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Using Social Media for Social Comparison and Feedback-Seeking: Gender and Popularity Moderate Associations with Depressive Symptoms



This study examined specific technology-based behaviors (social comparison and interpersonal feedback-seeking) that may interact with offline individual characteristics to predict concurrent depressive symptoms among adolescents. A total of 619 students (57 % female; mean age 14.6) completed self-report questionnaires at 2 time points. Adolescents reported on levels of depressive symptoms at baseline, and 1 year later on depressive symptoms, frequency of technology use (cell phones, Facebook, and Instagram), excessive reassurance-seeking, and technology-based social comparison and feedback-seeking. Adolescents also completed sociometric nominations of popularity. Consistent with hypotheses, technology-based social comparison and feedback-seeking were associated with depressive symptoms. Popularity and gender served as moderators of this effect, such that the association was particularly strong among females and adolescents low in popularity. Associations were found above and beyond the effects of overall frequency of technology use, offline excessive reassurance-seeking, and prior depressive symptoms. Findings highlight the utility of examining the psychological implications of adolescents' technology use within the framework of existing interpersonal models of adolescent depression and suggest the importance of more nuanced approaches to the study of adolescents' media use.
Using Social Media for Social Comparison and Feedback-Seeking:
Gender and Popularity Moderate Associations with Depressive
Jacqueline Nesi
&Mitchell J. Prinstein
#Springer Science+Business Media New York 2015
Abstract This study examined specific technology-based be-
haviors (social comparison and interpersonal feedback-
seeking) that may interact with offline individual characteris-
tics to predict concurrent depressive symptoms among adoles-
cents. A total of 619 students (57 % female; mean age 14.6)
completed self-report questionnaires at 2 time points.
Adolescents reported on levels of depressive symptoms at
baseline, and 1 year later on depressive symptoms, frequency
of technology use (cell phones, Facebook, and Instagram),
excessive reassurance-seeking, and technology-based social
comparison and feedback-seeking. Adolescents also complet-
ed sociometric nominations of popularity. Consistent with hy-
potheses, technology-based social comparison and feedback-
seeking were associated with depressive symptoms.
Popularity and gender served as moderators of this effect, such
that the association was particularly strong among females and
adolescents low in popularity. Associations were found above
and beyond the effects of overall frequency of technology use,
offline excessive reassurance-seeking, and prior depressive
symptoms. Findings highlight the utility of examining the
psychological implications of adolescentstechnology use
within the framework of existing interpersonal models of ad-
olescent depression and suggest the importance of more nu-
anced approaches to the study of adolescentsmedia use.
Keywords Adolescents .Depressive symptoms .
Tec hno logy .Social media .Interpersonal feedback-seeking .
Social comparison
Interpersonal models of depression in adolescents emphasize
the cyclical associations among social experiences,
depressogenic interpersonal behaviors, and depressive symp-
toms (Hames et al. 2013). During the adolescent transition,
changes in the social environment (e.g., more frequent, com-
plex, and unsupervised peer contact) may complicate this pro-
cess (Choukas-Bradley and Prinstein 2014), particularly as
social relationships take on increased importance in shaping
self-esteem, well being, and identity (Harter et al. 1996;
Rudolph 2009). For example, as adolescents move toward
developing cohesive self-identities, they typically engage in
greater levels of social comparison and interpersonal
feedback-seeking (Harter 2012). However, depressed or
depression-prone individuals may engage in these social be-
haviors in maladaptive ways, such that they represent unique
depressogenic-interpersonal behaviors (Hames et al. 2013).
Although much research has investigated the interactions
among these social behaviors, depressive symptoms, and in-
person peer interactions in this age group, relatively little is
known regarding social experiences that occur through tech-
nological media, including Social Networking Sites (SNS,
e.g., Facebook) and text messages. These media have had a
revolutionizing impact on both the frequency and types of
peer interaction afforded to todays teenagers. In fact, these
media may facilitate certain technology-based behaviors, or
behaviors that occur as a result of, or in conjunction with,
technology use, such as technology-based social comparison
and feedback-seeking (SCFS; Manago et al. 2008).
Thus, an investigation of associations between adolescents
interpersonal behaviors and depressive symptoms within this
*Jacqueline Nesi
*Mitchell J. Prinstein
Department of Psychology, University of North Carolina at Chapel
Hill, Davie Hall, Campus Box 3270, Chapel Hill, NC 27599-3270,
J Abnorm Child Psychol
DOI 10.1007/s10802-015-0020-0
online social context remains critical. The current study will
investigate technology-based SCFS as one important behavior
that may be associated with depressive symptoms, while con-
sidering individual characteristics that may influence this as-
sociation (i.e. gender and popularity). In examining these as-
sociations, it is important to understand the unique features of
adolescentsonline social worlds, mixed evidence for the as-
sociation between technology use and psychological adjust-
ment, the theoretical relevance of SCFS, and the potential
influence of gender and popularity. Each of these factors will
be discussed in turn.
AdolescentsOnline Social World
The ubiquity of technology in the interpersonal environments
of modern adolescents makes its influence impossible to ig-
nore, with recent years marking a dramatic increase in tech-
nology use that has transformed the adolescent social world.
Over 93 % of American teenagers (ages 12 to 17) are now
connected to the Internet, more than any other age group, with
an estimated 73 % belonging to at least one SNS (Jones and
Fox 2009; Lenhart et al. 2010). The average young person
now spends approximately 7 hours a day connected to elec-
tronic media (Rideout et al. 2010). At least 78 % of adoles-
cents own a cell phone, with at least half of those being a smart
phone (Madden et al. 2013b). Adolescents, in a developmen-
tal period during which peer contact is already increasing, are
now afforded almost constant communication with peers, an
experience that may actually increase the importance of the
role that peer groups play in development (Uhls et al. 2011).
In addition to the amount of peer contact, the type of peer
interaction afforded by new media is unique to the current
generation of adolescents. SNS, such as Facebook,
MySpace, Twitter, and Instagram, have a number of unique
features: a personal profile with photos, links, and text meant
to represent the user; friends, or the collection of other users an
individual has allowed into his or her network; and public
commentary on a users profiles and photos, visible to others
in the social network (boyd
2007). These features create an
online social world that is fundamentally different than its
offline counterpart.
Additionally, these features allow adolescents to engage
with these technologies in unique ways. The typical adoles-
cent maintains a network of about 300 online friends (Madden
et al. 2013a), meaning that behaviors on social networking
sites are performed in the presence of an audience; every pho-
to, comment, and new online friend provides details about the
user to his or her public network (Manago et al. 2012). Thus,
adolescents use the features of SNS to both create and
consume online content, including profiles, photos, and posts.
This allows adolescents to receive constant feedback from
their peers and to engage in social comparison processes with
them online (Manago et al. 2008).
Technology Use and Psychological Adjustment
A review of the current literature reveals extremely mixed
findings regarding the reciprocal associations between fre-
quent technology use and psychological adjustment among
adolescents. Some studies have suggested that frequent use
of technology may be associated with negative outcomes.
For example, frequent use of social networking sites may be
associated with depressive symptoms (van den Eijnden et al.
2008), short-term declines in subjective well-being (Kross
et al. 2013), romantic jealousy (Muise et al. 2009), and the
belief that others are happier and living better lives than ones
self (Chou and Edge 2012). Other studies have indicated the
opposite: that frequent technology use may be associated with
positive adjustment, including increases in self-disclosure and
friendship quality (Valkenburg and Peter 2009). A recent nar-
rative review reflects these mixed results, indicating associa-
tions between online technologies and positive outcomes such
as self-esteem, social support, and self-disclosure, as well as
negative outcomes, such as social isolation, depression, and
cyber-bullying (Best et al. 2014).
Importantly, a number of studies have found no association
between frequency of technology use and general well being
(Gross 2004), nor between frequency of technology use and
depressive symptoms (Davila et al. 2012; Jelenchick et al.
2013), further highlighting the inconclusive nature of attempts
to characterize overall associations between technology use
and psychological outcomes. In fact, it may not be the quantity
of social networking site usage that longitudinally influences
depressive symptoms, but rather the positive or negative qual-
ity of peer interactions or behaviors that occur via these tech-
nologies (Davila et al. 2012). As such, researchers have sug-
gested the need to examine specific technology-based behav-
iors, as well as individual, offline characteristics, that may
help explain associations between psychological adjustment
and frequent technology use (Bessière et al. 2008; Valkenburg
and Peter 2013). An investigation of these two factors in rela-
tion to depressive symptoms is the focus of the current study.
In line with this approach, preliminary findings have impli-
cated reciprocal associations between various offline charac-
teristics, technology-based behaviors, and negative outcomes.
For example, research suggests that individuals with lower
self-esteem and poorer offline friendship quality are more
likely to engage in maladaptive behaviors using technology,
such as posting negative or inappropriate content and using
the Internet primarily for content consumption, rather than
direct communication with peers (Mikami et al. 2010;
Selfhout et al. 2009). Maladaptive technology-based
danah boyd has chosen not to capitalize her name; more
information about her decision can be found at
behaviors, in turn, may be associated with negative online
peer feedback, as well as increases in loneliness and depres-
sive symptoms (Burke et al. 2010;ForestandWood2012;
Selfhout et al. 2009), which may influence future technology
use in a cyclical fashion.
Technology-Based Social Comparison
and Feedback-Seeking
An important next step in the literature is to draw on existing
interpersonal models of psychopathology to identify specific
technology-based behaviors that may act as precursors to and
correlates of depressive symptoms among adolescents.
Interpersonal feedback-seeking and social comparison, which
have often been conceptualized as depressogenic interperson-
al behaviors (e.g., Borelli and Prinstein 2006), may actually be
facilitated by modern technologies. These behaviors are im-
portant to adolescent identity construction processes, as ado-
lescents seek to ascertain normative standards of behavior and
engage in reflected appraisal (i.e. evaluation of the self based
on perceived opinions of others; Harter et al. 1996). However,
high levels of technology use may be associated with in-
creased levels of these behaviors among some adolescents.
Modern technologies have transferred many social rela-
tions from the private to public sphere (Subrahmanyam and
Greenfield 2008), creating an atmosphere of public commen-
tary and performance online. As adolescents use selective self-
presentation strategies to portray themselves online in an ideal
manner (Chou and Edge 2012; Gonzales and Hancock 2011),
self-focus is heightened, increasing feedback-seeking and so-
cial comparison (Manago et al. 2008), perhaps especially up-
ward comparisons, or comparisons with those believed to be
of higher status than the self. This may serve to intensify the
issues of identity development and interpersonal connected-
ness, challenging adolescents to confront them with greater
constancy and urgency (Uhls et al. 2011). Furthermore, pre-
liminary findings suggest that negative social comparison on
Facebook may be associated with lower life satisfaction
(Krasnova et al. 2013), as well as increased rumination and
depressive symptoms (Feinstein et al. 2013).
Technology Use and Individual Characteristics
In understanding the associations between technology-based
SCFS and depressive symptoms, it is essential to consider pre-
existing, offline characteristics that may affect these associa-
tions. For example, popularity and gender may play a role.
Popular adolescents, who are higher in self-esteem (Babad
2001), may engage in fewer upward comparisons, experience
less negative affect as a result of these comparisons, and re-
ceive more positive feedback from peers (Mikami et al. 2010).
In terms of gender, females may be more likely to compare
themselves on dimensions of physical attractiveness based on
online photos (Haferkamp and Krämer 2011), perhaps making
such photos more self-relevant and threatening to self-worth
(Stefanone et al. 2011). Furthermore, associations between
reassurance-seeking behaviors and depressive symptoms
may be particularly strong among adolescent girls, for whom
rates of depression are higher in general (Starr and Davila
2008), and given known situational continuities between on-
line and offline contexts (Mikami et al. 2010), this effect may
occur online, as well. However, the possibility that gender and
popularity differences may occur within online contexts has
not yet been rigorously examined.
Study Hypotheses
First, it is hypothesized that higher levels of technology-based
social comparison and feedback-seeking behaviors (SCFS)
will be concurrently associated with higher levels of depres-
sive symptoms, controlling for offline ERS, prior depressive
symptoms, and overall frequencies of technology use (i.e.,
total use of cell phones, Facebook, and Instagram).
Second, it is hypothesized that peer popularity will mod-
erate this association, such that individuals low in popu-
larity will show the strongest negative association be-
tween technology-based SCFS and depressive symptoms.
Third, it is hypothesized that gender will also moderate
this association, such that the effect will be stronger for
The current study included 619 participants. Students were
eighth and ninth grade students in low to middle socio-
economic status (SES) schools (67 % free or reduced price
lunch). Participants were between the ages of 12 and 16 (mean
age 14.6), and 57.3 % were female. The ethnic composition of
the sample included 47.9 % White/Caucasian, 21.1 % African
American/Black, 23.4 % Hispanic/Latino, 0.5 % Asian,
and 5.5 % other. This sample closely matched the demo-
graphic makeup of the district from which participants
were recruited. All procedures discussed below were ap-
proved by the university human subjects committee.
All students in seventh and eighth grade were recruited, except
for those in self-contained special education classrooms, using
parental consent and adolescent assent. A total of 868 students
participated in the studysfirstwave(baseline). The current
study was conducted 1 year later, when students were in
eighth and ninth grades. Of the original sample of 868, 90 %
J Abnorm Child Psychol
of students participated (n=779). Attrition was due to partic-
ipantsmoving away from the area (n=14), moving to a dif-
ferent school (n=20), withdrawal from the school (n=18),
withdrawal from the study (n=20), and absenteeism (n=17).
Out of the 779 students surveyed, 130 students were ex-
cluded from the analysis. Of these 130, 53 students were ex-
cluded because they indicated that they did not use technology
as defined in the study (use of cell phones, Facebook, or
Instagram). The other 77 students did not complete any of
the measures related to technology use, due to the placement
of these questions at the end of the study protocol. Attrition
analyses indicated that excluded students were, on average,
more likely to be male, t(775)=3.13, p=0.002, and African
American, χ
(3)= 24.77, p<0.001.
After preliminary analyses, two outliers were identified in
the data, with values more than four standard deviations below
the mean for popularity. These outliers were removed for fur-
ther analyses. A separate analysis was conducted using a
Winsorising approach, in which outlierspopularity were set
to equal the next closest values (approximately 3.25 standard
deviations below the mean). The pattern of results was con-
sistent in both treatments of outliers; thus, results for the anal-
ysis in which outliers were deleted are reported here. In addi-
tion, 14 subjects did not provide information on baseline de-
pressive symptoms and 14 subjects did not complete measures
of technology-based SCFS. Thus, final model sample was n=
619. Participants were compensated with $10 gift cards.
All measures were self-reported and administered to students
in classrooms during the school day using computer-assisted
self-interviews (CASI). For all variables except popularity and
socioeconomic status, a mean score of items was computed,
with higher scores indicating higher levels of that variable.
Socioeconomic Status Participantssocioeconomic status
(SES) was computed by calculating median household in-
come from US Census tract data ( for each
students home address. The mean SES for this sample was
identical to the average household income for the town in
which the data were collected, according to census data.
Depressive Symptoms The Short Mood and Feelings
Questionnaire (SMFQ; Angold et al. 1995) was used to assess
depressivesymptoms, both atthe current time point and 1 year
prior (baseline). The SMFQ is a 13-item, unifactorial scale in
which subjects endorse statements describing depressive
moods and behaviors over the past 2 weeks on a 3-point scale
(0 for not true, 1 for sometimes true, and 2 for true). The
SMFQ has good psychometric properties (Sharp et al.
symptoms in adolescent samples (e.g., Rothon et al.
2009). The current sample yielded good internal consistency
(Cronbachs alpha 0.94).
Popularity Sociometric nomination procedures were used to
measure peer-reported popularity (Coie et al. 1983). As such,
all subjects were presented with a roster of all grademates.
Alphabetization of the roster was reversed for a random half
of the participants in order to control for order effects in par-
ticipantsselection of names. Subjects were asked to nominate
an unlimited number of grademates whom they believed to be
the most popular and the least popular (Prinstein and Cillessen
2003). The range of nominations that participants received
was between zero and 60. The vast majority of participants
received at least one nomination. Of the full sample, only 19
students did not receive any nominations. For each partici-
pant, two sums were calculated: one for the number of most
popular nominations, and one for the number of least popular
nominations. These sums were then standardized within each
school grade, and a difference score was taken between most
popular and least popular standardized scores. These differ-
ences scores were then re-standardized to create a measure of
popularity, where higher scores indicated higher levels of pop-
ularity (Prinstein and Cillessen 2003). Sociometric nomina-
tion procedures are largely considered the most reliable and
valid indices of adolescentspopularity among peers (Coie
et al. 1983).
Technology-Based Social Comparison and Feedback-
Seeking (Technology-Based SCFS) The Motivations for
Electronic Interaction Scale (MEIS) was designed in order to
assess subjectsattitudes and behaviors regarding the use of
technology, specified as Btexting, Facebook, and other social
media.^This measure was developed in three steps. First, a
focus group comprised of recent high school graduates was
conducted. Students were asked to generate examples of
technology-based behaviors and attitudes toward technology
use that are common among current high school students (e.g.,
BI often post a status update if I think it will make others think
I am funny, nice, or cool^). Based on their answers, a pool of
34 items was generated and administered to a sample of 261
adolescents, living in a nearby school district and comprised
of similar age, gender, and ethnic composition to the current
sample. In the second step, the scale was expanded to include
52 items, with more items added reflecting engagement in
social comparison and feedback-seeking behaviors online.
The measure was administered to 158 high school students.
Factor analysis revealed a single 10-item factor indicating
engagement in social comparison and feedback-seeking be-
haviors using technology, as well as other factors relating to
the use of technology for general communication with roman-
tic partners, social support seeking, and discussions about
sexual health topics (Widman et al. 2014).
These items were ultimately included in the final 22-item
scale, which was administered to subjects in the current study.
Subjects endorsed the personal relevance of a number of be-
haviors on a 5-point scale (1 for Not at all true and 5 for
Extremely true). Examples from the 10-item Social
Comparison and Feedback Seeking Subscale (MEIS-SCFS)
include, BI use electronic interaction to see what others think
about how I look^and BI use electronic interaction to compare
my life with other peoples lives.^The scale showed good
internal consistency (Cronbachs alpha 0.92).
Excessive Reassurance-Seeking (ERS) Joiner and Metalsky
(1995) developed the Reassurance-Seeking Scale (RSS) for
use with adults and later adapted it for use with children and
adolescents (Joiner 1999) as a subscale of the larger
Depressive Relationships Inventory (DIRI). One critique of
the original RSS is that it is very brief (four items), and that
it lacks developmental sensitivity (Starr and Davila 2008).
Thus, for the current study, a Revised ERS scale was created
to include 6 additional items, all believed to be developmen-
tally appropriate to adolescents (e.g. BI often ask people if they
think my clothes look okay^). Another criticism of the origi-
nal ERS scale is that it lacks detail, simply assessing how often
individuals request assurance that others like and care for
them. Thus, the Revised ERS scale sought to address multiple
domains of reassurance-seeking appropriate to adolescents,
including reassurance-seeking about appearance (e.g., BIoften
ask people if I look attractive^), gossip (e.g. BI often ask peo-
ple what other people say about me^), and general liking (e.g.,
BI often ask people if other people like me.^).
Similarly to the MEIS, these items were developed through
the use of a focus group of recent high school graduates,
followed by pilot testing with a sample of 158 high school
students. Ultimately, the Revised ERS scale was a 10-item
measure in which subjects endorsed reassurance-seeking be-
haviors on a 5-point scale (1 for Not at all true and 5 for
Extremely true). The original RSS has been shown to have
good psychometric properties (Joiner and Metalsky 2001)
and has been used to assess depressive symptoms in adoles-
cent samples (e.g., Prinstein et al. 2005). The Revised ERS
scale showed good internal consistency as a unifactorial scale
in this sample (Cronbachs alpha 0.90).
Frequency of Technology Use The Electronic Interaction
Scale for Time (EIS_T) was developed to determine the aver-
age amount of time subjects spend using specific technologies
on Ba typical day.^Similar to the MEIS, this measure was
developed over a three-step process. Through the use of mul-
tiple focus groups and pilot testing among a total of 429 high
school students, five communication items were chosen and
response options were created to capture the full range of daily
electronic interaction time. For the final EIS_T, subjects indi-
cated the amount of time they spent each day engaged in in-
person communication, voice communication, non-voice
cellphone use (i.e., for Btexting, games, or Internet^),
Facebook use, and Instagram use. Frequencies were indicated
on a 7-point scale (0 for Idont use this, 1 for Less than 1 h,6
for 5ormorehours). For the current study, an average of the
final three items (non-voice cellphone use, Facebook use, and
Instagram use) was used to indicate frequency of overall tech-
nology use. Self-report measures have been widely used in
previous studies assessing frequency of technology use.
Descriptive Statistics Descriptive statistics were conducted
to examine the means and standard deviations of all study
variables (see Table 2). Independent sample t-tests were used
to compare means on study variables by gender. Interestingly,
females reported higher average values of most study vari-
ables, including depressive symptoms, technology-based
SCFS, frequencies of technology use, and excessive reassur-
ance seeking. No gender differences were found in levels of
Further, participants who reported using technology as de-
fined in the study were compared to those who indicated that
they did not use technology (n=53) on key demographic var-
iables. No differences emerged between users and non-users
of technology in terms of SES or ethnicity. However, the 53
students who indicated that they did not use technology were
more likely to be male, t(698)=3.83, p<0.001; lower in de-
pressive symptoms, t(698)=2.86, p=0.004; lower in ERS, t
(698)=3.51, p<0.001;andlowerinpeer-reportedpopularity
t(698)=4.60, p<0.001.
Pearson correlations were conducted to examine bivariate
associations among all study variables (see Table 1).
Significant positive associations were found between frequen-
cy of technology use, technology-based SCFS, and offline
excessive reassurance-seeking. Popularity was positively as-
sociated with frequency of technology use and technology-
based SCFS; however, it was negatively associated with de-
pressive symptoms. Pearson correlations were also conducted
to examine associations between study variables and socio-
economic status (SES). Individuals lower in SES reported
higher frequencies of technology use and lower levels of pop-
ularity (see Table 1). Interestingly, although depressive symp-
toms were positively correlated with concurrent technology
use frequency, this association was no longer significant after
accounting for other variables in the full regression model, as
discussed below.
Analyses were also conducted to determine whether means
and standard deviations of study variables differed by ethnic-
ity (see Table 2). Results indicated that levels of ERS were
significantly lower among Latino/Hispanic participants versus
Caucasian and African American participants; levels of
J Abnorm Child Psychol
Tab l e 1 Bivariate Associations Between Study Variables, Full Sample and by Gender
Full sample By gender
1. Frequency of technology use ––0.25*** 0.09 0.13* 0.03 0.09 0.10
2. Technology-based SCFS 0.28*** 0.24*** 0.22*** 0.38*** 0.16* 0.15* 0.11
3. Depressive symptoms 0.11** 0.34*** 0.02 0.35*** 0.44*** 0.50*** 0.16* 0.04
4. Excessive reassurance seeking 0.15*** 0.50*** 0.47*** 0.09 0.54*** 0.44*** 0.28*** 0.08 0.06
5. Baseline depressive symptoms 0.06 0.23*** 0.60*** 0.38*** 0.06 0.22*** 0.57*** 0.38*** 0.19** 0.01
6. Popularity 0.12** 0.17*** 0.11** 0.06 0.11** 0.14** 0.18*** 0.12* 0.07 0.09 0.24***
7. Socio-economic status 0.13*** 0.01 0.07 0.02 0.09* 0.17*** 0.12* 0.05 0.05 0.02 0.11* 0.11*
For associations by gender, males reported above the diagonal in bold, females reported below the diagonal
*p<0.05 **p< 0.01 ***p< 0.001
Tab l e 2 Means (and Standard Deviations) of Study Variables, with Gender and Race/Ethnicity Comparisons
Full sample Girls Boys t(df) American African Caucasian Latino/Hispanic F(df)
Frequency of technology use 2.80 (1.57) 3.23 (1.54) 2.21 (1.43) 8.39 (617)** 3.42 (1.76)
2.50 (1.38)
2.81 (1.57)
11.15 (615)**
Technology-based SCFS 1.73 (0.77) 1.82 (0.82) 1.61 (0.68) 3.47 (617)** 1.87 (0.81)
1.72 (0.75)
1.56 (0.63)
5.51 (615)**
Depressive symptoms 0.48 (0.51) 0.62 (0.56) 0.28 (0.35) 8.83 (617)** 0.49 (0.51) 0.44 (0.49) 0.52 (0.51) 1.32 (615)
Excessive reassurance seeking 1.50 (0.66) 1.60 (0.73) 1.36 (0.50) 4.68 (617)** 1.55 (0.66)
1.55 (0.67)
1.35 (0.53)
3.95 (615)*
Baseline depressive symptoms 0.47 (0.49) 0.58 (0.53) 0.33 (0.38) 6.53 (617)** 0.52 (0.52) 0.44 (0.48) 0.46 (0.43) 1.58 (615)
Popularity 0.07 (1.00) 0.11 (0.92) 0.03 (1.09) 0.992 (617) 0.07 (0.85) 0.08 (1.19) 0.005 (0.66) 0.833 (615)
Row means that do not share superscripts are significantly different
*p<0.01 **p< 0.001
technology-based SCFS were significantly higher among
African American participants versus Latino/Hispanic partic-
ipants; and frequency of technology use was significantly
higher among African American participants versus Latino/
Hispanic and Caucasian participants. No differences were
found in levels of depression between racial/ethnic groups.
Hypothesis Testing Hypotheses were tested within a hierar-
chical multiple linear regression framework using maximum
likelihood estimation in SPSS 22.0. All continuous predictor
variables were mean centered to reduce multicollinearity and
to aid in ease of interpretation. Baseline depressive symptoms,
excessive reassurance seeking, and overall frequency of tech-
nology use were entered as covariates in an initial step. The
main effects of gender, popularity, and technology-based
SCFS were added in the second step.
In order to test the hypothesis that popularity moderates the
relationship between technology-based SCFS and depression
symptoms, an interaction term was created by computing the
product of the centered values of technology-based SCFS and
popularity. In order to test the gender moderation hypothesis,
another interaction term was created by computing the product
of gender and the centered value of technology-based SCFS.
A third interaction term was created between gender and pop-
ularity. These three two-way interaction terms were added at
the third step. A three-way interaction term was created and
entered on a fourth step (see Table 3).
The full regression model explained a significant propor-
tion of the variance in depressive symptoms, R
=0.470, p<
0.001. In support of the first hypothesis, results revealed a
significant main effect of technology-based SCFS on depres-
sive symptoms (B=0.21, p<0.001). Additionally, in support
of the second and third hypotheses, analyses revealed a sig-
nificant technology-based SCFS x gender interaction effect
(B=0.08, p<0.05), as well as a significant technology-
based SCFS x popularity effect (B=0.11, p<0.01). This
model was further analyzed with the addition of ethnicity
and SES variables as covariates. The pattern of significant
and non-significant effects remained the same; thus, to present
a more parsimonious model, ethnicity and SES were not in-
cluded in results.
Interactions were probed following procedures outlined by
Aiken and West (1991) and using interaction utilities provided
by Preacher et al. (2006). First, models were reduced by re-
moving covariates and non-significant interaction terms
(Holmbeck 2002). For the gender interaction, simple inter-
cepts and slopes for the regression of technology-based
SCFS on depressive symptoms were computed for both males
and females. Results revealed significant slopes for both girls,
b(se) = 0.239 (0.03); p<0.001, and boys, b(se) =0.133 (0.04);
p<0.001, indicating that greater levels of technology-based
SCFS were associated with greater levels of depressive symp-
toms for both genders. The slope for girls was significantly
steeper than for boys, indicating that the effect of technology-
based SCFS on depressive symptoms is stronger among
females (see Fig. 1).
For the popularity interaction, simple intercepts and slopes
for the regression of technology-based SCFS on depressive
Tabl e 3 Prediction of Depressive
Symptoms by Technology-Based
Social Comparison and
Feedback-Seeking (SCFS),
Popularity, and Gender
Depressive symptoms
Step statistics Final statistics
Predictors ΔR
b(seb) βb(seb) β
Step 1, covariates 0.42**
Baseline depressive symptoms 0.51 (0.04) 0.48*** 0.45 (0.04) 0.43***
Excessive reassurance seeking 0.21 (0.03) 0.28*** 0.15 (0.03) 0.19***
Frequency of technology use 0.01 (0.01) 0.04 0.01 (0.01) 0.04
Step 2, main effects 0.04**
Technology-based SCFS 0.09 (0.02) 0.13*** 0.14 (0.03) 0.21***
Gender (female) 0.19 (0.03) 0.18*** 0.20 (0.03) 0.19***
Popularity 0.04 (0.02) 0.07* 0.04 (0.02) 0.08
Step 3, two way interactions 0.01*
SCFS × gender 0.09 (0.04) 0.08* 0.09 (0.04) 0.08*
SCFS × popularity 0.05 (0.02) 0.09** 0.07 (0.03) 0.11**
Popularity × gender 0.02 (0.03) 0.02 0.02 (0.03) 0.02
Step 4, three way interaction 0.00
SCFS × gender × popularity 0.03 (0.04) 0.03
Tot al R
Gender was coded as 0 for females, and 1 for males. SCFS= social comparison and feedback-seeking
p<0.06; * p<0.05; ** p<0.01; *** p<0.001; All variables mean centered with the exception of Gender
J Abnorm Child Psychol
symptoms were computed at the mean of popularity, as well as
at one standard above and below the mean. Results suggested
stronger associations between technology-based SCFS and
depressive symptoms for individuals low in popularity, (1
SD), b(se)=0.329 (0.04), p< 0.001, than for individuals high
in popularity (+SD), b(se)= 0.181 (0.03), p< 0.001 (see
Fig. 2). To further explore this effect, the Johnson-Neyman
(J-N) technique was used to identify the region of significance
(Bauer and Curran 2005), that is, the values of popularity for
which technology-based SCFS had a significant effect on de-
pressive symptoms. Results suggested that the association was
significant at all centered values of popularity less than 2.08
(approximately two standard deviations above the mean). The
slope at this boundary was b(se)=0.10 (0.05), p<0.05. This
indicates that technology-based SCFS may be associated with
depressive symptoms for the majority of individuals, but with
stronger associations for those lower in popularity.
Interpersonal theories of adolescent depression highlight bidi-
rectional associations among depressive symptoms, social ex-
periences, and interpersonal behaviors that occur within an
increasingly complex social environment during this time pe-
riod. In recent years, the widespread adoption of social tech-
nologies, including text messaging, cell phone use, and SNS,
has fundamentally transformed the adolescent social land-
scape; however, little is known about the role of technology-
based social experiences in these depressogenic interpersonal
-1 0 1 2 3 4
Depressive Symptoms
y-Based Social Comparison and Feedback-Seekin
-1 SD
+1 SD
Fig. 2 Plot of simple slopes for
technology-based social
comparison and feedback-seeking
(SCFS) by popularity interaction
on depressive symptoms. Note
that technology-based SCFS
is mean centered
-1 0 1 2 3 4
Depressive Symptoms
Technology-Based Social Comparison and Feedback-Seeking
Fig. 1 Plot of simple slopes for
technology-based social
comparison and feedback-seeking
(SCFS) by gender interaction on
depressive symptoms. Note that
technology-based SCFS is mean
processes. Researchers have called for a more nuanced under-
standing of the specific online behaviors and pre-existing in-
dividual characteristics that may influence these associations
(Bessière et al. 2008;Davilaetal.2012; Kraut et al. 2002;
Val k e nb urg a nd Pe t e r 2013). Findings have the potential to
inform interpersonal models of adolescent depression that bet-
ter account for modern adolescentssocial environments.
Although results are preliminary, and no strong conclu-
sions about directionality can be made given the concurrent
nature of the data, the current investigation provides a valu-
able contribution to the literature. Results identified
technology-based SCFS as one online behavior that may have
implications for both online and offline functioning. In sup-
port of the first hypothesis, technology-based SCFS was
found to be associated with depressive symptoms, controlling
for overall frequencies of technology use, offline ERS, and
prior depressive symptoms. In support of the second hypoth-
esis, findings indicated that this association might be depen-
dent on the offline characteristic popularity, with the strongest
associations between technology-based SCFS and depressive
symptoms found for adolescents lower in popularity. In sup-
port of the third hypothesis, gender was also found to moder-
ate the association between technology-based SCFS and de-
pressive symptoms, with the association stronger for females.
Each of these findings will be discussed in turn.
Associations Between Technology-Based SCFS
and Depressive Symptoms
Findings regarding the association between technology-based
SCFS and depressive symptoms may be understood in the
context of several theories. First, it may be that the online
environment facilitates higher levels of SCFS. The
hyperpersonal model of computer-mediated communication
(CMC; Walther et al. 2011) suggests that certain components
of technology-based interaction serve to intensify the process
of identity construction through increased feedback and
decision-making within these environments. Specific compo-
nents of this model include selective self-presentation, or the
potential for more deliberate portrayals of the self in an online
context, and idealization, or positive assumptions about others
for whom limited online information is available. In other
words, the online context fosters idealized self-presentations
and individuals are likely to make positive assumptions about
others subsequent to viewing online content. Given that young
people spend the majority of their time on SNS looking at
peersprofiles and photos, rather than posting or updating
their own profiles (Pempek et al. 2009), adolescents may be
especially likely to engage in technology-based SCFS and
may be vulnerable to negative comparisons with their peers.
Given that identity development is a stage-salient task charac-
teristic of adolescence, and that social comparison and
feedback-seeking are essential to this process, it is not
surprising that a forum that may serve to intensify this process
may be associated with higher levels of these behaviors.
Second, adolescents engaging in technology-based SCFS
may form distorted perceptions of their peers, leading them to
engage in harmful upward comparisons, or to doubt the sin-
cerity of positive feedback that is sought online, and experi-
ence decreases in mood or self-esteem. Chou and Edge(2012)
suggest that frequent users of technology employ certain heu-
ristics that lead them to believe that Blife is not fair^and
Bothers are happier and living better lives.^For example, ac-
cording to the availability heuristic (Tversky and Kahneman
1973), young people who frequently engage with technology
may more easily recall information encountered online when
forming impressions of others. The tendency for selective self-
presentation online may increase the probability that adoles-
cents encounter, and thus recall, distorted positive perceptions
of their peerslives. Furthermore, given the very large size of a
typical adolescents online social network, it is likely that
many online connections are mere acquaintances offline.
Thus, in forming impressions of individuals that they do not
know well offline, correspondence bias (Gilbert and Malone
1995) may lead adolescents to assume that othersphotos and
text reflect stable personality traits, rather than situational
Third, it may be that depressive symptoms precede and
predict technology-based SCFS. Some research suggests that
depressed and dysthymic individuals may be motivated to
seek out negative information about others (Wenzlaff and
Beevers 1998). Interpersonal theories of depression similarly
suggest that within offline social worlds, depressed and
depression-prone individuals are more likely to engage in
overall higher levels of social comparison (Gibbons and
Buunk 1999) and feedback-seeking behaviors (Hames et al.
2013). Situational continuity between offline and online con-
texts (Mikami et al. 2010) suggest that this effect is likely to
occur online, as well. In doing so, depressed individuals may
prefer to compare themselves to others perceived to be equal
to or less fortunate than them. This may explain why depres-
sion and technology-based SCFS are significantly associated.
Unfortunately, within a computer-mediated, hyperpersonal
environment in which peers selectively portray the most pos-
itive aspects of their lives, however, these downward compar-
isons may not be possible. In an online world where users
portray themselves in an ideal manner, depressed individuals
may be stymied in their efforts to seek out negative informa-
tion about others, potentially experiencing a worsening of
symptoms in light of othersperceived happiness.
Effects of Popularity
Consistent with the second hypothesis, another important
finding revealed that popularity moderated of the association
between technology-based SCFS and depressive symptoms.
J Abnorm Child Psychol
Notably, findings suggested stronger associations between
technology-based SCFS and depressive symptoms for unpop-
ular individuals. Downward comparisons may be especially
challenging for adolescent low in popularity. Prior research
suggests that individuals lower in popularity and self-esteem
receive less positive feedback on their social networking pro-
files (Mikami et al. 2010) and post updates that are higher in
negativity and lower in positivity (Forest and Wood 2012).
While positive feedback on SNS has been found to enhance
adolescentsself-esteem, negative feedback has been found to
decrease self-esteem (Valkenburg et al. 2006). Thus, it may be
that unpopular adolescents are not only more likely to post or
send negative content, but also to receive negative feedback.
In seeking out feedback from peers, unpopular adolescents
may actually be garnering self-relevant information that is
harmful to their self-esteem and related to increases in depres-
sive symptoms. Additionally, given the substantial overlap
between online and offline networks (Reich et al. 2012), it is
likely that adolescents who are unpopular offline have fewer
online friends. Manago et al. (2012) posit that larger online
networks and perceived audiences predict life satisfaction and
perceived social support. Thus, it may be that, when seeking
feedback online, lower status adolescents perceive smaller
audience sizes for their posted content, resulting in feelings
of decreased peer support and overall life satisfaction.
Effects of Gender
Finally, consistent with the third hypothesis, gender moderat-
ed the association between technology-based SCFS and de-
pressive symptoms. In particular, results suggested that the
association between technology-based SCFS and depressive
symptoms was particularly strong for females compared to
males. Prior work has indicated important differences between
adolescent girls and boys that may be relevant to an online
context, with girls more likely experience depressive symp-
toms as the result of reassurance-seeking behaviors offline
(Prinstein et al. 2005). Furthermore, girls are more likely to
prioritize and compare themselves on dimensions of physical
attractiveness online (Haferkamp and Krämer 2011; Jones
2001). Given the emphasis on photo sharing in todayspopu-
lar social networking tools (i.e., Facebook, Instagram), as well
as the increased likelihood that girls will post photos com-
pared to boys, it may be that online, girls are drawn to com-
parisons that are more self-relevant, and thus more threatening
to self-worth (Stefanone et al. 2011).
Furthermore, established interpersonal theories of depres-
sion show that the link between interpersonal stressors and
depressive symptoms may be particularly strong for girls
(Rudolph 2002); thus, insofar as technology-based SCFS
present a source of interpersonal stress for girls, these behav-
iors are likely to be associated with depressive symptoms. It is
important to note that given limited research in this field,
proposed theories on the moderating influences of gender
are speculative; more research is needed to clarify and expand
upon this potential moderator.
Findings from this study offer rare data to understand the
association between offline characteristics, online behavior,
and adolescent depressive symptoms. Importantly, results
did not identify any overall association between frequency
of technology use and depressive symptoms. Rather, findings
suggest the importance of exploring specific technology-
based behaviors and offline, individual characteristics in iden-
tifying for whom and under what conditions associations with
depressive symptoms may be present. Although preliminary,
these findings may have important implications for identify-
ing adolescents for whom frequent technology use may be
both a precursor to and an outcome of maladaptive psycho-
logical adjustment. In this emerging field, relevant theories
remain speculative and suggest the need for further research.
Limitations and Conclusions
This study provides a critical initial exploration of the associ-
ations among popularity, gender, online behavior, and depres-
sive symptoms among adolescents and provides a much-need
contribution to the literature on the psychosocial correlates of
technology-based behaviors. However, future research should
address these preliminary findings within a prospective longi-
tudinal framework. Although statistical controls in the model
allowed for examination of effects over and above those of
prior depressive symptoms, further work is needed to rigor-
ously assess temporal relationships between study variables,
perhaps testing for the presence of transactional effects be-
tween depressive symptoms and technology-based behaviors
(Valkenburg and Peter 2013).
Another limitation of this study is its reliance on self-report
measures, which are subject to recall and other biases.
Adolescentsreports of technology use frequency in the study
are consistent with nationally representative statistics of over
2000 students, collected by the Kaiser Family Foundation
(Rideout et al. 2010). However, future research should incor-
porate naturalistic methods, including observational coding of
adolescentsmedia output, to determine the accuracy of re-
ports on technology-based SCFS and other variables. Initial
studies using direct observation of adolescent media content
(e.g. Underwood et al. 2012) have shown promising results.
Similarly, given the lack of established measures regarding
technology-based behaviors, future research should aim to
develop and validate assessments of adolescentsengagement
with social technologies. It should also be noted that, as is
typical in school-based samples, mean levels of depressive
symptoms were very low in the current analyses. Thus, al-
though providing preliminary insight into these effects within
a community setting, results may not generalize to a clinical
sample. A final limitation of the current study was the inability
to fully assess differences in outcomes by ethnicity or SES.
Although the studys large and diverse sample provided the
opportunity to examine effects across different ethnicities, it is
possible that cell sizes were too small to examine potential
interaction effects. Additionally, the use of census tract data
provides only a rough estimate of familiestrue SES; more
sensitive measures of SES should be used in future studies.
In summary, the current study provides novel preliminary
evidence that technology-based social comparison and
feedback-seeking behaviors may be associated with depres-
sive symptoms among adolescents, controlling for overall
technology use, prior depressive symptoms, and offline
ERS. Furthermore, popularity and gender may play a
role in this effect, such that the association between these
behaviors and depressive symptoms is particularly strong
among adolescents low in popularity and among females.
Adolescentssocial environments are increasingly depen-
dent on the existence of social technologies, including
cell phones, text messaging, and SNS. The current find-
ings highlight the importance of understanding how these
modern social environments may intersect with existing
interpersonal models of psychopathology.
Acknowledgments This work was supported in part by National Insti-
tutes of Health Grant R01-HD055342 awarded to Mitchell J. Prinstein.
This work was also supported in part by the National Science Foundation
Graduate Research Fellowship DGE-1144081 awarded to Jacqueline
Nesi. We wish to sincerely thank the many research assistants and re-
search participants who made this study possible.
Disclaimer Any opinions, findings, and conclusions or recommenda-
tions expressed in this material are those of the authors and do not nec-
essarily reflect the views of the National Institutes of Health or the
National Science Foundation.
Conflict of Interest The authors declare that they have no conflict of
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and
interpreting interactions. Thousand Oaks: Sage.
Angold, A., Costello, E., Messer, S., Pickles, A., Winder, F., & Silver, D.
(1995). Development of a short questionnaire for use in epidemio-
logical studies of depression in children and adolescents.
International Journal of Methods in Psychiatric Research, 5,237
Babad, E. (2001). On the conception and measurement of popularity:
more facts and some straight conclusions. Social Psychology of
Education, 5,330.
Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and
multilevel regression: inferential and graphical techniques.
Multivariate Behavioral Research, 40,373400.
Bessière, K., Kiesler, S., Kraut, R., & Boneva, B. S. (2008). Effects of
internet use and social resources on changes in depression.
Information, Communication & Society, 11,4770.
Best, P., Manktelow, R., & Taylor, B. (2014). Online communication,
social media and adolescent wellbeing: a systematic narrative re-
view. Children and Youth Services Review, 41,2736.
Borelli, J. L., & Prinstein, M. J. (2006). Reciprocal, longitudinal associ-
ations among adolescentsnegative feedback-seeking, depressive
symptoms, and peer relations. Journal of Abnormal Child
Psychology, 34,154164.
boyd, d. (2007). Social networking sites: public, private, or what?
Knowledge Tree, 13,28.
Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and
social well-being. In Proceedings of the 28th Conference on Human
Factors in Computing Systems. Atlanta, GA.
Chou, H.-T. G., & Edge, N. (2012). BThey are happier and having better
lives than I am^: the impact of using facebook on perceptions of
otherslives. Cyberpsychology, Behavior and Social Networking,
Choukas-Bradley, S., & Prinstein, M. J. (2014).Peer relationships and the
development of psychopathology. In Handbook of developmental
psychopathology (pp. 185204). New York: Springer.
Coie, J., Dodge, K., & Coppotelli, H. (1983). Dimensions and types of
social status: a cross-age perspective. Developmental Psychology,
Davila, J., Hershenberg, R., Feinstein, B. A., Gorman, K., Bhatia, V., &
Starr, L. R. (2012). Frequency and quality of social networking
among young adults: associations with depressive symptoms, rumi-
nation, and co-rumination. Psychology of Popular Media Culture, 1,
Feinstein, B. A., Hershenberg, R., Bhatia, V., Latack, J. A., Meuwly, N.,
& Davila, J. (2013). Negative social comparison on facebook and
depressive symptoms: rumination as a mechanism. Psychology of
Popular Media Culture, 2,161170.
Forest, A. L., & Wood, J. V. (2012). When social networking is not
working: individuals with low self-esteem recognize but do not reap
the benefits of self-disclosure on facebook. Psychological Science,
Gibbons, F., & Buunk, B. (1999). Individual differences in social com-
parison: development of a scale of social comparison orientation.
Journal of Personality and Social Psychology, 76,129142.
Gilbert, D. T., & Malone, P. S. (1995). The correspondence bias.
Psychological Bulletin, 117,21.
Gonzales, A. L., & Hancock, J. T. (2011). Mirror, mirror on my facebook
wall: effects of exposure to facebook on self-esteem.
Cyberpsychology, Behavior and Social Networking, 14,7983.
Gross, E. F. (2004). Adolescent internet use: what we expect, what teens
report. Journal of Applied Developmental Psychology, 25,633649.
Haferkamp, N., & Krämer, N. C. (2011). Social comparison 2.0: exam-
ining the effects of online profiles on social-networking sites.
Cyberpsychology, Behavior and Social Networking, 14,309314.
Hames, J. L., Hagan, C. R., & Joiner, T. E. (2013). Interpersonal process-
es in depression. Annual Review of Clinical Psychology, 9,355377.
Harter, S. (2012). The construction of the self: Developmental and socio-
cultural foundations. New York: Guilford.
Harter, S., Stocker, S., & Robinson, N. S. (1996). The perceived direc-
tionality of the link between approval and self-worth: the liabilities
of a looking glass self-orientation among young adolescents.
Journal of Research on Adolescence, 6,285308.
Holmbeck, G. (2002). Post-hoc probing of significant moderational and
meditational effects in studies of pediatric populations. Journal of
Pediatric Psychology, 27,8796.
Jelenchick, L. A., Eickhoff, J. C., & Moreno, M. A. (2013). BFacebook
depression?^Social networking site use and depression in older
adolescents. Journal of Adolescent Health, 52,128130.
Joiner, T. E., Jr. (1999). A test of interpersonal theory of depression in
youth psychiatric inpatients. Journal of Abnormal Child
Psychology, 27,7785.
J Abnorm Child Psychol
Joiner, T. E., & Metalsky, G. I. (1995). A prospective test of anintegrative
interpersonal theory of depression: a naturalistic study of college
roommates. Journal of Personality and Social Psychology, 69,
Joiner, T. E., & Metalsky, G. I. (2001). Excessive reassurance seeking:
delineating a risk factor involved in the development of depressive
symptoms. Psychological Science, 12,371378.
Jones, D. C. (2001). Social comparison and body image: attractiveness
comparisons to models and peers among adolescent girls and boys.
Sex Roles, 45,645664.
Jones, S. & Fox, S. (2009). Generations online in 2009. Pew Internet &
American Life Project. Retrieved from http://www.floridatechnet.
Krasnova, H., Wenninger, H., Widjaja, T., & Buxmann, P. (2013). Envy
on Facebook: A hidden threat to userslife satisfaction? In
International Conference on Wirtschaftsinformatik,Leipzig.
Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., &
Crawford, A. (2002). Internet paradox revisited. Journal of Social
Issues, 58,4974.
Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N.,
Ybarra, O. (2013). Facebook use predicts declines in subjective
well-being in young adults. PLoS ONE,8. doi: 10.1371/journal.
Lenhart, A., Purcell, K., Smith, A., & Zickhur, K. (2010). Social media &
mobile Internet use among teens and young adults. Pew Internet &
American Life Project. Retrieved from
Madden, M., Lenhart, A., Cortesi, S., Gasser, U., Duggan, M., Smith, A.,
& Beaton, M. (2013a). Teens, social media, and privacy.Pew
Research Center. Retrieved from
(2013b). Teens and technology 2013. Pew Internet & American
Life Project. Retrieved from
Manago, A. M., Graham, M. B., Greenfield, P. M., & Salimkhan, G.
(2008). Self-presentation and gender on MySpace. Journal of
Applied Developmental Psychology, 29,446458.
Manago, A. M., Taylor, T., & Greenfield, P. M. (2012). Me and my 400
friends: the anatomy of college studentsfacebook networks, their
communication patterns, and well-being. Developmental
Psychology, 48,369380.
Mikami, A. Y., Szwedo, D. E., Allen, J. P., Evans, M. A., & Hare, A. L.
(2010). Adolescent peer relationships and behavior problems predict
young adultscommunication on social networking websites.
Developmental Psychology, 46,4656.
Muise, A., Christofides, E., & Desmarais, S. (2009). More information
than you ever wanted: does facebook bring out the green-eyed mon-
ster of jealousy? CyberPsychology & Behavior, 12,441444.
Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009). College
studentssocial networking experiences on facebook. Journal of
Applied Developmental Psychology, 30, 227238. doi:10.1016/j.
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools
for probing interactions in multiple linear regression, multilevel
modeling, and latent curve analysis. Journal of Educational and
Behavioral Statistics, 31,437448.
Prinstein, M. J., & Cillessen, A. H. (2003). Forms and functions of ado-
lescent peer aggression associated with high levels of peer status.
Merrill-Palmer Quarterly (1982-),310342.
Prinstein, M. J., Borelli, J. L., Cheah, C. S. L., Simon, V. A., & Aikins, J.
W. (2005). Adolescent girlsinterpersonal vulnerability to depres-
sive symptoms: a longitudinal examination of reassurance-seeking
and peer relationships. Journal of Abnormal Psychology, 114,
Reich, S. M., Subrahmanyam, K., & Espinoza, G. (2012). Friending,
IMing, and hanging out face-to-face: overlap in adolescentsonline
and offline social networks. Developmental Psychology, 48,356368.
M2: Media in the lives of 8-to-18-year-olds. Retrieved from
Rothon, C., Head, J., Clark, C., Klineberg, E., Cattell, V., & Stansfeld, S.
(2009). The impact of psychological distress on the educational
achievement of adolescents at the end of compulsory education.
Social Psychiatry and Psychiatric Epidemiology, 44,421427.
Rudolph, K. D. (2002). Gender differences in emotional responses to
interpersonal stress during adolescence. Journal of Adolescent
Health, 30,313.
Rudolph, K. D. (2009). Adolescent depression. In I. H. Gotlib & C. L.
Hammen (Eds.), Handbook of Depression: Second Edition (444467).
Selfhout, M. H. W., Branje, S. J. T., Delsing, M., ter Bogt, T. F. M., &
Meeus, W. H. J. (2009). Different types of internet use, depression,
and social anxiety: the role of perceived friendship quality. Journal
of Adolescence, 32,819833.
Sharp, C., Goodyer, I. M., & Croudace, T. J. (2006). The short mood and
feelings questionnaire (SMFQ): a unidimensional item responsethe-
ory and categorical data factor analysis of self-report ratings from a
community sample of 7-through 11-year-old children. Journal of
Abnormal Child Psychology, 34,379391.
Starr, L. R., & Davila, J. (2008). Excessive reassurance seeking, depres-
sion, and interpersonal rejection: a meta-analytic review. Journal of
Abnormal Psychology, 117,762775.
Stefanone, M. A., Lackaff, D., & Rosen, D. (2011). Contingencies of self-
worth and social-networking-site behavior. Cyberpsychology,
Behavior and Social Networking, 14,4149.
Subrahmanyam, K., & Greenfield, P. (2008). Online communication and
adolescent relationships. TheFutureofChildren,18,119146.
Tversky, A., & Kahneman, D. (1973). Availability: a heuristic for judging
frequency and probability. Cognitive Psychology, 5,207232.
Uhls, Y. T., Espinoza, G., Greenfield, P., Subrahmanyam, K., & Smahel,
D. (2011). Internet and other interactive media. Encyclopedia of
Adolescence, 2,160168.
Underwood, M. K., Rosen, L. H., More, D., Ehrenreich, S. E., & Gentsch,
J. K. (2012). The BlackBerry project: capturing the content of ado-
lescentstext messaging. Developmental Psychology, 48,295302.
Valkenburg, P. M., & Peter, J. (2009). The effects of instant messaging on
the quality of adolescentsexisting friendships: a longitudinal study.
Journal of Communication, 59,7997.
Valkenburg, P. M., & Peter, J. (2013). Five challenges for the future of
media-effects research. International Journal of Communication, 7,
Valkenburg, P. M., Peter, J., & Schouten, A. P. (2006). Friend networking
sites and their relationship to adolescentswell-being and social self-
esteem. CyberPsychology & Behavior, 9,584590.
van den Eijnden, R. J. J. M., Meerkerk, G.-J., Vermulst, A. A.,
Spijkerman, R., & Engels, R. C. M. E. (2008). Online communica-
tion, compulsive internet use, and psychosocial well-being among
adolescents: a longitudinal study. Developmental Psychology, 44,
Walther, J. B., Liang, Y. J., DeAndrea, D. C., Tong, S. T., Carr, C. T.,
Spottswood, E. L., & Amichai-Hamburger, Y. (2011). The effect of
feedback on identity shift in computer-mediated communication.
Media Psychology, 14,126.
Wenzlaff, R. M., & Beevers, C. G. (1998). Depression and interpersonal
responses to othersmoods: the solicitation of negative information
about happy people. Personality and Social Psychology Bulletin,
Widman, L., Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2014).
Safe sext: adolescentsuse of technology to communicate about
sexualhealthwithdatingpartners.The Journal of Adolescent
Health, 54,612614.
... One denotes more negative in his feelings when one compares oneself with someone inferior (Haferkamp & Krämer, 2011). Responses may be frustrating, and this may be reflected in one's identity as one differs oneself based on the perceived opinions of others (Nesi & Prinstein, 2015). ...
... The false self may be a means to modify the personal appearance of the individual or enhance some aspects related to his life in a way that positively enhances his openness towards life again (Nesi & Prinstein, 2015). Accordingly, these sites affect the individual's motivation and psychological well-being. ...
... The reason for the direct association between neuroticism and social comparison may be due in the case of the downward comparison, or it may be caused by the frustrating feelings he receives within the comments of his colleagues on promoting his person with a false self, which gives him feedback opposite to expectations about the perceptions of others and this conclusion agrees with (Nesi & Prinstein, 2015). ...
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The study aimed at Predictive Accuracy of Social Comparison, Five Big Factor of Personality in predicting of Mood Contagion among Social Networking Users of Universities students. The sample consisted of 288 students from volunteers' university stage students. The scales application was performed electronically by the google form platform. The study depended on a correlational approach. The paper used the Mood contagion, five big factors of personality in social networking scales, then the study produced the social comparison scale. The findings proved that two factors Neuroticism and Extraversion had positive effects on mood contagion. Finally, the social comparison had a positive effect on mood contagion.
... Earlier work suggested that frequent smartphone use lead to mental health problems and addictive behaviours (Kuss et al., 2014;Thomée et al., 2011); however, more recently research implicates smartphone use as a coping mechanism for stressors of everyday life (Kuss et al., 2018). Undoubtedly, smartphones are integral to the social lives of today's adolescents; constantly monitoring peer activity, peer feedback, and encountering peers as (idealised) images (Konijn et al., 2015;Ma & Yang, 2016;Nesi & Prinstein, 2015). ...
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Smartphones have many characteristics that make them attractive to adolescents. Recent work, however, has established a link between smartphone-related problems and self-esteem (self-worth) and social anxiety (fear of social relations). To date, little is known about the characteristics underpinning these relationships in combination. Driven by theory, the present study examined the association between self-esteem, social anxiety, and nomophobia (fear of being without access to a smartphone) and problem smartphone use (PSU) in a sample of early adolescents ( N = 254). Self-esteem (Rosenberg Self Esteem Scale), social anxiety (Social Avoidance and Distress Scale) and their combined contribution (self-esteem × social anxiety) served as predictor variables of nomophobia (Nomophobia Questionnaire) and PSU (Mobile Phone Problem Usage Scale – Adolescent version) in separate moderated regression analyses. We found that lower self-esteem was associated with higher nomophobia and PSU, and that higher self-esteem may be a protective factor in those lower in social anxiety, such that they are not prone to excessive smartphone use. Our findings offer preliminary markers for psychologists addressing the challenges related to excessive smartphone use in this age group.
... Users on social media share a content and expect other users to approve it by giving it a like (Lee et al., 2020). Likes stand for the quantifiable and highly visible signs of status on social media (Nesi & Prinstein, 2015). Getting likes implies a public reward and recognition, which symbolizes social approval (Martinez-Pecino & Garcia-Gavilán, 2019). ...
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Joyful selfies taken at disaster sites create a controversial topic in terms of moral boundaries in digital life. While some consider it acceptable to take smiley selfies in a tragedy zone, others find this behavior morally questionable. This article demonstrates empirically that excessive time spent on social media explains, at least partially, a greater tendency to like morally ambiguous content on social media. Specifically, this article shows that consumers tend to like more questionable content (such as smiley disaster selfies) on social media when they spend more time online. Further, this article shows that this effect is mediated by increased individualistic thinking. Responses to the survey experiment (N=206) compared the tendency to like morally ambiguous content between groups of little, moderate and excessive use of social media, and tested for the mediating role of individualistic thinking on the relationship between time spent and liking behavior. Secondly, the moderating role of an ethical reminder on time spent and the fact of liking morally ambiguous contents is demonstrated. In the presence of an ethical reminder, the effect of time spent on social media and liking morally ambiguous content becomes insignificant. This article contributes to theory on social media consumption by offering a novel underlying mechanism, such as increased individualistic thinking, as one variable that partly explains the liking for morally ambiguous content. This article also offers practical contributions for social media platforms and policy makers, showing that ethical reminders could be a possible and simple nudge to help consumers act more morally or become aware of morally questionable content.
... The authors found that exposure to content featuring models with idealized bodies and the degree to which audiences engaged in social comparison were predictors of participants' perceived self-appearance, self-esteem, and other troublesome behavioral outcomes such as the number of diets to lose weight, use of pathogenic weight control practices, and use of steroids to increase muscle mass. In a study investigating the outcomes of social comparison to social media referents among adolescent students, Nesi and Prinstein (2015) asked respondents to report on levels of depressive symptoms (at baseline and after a year), technology use frequency, and social comparison and feedback-seeking measures. Consistent with their hypotheses, the researchers found that technology-based social comparison and feedback-seeking behaviors were associated with depressive symptoms. ...
Scholars have noted that consumer processing of ads leads to a number of unintended effects, such as irrationality, depression and lower self-esteem, and marginalization. The call for more research on how advertising hinders well-being is ever prevalent (Stafford & Pounders, 2019). Influencers have become particularly important for marketers given that they hold a level of trust akin to that of close friends (Swant, 2016), which makes their content more persuasive than other strategies (Lou & Yuan, 2019). As social media users are cutting back social media consumption because of negative outcomes such as problematic social media use (Marino et al., 2016), fatigue (Dhir, Yossatorn, Kaur, & Chen, 2018), addiction (Su; Han, Yu, Wu & Potenza, 2020), and life dissatisfaction (Chu, Windels, & Kamal, 2016), a question remains: what are the unintended effects of exposure to influencer marketing?
... However, the study did not clarify specific social comparisons, namely, upward, or downward comparisons. Nesi and Prinstein (2015) showed that upward social comparison on social media sites was linked to depressive symptoms. Furthermore, Liu et al. (2019) focused on upward social comparison and showed that negative affect played a pivotal role in evaluating upward social comparison on online communication to forecast impulse buying. ...
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Many indicators have been proposed that can contribute to impulse buying. However, few studies have examined the role of social comparison in impulse buying, materialism, and negative affect, and even less is known about the underlying processes that may moderate these relationships. The objective of this study was to create a framework that included social comparison, materialism, negative affect, impulse buying, and the moderator variable confidence in Vietnamese e-commerce. A total of 249 completed questionnaires were received from young people who frequently shop online. The study used a structural model and experimentally analyzed the links between materialism, social comparison, impulse buying, and negative affect, and how the moderating variable confidence influenced these interactions. The study finds that social comparison has a significant influence on materialism but has no impact on negative affect. However, negative affect significantly influences impulse buying. Materialism also has an impact on negative affect and impulse buying. Additionally, confidence has a beneficial moderating effect on the relationship between social comparison and impulse buying as well as social comparison and materialism. The limitations and implications of both the scientific and managerial aspects of the study were also addressed. The results will improve marketers' understanding of impulse buying behaviors by evaluating the connection between materialism and negative affect, which will allow them to plan effective marketing strategies to increase future impulse buying and profits.
Instagram is a leading social network for information sharing and communication. Rigorous studies are missing that leverage bibliometric techniques to comprehensively portray the field. To fill this knowledge gap, this study carries out a holistic bibliometric and network analysis of Instagram research, illustrating the dynamic evolution from 2013 to 2021. On the basis of 2,242 publications from the Web of Science database, which were authored by 6,206 researchers, this study identifies the most prominent scholars and articles in the literature. Furthermore, it analyzes diverse bibliometric networks, such as citation, co-citation, collaboration and keyword co-occurrence networks, and presents two intellectual structure maps (i.e., conceptual structure map, thematic map). The results indicate that the number of academic studies about Instagram has been growing significantly over time and that the dominant topics are the psychological motivation of Instagram use, the COVID-19 pandemic, Instagram marketing, social media platforms, and healthcare.
This paper explores the motivations and priorities of Chinese Millennials' use of social media with regard to the sharing of content. A commercially important demographic, this group are highly active on social media. The amount of content that is shared online is immense. Some shared content “goes viral” and can be seen by vast numbers of users. The findings of this study are based on the results of over 650 online surveys and include both theoretical and practical contributions to the body of knowledge regarding the nature of viral propagation of content in Chinese social media. This contribution to the understanding and insight social media activities of this significant and commercially consumer demographic may be of value to online promoters and marketers as well those interested in the use of social media for commercial purposes in the design and management of their online and social media presence, marketing, and advertising strategies.
Replicated evidence shows that adolescents enrolled in high-achieving schools exhibit elevated mental health problems relative to national norms, reflecting risk factors such as achievement and social pressures. The frequency of digital media use is similarly a potential risk factor for poor youth mental health, although mediators of this association have not been identified. 2952 youth from three high-achieving U.S. high schools reported the frequency of their digital media use as well as internalizing and externalizing problems and substance use. Using a multiple mediation framework, the frequency of social comparison, receiving negative feedback, and risky self-presentation online each uniquely mediated the association of digital media use with internalizing and externalizing problems in boys and girls; for substance use, risky self-presentation mediated this association in both boys and girls and negative feedback mediated substance use in girls only. Measurable online behaviors in the form of social comparison, negative feedback, and self-presentation may crucially underlie the association of digital media use frequency with socio-emotional development in adolescents. Implications for intervention focused on impacting online behaviors for improving youth mental health are discussed.
The influence of social class on prosocial behaviour has long been a focus of intense research interest. The present research involved four studies that covered four moral exemplars (villains, victims, heroes, and beneficiaries) to test whether people of different social classes are treated equally in moral judgements. We described moral events experienced by different agents of varying social classes and asked participants to rate the morality of the events and their emotional responses. The results revealed that compared with the low‐class condition, high‐class individuals had an overall moral disadvantage when they were regarded as villains, victims, and beneficiaries. For the exemplar of hero, the high‐class condition was no different from the low‐class condition except that high‐class heroes evoked less elevation than low‐class heroes. The results reveal that people hold a biased moral attitude toward individuals in different social classes.
Objective: This study examined socio-demographic characteristics and COVID-19 experiences as concurrent predictors of perceived familial and friend social support, social media use, and socio-emotional motives for electronic communication during the COVID-19 pandemic among college students. Participants: Participants were 619 emerging adults (18-29-year-olds) currently enrolled at, or recently graduated from, a U.S.-based college or university (Mean age = 21.8, SD = 2.2; 64% female; 60% Non-Hispanic White). Methods: Online surveys were administered between May and June 2020. A path analysis model was conducted to examine the concurrent associations between socio-demographic factors, COVID-19-related experiences, social media/electronic engagement, and perceived social support. Results: Findings indicated significant differences in perceived social support, social media use, and socio-emotional motives for electronic communication as a function of gender, race, sexual orientation, first-generation status, and relationship status. Conclusions: Our findings highlight the role of both individual and situational differences in interpersonal functioning and demonstrate how college students differently engaged with social media for socio-emotional purposes during the COVID-19 pandemic.
Merely a half century ago, research examining contextual correlates of youth psychopathology focused almost exclusively on parental factors (Hartup, 1970). Several influential initial studies revealed that children and young adults experiencing significant emotional difficulties could be identified by their troubling experiences with peers earlier in childhood (e.g., Roff, 1961). Soon after, follow-forward studies revealed that children who were disliked by their peers appeared to be at greater risk for a host of later negative outcomes, including delinquent or criminal activity and various symptoms of psychopathology (e.g., Coie, Terry, Lenox, Lochman, & Hyman, 1995). These findings contributed to an emphasis on understanding how children’s peer status, or acceptance/rejection among peers, may be associated with later psychopathology. Over time, researchers began to take interest in developmental antecedents or determinants of children’s peer status and in more broadly understanding the nature of early childhood peer experiences. Soon, an awareness of other types of peer relationships began to dominate researchers’ interest. For instance, studies revealed that youths’ success in dyadic relationships was orthogonal to their status within the overall peer group (Hartup, 1996). Children’s formation, maintenance, and quality of friendships soon became a focus of research; associations among aspects of friendships and adjustment also proliferated.
This study examined three orientations toward the relation between peer approval and global self-worth among young adolescents: (a) Self-worth is based upon peer approval of the self, a looking glass self-orientation; (b) self-worth is viewed as preceding approval from others; and (c) no connection is reported between self-worth and peer approval. A number of liabilities of a looking glass self-orientation were hypothesized and supported by the findings. Participants basing their self-worth on peer approval reported the greatest preoccupation with peer approval, they were most likely to be distracted from their schoolwork by peers (according to teachers' reports), they perceived the greatest fluctuations in both classmate approval and their self-worth, and they reported lower levels of classmate approval (confirmed by teachers) and self-worth, compared to those reporting that self-worth precedes approval. Findings were discussed in terms of the need for a model that will elucidate the precursors of these three orientations and their correlates.
Adolescents today interact with digital media in almost all aspects of their lives, including communication, entertainment, and formal education. Research indicates that adolescents are using interactive media to grapple with many normative offline developmental issues such as identity development, peer interaction, relationship building, and learning. This article describes how interactive media are used by adolescents in the first decade of the twenty-first century; and then connects this media use to issues concerning adolescent development.