Follow Friends One Hour a Day: Limiting Time on Social Media and Muting Strangers Improves Well-Being

Abstract

Introduction: Social media use is ubiquitous among young adults, and empirical research is increasingly suggesting that how it is used and how much time is spent using it have significant implications for psychological well-being and mental health. Most recent studies find that limiting but not eliminating social media has beneficial effects. Correlational findings suggest that following actual friends is beneficial, while following strangers can be harmful. Method: This study sought to test the impact of limiting time spent on social media as well as “muting” strangers on Instagram and eliminating TikTok use in an experimental paradigm. Results: Replicating prior studies, we found that limiting social media use to 60 minutes per day (versus unlimited use) led to reductions in depression, F(1,96) = 5.84, p = .018, for the most depressed participants. Moreover, limiting stranger content (by muting strangers on Instagram and eliminating TikTok use), in addition to limiting time, led to significant reductions in fear of missing out, F(2,138) = 4.806, p = .01, for the most depressed participants and to significant reductions in social comparison, F(2,138) = 4.367, p = .015. Discussion: In conclusion, it is not just how much time one spends on social media that matters to well-being, but how one uses that time and who one is interacting with.

Full text

Journal of Social and Clinical Psychology, Vol. 42, No. 3, 2023, pp. 187–213
© 2023 Guilford Publications. https://doi.org/10.1521/jscp.2023.42.3.187
FOLLOW FRIENDS ONE HOUR A DAY: LIMITING
TIME ON SOCIAL MEDIA AND MUTING
STRANGERS IMPROVES WELL-BEING
MELISSA G. HUNT, ELISA XU, ALISSA FOGELSON, AND JULIA RUBENS
University of Pennsylvania, Philadelphia
Introduction: Social media use is ubiquitous among young adults, and empiri-
cal research is increasingly suggesting that how it is used and how much time
is spent using it have signicant implications for psychological well-being and
mental health. Method: Most recent studies nd that limiting but not eliminating
social media has benecial effects. Correlational ndings suggest that follow-
ing actual friends is benecial, while following strangers can be harmful. This
study sought to test the impact of limiting time spent on social media as well as
“muting” strangers on Instagram and eliminating TikTok use in an experimental
paradigm. Results: Replicating prior studies, we found that limiting social media
use to 60 minutes per day (versus unlimited use) led to reductions in depression,
F(1,96)=5.84, p=.018, for the most depressed participants. Moreover, limiting
stranger content (by muting strangers on Instagram and eliminating TikTok use),
in addition to limiting time, led to signicant reductions in fear of missing out,
F(2,138)=4.806, p=.01, for the most depressed participants and to signicant
reductions in social comparison, F(2,138)=4.367, p=.015. Discussion: In con-
clusion, it is not just how much time one spends on social media that matters to
well-being, but how one uses that time and who one is interacting with.
Keywords: social media, depression, Instagram, well-being, fear of missing out,
social comparison
INTRODUCTION
Social media has quickly become ubiquitous worldwide. In 2021,
over 4.26 billion people had accounts on social media platforms,
Address correspondence to Melissa Hunt, PhD, Department of Psychology,
University of Pennsylvania, 425 S. University Avenue, Philadelphia, PA 19104; Email:
mhunt@psych.upenn.edu
G5215.indd 187G5215.indd 187 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

188 HUNT ET AL.
and this number is projected to increase to almost 6 billion in 2027
(Dixon, 2022). As of April 2021, 7 in 10 Americans used social
media. Among 18- to 29-year-olds, 8 in 10 used social media, and
use of Instagram, Snapchat, and TikTok were especially common
(Pew Research Center, 2021). Facebook remains one of the most
popular sites among US adults, but Instagram, Snapchat, and
TikTok have attracted a strong following among young adults
(Perrin & Anderson, 2019), with 72% of teens saying they use
Instagram (Auxier, 2020).
Widespread use of social media has given rise to growing
concerns about potential negative effects of its use on young
adults’ psychological well-being and mental health (e.g. Twenge
& Campbell, 2018; Twenge etal., 2018). Research is increasingly
suggesting that time spent on social media is linked with depres-
sive symptoms, body dissatisfaction, anxiety, impaired sleep,
loneliness, low self-esteem, fear of missing out, and upward
social comparison (comparing oneself to others who are per-
ceived as happier, more attractive, etc.) (Schønning etal., 2020;
Twenge, 2019). Some research suggests that social media use
can have benecial effects in specic situations. For example,
O’Reilly etal. (2019) found that social media provides oppor-
tunities for mental health awareness and promotion for adoles-
cents. Social media can be a lifeline for marginalized minorities,
especially rural LGBTQ adolescents, who can nd community
and get support online, although constant surveillance of one’s
social media prole can become a stressor, potentially leading to
depression (Escobar-Viera etal., 2018; Karim etal., 2022). Shensa
et al. (2021) explored positive and negative effects of social
media for individuals with and without depressive symptoms
and found that connection with others and exposure to negativ-
ity were the most frequently mentioned positive and negative
effects, and that non-depressed individuals were more likely to
reap the benets. In sum, while social media use can be a posi-
tive, there are numerous reasons to believe that it can have a net
negative effect on adolescent and young adult mental health. A
recent empirical meta-review concluded there is indeed an asso-
ciation between social media use and mental health outcomes,
but that the effects are complex and depend on which aspects of
social media usage and mental health variables are investigated
(Meier & Reinecke, 2020).
G5215.indd 188G5215.indd 188 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

FOLLOW FRIENDS ONE HOUR A DAY 189
Unfortunately, much of the early research on social media
and psychological well-being was correlational, and much of
it focused only on Facebook. For example, self-reported Face-
book use was found to be directly and indirectly positively cor-
related with depressive symptoms (Steers etal., 2014; Tandoc
etal., 2015), low self-esteem (Kalpidou et al., 2011), loneliness
(Song etal., 2014), and low life satisfaction (Kross etal., 2013).
Self-reported Instagram use has also been found to be directly
and indirectly positively correlated with depressive symptoms
among young adults (Donnelly & Kuss, 2016; Lup etal., 2015).
Of course, such correlational ndings do not establish causality.
More recent studies have examined the effects of experimen-
tally eliminating social media use on well-being. Tromholt
(2016) randomly assigned participants to stop using Facebook
for a week or continue using it as usual, and found that quit-
ting Facebook led to increases in life satisfaction and more posi-
tive emotions. In contrast, other studies resulted in participants
reporting lower life satisfaction after giving up Facebook for ve
days, although they did experience lower levels of physiological
stress measured using cortisol levels (Vanman etal., 2018). Addi-
tionally, some participants who quit all forms of social media for
seven days experienced increases in cravings and boredom, but
this may be more indicative of the addictive qualities of social
media than any positive effects of its use (Stieger and Lewetz,
2018). However, eliminating social media entirely is unrealistic
for most young people, meaning that these studies had poor
ecological validity, and cannot be generalized to natural behav-
ior in the world (Schmuckler, 2001). Moreover, adolescents who
engage in light use of social media (between .5 and 2 hours),
appear to experience the highest levels of well-being compared
to both non-users and heavy users (Twenge, 2019), suggesting
that limited use may indeed be benecial. Additionally, light
Facebook users experienced no positive effects of quitting Face-
book, in contrast to medium and heavy users who improved in
well-being (Tromholt, 2016). Thus, experimental studies should
seek to limit, but not eliminate, social media use.
In addition, the amount of time spent on social media is a cru-
cial variable in studies of social media use and well-being, but
has historically been measured by self-report, sometimes by a
single item in which users are asked to estimate the amount of
G5215.indd 189G5215.indd 189 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

190 HUNT ET AL.
time they spend on social media in a “typical” day (e.g., Coyne
etal., 2020). Such self-report data are both unreliable and inaccu-
rate, subject to numerous self-report and retrospective memory
biases (Rideout, 2016; Ernala etal., 2020). Coyne and colleagues
(2020) found that social media use was unrelated to well-being
over an 8-year longitudinal study, but their measure of social
media use was so gross that such a conclusion is highly suspect.
Thus, experimental studies should measure social media use
objectively, rather than relying on retrospective self-report.
Hunt and colleagues have published several experimen-
tal studies that address these methodological challenges with
higher ecological validity and objective measurement of social
media use. College students who limited their use of Face-
book, Instagram, and Snapchat to 30 minutes a day, rather than
eliminating use, measured objectively through screenshots
of iPhone battery use, reported reductions in loneliness, and
depressive symptoms (Hunt etal., 2018). This was particularly
apparent for participants who reported higher levels of depres-
sive symptoms at baseline. These ndings were replicated in
another experimental study where more depressed college stu-
dents who limited social media time to 30 minutes a day (veri-
ed by app usage screenshots), but did not change how they
interacted with the platforms, reported decreases in depression
and signicantly less depression than those who did not limit
use (Hunt etal., 2020).
How one engages with social media is equally important.
There is a difference between passive and active use of social
media. Active use involves producing content and communi-
cating with others, such as posting photos, sharing statuses, or
commenting on another person’s post (Verduyn etal., 2015). Pas-
sive use involves consuming content without directly interact-
ing with others, such as scrolling through one’s feed or viewing
others’ posts or stories (Hanley etal., 2019). Passive use is asso-
ciated with declines in subjective well-being (Krasnova et al.,
2013; Verduyn etal., 2017), whereas active use is associated with
improvements in subjective well-being (Verduyn etal., 2017) and
decreased loneliness (Burke etal., 2010). Hunt etal. (2020) also
tracked active versus passive use and included a third experi-
mental group that increased their active posting. They found
that moderately active use was associated with the greatest well-
being (Hunt etal., 2020).
G5215.indd 190G5215.indd 190 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

FOLLOW FRIENDS ONE HOUR A DAY 191
Finally, who one engages with on social media is also impor-
tant to one’s well-being. Chou and Edge (2012) found that
people who had more Facebook “friends” whom they did not
actually know more strongly agreed that others led better lives.
Hunt etal. (2020) asked participants to identify how many of the
people they followed were actual friends, versus acquaintances
or strangers. Based on correlational ndings, the more actual
friends people followed, the less lonely they tended to be. The
more strangers people followed, however, the more depressed
they tended to be and the more they experienced fear of missing
out (FOMO).
Following strangers on Instagram may be particularly prob-
lematic. Although Instagram is the fastest growing major social
networking site (Wagner, 2015), few studies have investigated
its impact on psychological well-being. Unlike Facebook, where
user connections are reciprocal, following someone on Instagram
can be one-directional (Hu etal., 2014). Instagram posts tend to
show highly idealized images (often ltered and edited for max-
imum effect), and viewing such posts can cause sad mood and
decreased life satisfaction (Lup etal., 2015). For example, many
Instagram posts are tagged as #tspiration, and showcase ide-
alized images of highly muscular, very lean people engaged in
exercise or “healthful” eating (Alberga etal., 2018). Such images
have both direct and indirect negative effects on body image and
self-esteem in both women (Chansiri & Wongphothiphan, 2021)
and men (Sumter etal., 2021). Lup etal. (2015) also found that
18- to 29-year-old Instagram users who followed more strang-
ers tended to experience more upward social comparison and
depressive symptoms. Additionally, Instagram use was corre-
lated with less social comparison and fewer depressive symp-
toms for people who followed fewer strangers (Lup etal., 2015).
Intriguing though these results may be, the ndings of both
Hunt etal. (2020) and Lup et al. (2015) with regard to strang-
ers and psychological well-being were correlational, and thus no
causal conclusions can be drawn.
Several experimental studies have examined the impact of fol-
lowing strangers on Instagram. De Vries etal. (2018) found that
viewing strangers’ positive Instagram posts decreased positive
affect among individuals with high levels of social comparison
orientation, but increased positive affect among individuals with
low levels of social comparison orientation. Other experimental
G5215.indd 191G5215.indd 191 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

192 HUNT ET AL.
studies showed that exposure to beauty images on Instagram led
to a decrease in well-being and increase in body dissatisfaction
(Brown & Tiggermann, 2016; Sherlock & Wagstaff, 2019). Finally,
one longitudinal experiment that looked at the effects of follow-
ing celebrities on Instagram showed that one subgroup of college
women who initially followed a minimal number of celebrities
before the experiment experienced a decline in their life satis-
faction after being assigned to follow 15 specic celebrities for
4–6 weeks (Dion, 2016). However, these study designs all had
limited ecological validity, as participants were shown sourced
images from public Instagram proles, or planted images, or
assigned strangers to follow, none of which accurately reected
the users’ own uniquely curated Instagram accounts.
Recently, TikTok, an app for making and sharing short-form
videos, has also emerged as a popular social media platform
for adolescents. It has been downloaded more than 2 billion
times globally (Strategist, 2020). Current research on TikTok has
revealed a growing concern about the risk of excessive use and
addiction to the application (Kumar & Prabha, 2019; Montag
etal., 2021; Zhang etal., 2019). TikTok’s short-form video format
is similar to the centrality of images on Instagram, which sets
these apps apart from more text-central apps such as Twitter and
Facebook. Moreover, TikTok consists almost entirely of highly
edited content produced by strangers, which people passively
consume. Thus, we might expect the impact of TikTok use to be
similar to passive following of stranger content on Instagram.
This study is a replication and extension of Hunt etal. (2018)
and Hunt etal. (2020). The novel aspect of this study is an experi-
mental investigation of the effects of following strangers versus
friends, with special attention to the impact of Instagram and Tik-
Tok. In particular, we explore how limiting not only time spent
on social media but also the number of strangers followed affects
well-being. The study was experimental, allowing for causal
inferences, and maximized ecological validity by using partici-
pants’ own personal accounts, and taking advantage of built-in
features of Instagram. Participants were randomly assigned to
one of three groups, a control group, a limited use group, and
a third group in which participants both limited their use and
muted acquaintances and strangers on Instagram and refrained
from using TikTok. Muting someone on Instagram allows for a
G5215.indd 192G5215.indd 192 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

FOLLOW FRIENDS ONE HOUR A DAY 193
user to appear to continue following someone without actually
being shown their content on their Instagram feed. Since almost
all the content on TikTok is produced by strangers, we had users
abstain from TikTok entirely. Furthermore, participants in the
limited and muting conditions were asked to limit social media
use to 60 minutes per day, unlike the 30-minute limit in Hunt
etal. (2018) and Hunt et al. (2020). This change was made to
reect research suggesting that the happiest users report around
one hour of daily social media use (Twenge etal., 2018) and also
to accommodate for an increased reliance on social media for
social connection during the COVID-19 pandemic. Participants
were taking classes remotely at the time, which decreased their
opportunities for in-person social connection. All groups partici-
pated in a 4-week study where social media use was measured
objectively through the screen time app included in iPhones and
Androids, including a week of baseline monitoring followed by
a 3-week intervention period. Screen time apps record exactly
how much time an app has been open on the smart phone’s
screen over a specied period of time (e.g., one day, one week).
We used the weekly measure as it is less vulnerable to error vari-
ance based on the time of day the measure is assessed.
Our hypotheses were as follows:
1. We expected that individuals in the limited condition who
were highly depressed at baseline would report greater well-
being (i.e., lower depressive symptoms, less loneliness, and
lower fear of missing out and social comparison) at week 4
than individuals in the control condition, as was found in
Hunt etal. (2018) and Hunt etal. (2020).
2. We expected individuals in the muting condition to report
higher levels of well-being (less depression, loneliness, anx-
iety, fear of missing out, and social comparison and higher
self-esteem) at week 4 than individuals in the control or lim-
ited conditions, based on correlational ndings in Hunt etal.
(2020).
This study was approved by the University’s Institutional
Review Board, and informed consent was obtained from every
participant prior to their enrolling and sharing data with the
research team.
G5215.indd 193G5215.indd 193 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

194 HUNT ET AL.
METHODS
PARTICIPANTS
Informed consent was obtained from all potential participants.
A total of 182 Penn undergraduates (129 women, 53 men) com-
pleted the initial baseline survey. However, due to participant
withdrawal and study noncompliance (e.g., refusing to delete
TikTok, refusing to mute strangers on Instagram, not sending
screen time screenshots), 142 participants (103 women, 39 men)
completed all four weeks of the study. Participants began the
study on a rolling basis. The subject pool consisted of under-
graduates at a selective, private East Coast University enrolled
in psychology courses for which they could participate in stud-
ies to earn course credit. Participants were required to have a
Screen Time indicator and an Instagram account.
PROCEDURE
All participants were recruited using the university’s undergrad-
uate research pool. Participants were directed to a secure Qual-
trics platform, where they consented to the study and completed
the baseline survey of estimated use on Facebook, Instagram,
Snapchat, Twitter, and TikTok, as well as mood and well-being
measures. Participants were also told to estimate how many of
the people they followed on each platform were friends, acquain-
tances, or strangers. We dened friends as people they had spo-
ken to in person and felt close to in some way. An acquaintance
was dened as someone they had met in person, but did not
consider part of their intimate group. A stranger was dened as
someone they had never met.
Participants were then sent a welcome email including detailed
instructions for things like how to make a screen capture of the
app usage data and study procedures for the following four
weeks. They were told that at the end of the baseline week, they
would be emailed with their random assignment to one of three
groups. Participants were instructed to use social media as they
normally would for the next week. Additionally, all participants
were instructed to follow the study’s Instagram account in order
G5215.indd 194G5215.indd 194 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

FOLLOW FRIENDS ONE HOUR A DAY 195
to allow for an objective measure of their followers and follow-
ing, as well as the frequency of their posts and stories.
After completing the baseline week, participants were ran-
domly assigned using Random.org to one of three groups and
given instructions for their group. Group 1, referred to as the
control group, would continue to use social media as they usu-
ally do. Group 2 would be limiting activity on Facebook, Insta-
gram, Snapchat, Twitter, and TikTok to one hour per day across
all apps. Group 3 would be limiting activity on Facebook, Ins-
tagram, Snapchat, and Twitter to one hour per day across all
apps, would refrain from using Tik Tok, and would be muting
anyone whom they do not consider an actual friend on Insta-
gram. These participants were then required to send in screen-
shots listing the accounts they muted. An actual friend was
dened as someone who a participant has met in person and
feels friendly toward. Every week, participants sent in screen-
shots of their screen time use for their social media apps. Par-
ticipants also completed a weekly survey with the same mood
and well-being measures as the baseline survey. After par-
ticipants had completed the Week 4 survey and sent in their
weekly screenshot, they were sent a wrap-up email explaining
that they would be receiving course credit shortly and that they
were nished with the study.
Well-being questionnaires were closely monitored, and any
participant endorsing severe depression and/or suicidal ide-
ation was contacted by the PI, who is a licensed clinical psy-
chologist, to conduct a safety evaluation and provide referrals,
if desired, to local mental health resources. Three individuals
met criteria for this and were contacted by the PI. In each case,
the participant expressed their appreciation for the concern, and
assured the PI that they were either with their family and/or in
treatment and were not at risk.
MEASURES
To measure well-being, we used a battery consisting of six vali-
dated scales measuring well-being variables that have been found
to correlate with social media use. The survey also included a
consent form, questions regarding demographic information
(age, sex, and race), number of social media applications used,
G5215.indd 195G5215.indd 195 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

196 HUNT ET AL.
and questions about the users’ relationship to their followers/
following. The scales that make up the subjective well-being sur-
vey are listed below.
Fear of Missing Out. The Fear of Missing Out Scale (FOMO; Przy-
bylski etal., 2013) is a validated measure of distress related to
missing out on social experiences (α=.87). It consists of 10 items
scored on a scale of 1 (not at all true of me) to 5 (extremely true
of me). Items include statements such as “I get anxious when I
don’t know what my friends are up to”; “Sometimes, I wonder if
I spend too much time keeping up with what is going on”; and
“I fear others have more rewarding experiences than me.” This
measure has good construct validity and internal consistency
(α=.82), as well as strong reliability (α=0.90; Lai etal., 2016;
Przybylski etal., 2013).
Loneliness. The UCLA Loneliness Scale (revised UCLA Loneli-
ness Scale; Russell etal., 1980) measures perceived social isola-
tion. The original version was revised to include reverse-scored
items and consists of 20 items, scored on a scale of 1 (never)
to 4 (often). Sample items include statements such as “No one
really knows me well,” “My interests and ideas are not shared
by those around me,” and “I feel in tune with the people around
me” (reverse scored). The scale has good construct validity and
internal consistency with α=0.94 (Russell etal., 1980). Addition-
ally, this measure has been shown to be reliable, with test-retest
reliability over a one-year period (r=0.73; Keeling etal., 2006).
Depression. The Beck Depression Inventory (BDI-II; Beck etal.,
1996) is a standard clinical measure of depressive symptoms. It
consists of 21 items covering the vegetative, affective, and cog-
nitive symptoms of depression. Respondents can indicate the
severity of each symptom on a scale of 0–3 (e.g., for the symptom
loss of pleasure, one can respond: “I get as much pleasure as I
ever did from the things I enjoy”; “I don’t enjoy things as much
as I used to”; “I get very little pleasure from the things I used
to enjoy”; or “I can’t get any pleasure from the things I used to
enjoy”). The BDI-II has been shown to have good validity in a
prior sample of college students, as well as good reliability with
α=0.89 (Steer & Clark, 1997).
G5215.indd 196G5215.indd 196 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

FOLLOW FRIENDS ONE HOUR A DAY 197
Self-Esteem. The Rosenberg Self-Esteem Scale (RSES; Rosen-
berg, 1989) assesses how one feels about oneself. It consists of
10 items, scored 0 (strongly disagree) to 3 (strongly agree), with
higher scores indicating more positive feelings about oneself.
Items include “I feel that I have a number of good qualities”; “I
feel I do not have much to be proud of” (reverse scored); and “I
take a positive attitude toward myself.” The RSES has good reli-
ability over various demographic groups with α=0.91 (Sinclair
etal., 2010).
Social Comparison. The Iowa-Netherlands Comparison Orienta-
tion Measure (INCOM; Gibbons & Buunk, 1999) measures one’s
tendency to engage in social comparisons. It consists of 11 items,
scored 1 (disagree strongly) to 5 (agree strongly), with higher
scores indicating stronger tendencies to engage in social compar-
ison. The items cover social comparisons regarding ability (e.g.,
“I always pay a lot of attention to how I do things compared
with how others do things”) and opinion (e.g., “I often like to
talk with others about mutual opinions and experiences”). The
scale has good internal consistency with α ranging from .78 to
.85 across 10 different US samples (Gibbons & Buunk, 1999). The
scale has good reliability with α ranging from 0.78 to 0.85 across
10 different US samples (Gibbons & Buunk, 1999).
Participants were required to complete all 92 questions on the
battery, and the questions were presented in the same order each
time. Although this sounds like a long battery, the median time
to completion was about 9 minutes.
SOCIAL MEDIA USE
Participants rst emailed screenshots of their social media use of
Snapchat, Instagram, Facebook, Twitter, and TikTok during the
baseline week. They continued to send screenshots of the time
they spent on each of these platforms in the following four weeks
of the study. The data were sent at the end of each of the four
weeks. iPhones and Androids automatically track the total min-
utes each application is actively open and in use on the screen
during each day. Additionally, participants agreed to follow our
study’s Instagram account so that we could record how much
G5215.indd 197G5215.indd 197 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

198 HUNT ET AL.
they posted and how many people they followed and were fol-
lowed by on the platform. We recorded how many times they
posted and added to their stories every day over the course of
four weeks. At the end of each week, we recorded how many
people they were following on Instagram, and how many people
were following them.
Finally, participants assigned to Group 3 sent screenshots of
the individuals they muted on Instagram. They did this at the
start of Week 1 right after completing the baseline week.
RESULTS
BASELINE RESULTS
Attrition. Eighteen individuals did not complete the rst week of
baseline monitoring and were dropped from the study. Twenty-
one individuals completed the rst week of baseline monitor-
ing before dropping out. There was no signicant difference in
baseline social media use or mood variable between these 21
individuals who dropped out and individuals who completed
the study t(159)=0.23, ns. There were slightly more dropouts in
the Limited and Muting groups than in the Control group, sug-
gesting that it is indeed difcult for young people to limit their
use of social media. However, since there were no differences in
baseline use between those who dropped out and those who did
not, this does not appear to have biased the outcome.
Randomization. There were no signicant differences in the
number of platforms people used across condition. There was
a marginally signicant difference in baseline social media use,
however, such that individuals who were randomized to the
Muted condition had spent slightly less time overall on social
media, F(2,139)=2.86, p=.061, than people in the Limited group.
However, once one univariate outlier value in the Limited con-
dition was removed, that effect dropped to non-signicance.
There were no differences in baseline variables across the cohorts
recruited in the fall and the spring.
Correlational Results for People Followed on Instagram and Well-Being.
The number of people that participants followed and considered
a friend on Instagram was negatively correlated with loneliness
G5215.indd 198G5215.indd 198 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

FOLLOW FRIENDS ONE HOUR A DAY 199
(r = –.22, p < .05), replicating ndings by Hunt etal. (2020).
Additionally, the number of friends followed was positively cor-
related with self-esteem (r=.24, p<.01). The number of people
that participants followed and considered an acquaintance was
positively correlated with fear of missing out (r=.24, p<.01).
However, following more strangers on Instagram was not cor-
related with any measure of well-being.
EXPERIMENTAL RESULTS
MANIPULATION CHECK
First, we ensured that participants in the limited and muting
conditions decreased their time spent on social media, and we
checked that those in the muting condition actually muted strang-
ers. (Because participants followed the study Instagram account,
we could view individuals they followed on their accounts.) We
had data on the use of each individual app for each week of the
study, as well as total use from that week. We decided to elimi-
nate participants in the limited and muting conditions who did
not follow the instructions to limit their social media use to 60
minutes per day, which corresponds to a total of 1260 minutes
throughout the 3-week intervention period. Out of 142 partici-
pants, there were several extreme outliers who exceeded the 1260
minutes cutoff in both the limited (n=5) and muting conditions
(n=4). These participants were removed from further analysis.
Over the 3-week intervention period, the remaining participants
in the limited condition, t(8.58), p<.001, and muting condition,
t(9.30), p<.001, spent signicantly less time on social media than
the control group. As expected, time spent on social media did
not differ signicantly between the limited and muted groups
(see Figure 1).
Effect of Condition on Depression. Replicating the core nding of
Hunt etal. (2018) and Hunt etal. (2020), an ANCOVA predict-
ing depression at week 4 by baseline depression, condition, and
their interaction was signicant. There was a signicant inter-
action effect between baseline depression and condition, F(2,
138)=4.98, p=.008, such that those participants who reported
more depressive symptoms at baseline showed signicantly
G5215.indd 199G5215.indd 199 4/18/2023 11:01:31 AM4/18/2023 11:01:31 AM

200 HUNT ET AL.
fewer depressive symptoms after the intervention when they
limited their social media use than when they did not. Highly
depressed participants in the limited condition reported sig-
nicant decreases in depression (at baseline, mean BDI=21.62,
SD=6.428, at week 4, mean BDI=9.38, SD=6.520; t(15)=7.96,
p<.001), as did participants in the limited muted group (Baseline
BDI= 22.9, SD=8.8, at week 4 BDI=11.3, SD=11.3, t(10)=4.76,
p = .001). Participants in the control group, however, showed
non-signicant declines in depressive symptoms (Baseline
BDI=20.8, SD=6; week 4 BDI=17, SD=11.6, t(18)=1.36, ns.)
(see Figure 2). For the purposes of graphing, individuals who
scored a 14 or above on the BDI at baseline are represented. Par-
ticipants who had few depressive symptoms at baseline did not
change signicantly across condition, F(2,87]=0.85, ns, or from
baseline to week 4, presumably because they were already close
to the oor.
Effect of Condition on Fear of Missing Out. Similarly, an ANCOVA
predicting fear of missing out at week 4 from condition, fear of
FIGURE 1. Total social media use by condition.
G5215.indd 200G5215.indd 200 4/18/2023 11:01:34 AM4/18/2023 11:01:34 AM

FOLLOW FRIENDS ONE HOUR A DAY 201
missing out at baseline, baseline depression, and the interac-
tion between condition and baseline depression showed a sig-
nicant interaction between baseline depression and condition
on fear of missing out, F(2, 138)= 3.097, p=.048. Participants
in the muting condition who reported more depressive symp-
toms at baseline showed signicantly less fear of missing out
than participants in non-muting conditions after the interven-
tion, F(2, 138)=4.806, p=.01. Highly depressed participants in
the muting condition reported decreases in fear of missing out,
at baseline, mean FOMO=27.55, SD=8.664, at week 4, mean
FOMO= 20.64, SD =7.775; t(10) =2.95, p  =.015. The limited
group, however, reported non-signicant declines in fear of
missing out, at baseline, mean FOMO = 26.25, SD = 4.655, at
week 4, mean FOMO=23.50, SD=7.099; t(15)=1.75, p=.10. In
contrast, the control group reported no decline at all, at baseline,
mean FOMO=27.58, SD=6.915, at week 4, mean FOMO=26.89,
SD=7.370; t(18)=0.46, ns; see Figure 3). However, participants
in the muting condition who were not highly depressed did
not show signicant reductions in fear of missing out symp-
toms (at baseline, mean FOMO=25.07, SD= 6.771, at week 4,
mean FOMO=21.14, SD= 7.322). In other words, only highly
FIGURE 2. Change in Beck Depression Inventory (BDI) at Week 4 by
condition.
G5215.indd 201G5215.indd 201 4/18/2023 11:01:35 AM4/18/2023 11:01:35 AM

202 HUNT ET AL.
depressed participants who muted Instagram strangers and
refrained from using TikTok in addition to limiting their social
media use experienced reductions in fear of missing out from
baseline to week 4.
Effect of Condition on Social Comparison. In support of our
hypothesis, there was also a signicant effect of condition on
social comparison, F(2, 138)=4.37, p = .015, such that partici-
pants in the muting condition showed signicantly less social
comparison than participants in non-muting conditions after
the intervention. Participants in the muting condition reported
modest but signicant decreases in social comparison, at
baseline, mean INCOM = 40.38, SD = 5.076, at week 4, mean
INCOM=38.23, SD=6.085, t(38)=3.05, p=.004. Participants in
the limited and control groups, however, reported no declines
in social comparison, for the limited group, at baseline, mean
INCOM=39.06, SD= 6.693, at week 4, mean INCOM=38.47,
SD=7.077, t(35)=0.64, ns; for the control group at baseline, mean
INCOM= 39.09, SD = 5.49, at week 4, mean INCOM =39.74,
SD=5.32; t(57)=1.19, ns (see Figure 4). Unlike the effect of the
limited condition on depression or the effect of muting on fear of
FIGURE 3. Change in fear of missing out (FOMO) by condition for
depressed participants.
G5215.indd 202G5215.indd 202 4/18/2023 11:01:36 AM4/18/2023 11:01:36 AM

FOLLOW FRIENDS ONE HOUR A DAY 203
missing out, the effect of muting on social comparison was not
contingent upon participants being highly depressed. In other
words, all participants who muted Instagram strangers and
refrained from using TikTok in addition to limiting their social
media use experienced reductions in social comparison from
baseline to week 4.
Finally, because we had access to the prior data sets from Hunt
etal. (2018) and Hunt etal. (2020) we were able to compare both
baseline social media use and well-being across all three cohorts.
Consistent with national data, we found that Facebook use
declined over time in this age group, F(2, 306)=11.42, p<.001,
from an average of 87 (SD=86) minutes per week in 2018 to only
46 (SD=78) minutes per week in 2021. In contrast, Instagram use
increased dramatically over the same period, F(2, 354)=32.26,
p<.001, from an average of 127 (SD=92) minutes per week in
2018 to 323 (SD=266) minutes in 2021.
With respect to well-being, it is perhaps not surprising that
depressive symptoms were higher in the current cohort, which
bore the brunt of COVID 19, than in either preceding cohort
F(2,378)=4.24, p=.015. For 2018, M=8.89 (SD=9.5). For 2020,
M=8.38 (SD=7.9). For 2021, M=11.46 (SD=9.1). Perhaps more
FIGURE 4. Change in social comparison by condition (INCOM=Iowa-
Netherlands Comparison Orientation Measure).
G5215.indd 203G5215.indd 203 4/18/2023 11:01:37 AM4/18/2023 11:01:37 AM

204 HUNT ET AL.
striking is the percentage of individuals in each cohort who
scored 14 or higher on the BDI, suggesting clinically signicant
depressive symptoms. For the 2018 sample, 22% scored in that
range. For the 2020 sample, 20% scored in that range. For the 2021
sample, however, 35% of the sample started the study at least
mildly depressed, χ2= 8.37, p =.015. On the other hand, lone-
liness did not change signicantly. There was a non-signicant
trend toward a decrease in fear of missing out, F(2,378)=2.16,
p = .12. This is also not surprising. When the world is shut
down and no one can get together or travel or eat out, there is
not much to miss out on. There was also a statistically signi-
cant, but very small decrease in social comparison from 2020 to
2021, F(1,236)=4.49, p=.035. Again, when everyone’s life is cur-
tailed and their activities are very limited, there is less scope for
upward social comparison.
DISCUSSION
Our hypotheses regarding our replication of Hunt etal. (2018)
and Hunt etal. (2020) were conrmed. We replicated their central
nding that participants who were highly depressed at baseline
reported lower depression at week 4 after limiting social media
use, compared with participants in the control group. Similar to
prior studies, these differences did not appear in non-depressed
participants, suggesting that limiting social media use appears to
be the most benecial for individuals who are already somewhat
depressed. Additionally, we allowed participants in the limited
and muting groups 60 minutes of social media use per day, dif-
fering from the Hunt etal. (2018) and Hunt etal. (2020) stud-
ies, which restricted participants to 30 minutes of social media
use. By showing the benet of limiting social media to one hour,
we were able to expand upon existing epidemiological ndings
that social media use exceeding 60 minutes per day becomes sig-
nicantly correlated with unhappiness (Twenge, 2018). As such,
30 to 60 minutes of social media use per day appears to be the
sweet spot for individuals who are already experiencing depres-
sive symptoms.
Our hypotheses regarding the effect of the muting condition
on improved well-being was also conrmed. Prior to our experi-
mental manipulation, we were also able to replicate correlational
G5215.indd 204G5215.indd 204 4/18/2023 11:01:37 AM4/18/2023 11:01:37 AM

FOLLOW FRIENDS ONE HOUR A DAY 205
ndings by Hunt etal. (2020) such that baseline data showed a
signicant negative correlation between number of actual friends
followed on social media and loneliness. We also found a signi-
cant positive correlation between the number of friends followed
and self-esteem. However, we were not able to replicate the posi-
tive correlation between the number of strangers followed and
depressive symptoms found by Hunt etal. (2020). Nevertheless,
we were able to establish a causal relationship between mut-
ing Instagram strangers and a reported increase in well-being.
First, we found that highly depressed individuals who muted
Instagram strangers, refrained from using TikTok, and limited
social media use showed signicant reductions in fear of miss-
ing out. Second, we found that individuals who muted Insta-
gram strangers, refrained from using TikTok, and limited social
media use showed signicant reductions in social comparison.
Indeed, simply limiting time spent did not have nearly as large
an impact on fear of missing out and social comparison as limit-
ing time and muting Instagram strangers and eliminating TikTok.
This is in contrast to reductions in depressive symptoms, which
was equal across both intervention groups. This makes sense,
because consuming stranger content (especially highly edited,
idealized stranger content posted by celebrities and inuencers)
may be especially damaging to one’s own sense of satisfaction
with one’s life.
Interestingly, the degree to which participants actively posted
on Instagram (as opposed to spending time passively consum-
ing content) did not have an impact on their well-being. After
monitoring for each post and story that participants posted on
Instagram throughout the intervention, we found that the aver-
age participant posted around 3 stories and less than 1 post
throughout the 4 weeks. It is likely that most participants’ high
Instagram use consisted of passive use: passive scrolling, liking
posts, and watching Instagram stories. However, one extremely
active outlier participant posted 65 Instagram stories and 2
Instagram posts and also reported high depressive symptoms.
This is consistent with Hunt etal.’s (2020) nding that highly
depressed participants who were also highly active on social
media reported far greater depression than those who were less
active. Although we were not able to replicate this nding more
broadly, the fact that overly active social media use appears to be
harmful for highly depressed participants is consistent with our
G5215.indd 205G5215.indd 205 4/18/2023 11:01:37 AM4/18/2023 11:01:37 AM

206 HUNT ET AL.
experimental nding that highly depressed participants benet
from limiting time spent on social media.
LIMITATIONS AND FUTURE DIRECTIONS
This study had several important limitations. Participants were
instructed to identify the number of friends, acquaintances, or
strangers out of the users that they followed on each platform.
While we gave participants denitions of these variables, they
may have interpreted how to apply those denitions in idiosyn-
cratic ways. Therefore, we lacked an objective measure to verify
participants’ estimates of friends/acquaintances/strangers. For
example, one participant reported having 500 Instagram friends.
This large number did not seem to plausibly t our denition of
friend—someone you have not only met in person and spoken
to but also feel close to in some way—leading us to believe that
not everyone applied our denition in the same way. If partici-
pants varied in how they interpreted the denition for friends/
acquaintances/strangers estimates, then participants may have
also varied in applying the denition for muting Instagram
strangers. Participants in the muting condition were asked
to mute anyone who did not t the denition of a “friend.” If
participants in the muting condition overestimated or underes-
timated their number of close friends, the effect of the muting
condition on their well-being may be hard to interpret.
Importantly, we had difculties with implementing the mut-
ing intervention. Multiple participants in the muting condition
reported that they followed more than 100 strangers on Insta-
gram but that they ran into technical difculties when they
attempted to mute more than 100 users. The platform would
give them an error message and not allow them to mute fur-
ther accounts. Since most participants were unable to mute
more than 100 users, and we found no correlation between the
actual number of users muted and increased well-being, muting
all strangers on Instagram not only proved to be an impossible
task but also an impractical intervention. Instagram’s newly
launched features such as its shopping function and Instagram
reels also rendered it impossible to avoid strangers on the plat-
form. Although research is beginning to delineate how to best
use social media platforms to enhance well-being, it appears that
G5215.indd 206G5215.indd 206 4/18/2023 11:01:37 AM4/18/2023 11:01:37 AM

FOLLOW FRIENDS ONE HOUR A DAY 207
proven interventions such as limiting time spent on social media
and using the platform to stay connected only to close friends
run counter to Instagram’s business model.
There is also a possibility that participants’ screen time screen-
shots may not reect their total social media use. While we
instructed participants to use social media only on their mobile
phones, participants still had the ability to use social media
on their or their friends’ computers and other devices. We did
include both iPhone and Android users in our study, but studies
that also track use on other devices, such as computers, might
yield an even more accurate accounting of how much social
media participants are truly using.
Another possible limitation is that individuals in the two lim-
ited use groups were slightly more likely to drop out of the study.
Although baseline use was essentially equivalent across groups
and did not predict attrition, there is a possibility that users who
were more dependent on or simply enjoyed social media more
were more likely to drop out from the two limited use groups.
This might have introduced some bias into the results.
Lastly, we conducted our study during the COVID 19 pan-
demic, which might have affected the generalizability of our
ndings. Participants may have been using social media more
than usual to stay connected with their friends and family. We
also know that participants in this cohort started the study with
higher levels of depressive symptoms on average, compared to
the cohorts assessed in Hunt etal. (2018) and Hunt etal. (2020),
presumably as a consequence of the pandemic. For these and
other reasons, the pandemic likely impacted the ndings of our
intervention.
Future studies should investigate how people are using social
media platforms at a more granular level, including who they
are following, what content they are consuming, and what they
are posting. Objective measures of content via coding or images,
text mining, and other methods might allow a richer understand-
ing. Researchers are also in an arms race to keep up with the
impact of the constant iterations of these platforms. As the plat-
forms themselves morph and introduce new features, the impact
on well-being may also change. Furthermore, more research
on TikTok is necessary as it has begun to drastically increase
in popularity, particularly among younger users. By digging
deeper into how individuals engage with different social media
G5215.indd 207G5215.indd 207 4/18/2023 11:01:37 AM4/18/2023 11:01:37 AM

208 HUNT ET AL.
platforms, researchers can identify more nuanced ways to help
people use social media more adaptively to foster genuine social
connectivity.
We also did not assess what people did with the time that was
made available when they limited their social media use. It is
possible that people turned to other activities (such as watching
television or streaming shows) that are equally distracting and
stranger focused. It is also possible that people used the time
to get their work done, or exercise, or get more sleep, or to con-
nect with people in real life. Each of these activities might have
increased opportunities for pleasure, mastery, or stress regula-
tion, and might be expected to contribute to well-being. Future
studies should attempt to assess what people do with the found
time when they reduce their social media use.
CONCLUSION
Much of the prior research examining the relationship between
social media and well-being has been correlational in nature,
reliant on self-reports, and primarily focused on Facebook. A
few experimental studies have been conducted on Instagram,
but they lacked ecological validity. Far fewer studies have been
conducted on TikTok. Our study experimentally investigated
multiple social media platforms and tracked various aspects of
social media use objectively. We were able to replicate past nd-
ings that for highly depressed individuals, limiting time spent
on social media led to decreased depression. Our study was also
the rst we know of to establish a causal relationship between
muting strangers on Instagram and stopping TikTok use, and
increased subjective well-being. Through our experimental
manipulation of who individuals interact with and how much
time they spend on social media, we have demonstrated that
both limiting social media use and interacting with fewer strang-
ers can be highly benecial for depressed individuals. Following
friends, for about an hour a day, seems to be the most benecial
way to use social media. As social media platforms continue to
evolve and novel platforms such as TikTok begin to dominate
the user base, more research is needed to delineate the best ways
to use these tools to improve well-being, rather than to harm it.
G5215.indd 208G5215.indd 208 4/18/2023 11:01:37 AM4/18/2023 11:01:37 AM

FOLLOW FRIENDS ONE HOUR A DAY 209
Funding statement:
Declaration of interest: None.
REFERENCES
Alberga, A. S., Withnell, S. J. & von Ranson, K. M. (2018). Fitspiration and thinspi-
ration: A comparison across three social networking sites. Journal of Eating
Disorders, 6, 39. https://doi.org/10.1186/s40337-018-0227-x
Auxier, B. (2020, October 21). 8 facts about Americans and Instagram. Retrieved
April 27, 2021, from https://www.pewresearch.org/fact-tank/2020/10/
21/8-facts-about-americans-and-instagram/
Auxier, B., & Anderson, M. (2021, April 9). Social Media Use in 2021. Retrieved
April 9, 2021 from https://www.pewresearch.org/internet/2021/04/07/
social-media-use-in-2021/
Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck Depression
Inventory–II. Psychological Corporation.
Brown, Z., & Tiggemann, M. (2016). Attractive celebrity and peer images on Insta-
gram: Effect on women’s mood and body image. Body Image, 19, 37–43.
Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and social well-
being. In Proceedings of the SIGCHI conference on human factors in com-
puting systems (CHI’10) 1909–1912. Association for Computing Machinery.
https://doi.org/10.1145/1753326.1753613
Chansiri, K., & Wongphothiphan, T. (2021). The indirect effects of Instagram images
on women’s self-esteem: The moderating roles of BMI and perceived weight.
New Media and Society, https://doi.org/10.1177%2F14614448211029975.
Chou, H. T. G., & Edge, N. (2012). “They are happier and having better lives than I
am”: The impact of using Facebook on perceptions of others’ lives. Cyberpsy-
chology, Behavior, and Social Networking, 15(2), 117–121.
Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., & Booth, M. (2020). Does
time spent using social media impact mental health?: An eight year longi-
tudinal study. Computers in Human Behavior, 104, https://doi.org/10.1016/j
.chb.2019.106160.
De Vries, D. A., Möller, A. M., Wieringa, M. S., Eigenraam, A. W., & Hamelink,
K. (2017). Social comparison as the thief of joy: Emotional consequences
of Viewing strangers’ Instagram posts. Media Psychology, 21(2), 222–245.
https://doi.org/10.1080/15213269.2016.1267647
Dion, N. A. (2016). The effect of Instagram on self-esteem and life satisfaction (Doctoral
dissertation).
Dixon, S. (2022, December 16). Number of worldwide social network users
2027. Statista. Retrieved January 4, 2023, from https://www.statista.com/
statistics/278414/number-of-worldwide-social-network-users/#:~:
text=Social%20media%20usage%20is%20one,almost%20six%20billion%20
in%202027
G5215.indd 209G5215.indd 209 4/18/2023 11:01:38 AM4/18/2023 11:01:38 AM

210 HUNT ET AL.
Donnelly, E., & Kuss, D. J. (2016) Depression among users of social networking
sites (SNSs): The role of SNS addiction and increased usage. Journal of Addic-
tion and Preventive Medicine, 1(2), 107.
Ernala, S. K., Burke, M., Leavitt, A., & Ellison, N. B. (2020). How well do peo-
ple report time spent on Facebook?: An evaluation of established survey
questions with recommendations. Proceedings of the 2020 CHI Conference on
Human Factors in Computing Systems. Association for Computing Machinery,
New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376435
Escobar-Viera, C. G., Whiteld, D. L., Wessel, C. B., Shensa, A., Sidani, J. E., Brown,
A. L., Chandler, C. J., Hoffman, B. L., Marshal, M. P., & Primack, B. A. (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 Men-
tal Health, 5(3), e10496.
Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in social compari-
son: Development of a scale of social comparison orientation. Journal of
Personality and Social Psychology, 76(1), 129–142. https://doi.org/10.1111/
j.1083-6101.2007.00367.x
Gustafson, L. W., Gabel, P., Hammer, A., Lauridsen, H. H., Petersen, L. K., Ander-
sen, B., Bor, P., & Larsen, M. B. (2020). Validity and reliability of State-Trait
Anxiety Inventory in Danish women aged 45 years and older with abnormal
cervical screening results. BMC medical research methodology, 20(1), 1–9.
Hanley, S. M., Watt, S. E., & Coventry, W. (2019). Taking a break: The effect of tak-
ing a vacation from Facebook and Instagram on subjective well-being. PloS
ONE, 14(6), e0217743. https://doi.org/10.1371/journal.pone.0217743
Hu, Y., Manikonda, L., & Kambhampati, S. (2014, May). What we Instagram: A
rst analysis of Instagram photo content and user types. In Proceedings of the
International AAAI Conference on Web and Social Media (8,1).
Hunt, M., All, K., Burns, B., & Li, K. (2020). Too much of a good thing: Who we
follow, what we do, and how much time we spend on social media affects
well-being. Journal of Social and Clinical Psychology, 39(10), 47–69.
Hunt, M. G., Marx, R., Lipson, C., & Young, J. (2018). No more FOMO: Limiting
social media decreases loneliness and depression. Journal of Social and Clini-
cal Psychology, 37, 751–768. https://doi.org/10.1521/jscp.2018.37.10.751
Kalpidou, M., Costin, D., & Morris, J. (2011). The relationship between Facebook
and the well-being of undergraduate college students. CyberPsychology,
Behavior, and Social Networking, 14(4), 183–189. https://doi.org/10.1089/
cyber.2010.0061
Karim, S., Choukas-Bradley, S., Radovic, A., Roberts, S. R., Maheux, A. J., & Esco-
bar-Viera, C. G. (2022). Support over social media among socially isolated
sexual and gender minority youth in rural US during the COVID-19 Pan-
demic: Opportunities for intervention research. International Journal of Envi-
ronmental Research and Public Health, 19(23), 15611.
Keeling, J. A., Rose, J. L., & Beech, A. R. (2006). An investigation into the effective-
ness of a custody-based cognitive-behavioral treatment for special needs sex-
ual offenders. The Journal of Forensic Psychiatry & Psychology, 17(3), 372–392.
Krasnova, H., Wenninger, H., Widjaja, T., & Buxmann, P. (2013). Envy on Facebook:
A hidden threat to users’ life satisfaction? Conference proceedings: Interna-
tional Conference on Wirtschaftsinformatik (WI)At: Leipzig, Germany.
G5215.indd 210G5215.indd 210 4/18/2023 11:01:38 AM4/18/2023 11:01:38 AM

FOLLOW FRIENDS ONE HOUR A DAY 211
Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Shablack, H.,
Jonides, J., & Ybarra, O. (2013). Facebook use predicts declines in subjective
well-being in young adults. PloS One, 8(8), e69841. https://doi.org/10.1371/
journal.pone.0069841
Kumar, V., & Prabha, M. (2019). Getting glued to TIKTOK® — undermining the
psychology behind widespread inclination toward Dub-mashed videos.
Archives of Mental Health, 20(2), 76. https://doi.org/10.4103/amh.amh_7_19
Lai, C., Altavilla, D., Ronconi, A., & Aceto, P. (2016). Fear of missing out (FOMO) is
associated with activation of the right middle temporal gyrus during inclu-
sion social cue. Computers in Human Behavior, 61, 516–521
Lup, K., Trub, L., & Rosenthal, L. (2015). Instagram#Instasad?: Exploring associa-
tions among Instagram use, depressive symptoms, negative social compari-
son, and strangers followed. Cyberpsychology, Behavior, and Social Network-
ing,18(5), 247–252. https://doi.org/10.1089/cyber.2014.0560
Meier, A., & Reinecke, L. (2020). Computer-mediated communication, social media,
and mental health: A conceptual and empirical meta-review. Communication
Research. https://doi.org/10.1177/0093650220958224
Montag, C., Yang, H., & Elhai, J. D. (2021). On the psychology of TikTok use: A
rst glimpse from empirical ndings. Frontiers in Public Health, 9 https://
doi.org/10.3389/fpubh.2021.641673
O’Reilly, M., Dogra, N., Hughes, J., Reilly, P., George, R., & Whiteman, N. (2019).
Potential of social media in promoting mental health in adolescents. Health
Promotion International, 34(5), 981–991.
Perrin, A., & Anderson, M. (2021). Share of U.S. adults using social media, includ-
ing Facebook, is mostly unchanged since 2018. Pew Research Center. Retrieved
April 7, 2021, from https://www.pewresearch.org/fact-tank/2019/04/10/
share-of-u-s-adults-using-social-media-including-facebook-is-mostly
-unchanged-since-2018/
Pew Research Center (2021). Social Media Fact Sheet. Retrieved June 10, 2021, from
https://www.pewresearch.org/internet/fact-sheet/social-media/
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motiva-
tional, emotional, and behavioral correlates of fear of missing out. Comput-
ers in Human Behavior, 29, 1841–1848. https://doi.org/10.1016/j.chb.2013
.02.014
Rideout, V. (2016). Measuring time spent with media: The common sense census
of media use by US 8- to 18-year-olds. Journal of Children and Media, 10(1),
138–144, https://doi.org/10.1080/17482798.2016.1129808
Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The revised UCLA Loneliness
Scale: Concurrent and discriminant validity evidence. Journal of Personality and
Social Psychology, 39, 472–480. https://doi.org/10.1037/0022-3514.39.3.472
Rosenberg, M. (1989). Society and the adolescent self-image. Revised edition. Wes-
leyan University Press.
Schmuckler, M. A. (2001). What is ecological validity? A dimensional analysis.
Infancy, 2(4), 419–436.
Schønning, V., Hjetland, G. J., Aarø, L. E., & Skogen, J.C. (2020). Social media use
and mental health and well-being among adolescents — A Scoping review.
Frontiers in Psychology: Health Psychology, 14, 1949. https://doi.org/10.3389/
fpsyg.2020.01949
G5215.indd 211G5215.indd 211 4/18/2023 11:01:38 AM4/18/2023 11:01:38 AM

212 HUNT ET AL.
Shensa, A., Sidani, J. E., Hoffman, B. L., Escobar-Viera, C. G., Melcher, E. M., Pri-
mack, B. A., Myers, S. P., & Burke, J. G. (2021). Positive and negative social
media experiences among young adults with and without depressive symp-
toms. Journal of technology in behavioral science, 6(2), 378–387.
Sherlock, M., & Wagstaff, D. L. (2019). Exploring the relationship between fre-
quency of Instagram use, exposure to idealized images, and psychological
well-being in women. Psychology of Popular Media Culture, 8(4), 482.
Sinclair, S. J., Blais, M. A., Gansler, D. A., Sandberg, E., Bistis, K., & LoCicero, A.
(2010). Psychometric properties of the Rosenberg Self-Esteem Scale: Overall
and across demographic groups living within the United States. Evaluation
& the Health Professions, 33(1), 56–80.
Steer, R., & Clark, D. (1997). Psychometric characteristics of the Beck Depression
Inventory-II with college students. Measurement and Evaluation in Counsel-
ing and Development, 30(3), 128–136. https://doi.org/10.1080/07481756.1997
.12068933
Steers, M.L.N., Wickham, R. E., & Acitelli, L. K. (2014). Seeing everyone else’s high-
light reels: How Facebook usage is linked to depressive symptoms. Journal
of Social and Clinical Psychology, 33(8), 701–731. https://doi.org/10.1521/
jscp.2014.33.8.701
Stieger, S., & Lewetz, D. (2018). A week without using social media: Results from
an ecological momentary intervention study using smartphones. Cyberpsy-
chology, Behavior, and Social Networking, 21(10), 618–624. https://doi.org/10
.1089/cyber.2018.0070
Strategist, C. C. M. I. (n.d.). TikTok Crosses 2 Billion Downloads After Best Quar-
ter For Any App Ever. Sensor Tower Blog. https://sensortower.com/blog/
tiktok-downloads-2-billion.
Song, H., Zmyslinski-Seelig, A., Kim, J., Drent, A., Victor, A., Omori, K., & Allen,
M. (2014). Does Facebook make you lonely?: A meta analysis. Computers in
Human Behavior, 36, 446–452. https://doi.org/10.1016/j.chb.2014.04.011
Sumter, S. R., Cingel, D., & Hollander, L. (2021). Navigating a muscular and sexu-
alized Instagram feed: An experimental study examining how Instagram
affects both heterosexual and nonheterosexual men’s body image. Psychol-
ogy of Popular Media. Advance online publication. https://doi.org/10.1037/
ppm0000355
Tandoc Jr, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression
among college students: Is Facebooking depressing? Computers in Human
Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053
Tromholt, M. (2016). The Facebook experiment: Quitting Facebook leads to higher
levels of well-being. Cyberpsychology, Behavior, and Social Networking, 19(11),
661–666. https://doi.org/10.1089/cyber.2016.0259
Twenge, J. M. (2019). More time on technology, less happiness? Associations
between digital-media use and psychological well-being. Current Directions
in Psychological Science, 28(4), 372–379. https://doi.org/10.1177/096372141
9838244.
Twenge, J. M. & Campbell, W. K. (2018). Associations between screen time and
lower psychological well-being among children and adolescents: Evidence
from a population-based study. Preventative Medicine Reports, 12, 271–283.
G5215.indd 212G5215.indd 212 4/18/2023 11:01:38 AM4/18/2023 11:01:38 AM

FOLLOW FRIENDS ONE HOUR A DAY 213
Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depres-
sive symptoms, suicide-related outcomes, and suicide rates among US ado-
lescents after 2010 and links to increased new media screen time. Clinical
Psychological Science, 6(1), 3–17. https://doi.org/10.1177/2167702617723376
Vanman, E. J., Baker, R., & Tobin, S. J. (2018). The burden of online friends: The
effects of giving up Facebook on stress and well-being. The Journal of
Social Psychology, 158(4), 496–508. https://doi.org/10.1080/00224545.2018
.1453467
Verduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J., Ybarra, O.,
Jonides, J., & Kross, E. (2015). Passive Facebook usage undermines affective
well-being: Experimental and longitudinal evidence. Journal of Experimental
Psychology: General, 144(2), 480–488. http://doi.org/10.1037/xge0000057
Verduyn, P., Ybarra, O., Résibois, M., Jonides, J., & Kross, E. (2017). Do social net-
work sites enhance or undermine subjective well-being? A critical review.
Social Issues and Policy Review, 11(1), 274–302. https://doi.org/10.1111/sipr
.12033
Wagner, K. (2015, January 9). Instagram Is the Fastest Growing Major Social Network.
Vox. https://www.vox.com/2015/1/9/11557626/instagram-is-the-fastest
-growing-major-social-network
Zhang, X., Wu, Y., & Liu, S. (2019, July 4). Exploring short-form video application addic-
tion: Socio-technical and attachment perspectives. Telematics and Informatics, 42,
https://doi.org/10.1016/j.tele.2019.101243.
G5215.indd 213G5215.indd 213 4/18/2023 11:01:38 AM4/18/2023 11:01:38 AM