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Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence

23 Feb 2015-Journal of Experimental Psychology: General (American Psychological Association)-Vol. 144, Iss: 2, pp 480-488
TL;DR: It is demonstrated that passive Facebook usage undermines affective well-being, and this issue is examined using experimental and field methods.
Abstract: Prior research indicates that Facebook usage predicts declines in subjective well-being over time. How does this come about? We examined this issue in 2 studies using experimental and field methods. In Study 1, cueing people in the laboratory to use Facebook passively (rather than actively) led to declines in affective well-being over time. Study 2 replicated these findings in the field using experience-sampling techniques. It also demonstrated how passive Facebook usage leads to declines in affective well-being: by increasing envy. Critically, the relationship between passive Facebook usage and changes in affective well-being remained significant when controlling for active Facebook use, non-Facebook online social network usage, and direct social interactions, highlighting the specificity of this result. These findings demonstrate that passive Facebook usage undermines affective well-being.

Summary (2 min read)

Jump to: [Introduction][Method][Results][General Discussion][Caveats] and [Concluding Comment]

Introduction

  • The authors examined this issue in 2 studies using experimental and field methods.
  • The authors addressed this question by examining whether the way people use Facebook, in particular whether they do so actively or passively, explains how this technology impacts subjective wellbeing.
  • Theoretically, continually exposing oneself to positive information about others should elicit envy, an emotion linked to lower well-being (Salovey & Rodin, 1984; Smith & Kim, 2007).

Method

  • Eighty-nine people (Mage 20.23, SDage 2.10; 61 females; 53% European American, 34% Asian, 8% African American, and 5% other) were recruited for a study about Facebook through flyers posted around Ann Arbor, Michigan.
  • Prior to the start of the study, the authors operationalized active and passive Facebook usage for participants in the same way it was defined in Study 1.
  • Seventy-seven of the 80 participants that their analyses focused on returned to the laboratory following Phase 2 to complete another set of questionnaires, which included the Satisfaction With Life Scale (M 5.06, SD 1.13, .85; Diener et al., 1985).
  • In the present study this covariance was not significantly different from zero (covar .00, p .97) and will therefore not be discussed further.

Results

  • As Figure 2 illustrates, people interacted “directly” with other people more frequently than any other type of social interaction, ts(8332)s 12.85, ps .001.
  • Passive Facebook usage also remained a significant predictor of changes in affective well-being when controlling for the other social interaction variables the authors assessed: non-Facebook online social network usage, active Facebook use and direct social interaction.
  • As Table 5 illustrates, the only additional variable that was significantly related to changes in affective well-being over time in this analysis was direct social interaction.
  • Network usage to predict any of the results (ps .12).

General Discussion

  • This research adds to work indicating that interacting with Facebook has negative implications for subjective well-being (Krasnova et al., 2013; Kross et al., 2013).
  • This suggests that people spend most of their time on Facebook engaging in a behavior that undermines their affective well-being.
  • Because of this, the authors treated status updates as a binary predictor.
  • Correlations above the dashed diagonal line represent withinperson correlations obtained from multi-level analyses.
  • It is possible that people’s motivation to “stay in touch” outweigh concerns they have over how interacting with this technology influences their feelings.

Caveats

  • Three caveats are in order before concluding.
  • Together, these two sets of findings suggest that passive Facebook usage has a delayed, not immediate, effect on how people feel.
  • Finally, participants in the current studies were young adults.

Concluding Comment

  • Her response, “I’m a marketer, and sometimes I almost can’t take it out of my personal life.
  • I’ve had friends call me and say, ‘Your life looks so amazing.’.
  • Though the authors all “present” ourselves in daily life 8.
  • These findings, in conjunction with the absence of an immediate effect of manipulating Facebook usage on affect in Study 1, suggest that although some delay is necessary for passive Facebook usage to influence affect, the exact duration of this delay is less important.

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Passive Facebook Usage Undermines Affective Well-Being:
Experimental and Longitudinal Evidence
Philippe Verduyn
University of Leuven
David Seungjae Lee, Jiyoung Park, Holly Shablack,
Ariana Orvell, Joseph Bayer, Oscar Ybarra,
John Jonides, and Ethan Kross
University of Michigan, Ann Arbor
Prior research indicates that Facebook usage predicts declines in subjective well-being over time. How
does this come about? We examined this issue in 2 studies using experimental and field methods. In
Study 1, cueing people in the laboratory to use Facebook passively (rather than actively) led to declines
in affective well-being over time. Study 2 replicated these findings in the field using experience-sampling
techniques. It also demonstrated how passive Facebook usage leads to declines in affective well-being:
by increasing envy. Critically, the relationship between passive Facebook usage and changes in affective
well-being remained significant when controlling for active Facebook use, non-Facebook online social
network usage, and direct social interactions, highlighting the specificity of this result. These findings
demonstrate that passive Facebook usage undermines affective well-being.
Keywords: Facebook, social support, well-being, envy, online social networks
Just a decade ago people primarily relied on face-to-face inter-
actions, the phone, and e-mail to connect. Today, such connections
often occur instantly via online social networks such as Face-
book—but to what effect on well-being?
Kross et al. (2013) addressed this issue by examining the lon-
gitudinal implications of Facebook use for the two components of
subjective well-being: how people feel moment-to-moment and
how satisfied they are with their lives. They found that the more
people used Facebook during one time period, the worse they
subsequently felt; the more they used Facebook over 2-weeks, the
more their life satisfaction levels declined over time.
Although these findings begin to illuminate the relationship
between Facebook usage and subjective well-being, they raise an
important question: How does Facebook usage lead to these de-
clines? We addressed this question by examining whether the way
people use Facebook, in particular whether they do so actively or
passively, explains how this technology impacts subjective well-
being.
Prior research indicates that Facebook activities can be dichot-
omized into active and passive forms of usage (Burke, Marlow, &
Lento, 2010; Deters & Mehl, 2013; Krasnova, Wenninger, Wi-
djaja, & Buxmann, 2013). Active usage refers to activities that
facilitate direct exchanges with others (e.g., posting status updates,
commenting on posts); passive usage involves consuming infor-
mation without direct exchanges (e.g., scrolling through news
feeds, viewing posts). This distinction is important because cross-
sectional work has linked passive Facebook usage with reduced
levels of subjective well-being (Krasnova et al., 2013).
Why might passive Facebook usage undermine well-being?
On Facebook, people tend to portray themselves in overly
flattering ways (Barash, Ducheneaut, Isaacs, & Bellotti, 2010;
Kross et al., 2013; Mehdizadeh, 2010; Newman, Lauterbach,
Munson, Resnick, & Morris, 2011). They also communicate
positive life developments more frequently than negative ones
(Kross et al., 2013). Theoretically, continually exposing oneself
to positive information about others should elicit envy, an
emotion linked to lower well-being (Salovey & Rodin, 1984;
Smith & Kim, 2007). Although some cross-sectional evidence
supports this idea (Chou & Edge, 2012; Krasnova et al., 2013),
experimental and longitudinal evidence is needed to demon-
strate whether it is true.
In sum, previous research suggests that Facebook use negatively
influences subjective well-being. However, the mechanisms that
underlie this relationship are not well understood. Does the way
that people use Facebook, in particular whether they do so pas-
sively or actively, differentially impact subjective well-being? If
so, what role does envy play in explaining how passive Facebook
usage in particular negatively impacts subjective well-being? The
This article was published Online First February 23, 2015.
Philippe Verduyn, Department of Psychology, University of Leuven;
David Seungjae Lee, Jiyoung Park, Holly Shablack, and Ariana Orvell,
Department of Psychology, University of Michigan, Ann Arbor; Joseph
Bayer, Department of Communication Studies, University of Michigan,
Ann Arbor; Oscar Ybarra, John Jonides, and Ethan Kross, Department of
Psychology, University of Michigan, Ann Arbor.
This research was supported by funds provided by the University of
Michigan to EK and a postdoctoral research fellowship to PV from the
Fund for Scientific Research-Flanders (FWO). Author contributions: Con-
ceived and designed Study 1: PV, DSL, JP, JB, HS, AO, OY, JJ, EK;
conceived and designed Study 2: PV, DSL, JP, HS, JB, OY, JJ, EK;
performed Study 1: DSL, HS, AO; performed Study 2: HS; analyzed the
data: PV, DSL; wrote the article: PV, DSL, AO, EK; discussed the results
and commented on the manuscript: PV, DSL, JP, HS, AO, JB, JJ, EK.
Correspondence concerning this article should be addressed to Philippe
Verduyn, Tiensestraat 102 - box 3713, 3000 Leuven, Belgium, or Ethan Kross,
530 Church Street, Ann Arbor, MI 48109. E-mail: philippe.verduyn@
ppw.kuleuven.be or ekross@umich.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Experimental Psychology: General © 2015 American Psychological Association
2015, Vol. 144, No. 2, 480488 0096-3445/15/$12.00 http://dx.doi.org/10.1037/xge0000057
480

cross-sectional design used to address these questions in prior
work and exclusive focus on the cognitive component of subjec-
tive well-being (e.g., life satisfaction) makes it difficult to answer
these questions definitively.
Here we aimed to fill these knowledge gaps by using a combi-
nation of laboratory and experience sampling methods to illumi-
nate how Facebook use impacts subjective well-being. In Study 1,
we manipulated whether participants used Facebook passively or
actively in the laboratory, and examined the immediate and de-
layed effects of this manipulation on subjective well-being. In
Study 2, we performed a 1-week experience sampling study to
examine how active versus passive Facebook use impacts subjec-
tive well-being over time in daily life.
Study 1
Method
Participants. Eighty-four undergraduate students (M
age
19.93, SD
age
4.20; 52 females; 79% European American, 16%
Asian, 1% Middle Eastern, 2% African American, 2% other) were
randomly assigned to an active (N 42) or passive (N 42)
Facebook use condition in exchange for course credit. Participants
had to have an active Facebook account to participate. The Uni-
versity of Michigan institutional review board approved this study.
We aimed for at least 35 participants per condition. The research
coordinator was thus told to stop running the study after approx-
imately 80 participants.
Baseline measures. Upon arrival in the laboratory, partici-
pants were seated in front of a computer where they remained for
the entirety of the initial session. The session began with partici-
pants rating their affect (“How do you feel right now?” 0 very
negative, 100 very positive; M 67.77, SD 18.52); loneliness
(“How lonely do you feel right now?” 0 not at all lonely, 100
very lonely; M 34.22, SD 23.30); and life satisfaction (M
5.09, SD 1.24, ␣⫽.89; Diener, Emmons, Larsen, & Griffin,
1985).
We also assessed participants’ motivation for using Facebook
by asking them to indicate whether they use Facebook to keep in
touch with friends (77% answered yes), to find new friends (13%
answered yes), to share good things with friends (52% answered
yes), to share bad things with friends (7% answered yes), to
obtain new information (68% answered yes), or other: please
explain (20% answered yes). Examples of other reasons in-
cluded keeping in touch with family and organize photos.
Additional measures were administered during this session for
filler and/or exploratory purposes. The measures reported here are
those that were theoretically motivated and directly informed by
our previous research (Kross et al., 2013).
Experimental manipulation. After completing the baseline
measures, participants were randomly assigned to use Facebook
actively or passively for 10 min. The experimenter explained that
active Facebook use involved posting and communicating with
others on Facebook—for example, posting status updates or
sharing links, reacting and commenting on friends’ posts or
private messages; passive Facebook use involved browsing
Facebook—for example, scrolling through news feeds, looking
at friends’ pages and pictures, or a band’s page. Participants in
the active condition were instructed to use Facebook actively
and refrain from using it passively; those in the passive condi-
tion received the opposite instructions (for verbatim instruc-
tions, see Table 1).
Compliance. To ensure that participants followed protocol,
we recorded, unbeknownst to participants, their screen while they
were using Facebook with a software program called TeamViewer.
Nine participants (five in the active condition, four in the passive
condition) did not follow instructions (e.g., using Facebook ac-
tively when instructed to use it passively). They were excluded
from all analyses.
Postmanipulation questionnaire. After the 10-min Facebook
usage period was complete, the experimenter returned to the lab
and instructed the participant to complete another brief online
questionnaire via Qualtrics, which asked participants to rate their
Table 1
Cover Story and Manipulation Instructions for Study 1
Passive Facebook use instructions Active Facebook use instructions
Although there are many factors that contribute to Facebook’s popularity,
some studies suggest that one of the key reasons for Facebook’s
popularity is that it allows people to browse their social world
conveniently. By browsing, we mean scrolling through one’s news
feed, looking at one’s friends’ pages, pictures, and status updates.
Many people report that through browsing, they can easily connect to
and experience their social world.
Although there are many factors that contribute to Facebook’s popularity,
some studies suggest that one of the key reasons for Facebook’s
popularity is that it allows people to have direct communication with
others conveniently—by direct communication, we mean posting
status updates, sharing links, reacting and commenting on friends’
walls, or sending messages. Many people report that through direct
communication, they can easily connect to and experience their social
world.
So for the next 10 min, we ask that you try using Facebook only for
browsing: for example, scrolling through your news feed, looking
at your friends’ pages, pictures, and status updates, or a band’s
page, etc. In addition, we ask that you only use Facebook for
browsing and refrain from other activities, such as posting statuses,
sharing links, reacting and commenting on friends’ walls, or
sending messages.
So for the next 10 min, we ask that you try using Facebook only for
direct communication: for example, posting status updates, sharing
links, reacting and commenting on friends’ walls, or sending
messages. In addition, we ask that you only use Facebook for direct
communication and refrain from other activities, such as browsing,
scrolling through your news feed, looking at your friends’ pages,
pictures, and status updates, or a band’s page, etc.
While you are browsing we ask that you refrain from clicking on any
links that will lead to non-Facebook sites.
While you are browsing we ask that you refrain from clicking on any
links that will lead to non-Facebook sites.
Note. Bolded text reflects aspects of the manipulation that differed between conditions.
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481
FACEBOOK AND WELL-BEING

current mood (T
2
affect, M 69.51, SD 17.18); how lonely they
felt (T
2
loneliness, M 27.89, SD 21.30); how connected to
others they felt (T
2
connected, 0 not at all connected, 100
very connected, M 58.74, SD 21.56); and how much better
(T
2
better, 0 not at all better, 100 better, M 54.41, SD
26.54) or worse (T
2
Worse, 0 not at all worse, 100 worse,
M 21.99, SD 22.11) they thought their life seemed compared
to others. Because the latter two items both reflected how partic-
ipants viewed their own life compared with others’, we averaged
(reverse-coded T
2
better) them so that higher scores reflected the
tendency to view one’s life as worse off than other people (r .48,
M 33.76, SD 20.86).
Funneled debriefing and end-of-day instructions. Next, a
funneled debriefing was administered to determine whether
participants were aware of why we manipulated their Facebook
usage. Eight participants (three in the active condition, five in
the passive condition) were aware of the study hypotheses and
were thus excluded from all analyses on a priori grounds
leaving 67 participants (34 in the active and 33 in the passive
condition).
Participants were also asked whether they believed they were
watched while using Facebook. Nine participants reported suspi-
cion in this regard. As we had no predictions on the possible effect
of suspicion on our results we did not exclude these participants.
Nevertheless, it is notable that the conclusions remained identical
with or without these participants.
At this point participants were notified that the in-laboratory
session was complete. They were told that they would receive a
follow-up survey in the evening (9 p.m.), which they were asked
to complete upon receipt.
End-of-day questionnaire. Participants received a follow-up
survey at 9 p.m. All but four participants (two from each condi-
tion) completed the survey (95.2%). Another four participants
initially forgot to complete the follow-up survey and completed it
during the days following the experiment instead. Excluding these
participants from the analyses did not substantively influence the
results. The average time elapsed between the in-laboratory ses-
sion and the end-of-the-day questionnaire was 9.04 hr (SD
11.52). Controlling for the elapsed time did not substantively
influence the results.
The survey included all items from the post manipulation
questionnaire (T
3
affect, M 67.05, SD 21.06; T
3
loneliness,
M 25.77, SD 23.30; T
3
connected, M 61.84, SD
20.28; T
3
better, M 59.99, SD 21.02; T
3
worse, M 25.66,
SD 22.39). In addition, to assess potential lagged effects of
active versus passive Facebook usage on cognitive well-being,
we administered the Satisfaction with Life Scale again (T
3
SWLS, M 5.26, SD 1.20, ␣⫽.90).
Finally, for exploratory purposes we also asked participants
how much they used Facebook actively (T
3
active Facebook
use, M 22.61, SD 25.05) and passively (T
3
passive Face
-
book use, M 49.20, SD 30.28), and to indicate the degree
to which they used other non-Facebook online social network
sites actively (T
3
non-Facebook active social network use, M
32.78, SD 30.86) and passively (T
3
non-Facebook online
social network use, M 39.78, SD 33.93) since they left the
lab.
Results
We examined the effect of type of Facebook usage on affective
well-being (“How do you feel right now?”) by performing a 2
(Facebook Use: Active vs. Passive) 3 (Time of Assessment:
Baseline vs. Post Manipulation vs. End of Day) repeated measures
Analysis of Variance (ANOVA). This analysis revealed a signif-
icant interaction, F(2, 126) 4.04, p .02,
2
.06, uncorrected;
F(1.61, 101.1) 4.04, p .03,
2
.06, Greenhouse-Geisser
corrected.
1
As Figure 1 and Table 2 illustrate, neither passive use
nor active use participants displayed changes in affect immediately
following the manipulation. However, passive use participants
displayed a significant drop in affective well-being at the end of
the day relative to both their baseline and postmanipulation affect
levels; active use participants did not. The two groups also differed
significantly on end of the day affect, t(126) ⫽⫺3.07, p .01,
95% CI [16.78, 3.66]. Gender did not moderate the effect of
type of Facebook usage on affective well-being across time, F(2,
122 .31, p .73,
2
.01 uncorrected; F(1.61, 98.10) .31,
p .69,
2
.01, Greenhouse-Geisser corrected.
Although we expected passive Facebook usage would lead
participants to view their own life as worse compared to others, a
2 (Facebook Use) 2 (Time of Assessment: Postmanipulation vs.
End of the Day) repeated measures ANOVA on this variable did
not reveal a significant effect of condition or condition by time
interaction (Fs 1.32, ps .25). The complementary analysis
performed on life satisfaction likewise failed to reveal any signif-
icant effects involving condition (Fs 1.76, ps .19). We did,
however, observe a significant effect of time on life satisfaction,
F(1, 63) 6.96, p .01,
2
.10, 95% CI [.04, .25]; participants
scored higher on this measure at the end of the day compared to
baseline. This increase was not related to any type of social media
usage that we assessed (Fs 1.55, ps .21).
2
Note that unlike affective well-being (described earlier), both of
these measures were administered twice (not three times) to reduce
the likelihood that participants would discern the goals of the
study, which is also why 2 2 ANOVAs were performed.
Finally, the experimental manipulation did not influence how
much participants reported using Facebook overall, F(1, 63) .16,
p .69,
2
.003, 95% CI [15.17, 10.11]; or how actively, F(1,
63) .001, p .98,
2
.001, 95% CI [11.71, 11.97]; or
passively, F(1, 63) .37, p .55,
2
.006, 95% CI [19.65,
10.53] they used Facebook after they left the lab. It did, however,
influence how much people reported using non-Facebook online
social networks. Specifically, participants in the passive condition
1
Mauchly’s Test of Sphericity indicated that the assumption of sphe-
ricity had been violated,
2
(2) 17.54, p .0001, therefore degrees of
freedom were corrected using Greenhouse-Geisser estimates of sphericity
(ε .80).
2
Although our study focused on how type of Facebook usage influences
the two components of subjective well-being (affect and life satisfaction),
we also measured loneliness and social connection because some research
has linked active Facebook usage with changes in these constructs (Deters
& Mehl, 2013). Neither the main effect of condition nor the Condition
Time interaction was significant for these variables (Fs .51, ps .52).
However, the effect of time was significant for loneliness, F(2, 118)
8.60, p .001,
2
.13); all participants felt less lonely at the second,
t(118) ⫽⫺3.25, p .01, 95% CI [12.80, 3.10] and third assessment,
t(118) ⫽⫺3.86, p .001, 95% CI [14.29, 4.60]) compared with
baseline.
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482
VERDUYN ET AL.

reported engaging more in active, F(1, 63) 5.12, p .03,
2
.08, 95% CI [2.02, 32.69]; and passive, F(1, 63) 5.01, p .03,
2
.07, 95% CI [2.04, 35.80] forms of non-Facebook online
social interaction (e.g., Tumblr, Twitter, Blogger, MySpace, Insta-
gram) after they left the lab. Critically, controlling for these other
types of non-Facebook online social network use did not substan-
tively influence any of the aforementioned results.
Study 2
Study 1 demonstrated that passive Facebook usage decreases
affective well-being, but not life satisfaction. Study 2 examined
whether passive Facebook usage predicts similar outcomes
when people engage in this behavior spontaneously in daily life.
It also investigated the psychological mechanism underlying the
link between passive Facebook usage and affect. In Study 1, we
did not observe an effect of type of Facebook usage on people’s
tendency to view their life as worse off than others, which we
thought would exacerbate envy and lead to emotional declines.
One explanation for this null finding is that participants may not
have been aware of whether they compared their life to others
(Brickman & Bulman, 1977; Gilbert, Giesler, & Morris, 1995;
Goethals, 1986; Nisbett & Wilson, 1977; Wood, 1996). Conse-
quently, it is possible that people did engage in this comparison
process, but our measurement strategy may have failed to
capture it. Study 2 circumvented this issue by examining
whether passive Facebook usage influenced envy, a consciously
accessible subjective experience (Ellsworth, 1995; Robinson &
Clore, 2002) that we thought would be a more proximal pre-
dictor of affective well-being.
We examined these issues in Study 2 by text messaging
participants five times a day for 6 days. Each text contained a
link to an online survey, which asked participants to answer
questions that assessed affective well-being, envy, active Face-
book usage, passive Facebook usage, direct social interaction,
and non-Facebook online social network usage (for question
items, see Table 3). We performed lagged analyses on partici-
pants’ responses to these questions and their answers to the
Satisfaction With Life Scale (Diener et al., 1985), which they
completed before and after the experience-sampling phase of
the study, to examine whether type of Facebook usage predicts
changes in affective and cognitive well-being over time.
Method
Participants. Eighty-nine people (M
age
20.23, SD
age
2.10; 61 females; 53% European American, 34% Asian, 8% Af-
rican American, and 5% other) were recruited for a study about
Facebook through flyers posted around Ann Arbor, Michigan. To
qualify for the study participants had to possess a Facebook
account and a touch-screen smartphone. They received up to $40
and were entered into a raffle to receive an iPod Nano for partic-
ipating. The University of Michigan institutional review board
approved this study.
We determined our target sample size by referencing a recent
experience sampling study on Facebook and well-being, which
consisted of 82 participants (Kross et al., 2013). The research
coordinator was told to stop running participants after approx-
imately 80 participants were successfully run through the pro-
tocol.
Phase 1. Participants completed a set of questionnaires, which
included the Satisfaction With Life Scale (M 5.14, SD 1.08,
␣⫽.85; Diener et al., 1985), the Beck Depression Inventory (M
.43, SD .37, ␣⫽.90; Beck, Ward, Mendelson, Mock, &
Erbaugh, 1961), the Revised UCLA Loneliness Scale (M 1.72,
SD .51, ␣⫽.92; Russell, Peplau, & Cutrona, 1980), the
Rosenberg Self-Esteem Scale (Rosenberg, 1965; M 2.68, SD
.56, ␣⫽.90), and the Social Provision Scale (M 3.48, SD .36,
␣⫽.82; Cutrona, 1989), which we modified to assess perceptions
of Facebook support.
We also assessed participants’ motivation for using Facebook
by asking them to indicate whether they use Facebook to keep
in touch with friends (95% answered yes), to find new friends
Table 2
Simple Effects Demonstrating How the Passive and Active Facebook Conditions Affect Levels
Fluctuate Over Time
Condition
Postmanipulation vs.
baseline End of day vs. baseline
End of day vs.
postmanipulation
t 95% CI t 95% CI t 95% CI
Passive Facebook use .29 [5.67, 7.61] 2.74
ⴱⴱ
[2.55, 15.83] 2.45
[1.58, 14.86]
Active Facebook use 1.26 [10.69, 2.39] 1.24 [10.63, 2.45] .02 [6.48, 6.60]
Note. Degrees of freedom for all tests is 126.
p .05.
ⴱⴱ
p .01.
ⴱⴱⴱ
p .0001.
50
55
60
65
70
75
80
Baseline Post Manipulation End of Day
Affective Well-being
Active Passive
Figure 1. Affective well-being over time as a function of passive vs.
active Facebook use. Error bars represent / 1 standard error.
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483
FACEBOOK AND WELL-BEING

(19% answered yes), to share good things with friends (75%
answered yes), to share bad things with friends (34% answered
yes), to obtain new information (76% answered yes), or other:
please explain (14% answered yes). Examples of other reasons
included keeping in touch with family and playing games.
Phase 2. Participants were text-messaged five times per day
between 10 a.m. and midnight for 6 consecutive days. Text-
messages occurred at random times within five 168-min window
blocks each day. Controlling for the length of time between any
two text messages did not substantively influence the results. Each
text-message contained a link to an online survey, which asked
participants to answer questions on affective well-being and lone-
liness at the moment of completing the online survey, and degree
of envy, active Facebook usage, passive Facebook usage, direct
interactions, and non-Facebook social network usage since the
previous report. The original affective well-being response scale
ranged from 0 very positive to 100 very negative but ratings
were reversed prior to analyses to match the response format of
Study 1. Participants always answered the affect question first,
followed by the loneliness question. The rest of the questions were
presented randomly.
Prior to the start of the study, we operationalized active and
passive Facebook usage for participants in the same way it was
defined in Study 1. Subsequently, the experimenter walked partic-
ipants through the protocol for answering each experience-
sampling question to ensure that they understood how to respond
to them.
On average, participants responded to 80% of the text-
messages (range: 3%–100%). Following prior experience sam-
pling research with a similar study duration (Koval, Kuppens,
Allen, & Sheeber, 2012) we pruned the data by excluding all of
the data from nine participants who responded to 60% of the
texts, resulting in 2,084 total experience sampling observations
from 80 participants. This threshold was more conservative
than the one used in our prior work on Facebook and well-being
(Kross et al., 2013) as the duration of the present study was
shorter. Using the same cutoff score as we previously used did
not alter the results.
Phase 3. Seventy-seven of the 80 participants that our analy-
ses focused on returned to the laboratory following Phase 2 to
complete another set of questionnaires, which included the Satis-
faction With Life Scale (M 5.06, SD 1.13, ␣⫽.85; Diener
et al., 1985). We recorded participants’ number of Facebook
friends (M 783.17, SD 425.19) and obtained a screenshot of
their Facebook wall posts (so that we could record status updates)
that corresponded to the timespan that the experience-sampling
phase of the study took place during a subsequent session by
asking them to log into their accounts in the presence of the
experimenter. We were unable to obtain Facebook friend and/or
wall data for 14 participants either because this information was
hidden from their walls or because they did not return to have this
information recorded.
3
Analyses overview. Following prior work (Kross et al.,
2013) we examined the relationship between active and passive
Facebook usage and affect using multilevel analyses to account
for the nested data structure. Specifically, we examined whether
T
2
affect (i.e., How do you feel right now?) was predicted by
T
1–2
active Facebook usage (i.e., How much have you used
Facebook actively since the last time we asked?), or T
1–2
passive Facebook usage (i.e., How much have you used Face-
book passively since the last time we asked?), controlling for T
1
affect at level-1 of the model. Note that although this analysis
assesses Facebook usage at T
2
, the question refers to usage
between T
1
and T
2
(hence the notationT
1–2
). Thus, this analysis
allowed us to explore whether active or passive Facebook usage
during the time period separating T
1
and T
2
predicted changes
in affect over time. Following prior work (Koval et al., 2012),
we excluded between-day lags from the lagged analysis (i.e.,
participants first ratings in the morning were not predicted by
their last ratings on the previous day).
When noncompliant cases were observed, we used partici-
pants’ responses to the last text message they answered to
maximize power when examining the lagged effect of type of
Facebook usage on well-being. Thus, if we examined whether
T
2–3
active or passive Facebook usage predicted T
3
affect
controlling for T
2
affect, but did not have data on T
2
affect, then
we used T
1
affect instead. Excluding trials in which participants
did not respond to the previous texts (rather than following this
protocol) did not substantively alter any of the results we report.
All Level-1 predictors were group-mean centered, and intercepts
and slopes were allowed to vary randomly across participants.
Unstandardized regression weights are reported. Significance test-
ing of fixed effects was performed using t tests.
Degrees of freedom vary across analyses for the following
reasons. For the nonlagged analysis 2,084 observations were
used whereas for the lagged analysis only 1,609 observations
were entered into the analysis (because between-day lags were
excluded). When comparing the frequency of different types of
communication (i.e., active Facebook, passive Facebook, non-
3
Additional measures were included during Phases 1 and 3 either to
serve as filler questionnaires or for exploratory purposes. The only mea-
sures that were administered during Phase 1 and Phase 3 (in addition to life
satisfaction) were the BDI (M .36, SD .42, ␣⫽.93) and the Revised
UCLA Loneliness Scale (M 1.72, SD .46, ␣⫽.90). Neither active nor
passive Facebook use predicted changes on these measures (ps .24). The
measures reported in the text are those that were theoretically motivated
and directly informed by our previous research (Kross et al., 2013).
Participants engaged in a pilot study after Phase 3, the outcomes of which
have no bearing on the current results.
Table 3
Text Message Questions
Variable Question
Affective well-being How do you feel right now?
Envy How envious have you been of others since
the last time we asked?
Active Facebook use How much have you actively used Facebook
since the last time we asked?
Passive Facebook use How much have you passively used Facebook
since the last time we asked?
Direct social interaction How much have you interacted with other
people directly since the last time we
asked?
Non Facebook online
social networking use
How much time have you spent on social
network sites other than Facebook since the
last time we asked?
Note. Participants rated their affective well-being on a 0 very negative
to 100 very positive scale; all remaining questions used a 0 not at all
to 100 a lot scale.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
484
VERDUYN ET AL.

Citations
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TL;DR: The results show that while social media browsing has a strong negative impact on users’ subjective well-being, there is no significant impact generated by social media communication.
Abstract: Social media has profoundly reshaped the way people obtain and exchange information. Recently, concerns have been raised on its adverse impacts on people’s subjective well-being. Using a large and representative sample of Chinese individuals, we explore the effects of social media browsing and social media communication on users' life satisfaction. The results show that while social media browsing has a strong negative impact on users’ subjective well-being, there is no significant impact generated by social media communication. The relative income and social comparison mainly drive the result. The negative impact of social media browsing is more pronounced for low-income people than for high-income people. The latter is influenced more by updates shared by their friends, while the former is influenced more by news from public sources. Our results do not support other possible mechanisms like information cocoons or information fragmentation.

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Cites background from "Passive Facebook usage undermines a..."

  • ...Verduyn et al. (2015) show that the use of social media as an information source leads to a decline in the affective well-being, and this can be attributed to the social comparison and the envious feeling arising from the social comparison....

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  • ...Most of the previous studies on this topic are from psychology or communication study (e.g., Tandoc et al., 2014, Vogel et al., 2014, Verduyn et al., 2015)....

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  • ...…can fall into the following categories: active usage, which refers to activities that facilitate direct communications or interactions with others, and passive usage, which involves consuming information without direct exchanges (Wenninger et al., 2014, Verduyn et al., 2015, Verduyn et al., 2015)....

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TL;DR: Confirming evidence showed that enhanced Facebook usage was associated with anxious symptoms among individuals with impaired neural Facebook filtering ability, and additional suggestive evidence indicated that this specific Facebook filtering impairment was not better explained by a general filtering deficit.
Abstract: Is Facebook usage bad for mental health? Existing studies provide mixed results, and direct evidence for neural underlying moderators is lacking. We suggest that being able to filter social-network information from accessing working memory is essential to preserve limited cognitive resources to pursue relevant goals. Accordingly, among individuals with impaired neural social-network filtering ability, enhanced social-network usage would be associated with negative mental health. Specifically, participants performed a novel electrophysiological paradigm that isolates neural Facebook filtering ability. Participants' actual Facebook behavior and anxious symptomatology were assessed. Confirming evidence showed that enhanced Facebook usage was associated with anxious symptoms among individuals with impaired neural Facebook filtering ability. Although less robust and tentative, additional suggestive evidence indicated that this specific Facebook filtering impairment was not better explained by a general filtering deficit. These results involving a neural social-network filtering moderator, may help understand for whom increased online social-network usage is associated with negative mental health.

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Cites background from "Passive Facebook usage undermines a..."

  • ...…link between OSN usage and maladaptive psychological aspects (e.g., Brooks, 2015; Feinstein et al., 2015), several longitudinal experience-sampling studies were able that rule out reversed directionality that maladaptive aspects influence OSN usage (e.g., Kross et al., 2013; Verduyn et al., 2015)....

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  • ...Recent studies indicate that social networks activities can be classified into two broad categories: active and passive usage (Verduyn et al., 2015; Verduyn, et al., 2017)....

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  • ...…psychological aspects In order to examine maladaptive psychological aspects, we concentrated on two wellestablished questionnaires measuring depressive and anxious symptoms that were previously associated with social media usage (Acar, 2008; Feinstein et al., 2015; Verduyn et al., 2015)....

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  • ...Specifically, while studies consistently find a negative relationship between passive usage and aspects of well-being (Verduyn et al., 2015; Verduyn et al., 2017), the nature of the relationship between active usage and well-being is mixed (Verduyn et al., 2017)....

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DOI
TL;DR: In this paper, a trans-diagnostic cognitive behavioural conceptualisation of the positive and negative roles of social media use in adolescence, with a focus on how it interacts with common mental health difficulties is presented.
Abstract: Abstract Whilst research into the association between social media and mental health is growing, clinical interest in the field has been dominated by a lack of theoretical integration and a focus on pathological patterns of use. Here we present a trans-diagnostic cognitive behavioural conceptualisation of the positive and negative roles of social media use in adolescence, with a focus on how it interacts with common mental health difficulties. Drawing on clinical experience and an integration of relevant theory/literature, the model proposes that particular patterns of social media use be judged as helpful/unhelpful to the extent that they help/hinder the adolescent from satisfying core needs, particularly those relating to acceptance and belonging. Furthermore, it introduces several key interacting processes, including purposeful/habitual modes of engagement, approach/avoidance behaviours, as well as the potential for social media to exacerbate/ameliorate cognitive biases. The purpose of the model is to act as an aide for therapists to collaboratively formulate the role of social media in young people’s lives, with a view to informing treatment, and ultimately, supporting the development of interventions to help young people use social media in the service of their needs and values. Key learning aims (1) To gain an understanding of a trans-diagnostic conceptualisation of social media use and its interaction with common mental health difficulties in adolescence. (2) To gain an understanding of relevant research and theory underpinning the conceptualisation. (3) To gain an understanding of core processes and dimensions of social media use, and their interaction with common mental health difficulties in this age group, for the purpose of assessment and formulation. (4) To stimulate ideas about how to include adolescent service users’ online world(s) in treatment (where indicated), both with respect to potential risks to ameliorate and benefits to capitalise upon. (5) To stimulate and provide a framework for clinically relevant research in the field and the development of interventions to support young people to flourish online.

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TL;DR: The authors examined how technology impacts adolescents' perceptions of, and affective responses to solitude, as well as how adolescents' own motivations for solitude (shyness, affinity for aloneness) were related to these reactions.
Abstract: In this study, we examined how technology impacts adolescents’ perceptions of, and affective responses to solitude, as well as how adolescents’ own motivations for solitude (shyness, affinity for aloneness) were related to these reactions. Participants were N = 437 adolescents (297 girls; Mage = 16.15 years, standard deviation (SD) = .50) who were presented with a series of hypothetical vignettes asking them to imagine themselves in the context of pure solitude (alone in their room with the door closed), as well as being physically alone but engaged in increasing levels of virtual social engagement, including passive (e.g., watching videos, scrolling, but no direct social engagement), active (e.g., texting), and audio-visual (e.g., Facetime) technology use. Following each vignette, participants reported their perceptions of being alone and positive/negative affective responses. We also measured general motivations for solitude (shyness, affinity for aloneness). Among the results, adolescents perceived themselves as less alone in vignettes depicting increasing virtual social engagement. Affective benefits of increased virtual engagement were also found (e.g., less loneliness/boredom/sadness, greater social connection/contentment). However, these effects were moderated by solitude motivations, with different patterns evident as a function of participant shyness and affinity for aloneness. Findings highlight the importance of considering the nature of adolescents’ technology use when alone, as well as motivations for solitude, when considering links between solitude and well-being.

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TL;DR: For example, Curry et al. as mentioned in this paper found that doing good for others in ways that strengthen social bonds tends to feel good, while acting selfishly in a way that could harm relationships may not leave people feeling like criminals as Hume envisioned.

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References
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TL;DR: The difficulties inherent in obtaining consistent and adequate diagnoses for the purposes of research and therapy have been pointed out and a wide variety of psychiatric rating scales have been developed.
Abstract: The difficulties inherent in obtaining consistent and adequate diagnoses for the purposes of research and therapy have been pointed out by a number of authors. Pasamanick12in a recent article viewed the low interclinician agreement on diagnosis as an indictment of the present state of psychiatry and called for "the development of objective, measurable and verifiable criteria of classification based not on personal or parochial considerations, but on behavioral and other objectively measurable manifestations." Attempts by other investigators to subject clinical observations and judgments to objective measurement have resulted in a wide variety of psychiatric rating scales.4,15These have been well summarized in a review article by Lorr11on "Rating Scales and Check Lists for the Evaluation of Psychopathology." In the area of psychological testing, a variety of paper-and-pencil tests have been devised for the purpose of measuring specific

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TL;DR: For instance, in the case of an individual in the presence of others, it can be seen as a form of involuntary expressive behavior as discussed by the authors, where the individual will have to act so that he intentionally or unintentionally expresses himself, and the others will in turn have to be impressed in some way by him.
Abstract: hen an individual enters the presence of oth ers, they commonly seek to acquire information about him or to bring into play information about him already possessed. They will be interested in his general socio-economic status, his concep tion of self, his attitude toward them, his compe tence, his trustworthiness, etc. Although some of this information seems to be sought almost as an end in itself, there are usually quite practical reasons for acquiring it. Information about the individual helps to define the situation, enabling others to know in advance what he will expect of them and what they may expect of him. Informed in these ways, the others will know how best to act in order to call forth a desired response from him. For those present, many sources of information become accessible and many carriers (or “signvehicles”) become available for conveying this information. If unacquainted with the individual, observers can glean clues from his conduct and appearance which allow them to apply their previ ous experience with individuals roughly similar to the one before them or, more important, to apply untested stereotypes to him. They can also assume from past experience that only individuals of a par ticular kind are likely to be found in a given social setting. They can rely on what the individual says about himself or on documentary evidence he provides as to who and what he is. If they know, or know of, the individual by virtue of experience prior to the interaction, they can rely on assumptions as to the persistence and generality of psychological traits as a means of predicting his present and future behavior. However, during the period in which the indi vidual is in the immediate presence of the others, few events may occur which directly provide the others with the conclusive information they will need if they are to direct wisely their own activity . Many crucial facts lie beyond the time and place of interaction or lie concealed within it. For example, the “true” or “real” attitudes, beliefs, and emotions of the individual can be ascertained only indirectly , through his avowals or through what appears to be involuntary expressive behavior. Similarly , if the individual offers the others a product or service, they will often find that during the interaction there will be no time and place immediately available for eating the pudding that the proof can be found in. They will be forced to accept some events as con ventional or natural signs of something not directly available to the senses. In Ichheiser ’s terms, 1 the individual will have to act so that he intentionally or unintentionally expresses himself, and the others will in turn have to be impressed in some way by him.…

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"Passive Facebook usage undermines a..." refers background in this paper

  • ...Th is ar tic le is in te nd ed so le ly fo rt he pe rs on al us e of th e in di vi du al us er an d is no tt o be di ss em in at ed br oa dl y. (Goffman, 1959), the current findings suggest that how people do so on Facebook creates an environment that is difficult to passively navigate without…...

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  • ...(Goffman, 1959), the current findings suggest that how people do so on Facebook creates an environment that is difficult to passively navigate without negatively influencing how we feel....

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Abstract: This article reports the development and validation of a scale to measure global life satisfaction, the Satisfaction With Life Scale (SWLS). Among the various components of subjective well-being, the SWLS is narrowly focused to assess global life satisfaction and does not tap related constructs such as positive affect or loneliness. The SWLS is shown to have favorable psychometric properties, including high internal consistency and high temporal reliability. Scores on the SWLS correlate moderately to highly with other measures of subjective well-being, and correlate predictably with specific personality characteristics. It is noted that the SWLS is suited for use with different age groups, and other potential uses of the scale are discussed.

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"Passive Facebook usage undermines a..." refers background or methods in this paper

  • ...Participants completed a set of questionnaires, which included the Satisfaction With Life Scale (M !...

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  • ....85; Diener et al., 1985), the Beck Depression Inventory (M !...

    [...]

  • ....85; Diener et al., 1985)....

    [...]

  • ...Seventy-seven of the 80 participants that our analyses focused on returned to the laboratory following Phase 2 to complete another set of questionnaires, which included the Satisfaction With Life Scale (M ! 5.06, SD ! 1.13, " ! .85; Diener et al., 1985)....

    [...]

  • ...We performed lagged analyses on participants’ responses to these questions and their answers to the Satisfaction With Life Scale (Diener et al., 1985), which they completed before and after the experience-sampling phase of the study, to examine whether type of Facebook usage predicts changes in affective and cognitive well-being over time....

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TL;DR: The Satisfaction With Life Scale (SWLS) as mentioned in this paper is a scale to measure global life satisfaction, which does not tap related constructs such as positive affect or loneliness, and has favorable psychometric properties, including high internal consistency and high temporal reliability.
Abstract: This article reports the development and validation of a scale to measure global life satisfaction, the Satisfaction With Life Scale (SWLS). Among the various components of subjective well-being, the SWLS is narrowly focused to assess global life satisfaction and does not tap related constructs such as positive affect or loneliness. The SWLS is shown to have favorable psychometric properties, including high internal consistency and high temporal reliability. Scores on the SWLS correlate moderately to highly with other measures of subjective well-being, and correlate predictably with specific personality characteristics. It is noted that the SWLS is Suited for use with different age groups, and other potential uses of the scale are discussed.

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Abstract: Evidence is reviewed which suggests that there may be little or no direct introspective access to higher order cognitive processes. Subjects are sometimes (a) unaware of the existence of a stimulus that importantly influenced a response, (b) unaware of the existence of the response, and (c) unaware that the stimulus has affected the response. It is proposed that when people attempt to report on their cognitive processes, that is, on the processes mediating the effects of a stimulus on a response, they do not do so on the basis of any true introspection. Instead, their reports are based on a priori, implicit causal theories, or judgments about the extent to which a particular stimulus is a plausible cause of a given response. This suggests that though people may not be able to observe directly their cognitive processes, they will sometimes be able to report accurately about them. Accurate reports will occur when influential stimuli are salient and are plausible causes of the responses they produce, and will not occur when stimuli are not salient or are not plausible causes.

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"Passive Facebook usage undermines a..." refers background in this paper

  • ...One explanation for this null finding is that participants may not have been aware of whether they compared their life to others (Brickman & Bulman, 1977; Gilbert, Giesler, & Morris, 1995; Goethals, 1986; Nisbett & Wilson, 1977; Wood, 1996)....

    [...]

Frequently Asked Questions (9)
Q1. What are the contributions mentioned in the paper "Passive facebook usage undermines affective well-being: experimental and longitudinal evidence" ?

The authors examined this issue in 2 studies using experimental and field methods. 

A key challenge for future research is to identify when ( and why ) interacting with this technology leads to positive versus negative socioemotional outcomes. Together, these results suggest that passive Facebook usage predicts substantive declines in subjective well-being in both the lab and in daily life. First, a growing literature suggests that Facebook ( and social network sites more generally ) have addictive properties ( Ryan, Chester, Reece, & Xenos, 2014 ). 

For the nonlagged analysis 2,084 observations were used whereas for the lagged analysis only 1,609 observations were entered into the analysis (because between-day lags were excluded). 

When noncompliant cases were observed, the authors used participants’ responses to the last text message they answered to maximize power when examining the lagged effect of type of Facebook usage on well-being. 

Passive Facebook usage also remained a significant predictor of changes in affective well-being when controlling for the other social interaction variables the authors assessed: non-Facebook online social network usage, active Facebook use and direct social interaction. 

A key challenge for future research is to identify when (and why) interacting with this technology leads to positive versus negative socioemotional outcomes. 

Eight participants (three in the active condition, five in the passive condition) were aware of the study hypotheses and were thus excluded from all analyses on a priori grounds leaving 67 participants (34 in the active and 33 in the passive condition). 

In Study 1, the authors did not observe an effect of type of Facebook usage on people’s tendency to view their life as worse off than others, which the authors thought would exacerbate envy and lead to emotional declines. 

These findings, in conjunction with the absence of an immediate effect of manipulating Facebook usage on affect in Study 1, suggest that although some delay is necessary for passive Facebook usage to influence affect, the exact duration of this delay is less important.