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ORIGINAL PAPER
Social Media Use and Perceived Emotional Support Among US
Young Adults
Ariel Shensa
1,2
•Jaime E. Sidani
1,2
•Liu yi Lin
1
•Nicholas D. Bowman
4
•
Brian A. Primack
1,2,3
ÓSpringer Science+Business Media New York 2015
Abstract Low emotional support is associated with poor
health outcomes. Engagement with face-to-face social
networks is one way of increasing emotional support.
However, it is not yet known whether engagement with
proliferating electronic social networks is similarly asso-
ciated with increased emotional support. Thus, the purpose
of this study was to assess associations between social
media use and perceived emotional support in a large,
nationally-representative sample. In October 2014, we
collected data from 1796 U.S. adults ages 19–32. We
assessed social media use using both total time spent and
frequency of visits to each of the 11 most popular social
media platforms. Our dependent variable was perceived
emotional support as measured by the brief Patient-Re-
ported Outcomes Measurement Information System
(PROMIS) emotional support scale. A multivariable model
including all sociodemographic covariates and accounting
for survey weights demonstrated that, compared with the
lowest quartile of time on social media, being in the highest
quartile (spending two or more hours per day) was sig-
nificantly associated with decreased odds of having higher
perceived emotional support (AOR 0.62, 95 % CI 0.40,
0.94). However, compared with those in the lowest quar-
tile, being in the highest quartile regarding frequency of
social media use was not significantly associated with
perceived emotional support (AOR 0.70, 95 % CI 0.45,
1.09). In conclusion, while the cross-sectional nature of
these data hinder inference regarding directionality, it
seems that heavy users of social media may actually feel
less and not more emotional support.
Keywords Emotional support Social media Social
networks PROMIS (patient reported outcomes
measurement information system) Nationally-
representative data Young adults
Introduction
Low emotional support has been associated with poor
physical and mental health outcomes and increased overall
mortality risk [1–5]. Increased emotional support is also a
protective factor for vulnerable populations against nega-
tive health outcomes such as pre- and postnatal maternal
depression [6], stress, anxiety, and depression in HIV-
positive men [7], and increased survival rates among
African-American and White breast cancer patients [8].
While demographic characteristics such as age and gender
moderate the relationship between emotional support and
well-being [9,10], emotional support is consistently asso-
ciated with an overall beneficial effect on health [11].
One of the most effective ways of increasing emotional
support is through social network affiliation [12,13]. For
example, a 20-year longitudinal study found that one’s
happiness depends on the happiness of other individuals
with whom they are connected [14]. Similarly, results from
&Ariel Shensa
shensaa@upmc.edu
1
Division of General Internal Medicine, Department of
Medicine, University of Pittsburgh School of Medicine,
Pittsburgh, PA, USA
2
Center for Research on Media, Technology, and Health,
University of Pittsburgh, 230 McKee Place Suite 600,
Pittsburgh, PA 15213, USA
3
Division of Adolescent Medicine, Department of Pediatrics,
University of Pittsburgh School of Medicine, Pittsburgh, PA,
USA
4
Department of Communication Studies, West Virginia
University, Morgantown, WV, USA
123
J Community Health
DOI 10.1007/s10900-015-0128-8
Table 1 Whole sample
characteristics and bivariable
associations with perceived
emotional support (N =1785)
Independent variables Whole
sample
a
Perceived emotional support
a
pvalue
b
Low
(n =1096)
High
(n =689)
Social media use
Time per day, min .06
Q1 (0–30) 29.8 27.8 32.8
Q2 (31–60) 20.8 19.2 23.3
Q3 (61–120) 24.0 24.6 23.0
Q4 (121 and above) 25.5 28.5 20.8
Site visits per week
c
.64
Q1 (less than 9) 28.3 27.1 30.2
Q2 (9–30) 25.1 25.4 24.6
Q3 (31–57) 24.1 23.6 24.8
Q4 (58 and above) 22.5 23.9 20.4
Sociodemographic
Age, years .16
19–23 33.7 31.3 37.4
24–26 24.8 24.8 24.7
27–32 41.6 43.9 37.9
Sex .91
Female 50.3 50.5 50.0
Male 49.7 49.5 50.0
Race .004
White, non-Hispanic 57.5 53.3 63.9
Black, non-Hispanic 13.0 12.7 13.4
Hispanic 20.6 22.2 18.1
Other
d
8.9 11.8 4.6
Relationship status \.001
Single
e
44.5 51.1 34.1
In a committed relationship
f
55.6 48.9 65.9
Living situation .10
Parent/guardian 34.0 34.9 32.7
Significant other 35.6 32.7 40.1
Other
g
30.4 32.5 27.2
Household income .047
Low (under $30,000) 22.9 25.9 18.2
Medium ($30,000–$74,999) 38.4 36.7 41.2
High ($75,000 and above) 38.7 37.5 40.6
Education level .24
High school or less 36.0 37.3 33.8
Some college 38.3 39.0 37.2
Bachelor’s degree or higher 25.7 23.7 29.0
a
Values may not total 100 due to rounding. Column percentages are based upon survey weighted data,
therefore may not be congruent with the cell frequency proportion of total N
b
Pvalue derived using Chi square analyses comparing proportion of users in each category
c
Includes Facebook, Twitter, Google?, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine, Snap-
chat, and Reddit
d
Includes multiracial
e
Includes widowed, divorced, and separated
f
Includes engaged, married, and in a domestic partnership
g
Defined as not living with a parent/guardian or significant other
J Community Health
123
a 32-year longitudinal study indicated that positive health
behaviors appear to spread via social ties in a large social
network [15]. Moreover, a study on religiosity and life
satisfaction offered strong evidence that increased life
satisfaction stems from the social aspects involved in
religious identity such as building congregational social
networks [16].
With the substantial increase of Internet and social
media use in the 21
st
century, opportunities for connect-
edness and support have become more plentiful yet more
complex. From 2005 to 2013, the percentage of online
adults ages 18–29 who use social media has increased from
9 to 90 %, with over 74 % of all online adults currently
reporting social media use [17]. It may be that electronic
social networks mimic face-to-face social networks, which
are known to increase emotional support. Consistent with
this, some findings suggest that larger social networks and
perceived audiences predict higher levels of life satisfac-
tion [18]. Similarly, greater intensity of social network use
has been associated with increased social capital [19,20].
Additionally, in a study assessing Facebook user responses,
positive emotions were found to be more prevalent than
negative emotions, suggesting Facebook use may be
associated with happiness [21]. Furthermore, another
recent study of Facebook found that users report higher
perceived emotional support than other internet users of
similar demographic characteristics [22]. One possibility
for social media’s potential positive impact on emotional
support is the medium’s ability to help foster both strong
and weak social ties [23]. These ties may be important for
emotional strength or securing novel sources of informa-
tion, depending upon the strength of the tie [24]. Others
have argued that social media usage creates an ambient
awareness among users, keeping them loosely aware of
each other’s day-to-day social activities [25], which may
also enhance a user’s perception of emotional support.
However, increased online social media use may not
necessarily translate into increased emotional support. For
example, results from one study suggested that total time
spent on social media and a larger online social network
was neither associated with having a larger offline network
nor with feeling emotionally closer to one’s offline net-
works [26]. Additionally, a literature review of 43 empir-
ical studies found that—while most individuals are initially
motivated to use social media sites to stay connected with
established offline social networks—increased use was
associated with a decrease in real life social community,
lower academic achievement, as well as relationship
problems [27]. Furthermore, findings from another recent
study indicated that—while online social networking was
weakly associated with decreased depression—social sup-
port from Facebook had less of an association with reduced
depression than did face-to-face social network support
[28]. Some have argued that social media use may be
disruptive in that it pulls individuals away from their
physical surroundings, including people who occupy that
space. This may result in displacement or a tension of
attention [29,30].
It is important to note that the above-mentioned often
conflicting studies were limited in several respects. For
example, these studies nearly all used relatively small
convenience samples and/or focused on youth [19,20,31].
Due to the ubiquitous nature of social media use, including
use by young adults [32], it would be valuable to study
associations between social media use and perceived
emotional support in a large national sample more gener-
alizable to the population of U.S. users. Prior studies also
generally assessed social media using a single platform
(e.g., Facebook) [18,21,28,31]. However, multi-platform
use is on the rise, with 52 % of online adults and 71 % of
teens using two or more social media sites [32]. Hence, it
would be beneficial to assess social media use more com-
prehensively. According to Glanz, Rimer, and Viswanath,
both social network and social support are inter-related
theoretical constructs within a larger conceptual model of
health behavior and health outcomes [12]. Therefore, the
purpose of this study was to assess associations between
social media use and perceived emotional support in a
large, nationally-representative sample of young adults.
Based on prior literature [18–22] and theory [12], we
hypothesized that increased social media use would be
associated with greater perceived emotional support.
Methods
Participants and Procedures
Participants were recruited from a nationally-representative
probability-based online non-volunteer access panel known
as the KnowledgePanel
Ò
. This panel, which consists of
approximately 55,000 members ages 18 and older, is
recruited and maintained by Growth from Knowledge
(GfK) [33]. It was populated through both address-based
sampling and random digit dialing, allowing for a sampling
frame that covers approximately 97 % of U.S. households
[33]. Panel members are invited to participate in web-based
surveys via personal or GfK-provided e-mail addresses. In
March–April 2013, a total of 3254 panel members ages
18–30 (response rate =54 %) completed a web-based
survey about various health behaviors. In October 2014,
GfK sent a follow-up survey about these and other health
behaviors to the 3254 panel members who had completed
the baseline survey, who were then ages 19–32. Those who
completed the surveys were given a $15 cash-equivalent
incentive. Data from the current study were collected as
J Community Health
123
part of this follow-up wave of the longitudinal study;
baseline data were not used because social media use and
the dependent variable of perceived emotional support
were not collected at that time. This study was approved by
the University of Pittsburgh Institutional Review Board and
was granted a Certificate of Confidentiality from the
National Cancer Institute at the National Institutes of
Health.
Measures
Perceived Emotional Support (Dependent Variable)
We assessed perceived emotional support using a 4-item
scale developed by the Patient-Reported Outcomes Mea-
surement Information System (PROMIS). PROMIS is a
National Institutes of Health (NIH) Roadmap initiative
aiming to provide precise, reliable, valid, and standardized
questionnaires measuring patient–reported outcomes across
the domains of physical, mental, and social health [34–36].
The PROMIS emotional support item bank specifically
aims to assess perceived feelings of being cared for and
valued as a person [37]. Participants were presented with
the following items: ‘‘I have someone who will listen to me
when I need to talk’’; ‘‘I have someone to confide in or talk
to about myself or my problems’’; ‘‘I have someone who
makes me feel appreciated’’ and ‘‘I have someone to talk
with when I have a bad day.’’ Each item was followed by a
Likert-type response scale with possible responses of Never
(1), Rarely (2), Sometimes (3), Often (4), and Always (5).
We calculated a raw summary score ranging from 4 to 20,
using only the respondents who answered all 4 items.
Because of the non-normal distribution of the data, the
outcome was treated as categorical instead of continuous.
While our original analytic plan involved collapsing the
dependent variable into tertiles, this was ultimately not
appropriate because of violation of the proportional odds
assumption [38]. Therefore, the most appropriate way to
operationalize this variable for primary analyses was
dichotomously into low and high categories based upon the
distribution of the data.
Social Media Use (Independent Variables)
Social media use was assessed with multiple items devel-
oped to capture use in terms of both total time spent on
social media and frequency of use. The first item asked
participants to estimate total time per day spent on social
media for personal use. This item explicitly instructed
participants to not include work-related use in their esti-
mates. Participants were provided with open-ended boxes
for hours and minutes, and total time was converted to
minutes for analysis. Eleven subsequent items prompted
participants to indicate how frequently they visit the fol-
lowing 11 social media platforms: Facebook, Twitter,
Google?, YouTube, LinkedIn, Instagram, Pinterest, Tum-
blr, Vine, Snapchat, and Reddit. These platforms were
selected based on their popularity with the young adult age
group at the time of the study [32,39]. Seven response
categories were based on the Pew Research Center items
[32] and included: I don’t use this platform (0), less than
once a week (1), 1–2 days a week (2), 3–6 days a week (3),
about once a day (4), 2–4 times a day (5), and 5 or more
times a day (6). This item was used to calculate partici-
pants’ social media site visits per week (frequency) by
converting the response categories into numeric averages
based on a standardized unit of measurement rather than
general frequency. For example, 1–2 days a week was
recoded as 1.5 site visits per week and 2–4 times a day was
recoded as 21 site visits per week. Both independent
variables were collapsed into quartiles for primary analyses
to improve the interpretability of results.
Socio-demographic Factors (Covariates)
GfK maintains certain socio-demographic information
about panel members, including age, sex, race/ethnicity,
household income, and education level. For this study, age
was collapsed into three categories (19–23; 24–26; 27 and
above) based on the distribution of the data. Race/ethnicity
was divided into four categories (White, non-Hispanic;
Black, non-Hispanic; Hispanic; Other, non-Hispanic),
while household income and education level were each
divided into three categories (low, under $30,000; medium,
$30,000–74,999; high, $75,000 and above and high school
or less; some college; bachelor’s degree or higher,
respectively). Relationship status (single; in a committed
relationship) and living situation (with parent/guardian;
with significant other; other) were obtained via self-report
from participants.
Data Analysis
We included all participants who had complete data on the
dependent variable (perceived emotional support). Because
\1 % of participants had missing data for this variable,
removal of incomplete data is unlikely to have affected our
results. We first calculated descriptive statistics of the
dependent variable, each of the 2 independent variables
(time and frequency), and each of the 7 covariates.
We then used Chi square tests to determine bivariable
associations between our dependent variable and each of
our independent variables and covariates. We also used Chi
square tests to assess bivariable associations between each
covariate and each independent variable.
J Community Health
123
We then used logistic regression to determine bivariable
and multivariable associations between each independent
variable and our dependent variable. It was decided a priori
to adjust for all sociodemographic variables in our primary
multivariable models. Additionally, we used post-estima-
tion orthogonal polynomial tests to examine the overall
linear trend of each ordered categorical independent vari-
able in relation to our dependent variable.
All primary analyses were conducted using survey
weights provided by GfK in order to estimate effects for
the general U.S. population. Statistical analyses were per-
formed with Stata 12.1 [40], and two-tailed pvalues of
\.05 were considered to be significant.
To examine the robustness of our results, we conducted
three sets of sensitivity analyses. First, we conducted all
multivariable analyses without survey weights. Second, we
conducted all multivariable analyses using only a parsi-
monious set of covariates that had a bivariable association
of p\.10 with the dependent variable. Third, we con-
ducted all analyses with independent variables as contin-
uous instead of ordered categorical variables.
Results
Participants
Our final sample consisted of 1785 individuals with only
11 (\1 %) omitted for incomplete data on our outcome
variable. The sociodemographic characteristics of our
sample are reported in Table 1.
Perceived Emotional Support
Individual items demonstrated high internal consistency with
Cronbach’s alpha =0.96. Data for perceived emotional
support were non-normal and heavily skewed right. The
median score for perceived emotional support was 16 (in-
terquartile range [IQR] =12–20). As described above in the
methods section, analysis of this variable as continuous was
not viable because of non-normality and no appropriate
transformation. Additionally, collapsing in tertiles was ulti-
mately not appropriate because of violation of the propor-
tional odds assumption [38]. Thus, perceived emotional
support was dichotomized into ‘‘low’’ and ‘‘high’’ groups
based on the distribution of the data and correspondence with
T-scores. In particular, the 39 % of participants with raw
scores of 19 or 20 were defined as ‘‘high’’ emotional support
and the remaining 61 % were placed in the ‘‘low’’ category.
These categories corresponded well with T-scores, which
were recommended benchmarks for other PROMIS measures
under conditions of normality. For example, our median score
of 16 corresponded to a T-score of 49, which is only 1 point
below the standardized mean of 50. Our ‘‘high’’ group, scores
of 19 and 20, corresponded to T-scores of 55.6 and 62. These
scores are equivalent to greater than half and greater than one
standard deviations above the mean, respectively.
Social Media Use
Participants reported a median of 61 min (IQR =30–135)
for time per day on social media and a median of 30
(IRQ =9–57) site visits per week. The lowest quartile for
Table 2 Bivariable associations between sociodemographic covari-
ates and time per day on social media for personal use
Covariate Time per day, min
a
pvalue
b
0–30 31–60 61–120 121?
Age, years \.001
19–23 26.7 27.6 37.2 43.3
24–26 27.4 20.3 26.1 23.2
27–32 45.9 52.1 36.8 33.5
Sex
Female 42.7 43.4 53.4 61.0 \.001
Male 57.3 56.6 46.6 39.0
Race/ethnicity
White, non-Hispanic 63.5 63.7 54.0 48.4 .13
Black, non-Hispanic 10.5 10.4 15.0 16.6
Hispanic 16.5 17.3 23.3 25.4
Other
c
9.4 8.6 7.8 9.6
Relationship status .09
Single
d
41.3 38.3 46.8 50.5
Committed
relationship
e
58.7 61.7 53.2 49.5
Living situation .13
Parent/guardian 31.3 29.5 36.9 37.7
Significant other 41.0 40.4 31.2 29.1
Other
f
27.7 30.1 32.0 33.3
Household income .17
Under $30,000 18.2 20.7 24.4 28.0
$30,000–$74,999 41.4 36.2 41.4 34.1
$75,000 and above 40.4 43.2 34.1 37.9
Education level .003
High school or less 31.9 26.3 38.4 45.0
Some college 37.1 41.7 39.1 36.9
B.A. or higher 31.0 32.0 22.5 18.2
a
Values may not total 100 due to rounding. Numerals represent
column percentages
b
Pvalue derived using Chi square analyses comparing proportion of
users in each category
c
Includes multiracial
d
Includes widowed, divorced, and separated
e
Includes engaged, married, and in a domestic partnership
f
Defined as not living with a parent/guardian or significant other
J Community Health
123
time per day included 0–30 min; the second quartile
included 31–60 min; the third quartile included
61–120 min; and the fourth and highest quartile included
121 or more minutes. For site visits per week, the lowest
quartile included less than 9 site visits; the second quartile
included 9–30; the third quartile included 31–57; and the
fourth and highest quartile included 58 or more site visits.
Bivariable Analyses
We found no significant associations between any of the
social media use variables and perceived emotional sup-
port. However, bivariable analyses did show significant
associations between race/ethnicity, relationship status, and
household income and perceived emotional support
(pranging from \.001 to .05) (Table 1). Bivariable anal-
yses also showed significant associations between three
covariates (age, sex, and education level) and time per day
on social media (pranging from \.001 to .003) (Table 2).
Only age showed a significant association with site visits
per week (p\.001) (data not shown).
Multivariable Analyses
In fully-adjusted multivariable models, respondents in the
highest quartile of time per day on social media had sig-
nificantly decreased odds of high perceived emotional
support (AOR 0.62; 95 % CI 0.40–0.94). There was no
significant association between site visits per week and
perceived emotional support (AOR 0.70; 95 % CI
0.45–1.09) (Table 3).
Post-estimate tests using orthogonal polynomials
showed significant linear trends for time per day with
perceived emotional support (p=.02), whereas site visits
per week did not (p=.18) (Table 3). Results from all
sensitivity analyses were consistent with those from pri-
mary analyses.
Discussion
This study of a nationally-representative sample of young
adults found that participants who spend the most time per
day on social media sites had significantly lower odds of
reporting higher levels of perceived emotional support.
Additionally, we found a significant linear trend between
time and perceived emotional support; as time per day
spent on social media increased, perceived emotional
support decreased. A second independent variable, which
quantified the frequency of social media visits per week,
was not associated with perceived emotional support.
While there is conflicting literature on the association
between social media use and feeling emotionally sup-
ported, our results are consistent with research that sug-
gests that social media use may undermine subjective well-
being [41] and be associated with factors counteractive to
perceived emotional support such as loneliness [26,42,43]
and depression [44]. However, they do not support the
findings that state the psychological benefit of a social
media presence presented in the literature review. While
our study did not probe the psychological mechanisms
underlying observed effects, some have argued that social
Table 3 Bivariable and
multivariable associations
between social media use and
perceived emotional support
Social media use Perceived emotional support
a
OR (95 % CI) P
b
AOR
c
(95 % CI) P
b
Time per day, min .009 .02
Q1 (0–30) 1 [Reference] 1 [Reference]
Q2 (31–60) 1.03 (0.69–1.54) 0.96 (0.65–1.42)
Q3 (61–120) 0.79 (0.53–1.19) 0.78 (0.50–1.21)
Q4 (121 ?) 0.62 (0.41–0.92) 0.62 (0.40–0.94)
Site visits per week
d
.30 .18
Q1 (less than 9) 1 [Reference] 1 [Reference]
Q2 (9–30) 0.87 (0.58–1.30) 0.77 (0.52–1.16)
Q3 (31–57) 0.94 (0.63–1.42) 0.86 (0.56–1.33)
Q4 (58 and above) 0.77 (0.50–1.17) 0.70 (0.45–1.09)
OR odds ratio, AOR adjusted odds ratio, CI confidence interval
a
Perceived emotional support is divided into 2 categories; the upper level representing greater support
b
Pvalue derived using orthogonal polynomial tests for trend
c
Adjusted for age, sex, race, relationship status, living situation, household income, and education level
d
Includes Facebook, Twitter, Google?, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine, Snap-
chat, and Reddit
J Community Health
123
media platforms—despite their ability to help foster both
strong (emotionally supportive) and weak (information-
seeking) social ties [24]—can greatly disrupt the user’s
normal sense of place including the people in it [29,30].
Moreover, social media platforms require users to con-
stantly monitor and co-produce information with their
peers, which might place additional strain on their cogni-
tive, emotional, and social resources [45].
It is interesting to note that, while we found significant
associations between increased time on social media and
lower perceived emotional support, we did not find sig-
nificant associations between increased frequency of use
and lower emotional support. Because the point estimate
was substantially below 1.0 (it was 0.7), it is possible that
we simply did not have enough power for this result to be
significant. However, it is also possible that total time spent
is indeed more closely tied to lower perceived emotional
support for other reasons. For example, this might support
the ‘‘displacement hypothesis’’ whereby increased overall
time on social media simply makes less time available for
more beneficial face-to-face relationships. Similarly,
because it is generally an acceptable social convention
among young adults to check social media frequently—
even when also engaging in an in-person social activity
[30,46]—this frequency may not interfere with benefits
afforded by other social relationships. Conversely, it could
also be the case that frequent social media checks are more
functional than they are distracting. For example, one
might make frequent checks of their Twitter or Facebook
accounts throughout the day, but these checks could also be
both short and performed in spaces otherwise free from
social interaction. That is, one might share and view social
media content that are valuable without being demanding.
It would be beneficial for future studies to continue to
examine potentially differential effects of time and fre-
quency of use.
Because our data were cross-sectional, we could not
infer temporality. One explanation of our overall findings
may be that individuals who feel less emotionally sup-
ported in offline relationships subsequently spend more
time on social media to fill this void. Alternately, it could
be that individuals who first spend substantial time on
social media subsequently feel less emotional support.
This could be an effect of what is happening online or
increased exposure to negative social support. For
example, envy, fear of missing out, and contentious
interactions with others may occur with increased time
spent on social media and adversely affect one’s per-
ceived emotional support [47–50]. As noted above,
increasing time spent online may also result in being less
engaged in truly therapeutic face-to-face relationships
and/or interactions. Regardless of directionality, however,
it is an important and somewhat paradoxical finding that
those with increased social media use tend to perceive
less emotional support. Even if this is because individuals
with low perceived emotional support subsequently spend
increased time online, it is interesting that they do not
seem to find the emotional support they lack in that
medium. As is often the case, it may be that both direc-
tions are applicable—individuals with low perceived
support spend more time online, which subsequently
limits their ability to engage in more potentially valuable
in-person activities that might better serve their emotional
support needs. Qualitative work may be valuable in
helping describe these types of complex processes, as
well as longitudinal research such as cohort and panel
designs.
It is important to note that we only assessed overall time
and frequency, and that there are many qualitatively dif-
ferent ways of interacting on social media. For example,
some individuals have more ‘‘passive’’ experiences by
simply viewing posts; this is also known as browsing or
lurking. However, other individuals have substantially
more ‘‘active’’ experiences characterized by writing private
messages, commenting, posting, and/or actively searching
for old friends or acquaintances. It follows that users who
actively engage content—co-creating their and others’
stories through frequent and ritualized interactions—might
see these spaces as vehicles for expression and social
support [51]. Because the degree of passivity/activity may
indeed be associated with ultimately feeling emotional
support, it would be valuable for future work to assess the
character of online interactions in a more fine-grained
manner.
Similarly, there are a variety of different degrees of
emotionality that can be experienced online. Some indi-
viduals may have only relatively benign interactions, while
as noted above, others may tend to have aggressive inter-
actions. Still others may be prone to ‘‘overshare,’’ given the
disinhibited nature of mediated social interaction. This
phenomenon of over-sharing, especially among users with
low self-esteem, ultimately can lead to negative responses
from friends [52] and decreased relationship satisfaction
[53]. One theoretical mechanism underlying these effects is
that of communication privacy management, wherein
social media users often and unintentionally violate their
own boundary rules around content and information con-
sidered to be highly personal, but once divulged online
becomes essentially public domain [54]. Because the
specific ways in which individuals use social media was
not measured in this study, these could be valuable areas
for future research.
It was an important contribution of our study that we
assessed social media broadly—using the 11 most popular
platforms at the time—to measure frequency of use
instead of using a single platform such as Facebook. This
J Community Health
123
will be even more important in the future, as multi-plat-
form use among online adults increases about 10 % per
year (Pew Research Center, 2015). While we did not find
a significant association between overall frequency and
perceived emotional support, there are other aspects of
multi-platform use that may be interesting to assess in the
future. For example, media multi-tasking, especially
among adolescents, has been associated with negative
cognitive and/or emotional outcomes [55–58]. It may
therefore be interesting to determine if the diffusion of
social media use across multiple platforms is an inde-
pendent risk factor for negative outcomes such as lower
perceived emotional support.
Limitations
As noted above, the cross-sectional nature of the study
limits inference of directionality. Future longitudinal work
may improve assessment of temporality. Additionally,
although our study used a nationally-representative sample
of young adults, these findings cannot be generalized to a
younger or older population. It would be beneficial for
future research to assess other populations. This includes
older adult social media users, given this group’s rapidly
growing social media presence [32].
Another limitation of our study is that data were self-
reported. However, we assured all participants that their
responses were confidential and protected by a Certifica-
tion of Confidentiality, making it unlikely responses would
not be truthful.
Conclusion
This study of a nationally-representative sample of
young adults found that individuals who spent more than
two hours per day on any combination of the 11 most
popular social media platforms had significantly
decreased odds of reporting higher levels of perceived
emotional support, even when controlling for a com-
prehensive set of related sociodemographic characteris-
tics such as sex, relationship status, and living situation.
Individuals with low perceived emotional support may
subsequently spend more time on social media, social
media users may paradoxically begin to feel lower
emotional support, or both may be true. It would be
valuable for future research to examine these associa-
tions longitudinally and to more carefully assess differ-
ent types of social media interactions.
Acknowledgments Dr. Primack is supported in part by a grant from
the National Cancer Institute (R01-CA140150).
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict
of interest.
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