, with individuals ages 18-25 having the highest prevalence of major depression at 13.1%
(Major Depression, n.d.). Depression is the leading cause of disability worldwide, and is a major
contributor to the overall global burden of disease (Depression, 2018
b
). Decreased energy or
fatigue, loss of interest or pleasure in hobbies and activities and difficulty concentrating,
remembering, or making decisions are only some of the signs and symptoms an individual who
is depressed might experience (Depression, 2018
a
). In addition, Health Direct states “Good
mental health is more than just the absence of mental illness. A positive state of wellbeing
includes feeling good and functioning well (Good Mental Health, 2017).” To promote mental
and emotional well-being, the Surgeon General recommends “facilitating social connectedness
and community engagement across the lifespan” and “building strong, positive relationships with
family and friends (Mental and Emotional Well-being, n.d.).”
2
Social Media
Merriam-Webster (2004) defines social media as “forms of electronic communication (such as
websites for social networking and microblogging) through which users create online
communities to share information, ideas, personal messages, and other content (such as videos)
(Social Media, 2004).” With the popularity of social media applications (i.e. Facebook, Twitter,
Instagram, Snapchat and Youtube) (Smith & Anderson, 2018), people are now able to see status
updates of those they follow, which may generate feelings such as “fear of missing out” (FoMO)
or becoming envious (Zimmerman, n.d.). However, because users are also able to modify their
picture by using apps such as photoshop to “beautify” themselves, social media does not always
portray reality (Holland & Tiggemann, 2016). In 2018, about 88% of Americans 18-29 years-
old have indicated that they use a form of social media. Facebook is used by a majority of
Americans; however, Americans ages 18 to 24 are more likely to use Snapchat, Instagram and
Twitter (Smith & Anderson, 2018). Social media has been used for retrieving information
(Tang, Bie, Park & Zie, 2018), for intervention to promote positive behaviors (Hsu, Rouf &
Allman-Farinelli, 2018), and for social support (Klassen, Douglass, Brennan, Truby & Lim,
2018). Social media can also be used for individual entertainment, maintaining relationships or
occupying time, however, it can become an addiction developed from feeling better or more self-
assured when using social media (Guedes et. Al, 2016).
Social Media and Depression
When studying social media’s relationship with mental health outcomes such as depression,
findings suggest mixed results. The relationship is complex and may involve multiple
3
psychological, social, behavioral and individual factors (Baker & Algorta, 2016). Whether social
media is beneficial or harmful to mental health and well-being may partly depend on the quality
of social factors in the social network site’s (SNS) environment (Seabrook, Kern & Rickard,
2016). When studying child and adolescent populations, studies have been consistent in the
relationship between cyberbullying and depression (Hamm et al., 2015). Among the Lesbian,
Gay and Bisexual (LGB) community, social media has been seen to be both a protective (due to
easing geographic isolation and loneliness) and a risk factor (from negative experiences such as
cyberbullying) for depression (Escobar-Viera et. al, 2018). In 18-to-29-year-olds, it has been
found that positive social comparison was significantly negatively associated with depressive
symptoms (Lup, Trub & Rosenthal, 2015). A study has shown an increased odds of depression
in participants spending more time on social media and “Based on uses and gratification theory,
media used in a goal-directed way for individual gratification and satisfaction has similarities
with addiction (Lin, 2016) (Kuss & Griffiths, 2011).” Griffiths defines addictive behavior as
being characterized by six core components of addiction: salience, mood modification, tolerance,
withdrawal symptoms, conflict, and relapse and any behavior that fulfills these six criteria can be
considered as an addiction, including social networking (Guedes et al., 2016). Further research
is needed because of the high prevalence of social media usage (Smith & Anderson, 2018) and
high rates of depression in young adults (Depression, 2018
a
), with depression as the most
common health problem for college students (Depression and College Students, n.d.). A scoping
review can assess the potential size and scope of the available research literature with aims to
identify the nature and extent of current research (Grant & Booth, 2009). A scoping review on
the literature relating social media use and depression in college students in the last 5 years
4
(October 2013 – October 2018) was conducted to describe the association between time, social
media (SM) addiction and/or different behaviors on social media and depression and assist in
determining if a systematic review on this topic might be possible.
Public Health Significance
This scoping summarization of depression and social media usage in college students ages 18-25
has a public health significance with the following aspects: 1) college students between 18 and
25 are a large population; according to the National Center for Education Statistics, was
projected to be 3.2 million in 2018 (Digest of Education Statistics, 2017), 2) in 2018, 18-to-29-
year-olds have the highest percentage using any form of social media (88%) (Smith & Anderson,
2018), 3) in 2016, this age group was listed as having the highest prevalence of depression
(Depression, 2018
a
), 4), while they are more mature than adolescents, their brains are still
developing (Brain Maturity Extends Well Beyond Teen Years, 2011), 5) depression can lead to
difficulties with concentration (Depression, 2018
b
), and 6) students generally need their memory
to study and retain learned material. With young adults ages 18-25 having the highest
prevalence of an episode of major depression in the US in 2016 (Depression, 2018
a
), this study
seeks to describe the association between social media and depression specifically in college
students to provide insight on the focus of future studies to guide intervention and policy
development.
5
Hypothesis, Research Question, Specific Aims or Objectives
The research questions are: 1) What is the relationship between time on social media and
depression in college students? 2) Does social media addiction have a positive correlation with
depression? and 3) What types of social media behaviors are positively correlated with
depression? Studies have determined an association between social media usage and depression.
Different aspects of social media having been studied among young adults (Seabrook, Kern &
Rickard, 2016) such as 1) the association between real-life closeness of social media contacts
(Shensa et al. et al., 2018), 2) actual and perceived support (Park et al. 2016) or 3) negative
Facebook experiences (Rosenthal et al., 2016) and depression. This proposal seeks to perform a
scoping review of literature that explores the relationship between 1) time spent on social media
2) social media addiction and 3) social media behaviors and depression in college students 18-
25.
METHODS
Search Strategy
A modified version of the 14 review steps by the Joanna Briggs Institute was implemented
(Training, 2017). Electronic searches were performed using the following health databases:
PubMed, Embase and Scopus. Searches for articles on the three health databases took place in
January 2019
for articles published within the last five years (October 2013 – October 2018).
The main search terms were based on ‘social media’, ‘depression’ and ‘college’ where social
media was defined according to Merriam-Webster’s definition above and depression was defined
according to the National Institute of Mental Health as a common, but serious mood disorder
6
which can cause severe symptoms that affect feeling, thinking and handling daily activities such
as sleeping, eating or working (Depression, 2018
b
). The terms used in the search are shown in
table 1. Searches were performed for each key concept, with Boolean operator “OR” separating
each term. Key concepts were then all combined using “AND ” Boolean operator.
Table 1 Search terms used for the scoping review by main key concepts
Social Media
Depression
College
Facebook
Depressive disorder
Undergraduate
Instagram
Mental health
University
Reddit
Depression/diagnosis
College
Linkedin
Depression
Students
Twitter
Snapchat
Social media addiction
Social network
Problematic social media
use
Social networking sites
Social comparison
Social media utilization
Social desirability
Social media
Eligibility Criteria
The articles included meet the eligibility criteria presented in table 2. Because studies have
shown consistent findings of cyberbullying and depression, articles that focused on victims of
cyberbullying and depression were excluded from this study (Hamm et al., 2015 & Bottino et al.,
2015). Articles not accessible through open access or interlibrary loan or could not be retrieved
from the author and have not been published within the October 2013 – October 2018 were also
excluded.
7
Table 2 Study inclusion/exclusion criteria of articles for this scoping review on social media and
depression in college students
Criteria
Exclusion
Articles
Not written in English
Study
participants
Studies that are broad, not
focusing on social media
platforms (i.e. handheld device or
including searching the web),
depression or college students
Study design
Editorials, commentaries and
systematic reviews
Study
Outcomes
Variables
Articles focused on other mental
health outcomes other than
depression such as suicides or
anxiety, where depression is not
an outcome
Articles focusing on victims of
cyberbullying
Articles that do not include
independent variables associated
with outcome variables
Articles generated from searches were screened through article title and abstract on each health
database. Articles that did not meet inclusion criteria were not selected for full text screening.
Articles that required a full text screening were selected and imported into the citation manager,
RefWorks in folders named according to the heath database in which the article populated (i.e.
Pubmed, Scopus or Embase). Articles that were duplicates were identified through RefWorks
8
once articles were imported and removed prior to in-text screening. The option to view articles,
if accessible was available in Refworks for articles imported from Scopus or Pubmed. Full-text
for articles imported from Embase were searched through Google Scholar or Pubmed. Five
articles were not directly accessible and needed to be paid for. An attempt was made to retrieve
said articles through interlibrary loan; however, no response was received after two weeks. Full
text was requested from the first author of the five articles through e-mail. Four articles were e-
mailed back for full-text screening with one article meeting inclusion criteria. Articles meeting
inclusion criteria after in-text screening were saved as a PDF and remaining articles were deleted
from the RefWorks database. Excluded articles from in-text screening were documented in an
excel spreadsheet. The numbers of articles included and excluded through the in-text screening
process was documented as well as the reasoning for exclusion in a flow diagram according to
PRISMA guidelines (figure 1). This scoping review used published articles and was approved
for exemption by the Committee for the Protection of Human Subjects (CPHS) of UTHealth
(HSC-SPH-18-1030).
Documentation of Results
Four separate charting forms were developed to record the characteristics of included articles.
The first charting form included the author and published year of the article, the type of social
media used/studied, the population of study (including country), sample size of the study, age
range or mean age of the students included in the study and study design. The second charting
form recorded how measurements were operationalized for social media and how measurements
was operationalized for depression. The third charting form included the author’s last name,
9
publication year, social media measurement, depression measurement, the statistics of
association between the two measurements (i.e. odds ratios, risk ratios, mean rank, regression
coefficients and/or correlation coefficients) and whether the association showed a positive
correlation between social media and depression. Asterisks indicated the significance of the
statistic (one asterisk indicating <0.05 and two asterisks indicating <0.01). Other factors with
moderate correlations were included in this table. The final charting form listed what was
associated with depression, if the association was negative or positive and whether findings were
significant. These charting forms were used to assist in tallying results. Proportions of articles
exploring the associations between social media and depression are reported.
RESULTS
About 43% of the articles included populations in Asia (Singapore, South Korea, China, Hong
Kong, Taiwan, Japan and India) (Jang, Park & Song, 2016, Tang, Koh & Gan, 2017, Tang &
Koh, 2017, Zhang, 2017, Barman, Mukhopadhyay & Bandyopadhyay, 2018 & Tang et al., 2018)
(see table 3). Over a quarter (29%) of articles included other counties (Greece, Iran, Israel and
Mexico) (Kokkinos & Saripanidis, 2017, Rad & Ahmadi, 2018, Jasso-Medrano & Lopez-
Rosales, 2018 & Turel, Poppa & Gil-Or, 2018). Most (93%) of the articles were cross-sectional.
Over a fifth (21%) of articles had participants that were randomly selected (Zhang, 2017,
Barman et al., 2018 & Rad & Ahmadi, 2018). Half (50%) of the articles included participants
whose age in years ranged from 18-25 (Jang et al., 2016, Tang et al., 2017, Tang & Koh, 2017,
Zhang, 2017, Barman et al., 2018, Choukas-Bradley, Nesi, Widman & Higgins, 2018 & Jasso-
Medrano & Lopez-Rosales, 2018), 14% included participants with ages ranging from 18-30 (Rad
10
& Ahmadi, 2018 & Tang et al., 2018), 14% included students over age 30 (Kokkinos &
Saripanidis, 2017 & Kircaburn et al., 2018) and 21% of articles did not mention a maximum age
(Lauckner, Hill & Ingram, 2018, Shensa et al., 2018 & Turel et al., 2018). Half (50%) of the
articles focused on or included Facebook as their type of social media (Jang et al., 2016,
Kokkinos & Saripanidis, 2017, Zhang, 2017, Choukas-Bradley et al., 2018, Jasso-Medrano &
Lopez-Rosales, 2018, Lauckner et al., 2018 & Turel et al., 2018) and half (50%) used social
networking sites in general (Tang et al., 2017, Tang & Koh, 2017, Barman et al., 2018, Rad &
Ahmadi, 2018, Kircaburn et al., 2018, Shensa et al., 2018 & Tang et al., 2018). Instagram and
Twitter were also popular forms of social media and were each included in 21% of selected
articles (Choukas-Bradley et al., 2018, Rad & Ahmadi, 2018 & Lauckner et al., 2018) (see table
3).
Social media measurements
Over a tenth (14%) of articles estimated the amount of time on social media using measurements
(7% used a 6-point frequency response scale (Kokkinos & Saripanidis, 2017) and 7% used free
response, in which participants were asked the average number of hours and minutes spent using
any social media on a typical day (Choukas-Bradley et al., 2018)). To measure social network
addiction, 29% of articles used a modified 6-item Bergen Facebook Addiction Scale (BFAS)
(Tang et al., 2017, Tang & Koh, 2017, Tang et al., 2018 and Turel et al., 2018), 7% used a
different version of an addiction scale (seven-dimension online social networking addiction
scale) (Esmaeili, 2018) and 7% used a social network addiction questionnaire (Jasso-Medrano &
Lopez-Rosales, 2018). Over a tenth (14%) of articles asked questions about Facebook use with
11
answers based on a 5/6-point scale (Jang et al., 2016 & Zhang, 2017) and 14% used a
questionnaire about use of social networking sites (Barman et al., 2018 & Kircaburn et al., 2018).
Less than half (43%) of articles used measurements specific to social media behaviors (i.e. social
comparison on Facebook, impression management, enacted social support, self-disclosure, body
comparison, body survelliance, cyberbullying offending, social network intensity and real-life
closeness of social media contacts) (Jang et al., 2016, Zhang, 2017, Choukas-Bradley et al.,
2018, Kircaburn et al., 2018, Laukner, 2018 & Shensa et al., 2018) (see table 5).
Depression measurements
Most included articles (86%) measured depression; 7% measured mental health (Jang et al.,
2016) and 7% measured well-being (Turel et al., 2018). There were various depression
measurements. The 9-item patient health questionnaire (PHQ-9) (21%) (Kokkinos &
Saripanidis, 2017, Zhang, 2017 & Lauckner et al., 2018) and variations of the Diagnostic and
Statistical Manual of Mental Disorders (DSM-5) (21%) (Tang et al., 2017,
Tang & Koh, 2017 &
Tang 2018) were the most popular. The Becks Depression Inventory (BDI) was used in 14% of
articles (Barman et al., 2018 & Rad & Ahmadi, 2018). The Center for Epidemiologic Studies
Depression Scale (CES-D), 4-item patient reported outcomes measurement information system
(PROMIS), short mood and feelings questionnaire and short depression-happiness scale were
each used in 7% of articles (Jasso-Medrano & Lopez-Rosales, 2018, Choukas-Bradley et al.,
2018, Kircaburn et al., 2018 & Shensa et al., 2018).
12
Findings
Time spent on social media
Half (50%) of included articles studied the relationship between time spent on social media and
depression or well-being (Kokkinos & Saripanidis, 2017, Zhang, 2017, Barman et al., 2018,
Choukas-Bradley et al., 2018, Rad & Ahmadi, 2018, Jasso-Medrano & Lopez-Rosales, 2018 &
Turel et al., 2018) with a majority (71% of articles studying time) measured using correlation to
determine a positive or negative correlation with depression (i.e. correlation matrix, Spearman’s
rho, bivariate correlation and zero-order correlation) (Kokkinos & Saripanidis, 2017, Zhang,
2017, Choukas-Bradley et al., 2018, Jasso-Medrano & Lopez-Rosales, 2018 & Turel et al., 2018)
(see table 5); the remaining articles studying time on SM used mean rank comparing less than or
equal to 4 hours or greater than four hours or comparing time and depression before and after
treatment (Barman et al., 2018 & Rad & Ahmadi, 2018). Mean rank scores were correlations
between time spent on social media (including daily hours and daily frequency) and depression
and did not exceed 0.17 (see table 5). These correlations were relatively low compared to other
factors measured and the relationship between time on social media and depression in college
students could not be determined (see table 5).
Social media addiction
About half of included articles (43%) studied social media addiction with definitions including
stated ability to spend a day without SNS or terminology associating addiction with social media
use and all articles showing positive associations (Tang et al., 2017, Tang & Koh, 2017, Barman
et al., 2018, Jasso-Medrano & Lopez-Rosales, 2018, Tang 2018 & Turel et al., 2018). One third
13
of articles studying SM addiction and depression in college students compared US SM addiction
and depression rates in college students with other countries through regression, odds ratios or
relative risk (Tang et al., 2017 & Tang et al., 2018). Articles that reported significantly higher
risks/odds of depression with social media addiction were in China, Hong Kong/Macau and
Japan when compared with the United States. One third (2/6) of articles studying SM addiction
and depression or well-being used a correlation matrix or Spearman’s rho with a moderate
correlation (-0.57) found between SM addiction and well-being (Jasso-Medrano & Lopez-
Rosales, 2018 & Turel et al., 2018). One third of articles studying SM addiction used other
measures (comorbidity and mean rank) (Tang & Koh, 2017 & Barman et al., 2018) and one third
found SM addition and depression in college students more common in females than males
(Tang et al., 2017 & Tang & Koh, 2017). Findings from this scoping review suggest a positive
correlation between social media addiction and depression in college students (see table 5).
Social media behaviors
SM behaviors were studied in 64% of included articles (Jang et al., 2016, Lauckner et al., 2018,
Shensa et al., 2018, Zhang, 2017, Kokkinos & Saripanidis, 2017, Choukas-Bradley et al., 2018,
Kircaburn et al., 2018, Barman et al., 2018 & Turel et al., 2018). Impression management,
Facebook (FB) use, social comparison, enacted social support on FB, indiscreet FB content,
Facebook victimization, self-disclosure, amount/intimacy, honesty, intent, body surveillance,
body comparison, cyberbullying perpetration, problematic social media use, FB network size and
use of FB privacy settings were all measured using variations of correlation measures (i.e. zero-
order, bivariate, Pearson or Spearman’s rho correlations or through a correlation matrix) with
14
depression/mental health/well-being (Jang et al., 2016, Zhang, 2017, Kokkinos & Saripanidis,
2017, Choukas-Bradley et al., 2018, Kircaburn et al., 2018 & Turel et al., 2018). Problematic
SM use, FB victimization, body surveillance, body comparison, cyberbullying perpetration and
comparing oneself on SM were SM behaviors showing the highest correlations with depression,
0.37 being the highest and all showing significantly positive findings (Kircaburn et al., 2018,
Kokkinos & Saripanidis, 2018, Choukas-Bradley et al., 2018 & Jang et al., 2016). Other
measures used to compare social media behaviors include regression, odds ratios and mean rank
and measured excessive social media use, no/distant/close face-to-face (FTF) relationships and
whether social media was used late night/early morning, respectively (Lauckner et al., 2018,
Shensa et al., 2018 & Barman et al., 2018). Having close FTF relationships with social media
contacts had a significantly negative association with depression, while no FTF relationships
with SM contacts and excessive SM use had significantly positive associations with depression
(Shensa et al., 2018). Impression management, disclosure amount, disclosure honesty,
disclosure intent, use of Facebook privacy settings and enacted social support on Facebook did
not have significant evidence of an association with depression (Jang et al., 2016, Zhang, 2017 &
Kokkinos & Saripanidis, 2017) (see table 5).
Other factors measured on social media
Other factors measured and which showed a positive significant association with depression
included: being neurotic, loneliness, having negative social media experiences,
academic/environmental/interpersonal stressors and appearance-related social media
consciousness (Kokkinos & Saripanidis, 2017, Zhang, 2017 & Choukas-Bradley et al., 2018). A
15
majority (64%) of studies also found other factors that had a significant negative association with
depression (i.e. body esteem, life satisfaction, self-esteem, positive suicidal idealization, general
belongingness, age, extraversion, agreeableness, conscientiousness, sex, race and perceived
social support) (Choukas-Bradley et al., 2018, Rad & Ahmadi, 2018, Jang et al., 2016, Jasso-
Medrano & Lopez-Rosales, Kircaburn et al., 2018, Kokkinos & Saripanidis, Shensa et al., 2018,
Tang et al., 2017 and Zhang, 2017). The most common was self-esteem, which was measured in
21% of articles (Jang et al., 2016, Kokkinos & Saripanidis, 2017 & Kircaburn et al., 2018). Self-
esteem was measured with zero-order, bivariate or Pearson’s correlations, 14% correlated self-
esteem with depression with -0.41 being the highest correlation and -0.26 the lowest and 7% of
articles correlated self-esteem with mental health (0.44) (Jang et al., 2016) (see table 5). Other
factors measured using various correlation measures included perceived social support,
neuroticism, loneliness, academic stressors, environmental stressors, interpersonal stressors,
satisfaction with life, extraversion, conscientiousness, agreeableness, Facebook network size,
appearance related social media consciousness, body esteem, suicidal ideation, general
belongingness, sex and age (Jang et al., 2016, Kokkinos & Saripanidis, 2017, Zhang, 2017,
Choukas-Bradley et al., 2018, Jasso-Medrano & Lopez-Rosales, 2018 & Kircaburn et al., 2018).
Neuroticism had the highest correlations. When correlated with depression, neuroticism had a
correlation value of 0.58 and when correlated with well-being had a correlation value of -0.68
(Kokkinos & Saripanidis, 2017 & Turel et al., 2018). Loneliness, suicidal ideation and academic
stressors also had relatively high correlations with depression with correlation values 0.52, 0.48
and 0.42, respectively (Kokkinos & Saripanidis, 2017, Jasso-Medrano & Lopez-Rosales, 2018 &
Zhang, 2017). Other factors studied which showed significantly negative associations with
16
depression included: body esteem, general belongingness, and satisfaction with life (Choukas-
Bradley et al., 2018, Kircaburn et al., 2018 & Zhang, 2017). Indiscrete Facebook content,
knowledge of privacy settings, number of Facebook friends and Facebook network size did not
have significant evidence of an association with depression (Kokkinos & Saripanidis, 2017 &
Zhang, 2017). The number of social network sites used was measured for depression using mean
rank and showed no significance (Barman et al., 2018). The randomized clinical trial included in
this review used an app to control social media use with the intervention group for two weeks.
The study showed the app significantly reduced time spent on social media, anxiety, depression,
sleep quality and life satisfaction in the intervention group (Rad & Ahmadi, 2018).
DISCUSSION
The goal of this scoping review was to study the association between social media use and
depression in college students. This study specifically focused on SM use related to time spent
on social media, social media addiction and social media behaviors; however, individual
characteristics were extracted from final articles as well. There were conflicting findings
regarding time spent on social media and depression and studies showing a significant
relationship between time spent on social media and depression showed a smaller correlation
when comparing depression correlations with other measured factors. According to Choukas-
Bradley et al. (2018), when controlling for time, appearance-related social media consciousness
(ARSM) was significantly associated with depressive symptoms in college women. The
correlation between ARSM was higher than time spent on social media which suggests women’s
SM experience can be more predictive of depression than time spent on social media (Choukas-
17
Bradley et al., 2018). Time spent on social media might also be a result of individual
experiences. According to Kircaburn et al. (2018), students who feel socially isolated may spend
more time on social media to fulfill their need of socializing and feeling connected. The mixed
findings and smaller correlations could mean that time spent on social media is not directly
correlated with depression; however, it is important to note that measurements in studies
measuring time were all different and future studies utilizing a standardized measure could be
useful.
When looking at studies comparing the association between social media addiction and
depression, it was observed that some countries have a higher risk of depression when addicted
to social media. Addiction rates found in Online Social Networking Addiction Among College
Students in Singapore: Comorbidity with Behavioral Addiction and Affective Disorder was
shown to be relatively high (30%), falling in the range of 24-35% of Asian students, found in
previous studies. Chinese students spend more time on social networking sites than US students
(about 10 hours a week vs. 6.4 hours a week) and the long hours can result in getting addicted.
Directionality cannot be assumed due to the cross-sectional nature of the study and being
addicted could lead to more time on social media (Tang et al., 2017). Tang et al. (2017) suggests
SNS addition and depression could be due to FOMO, demands of relationship maintenance,
constant social comparison and frequent violation of privacy. In a three-way model observing
social media addiction, neuroticism and well-being, it was observed that high neuroticism
magnified negative associations between SNS addiction symptoms and well-being in females;
however, addiction symptoms were the same in men, despite levels of neuroticism (Turel et al.,
18
2018). Tang et al. (2017) suggests males tend to be addicted to online games where as females
tend to be addicted to social interaction, which could be due to the male need for power and
control or to explore sexual fantasies and for the female need to share feelings and emotions.
Close relationships with Facebook contacts, social comparison, problematic social media use,
being neurotic, extraversion, agreeableness, cyberbullying perpetration, body surveillance and
excessive social media use were significantly associated with depression. When looking at other
social media behaviors, individual differences appeared to have a higher correlation with
depression. In studies focusing on Facebook, a person having high self-esteem was shown to
have a lower risk of depression. According to Jang et al. (2016), those with low self-esteem
were more likely to compare on Facebook, than those with higher self-esteem, and were more
likely to feel hurt when comparing themselves to others; individuals who managed their
impression on Facebook were also more likely to engage in social comparison (Jang et al., 2016).
Body comparison and surveillance were more common in individuals more conscious of their
appearance on social media (Choukas-Bradley et al., 2018). No face-to-face (FTF) relationships
with social media contacts significantly increased odds of depressive symptoms, while close FTF
relationships with SM contacts significantly decreased odds of depressive symptoms. On social
media, users can present a modified version of themselves and might not provide authentic
relationships; however, using SM to develop online relationships can supplement established
relationships (Shensa et al., 2018). Lack of genuine relationships can lead to loneliness which
was highly correlated with depression (Kokkinos & Saripanidis 2017). Self-disclosure on
Facebook in Hong Kong students could have been due to stressful events and to gain social
19
support; however, posts were modified to maintain their image. Those who practiced self-
disclosure experienced higher life satisfaction, which aligns with findings that talking about your
problems is healthy; however, honest disclosures were associated with lower life satisfaction
(Zhang, 2017). This agrees with findings of posting indiscreet Facebook content being
positively associated with Facebook victimization (Kokkinos & Saripanidis, 2017). Kokkinos &
Saripanidis (2017) suggests posting indiscrete content can make an individual easier to bully and
depression is a predictor of Facebook victimization. Cyberbullying victimization has been found
to be a strong predictor of cyberbullying perpetration via anger and cyberbullying perpetration’s
association with depression has been found to be fully mediated by problematic social media use
(Kircaburan, 2018). Higher problematic social media use was indirectly associated with feeling
less social connectedness, belongingness, and self-esteem via depression (Kircaburan, 2018).
Most of the articles included in this scoping review were cross-sectional and were unable to
determine the directionality the association between the use of social media and depression. It is
possible that depression leads to increased time on social media, social media addiction and
social media behaviors such as problematic social media use; however, longitudinal studies are
needed to provide evidence of causality. According to Lauckner et al. (2018), students with
depressive symptoms could be receptive to technological based interventions. Designed mobile
apps as reality therapy have been shown to reduce social networking addiction and time spent on
social media and depression. The reduced depression rate found in the intervention group
provides possibility that depression related to social media use can be regulated through apps;
however, this RCT was assessed after 2 weeks of use and a longer study is important to confirm
20
findings (Rad & Ahmadi, 2018). Studies used a broad range of measurements to assess SM use
and depression, making comparisons difficult across studies; thus, it would be helpful to be
consistent in social media use and depression measurements to perform systematic reviews/meta-
analyses to determine association. Articles used self-reported measures which could have led to
under-reporting. This could be remedied with diagnostic tools or participation of mental health
professionals (Tang & Koh, 2017). Convenience sampling and lack of diversity could have also
limited generalizability (Lauckner et al., 2018).
Potential Strengths and Limitations
There are currently no scoping or systematic reviews focused on social media usage and
depression in college students and these findings could contribute to ideas for future research on
an issue that currently has a high prevalence. By not restricting to the US population, this
scoping review can observe similarities and differences between nations that have been studied.
A scoping review also reduces potential biases between studies; however, although only one
article was inaccessible, lack of access to articles could have introduced some bias. Limitations
present in this review include: 1) I was the only reviewer and could have missed articles through
the scanning process, 2) this was an exploratory study and including multiple countries may
confuse results and 3) limiting my articles to English-language only articles could eliminate
studies in other languages that would provide additional information.
21
CONCLUSION
This review reiterated the need for longitudinal studies to access directionality and the need to
standardize measures used to measure social media, depression in college students. Articles
consistently showed higher correlations (>.40) between loneliness, neuroticism, self-esteem, SM
addiction, SM comparison and depression in college students, calling for future studies to
continue to focus on the relationship between individual characteristics, SM addiction, SM
comparison and depression. Further studies could also perform RCTs, utilizing treatment
periods longer than 2-weeks.
22
Figure 1: Flowchart of scoping review process of social media and depression in college students
Records identified through database searching
Pubmed (153), Scopus (400), Embase (174)
Records screened in
Phase 1 (title/abstract)
(n=727)
Records excluded
(n = 643)
Records after duplicate removal (n= 63)
Records screened in Phase 2
(full-text)
(n=63)
Full-text articles excluded, with reasons
(n=49)
- Not specific to social media (n=15)
- Did not associate SM time, addiction
or behavior to depression (n=12)
- Not focused on depression (n=8)
- Not focused on college students (n=1)
- Review (n=3)
- Age not 18 or over (n=7)
- Not published in the last 5 years (n=1)
- Depression not an outcome (n=1)
- Interlibrary loan and e-mailed author,
but no response (n=1)
Publications included in
Scoping Review (n=14)
SEARCH
SCREENING
ELIGIBILITY
INCLUDED
23
Table 3 Characteristics of Articles Included in this Scoping Review
Author,
Year
Population of
study
Sample size
and
selection
Age
Study
design
Type of
social
media
included in
study
1
Jang et al.,
2016
Communication
students from a
large private
university in Seoul,
South Korea
313 –
volunteered
Average
age 21.17
years
SD: 1.95
Cross-
sectional
Facebook
2
Kokkinos
&
Saripanidis,
2017
University students
from Greece with a
Facebook account
240 -
availability
and
snowball
sampling
18-51
years
Cross-
sectional
Facebook
3
Tang et al.,
2017
Undergraduate
students from
China, Singapore
and US
3267 –
convenience
sample
18-25
years
Cross-
sectional
Social
Networking
Sites (SNS)
4
Tang &
Koh, 2017
College students
from a University
in Singapore
1110 –
convenience
sample
18-25
years
Cross-
sectional
Social
Networking
Sites (SNS)
5
Zhang,
2017
Students from a
university in Hong
Kong
560 –
random
sampling
18-25
years
Cross-
sectional
Facebook
6
Barman,
2018
Undergraduate
students from a
medical college in
Kolkata, West
Bengal
200 –
random
sampling
21.6 +/-
1.8 years
Cross-
sectional
Social
Networking
Sites (SNS)
7
Choukas-
Bradley,
2018
College women
enrolled in
psychology courses
at a large university
in the southeastern
United States
339 –
enrolled
students
18-19
years
Cross-
sectional
Facebook,
Instagram,
Snapchat,
Twitter,
etc.
8
Jasso-
Medrano &
Lopez-
Rosales,
2018
University students
from Mexico
374 -
recruited
18-24
years
Cross-
sectional
Whats App
and
Facebook
24
9
Kircaburn
et al., 2018
University students
from a state
university in the
US
760 -
volunteer
18-40
years
Cross-
sectional
Social
Networking
Sites (SNS)
10
Lauckner et
al., 2018
University students
at two universities
in Southeastern US
255-
convenience
sample
18 or
older
Cross-
sectional
Facebook,
Snapchat,
Instagram,
Twitter,
Yik Yak
and
Tumbler
11
Rad &
Ahmadi,
2018
Students from the
University of
Technology in
Urmia, Iran
200 –
random
sampling
18-28
years
Randomized
Controlled
Trial where
intervention
group used
mobile app
to control
social
network
addiction,
depression,
anxiety,
sleep
quality,
cognitive
failure and
life
satisfaction
SNS, most
popular
were
Telegram,
Instagram,
and Twitter
12
Shensa et
al., 2018
Students from Mid-
Atlantic US state
university
1124 –
convenience
sample
At least
18 years
old
Cross-
sectional
Social
media
(SM)
13
Tang et al.,
2018
College students
from United States,
Singapore,
HK/Macau, China
(Beijing), South
Korea (Seoul),
Taiwan (Taichung)
and Japan (Kyoto)
8067 –
convenience
sample
18-30
years
Cross-
sectional
Social
Networking
Sites (SNS)
14
Turel et al.,
2018
Israeli college
students who use
Facebook
215 –
convenience
sample
>=18
years
Cross-
sectional
Facebook
25
Table 4 Measurements used in Included Articles to Access Social Media Use and Depression
Author, Year
Measurement used to assess social media
use
Measurement used to
assess depression
1
Jang et al.,
2016
Facebook use was measured using 3
questions: 1) how often do you post on
Facebook, 2) how often do you look at other
people’s posts and how often do you use
Facebook. Social comparison on Facebook
and Impression Management were also
measured
Mental health was
measured with 5-item
RAND Mental Health
Inventory
2
Kokkinos &
Saripanidis,
2017
Time spent on social media (how much time
on a 6-point frequency scale) and Facebook
Victimization using a self-report
questionnaire
9-item patient health
questionnaire (PHQ-9)
3
Tang et al.,
2017
Social Network Addiction – Modified 6-
item Bergen Facebook Addiction Scale
(BFAS)
9-item Depression Scale
(adopted from Diagnostic
and Statistical Manual of
Mental Disorders (DSM-
5))
4
Tang & Koh,
2017
Modified 6-item Bergen Facebook
Addiction Scale (BFAS)
Diagnostic and Statistical
Manual of Mental
Disorders (DSM-5)
5
Zhang, 2017
Facebook use: asked if they had a Facebook
profile, how many minutes per day on
average was spent on Facebook in the past
week and how many Facebook friends they
had. Enacted social support and self-
disclosure were also measured
Depression was
measured with the Patient
Health Questionnaire.
(PHQ-9)
6
Barman, 2018
Structured questionnaire for assessment of
the use of SNS
Becks depression
inventory (BDI)
7
Choukas-
Bradley, 2018
Time spent on social media – free response
format of average number of hours and
minutes participants use social media on a
typical day. Body comparison and body
surveillance were also measured
13-item short mood and
feelings questionnaire
8
Jasso-Medrano
& Lopez-
Rosales, 2018
Cuestionario de Addiccion a Redes Sociales
(ARS) (Social Network Addiction
Questionnaire)
Center for Epidemiologic
Studies Depression Scale
(CES-D)
26
9
Kircaburn et
al., 2018
Social Media Use Questionnaire (SMUQ)
and Cyberbullying Offending Scale (CBOS)
Short Depression-
Happiness Scale (SDHS)
10
Lauckner et
al., 2018
Social network intensity
Patient health
questionnaire-9 (PHQ-9)
11
Rad &
Ahmadi, 2018
Social media: seven-dimension online social
networking addiction scale
Becks depression
inventory (BDI)
12
Shensa et al.,
2018
Real-life closeness of social media contacts
– 1) no face-to-face (FTF) relationship; 2) a
distant FTF relationship and 3) a close FTF
relationship
4-item patient reported
outcomes measurement
information system
(PROMIS)
13
Tang et al.,
2018
Modified six-item Bergen Facebook
Addiction Scale (BFAS)
9-item depression scale
adopted from DMS-5
14
Turel et al.,
2018
Hebrew translation of the 6-item Bergen
Facebook Addiction Scale
World Health
Organization (WHO) five
item Wellbeing Index
(WHO-5)
27
Table
5 Statistics of Social Media Use and Depression/Mental Health/Well
-being in College Students from Scoping Review
Author, Year
Social Media
Measurement
Depression
Measurement
Association
Measurement
Statistic
Positively
associated
with
depression
Association Between Time on Social Media and Depression
Hours spent on SM
1
Kokkinos &
Saripanidis,
2017
6-point scale (
≤10 minutes
(min.), 10
-30 min., 30
-60
min., 1
-2 hours (hrs.), 2
-3
hrs. and
≥ 3 hrs
.)
PHQ
-9
Spearman's Rho
0.15*
Yes
2
Zhang, 2017
6-point scale (
≤ 10 min.,
10-30 min., 31
-60 min., 1
-
2 hrs., 2
-3 hrs. and
≥ 3
hrs.)
PHQ
-9
Zero-order
correlation
0.02
Yes
3
Barman, 2018
Questionnaire
BDI
Mean Rank (<=4 vs
>4 hours)
79.91 vs. 103.85*
Yes
4
Choukas
-
Bradley, 2018
Free response
13-item Short
Mood and
Feelings
Questionnaire
Bivariate
Correlation
0.17**
Yes
5
Jasso
-Medrano
& Lopez
-
Rosales, 2018
Social Network Addiction
Questionnaire
CES-D
Spearman's Rho
-0.07
No
6
Rad & Ahmadi,
2018
Seven
-dimension online
social networking
addiction scale
BDI
Mean
Rank
(intervention
-
before vs. after
treatment)
39.00 vs. 29.00**
(time)
48.10 vs. 50.96**
(depression)
Yes
Mean Rank
(control
- before vs.
after treatment)
37.45 vs. 36.54
(time)
48.10 vs. 50.96
(depression)
No
28
Table 5
Continued
Daily Use Frequency
1
Barman, 2018
Questionnaire
BDI
Mean rank (<4 vs.
>=4 times
77.52 vs. 104.24*
Yes
Association Between Time on Social Media and Wellbeing
Hours spent on SM
1
Turel et al.,
2018
BFAS
WHO
-5
Correlation Matrix
-0.19
Yes
Daily Use Frequency
1
Turel et al.,
2018
BFAS
WHO
-5
Correlation Matrix
-0.07
Yes
Association Between Social Media Addiction and Depression
1
Tang et al., 2017
BFAS
Modified
DSM
-5
Multivariate
Logistic Regression
0.13*
Yes
Odds Ratio
China vs. US
1.398 (1.008
-1.932)
Yes
Singapore vs.
US
1.097 (0.778
-1.547)
Yes
2
Tang & Koh,
2017
BFAS
DSM
-5
Comorbidity
-
Total
SN addition
- 29.5%
Yes
Depression
- 21.0%
Comorbidity
- Male
SN addition
- 22.1%
Yes
Depression
- 13.9%
Comorbidity
-
Female
SN addition
- 33.9%
Yes
Depression
- 25.1%
3
Barman, 2018
Questionnaire
BDI
Mean rank (stated
ability to spend a
day without
accessing SNSs
-yes
vs. no)
92.22 vs. 126.71**
Yes
29
Table 5
Continued
4
Jasso
-Medrano
& Lopez
-
Rosales, 2018
Social Network Addiction
Questionnaire
CES-D
Spearman's Rho
coefficient
0.25**
Yes
5
Tang et al., 2018
BFAS
Modified
DSM
-5
Risk Ratio
Singapore vs. US
1.02 (0.92
- 1.12)
HK/Macau vs. US
1.28 (1.19
-1.39)
China vs. US
1.06 (0.97
- 1.16)
South Korea vs. US
1.02 (0.93
-1.13)
Taiwan vs. US
0.97 (0.89
-1.07)
Japan vs. US
1.12 (1.02-1.22)
Association Between Social Media Addiction and Wellbeing
1
Turel et al.,
2018
BFAS
WHO
-5
Correlation Matrix
-0.57**
Yes
Association Between Social Media Behaviors and Mental Health
Social Comparison
1
Jang et al., 2016
Iowa
-Netherlands
Comparison Orientation
Measure
RAND Mental
Health
Inventory
Zero-order
correlation
-0.15**
Yes
Facebook Use
2
Jang et al., 2016
Free response
RAND Mental
Health
Inventory
Zero-order
correlation
-0.05
Yes
Impression Management
3
Jang et al., 2016
Self-promotion and
ingratiation among the
impression management
scale
RAND Mental
Health
Inventory
Zero-order
correlation
0.01
No
Association Between Social Media Behaviors and Depression
Social Network Intensity
1
Lauckner
et al.,
2018
5-point scale (strongly
disagree to strongly agree)
PHQ
-9
Regression
-0.1
No
30
Table 5
Continued
Face-to-Face Relationship with FB Contacts
2
Shensa et al.,
2018
Proportion of SM
friends
(on platform most used)
with whom they have:
PROMIS
Odds Ratio
-
increased odds of
moderate/severe
depressive
symptoms for every
10% increase in
independent
variable
No FTF Relationship
1.09 (1.05
-1.13)
Yes
Distant FTF
Relationship
1.01 (0.96
-1.05)
Yes
Close FTF
Relationship
0.93 (0.88
-0.97)
No
Enacted Social Support on FB
3
Zhang, 2017
5-item Scale (never to all
the time)
PHQ
-9
Zero-order
correlation
-0.03
No
Self-Disclosure
4
Kokkinos &
Saripanidis,
2017
10-item Self
-Disclosure
Index
PHQ
-9
Pearson correlation
0.14*
Yes
5
Zhang, 2017
General Disclosiveness
Scale
PHQ
-9
Zero-order
correlation
Honesty
-0.07
No
Intent
0.04
Yes
Amount/Intimacy
0.02
Yes
Indiscreet Facebook Content
6
Kokkinos &
Saripanidis,
2017
30-item Questionnaire
PHQ
-9
Pearson correlation
0.16
Yes
Body Surveillance
7
Choukas
-
Bradley, 2018
Objectified Body
Consciousness Scale
13-item Short
Mood and
Feelings
Questionnaire
Bivariate
Correlation
0.25**
Yes
31
Table 5
Continued
Body Comparison
8
Choukas
-
Bradley, 2018
6-item Body Comparison
Orientation Scale
13-item Short
Mood and
Feelings
Questionnaire
Bivariate
Correlation
0.24**
Yes
Problematic Social Media Use
9
Kircaburn et al.,
2018
SMUQ
SDHS
Pearson correlation
0.37**
Yes
Facebook Victimization
10
Kokkinos &
Saripanidis,
2017
Self-report Questionnaire
PHQ
-9
Pearson
correlation
0.29**
Yes
Cyberbullying Perpetration
11
Kircaburn et al.,
2018
Cyberbullying offending
scale
SDHS
Bivariate
Correlation
0.19**
Yes
Used late night/early morning
12
Barman et al.,
2018
Questionnaire
BDI
Mean rank (yes vs.
no)
112.45 vs
85.90**
Yes
Facebook Network Size
13
Zhang, 2017
6-point scale (
≤ 10, 11
-
100, 101
-200, 201
-30,
201-400 and
> than 400)
PHQ
-9
Zero-order
correlation
0
--
Use of FB Privacy Setting
14
Kokkinos &
Saripanidis,
2017
yes/no question
PHQ
-9
Spearman's rho
0.09
Yes
Correlation Between Individual Factors and Mental Health
Self-Esteem
1
Jang et al., 2016
Rosenberg's Self
-Esteem
Scale
RAND Mental
Health
Inventory
Zero-order
correlation
0.44**
Yes
32
Table 5
Continued
Correlation Between Individual Factors and Depression
Self-Esteem
2
Kokkinos &
Saripanidis,
2017
Self-report Questionnaire
PHQ
-9
Pearson correlation
-0.41**
Yes
Neuroticism
3
Kokkinos &
Saripanidis,
2017
Self-report
Questionnaire
PHQ
-9
Pearson correlation
0.58**
Yes
Loneliness
4
Kokkinos &
Saripanidis,
2017
20-item UCLA Loneliness
Scale Version 3
PHQ
-9
Pearson correlation
0.52**
Yes
Academic Stressors
5
Zhang, 2017
5-point scale (did not
occur to occurred and
extremely stressful)
PHQ
-9
Zero-order
correlation
0.42**
Yes
Suicidal Ideation
6
Jasso
-Medrano
& Lopez
-
Rosales, 2018
Positive and Negative
Suicidal Ideation
Inventory
CES-D
Spearman's rho
0.48**
Yes
Correlation Between Individual Factors and
Wellbeing
Neuroticism
1
Turel et al.,
2018
Big five inventory
WHO
-5
Correlation Matrix
-0.68**
Yes
*p<0.05, **p<0.01
33
Table 6 Association between Time on SM, SM Addiction and SM Behaviors and Depression
and Significance
Social Media Factors Studied
Association with
depression
Significant
Time on Social Media
Amount in hours
Mixed
Mixed
Daily frequency
Positive
Mixed
Social Media Addiction
Positive
Yes
Social Media Behavior
Social Comparison
Positive
Yes
Facebook Use
Positive
No
Impression Management
Negative
No
Social Network Intensity
Positive
No
Close face-to-face relationship with FB
Contacts
Negative
Yes
Distant face-to-face relationship with FB
Contacts
Positive
No
Negative face-to-face relationship with FB
Contacts
Positive
Yes
Enacted social support on FB
Negative
No
Self-disclosure on FB
Mixed
Mixed
Indiscreet FB content
Positive
No
Body surveillance
Positive
Yes
Body comparison
Positive
Yes
Problematic social media use
Positive
Yes
Facebook Victimization
Positive
Yes
Cyberbullying perpetration
Positive
Yes
Used late-night/early morning
Positive
Yes
Use of privacy settings
Positive
No
34
REFERENCES
Baker, D. A., & Algorta, G. P. (2016). The relationship between online social networking and
depression: A systematic review of quantitative studies. Cyberpsychology, Behavior and
Social Networking, 19(11), 638-648. doi:10.1089/cyber.2016.0206
Barman, L., Mukhopadhyay, D. K., & Bandyopadhyay, G. K. (2018). Use of social networking
site and mental disorders among medical students in kolkata, west bengal. Indian Journal of
Psychiatry, 60(3), 340-345. doi:10.4103/psychiatry.IndianJPsychiatry_210_18
Bottino, S. M., Bottino, C. M., Regina, C. G., Correia, A. V., & Ribeiro, W. S. (2015).
Cyberbullying and adolescent mental health: Systematic review. Cadernos De Saúde
Pública, 31(3), 463-475. doi:10.1590/0102-311x00036114
Brain Maturity Extends Well Beyond Teen Years. (2011, October 10). Retrieved March 28,
2019, from https://www.npr.org/templates/story/story.php?storyId=141164708
Choukas-Bradley, S., Nesi, J., Widman, L., & Higgins, M. K. (2018). Camera-ready: Young
women’s appearance-related social media consciousness. Psychology of Popular Media
Culture, No Pagination Specified-No Pagination Specified. doi:10.1037/ppm0000196
Depression. (2018
a
, February). Retrieved March 28, 2019, from
https://www.nimh.nih.gov/health/topics/depression/index.shtml
Depression. (2018
b
, March 22). Retrieved March 28, 2019, from http://www.who.int/en/news-
room/fact-sheets/detail/depression
Depression and College Students. (n.d.). Retrieved from March 28, 2019,
https://www.nimh.nih.gov/health/publications/depression-and-college-students/index.shtml
35
Depression in the U.S. Household Population, 2009-2012. (2014, December 03). Retrieved
March 28, 2019, from https://www.cdc.gov/nchs/data/databriefs/db172.htm
Digest of Education Statistics, 2017. (n.d.). Retrieved March 28, 2019, from
https://nces.ed.gov/programs/digest/d17/tables/dt17_303.40.asp
Dmytriw, A. A., Sorenson, T. J., Morris, J. M., Nicholson, P. J., Hilditch, C. A., Graffeo, C. S.,
et al. (2018). #Fake news: A systematic review of mechanical thrombectomy results among
neurointerventional stroke surgeons on twitter. Journal of Neurointerventional Surgery,
doi:neurintsurg-2018-014319
Escobar-Viera, C. G., Whitfield, D. L., Wessel, C. B., Shensa et al., A., Sidani, J. E., Brown, A.
L., et al. (2018). For better or for worse? A systematic review of the evidence on social
media use and depression among lesbian, gay, and bisexual minorities. JMIR Mental Health,
5(3), e10496. doi:10.2196/10496
Good mental health. (2017). Retrieved March 28, 2019, from
https://www.healthdirect.gov.au/good-mental-health
Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and
associated methodologies. Health Information & Libraries Journal, 26(2), 91-108.
doi:10.1111/j.1471-1842.2009.00848.x
Guedes, E., Sancassiani, F., Carta, M. G., Campos, C., Machado, S., King, A. L., & Nardi, A. E.
(2016). Internet Addiction and Excessive Social Networks Use: What About Facebook?
Clinical Practice & Epidemiology in Mental Health, 12(1), 43-48.
doi:10.2174/1745017901612010043
36
Hamm, M. P., Newton, A. S., Chisholm, A., Shulhan, J., Milne, A., Sundar, P., et al. (2015).
Prevalence and effect of cyberbullying on children and young people: A scoping review of
social media studies. JAMA Pediatrics, 169(8), 770-777.
doi:10.1001/jamapediatrics.2015.0944
Holland, G., & Tiggemann, M. (2016). A systematic review of the impact of the use of social
networking sites on body image and disordered eating outcomes. Body Image, 17, 100-110.
doi:10.1016/j.bodyim.2016.02.008
Hsu, M. S. H., Rouf, A., & Allman-Farinelli, M. (2018). Effectiveness and behavioral
mechanisms of social media interventions for positive nutrition behaviors in adolescents: A
systematic review. The Journal of Adolescent Health : Official Publication of the Society for
Adolescent Medicine, doi:S1054-139X(18)30249-0
Jang, K., Park, N., & Song, H. (2016). Social comparison on facebook: Its antecedents and
psychological outcomes doi:https://doi.org/10.1016/j.chb.2016.03.082
Jasso-Medrano & Lopez-Rosales, J. L., & López-Rosales, F. (2018). Measuring the relationship
between social media use and addictive behavior and depression and suicide ideation among
university students. Computers in Human Behavior, 87, 183-191.
doi:10.1016/j.chb.2018.05.003
Kircaburn et al., K., Kokkinos & Saripanidis, C. M., Demetrovics, Z., Király, O., Griffiths, M.
D., & Çolak, T. S. (2018). Problematic online behaviors among adolescents and emerging
adults: Associations between cyberbullying perpetration, problematic social media use, and
psychosocial factors. International Journal of Mental Health and Addiction, 1-18.
doi:10.1007/s11469-018-9894-8
37
Klassen, K. M., Douglass, C. H., Brennan, L., Truby, H., & Lim, M. S. C. (2018). Social media
use for nutrition outcomes in young adults: A mixed-methods systematic review. The
International Journal of Behavioral Nutrition and Physical Activity, 15(1), 70.
doi:10.1186/s12966-018-0696-y
Kokkinos & Saripanidis, C. M., & Saripanidis, I. (2017). A lifestyle exposure perspective of
victimization through facebook among university students. do individual differences matter?
Computers in Human Behavior, 74, 235-245. doi:10.1016/j.chb.2017.04.036
Kuss, D. J., & Griffiths, M. D. (2011). Online Social Networking and Addiction—A Review of
the Psychological Literature. International Journal of Environmental Research and Public
Health, 8(9), 3528-3552. doi:10.3390/ijerph8093528
Lauckner, C., Hill, M., & Ingram, L. A. (2018). An exploratory study of the relationship between
social technology use and depression among college students. Journal of College Student
Psychotherapy, doi:10.1080/87568225.2018.1508396
Lin, L. Y. (2016). Association between use of social networking media and depression in
adolescents and young adults. Depress Anxiety, 323-331. doi:10.5353/th_b5805152
Lup, K., Trub, L., & Rosenthal, L. (2015). Instagram #Instasad?: Exploring Associations Among
Instagram Use, Depressive Symptoms, Negative Social Comparison, and Strangers
Followed. Cyberpsychology, Behavior, and Social Networking, 18(5), 247-252.
doi:10.1089/cyber.2014.0560
Major Depression. (n.d.). Retrieved March 28, 2019, from
https://www.nimh.nih.gov/health/statistics/major-depression.shtml
38
Mental and Emotional Well-being. (n.d.). Retrieved April 10, 2019, from
https://www.surgeongeneral.gov/priorities/prevention/strategy/mental-and-emotional-well-
being.html
Park, J., Lee, D. S., Shablack, H., Verduyn, P., Deldin, P., Ybarra, O., . . . Kross, E. (2016).
When perceptions defy reality: The relationships between depression and actual and
perceived Facebook social support. Journal of Affective Disorders, 200, 37-44.
doi:10.1016/j.jad.2016.01.048
Rad, M.E., & Ahmadi, F. (2018). A new method to measure and decrease the online social
networking addiction. Asia-Pacific Psychiatry, 10(4) doi:10.1111/appy.12330
Rosenthal, S. R., Buka, S. L., Marshall, B. D., Carey, K. B., & Clark, M. A. (2016). Negative
Experiences on Facebook and Depressive Symptoms Among Young Adults. Journal of
Adolescent Health, 59(5), 510-516. doi:10.1016/j.jadohealth.2016.06.023
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016). Social networking sites, depression, and
anxiety: A systematic review. JMIR Mental Health, 3(4), e50. doi:v3i4e50
Shensa et al., A., Sidani, J. E., Escobar-Viera, C. G., Chu, K., Bowman, N. D., Knight, J. M., &
Primack, B. A. (2018). Real-life closeness of social media contacts and depressive
symptoms among university students. Journal of American College Health, 1-8.
doi:10.1080/07448481.2018.1440575
Smith, A., & Anderson, M. (2018, September 19). Social Media Use 2018: Demographics and
Statistics. Retrieved March 28, 2019, from http://www.pewinternet.org/2018/03/01/social-
media-use-in-2018/
39
Social Media. (2004). Retrieved March 28, 2019, from https://www.merriam-
webster.com/dictionary/social media
Tang, C. S., Koh, Y. W., & Gan, Y. (2017). Addiction to internet use, online gaming, and online
social networking among young adults in china, singapore, and the united states. Asia-
Pacific Journal of Public Health, 29(8), 673-682. doi:10.1177/1010539517739558
Tang, C. S. & Koh, Y. Y. W. (2017). Online social networking addiction among college students
in singapore: Comorbidity with behavioral addiction and affective disorder. Asian Journal
of Psychiatry, 25, 175-178. doi:10.1016/j.ajp.2016.10.027
Tang, C. S. K., Wu, A. M. S., Yan, E. C. W., Ko, J. H. C., Kwon, J. H., Yogo, M., . . . Koh, Y.
Y. W. (2018). Relative risks of internet-related addictions and mood disturbances among
college students: A 7-country/region comparison. Public Health, 165, 16-25.
doi:10.1016/j.puhe.2018.09.010
Tang, L., Bie, B., Park, S. E., & Zhi, D. (2018). Social media and outbreaks of emerging
infectious diseases: A systematic review of literature. American Journal of Infection
Control, 46(9), 962-972. doi:S0196-6553(18)30105-6
Training, C. (2017, August 31). Doing scoping reviews. Retrieved from March 28, 2019,
https://www.youtube.com/watch?v=kdsHk84X5g0
Turel, O., Poppa, N. T., & Gil-Or, O. (2018). Neuroticism magnifies the detrimental association
between social media addiction symptoms and wellbeing in women, but not in men: A
three-way moderation model. The Psychiatric Quarterly, 89(3), 605-619.
doi:10.1007/s11126-018-9563-x
40
Zhang, R. (2017). The stress-buffering effect of self-disclosure on facebook: An examination of
stressful life events, social support, and mental health among college students. Computers in
Human Behavior, 75, 527-537. doi:10.1016/j.chb.2017.05.043
Zimmerman, L., PhD. (n.d.). Do you fear missing out? Retrieved March 30, 2019, from
https://www.apa.org/gradpsych/2016/01/missing-out