URGENT! Disconnect Paper

Facebook Use Predicts Declines in Subjective Well-Being in Young Adults Ethan Kross1*, Philippe Verduyn2, Emre Demiralp1, Jiyoung Park1, David Seungjae Lee1, Natalie Lin1,

Holly Shablack1, John Jonides1, Oscar Ybarra1

1 Psychology Department, University of Michigan, Ann Arbor, Michigan, United States of America, 2 Psychology Department, University of Leuven, Leuven, Belgium

Abstract

Over 500 million people interact daily with Facebook. Yet, whether Facebook use influences subjective well-being over time is unknown. We addressed this issue using experience-sampling, the most reliable method for measuring in-vivo behavior and psychological experience. We text-messaged people five times per day for two-weeks to examine how Facebook use influences the two components of subjective well-being: how people feel moment-to-moment and how satisfied they are with their lives. Our results indicate that Facebook use predicts negative shifts on both of these variables over time. The more people used Facebook at one time point, the worse they felt the next time we text-messaged them; the more they used Facebook over two-weeks, the more their life satisfaction levels declined over time. Interacting with other people ‘‘directly’’ did not predict these negative outcomes. They were also not moderated by the size of people’s Facebook networks, their perceived supportiveness, motivation for using Facebook, gender, loneliness, self-esteem, or depression. On the surface, Facebook provides an invaluable resource for fulfilling the basic human need for social connection. Rather than enhancing well-being, however, these findings suggest that Facebook may undermine it.

Citation: Kross E, Verduyn P, Demiralp E, Park J, Lee DS, et al. (2013) Facebook Use Predicts Declines in Subjective Well-Being in Young Adults. PLoS ONE 8(8): e69841. doi:10.1371/journal.pone.0069841

Editor: Cédric Sueur, Institut Pluridisciplinaire Hubert Curien, France

Received January 31, 2013; Accepted June 12, 2013; Published August 14, 2013

Copyright: � 2013 Kross et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no support or funding to report.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

Online social networks are rapidly changing the way human

beings interact. Over a billion people belong to Facebook, the

world’s largest online social network, and over half of them log in

daily [1]. Yet, no research has examined how interacting with

Facebook influences subjective well-being over time. Indeed, a

recent article that examined every peer-reviewed publication and

conference proceeding on Facebook between 1/2005 and 1/2012

(412 in total) did not reveal a single study that examined how using

this technology influences subjective well-being over time (Text S1)

[2].

Subjective well-being is one of the most highly studied variables

in the behavioral sciences. Although significant in its own right, it

also predicts a range of consequential benefits including enhanced

health and longevity [3–5]. Given the frequency of Facebook

usage, identifying how interacting with this technology influences

subjective well-being represents a basic research challenge that has

important practical implications.

This issue is particularly vexing because prior research provides

mixed clues about how Facebook use should influence subjective

well-being. Whereas some cross-sectional research reveals positive

associations between online social network use (in particular

Facebook) and well-being [6], other work reveals the opposite

[7,8]. Still other work suggests that the relationship between

Facebook use and well-being may be more nuanced and

potentially influenced by multiple factors including number of

Facebook friends, perceived supportiveness of one’s online

network, depressive symptomatology, loneliness, and self-esteem

[9,10,11].

So, how does Facebook usage influence subjective well-being

over time? The cross-sectional approach used in previous studies

makes it impossible to know. We addressed this issue by using

experience-sampling, the most reliable method for measuring in-

vivo behavior and psychological experience over time [12]. We

text-messaged participants five times per day for 14-days. Each

text-message contained a link to an online survey, which

participants completed using their smartphones. We performed

lagged analyses on participants’ responses, as well as their answers

to the Satisfaction With Life Questionnaire (SWLS) [13], which

they completed before and immediately following the 14-day

experience-sampling period, to examine how interacting with

Facebook influences the two components of subjective well-being:

how people feel (‘‘affective’’ well-being) and how satisfied they are

with their lives (‘‘cognitive’’ well-being) [14,15]. This approach

allowed us to take advantage of the relative timing of participants’

natural Facebook behavior and psychological states to draw

inferences about their likely causal sequence [16–19].

Methods

Participants Eighty-two people (Mage = 19.52, SDage = 2.17; 53 females;

60.5% European American, 28.4% Asian, 6.2% African Amer-

ican, and 4.9% other) were recruited for a study on Facebook

through flyers posted around Ann Arbor, Michigan. Participants

needed a Facebook account and a touch-screen smartphone to

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qualify for the study. They received $20 and were entered into a

raffle to receive an iPad2 for participating.

Ethics Statement The University of Michigan Institutional Review Board

approved this study. Informed written consent was obtained from

all participants prior to participation.

Materials and Procedure Phase 1. Participants completed a set of questionnaires,

which included the SWLS (M = 4.96, SD = 1.17), Beck Depression

Inventory [20] (M = 9.02, SD = 7.20), the Rosenberg Self-Esteem

Scale [21] (M = 30.40, SD = 4.96), and the Social Provision Scale

[22] (M = 3.55, SD = .34), 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 (98% answered yes),’’ ‘‘to

find new friends (23% answered yes),’’ ‘‘to share good things with

friends (78% answered yes),’’ ‘‘to share bad things with friends

(36% answered yes),’’ ‘‘to obtain new information (62% answered

yes),’’ or ‘‘other: please explain (17% answered yes).’’ Examples of

other reasons included chatting with others, keeping in touch with

family, and facilitating schoolwork and business.

Phase 2. Participants were text-messaged 5 times per day

between 10am and midnight over 14-days. Text-messages

occurred at random times within 168-minute windows per day.

Each text-message contained a link to an online survey, which

asked participants to answer five questions using a slider scale: (1)

How do you feel right now? (very positive [0] to very negative [100];

M = 37.47, SD = 25.88); (2) How worried are you right now? (not at

all [0] to a lot [100]; M = 44.04, SD = 30.42); (3) How lonely do you

feel right now? (not at all [0] to a lot [100]; M = 27.61, SD = 26.13);

(4) How much have you used Facebook since the last time we

asked? (not at all [0] to a lot [100]; M = 33.90, SD = 30.48); (5) How

much have you interacted with other people ‘‘directly’’ since the

last time we asked? (not at all [0]to a lot [100]; M = 64.26,

SD = 31.11). When the protocol for answering these questions was

explained, interacting with other people ‘‘directly’’ was defined as

face-to-face or phone interactions. An experimenter carefully

walked participants through this protocol to ensure that they

understood how to answer each question and fulfill the study

requirements.

Participants always answered the affect question first. Next the

worry and loneliness questions were presented in random order.

The Facebook use and direct social interaction questions were

always administered last, again in random order. Our analyses

focused primarily on affect (rather than worry and loneliness)

because this affect question is the way ‘‘affective well-being’’ is

typically operationalized.

Phase 3. Participants returned to the laboratory following

Phase 2 to complete another set of questionnaires, which included

the SWLS (M = 5.13, SD = 1.26) and the Revised UCLA

Loneliness Scale [23] (M = 1.69, SD = .46). Participants’ number

of Facebook friends (M = 664.25, SD = 383.64) was also recorded

during this session from participants’ Facebook accounts (Text S2).

Results

Attrition and compliance Three participants did not complete the study. As the methods

section notes, participants received a text message directing them

to complete a block of five questions once every 168 minutes on

average (the text message was delivered randomly within this 168-

minute window). A response to any question within a block was

considered ‘‘compliant’’ if it was answered before participants

received a subsequent text-message directing them to complete the

next block of questions. Participants responded to an average of

83.6% of text-messages (range: 18.6%–100%). Following prior

research [24], we pruned the data by excluding all of the data from

two participants who responded to ,33% of the texts, resulting in

4,589 total observations. The results did not change substantively

when additional cutoff rates were used.

Analyses overview We examined the relationship between Facebook use and affect

using multilevel analyses to account for the nested data structure.

Specifically, we examined whether T2 affect (i.e., How do you feel

right now?) was predicted by T1–2 Facebook use (i.e., How much

have you used Facebook since the last time we asked?), controlling for

T1 affect at level-1 of the model (between-day lags were excluded).

Note that although this analysis assesses Facebook use at T2, the

question refers to usage between T1 and T2 (hence the notationT1–

2). This analysis allowed us to explore whether Facebook use

during the time period separating T1 and T2 predicted changes in

affect over this time span.

When non-compliant cases were observed, we used participants’

responses to the last text message they answered to examine the

lagged effect of Facebook use on well-being to maximize power.

So, if we were interested in examining whether T2–3 Facebook use

predicted T3 Affect controlling for T2 Affect, but did not have data

on T2 Affect, then we used T1 Affect instead. Excluding trials in

which participants did not respond to the previous texts (rather

than following the aforementioned analytical scheme) did not

substantively alter any of the results we report.

Significance testing of fixed effects was performed using chi-

squared distributed (df = 1) Wald-tests. All level-1 predictors were

group-mean centered, and intercepts and slopes were allowed to

vary randomly across participants (see Table 1 for zero-order

correlations). We tested for moderation by examining whether

each moderator variable was related to the slope of T1–2 Facebook

use when predicting T2 affect, controlling for T1 affect.

Data from one person who scored 4SDs above the sample mean

on the BDI were excluded from the BDI moderation analyses;

data from one person who scored 4SDs above the sample mean on

number of Facebook friends were excluded from the moderation

analyses based on Facebook friends.

The relationship between mean Facebook use and life

satisfaction was assessed using OLS regressions because these

data were not nested. Both unstandardized (B) and standardized

(b) OLS regression coefficients are reported (see Text S3).

Facebook use and well-being Affective well-being. We examined whether people’s ten-

dency to interact with Facebook during the time period separating

two text messages influenced how they felt at T2, controlling for

how they felt at T1. Nested time-lag analyses indicated that the

more people used Facebook the worse they subsequently felt,

B = .08, x 2

= 28.90, p,.0001, (see Figure 1, top). The reverse

pathway (T1 Affect predicting T1–2 Facebook use, controlling for

T0–1 Facebook use) was not significant, B = 2.005, x 2

= .05,

p = .82, indicating that people do not use Facebook more or less

depending on how they feel (see Text S4, S5).

Cognitive well-being. To examine how Facebook use

influenced ‘‘cognitive well-being,’’ we analyzed whether people’s

average Facebook use over the 14-day period predicted their life

satisfaction at the end of the study, controlling for baseline life

satisfaction and average emotion levels over the 14-day period.

The more participants used Facebook, the more their life

Facebook Use Predicts Declines in Well-Being

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satisfaction levels declined over time, B = 2.012, b = 2.124, t(73) = 22.39, p = .02, (see Figure 1, bottom).

Alternative explanations. An alternative explanation for

these results is that any form of social interaction undermines well-

being. Because we also asked people to indicate how frequently

they interacted with other people ‘‘directly’’ since the last time we

text messaged them, we were able to test this idea. Specifically, we

repeated each of the aforementioned analyses substituting ‘‘direct’’

social interaction for Facebook use. In contrast to Facebook use,

‘‘direct’’ social interaction did not predict changes in cognitive

well-being, B = 2.006, b = 2.059, t(73) = 1.04, p = .30, and pre- dicted increases (not decreases) in affective well-being, B = 2.15,

x 2

= 65.30, p,.0001. Controlling for direct social interaction did

not substantively alter the significant relationship between Face-

book use and affective well-being, B = .05, x 2

= 10.78, p,.01.

Table 1. Within-person and between-person zero-order correlations.

Experience-sampled variables Pre/post experience sampling

Affect Worry Loneliness Facebook use Direct contact

Pre life satisfaction

Post life satisfaction

Affect – .53 ***

.50 ***

.14 ***

2.29 ***

– –

Worry .77*** – .37*** .17*** 2.23*** – –

Loneliness .68 ***

.66 ***

– .22 ***

2.40 ***

– –

Facebook Use .07 .13 .22 *

– 2.24 ***

– –

Direct Contact 2.28* 2.09 2.39*** .26* – – –

Pre Life Satisfaction 2.55 ***

2.41 ***

2.40 ***

2.05 .29 **

– –

Post Life Satisfaction 2.66 ***

2.51 ***

2.48 ***

2.18 .23 *

.86 ***

–

Note. Correlations above the dashed diagonal line represent within-person correlations obtained from multi-level analyses. Correlations below the dashed diagonal line represent between-person correlations. *p,.05. **p,.01. ***p,.001. doi:10.1371/journal.pone.0069841.t001

Figure 1. Facebook use predicts declines in affect and life satisfaction over time. Interacting with Facebook during one time period (Time1–2) leads people to feel worse later on during the same day (T2) controlling for how they felt initially (T1); values are regression weights from multilevel analyses (Panel A). Average Facebook use over the course of the 14-day experience-sampling period predicts decreases in life satisfaction over time; values are standardized regression weights from OLS regression analysis (Panel B). *p,.05, ** p,.01, ***p,.001. doi:10.1371/journal.pone.0069841.g001

Facebook Use Predicts Declines in Well-Being

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Another alternative explanation for these results is that people

use Facebook when they feel bad (i.e., when they are bored lonely,

worried or otherwise distressed), and feeling bad leads to declines

in well-being rather than Facebook use per se. The analyses we

reported earlier partially address this issue by demonstrating that

affect does not predict changes in Facebook use over time and

Facebook use continues to significantly predict declines in life

satisfaction over time when controlling for affect. However,

because participants also rated how lonely and worried they felt

each time we text messaged them, we were able to test this

proposal further.

We first examined whether worry or loneliness predicted

changes in Facebook use over time (i.e., T1 worry [or T1 loneliness] predicting T1–2 Facebook use, controlling for T0–1 Facebook use). Worry did not predict changes in Facebook use,

B = .04, x 2

= 2.37, p = .12, but loneliness did, B = .07, x 2

= 8.54,

p,.01. The more lonely people felt at one time point, the more

people used Facebook over time. Given this significant relation-

ship, we next examined whether controlling for loneliness renders

the relationship between Facebook use and changes in affective

and cognitive well-being non-significant—what one would predict

if Facebook use is a proxy for loneliness. This was not the case.

Facebook use continued to predict declines in affective well-being,

B = .08, x 2

= 27.87, p,.0001, and cognitive well-being, B = 2.012,

b = 2.126, t(72) = 2.34, p = .02, when loneliness was controlled for in each analysis. Neither worry nor loneliness interacted signifi-

cantly with Facebook use to predict changes in affective or

cognitive well-being (ps..44).

Moderation. Next, we examined whether a number of

theoretically relevant individual-difference variables including

participants’ number of Facebook Friends, their perceptions of

their Facebook network support, depressive symptoms, loneliness,

gender, self-esteem, time of study participation, and motivation for

using Facebook (e.g., to find new friends, to share good or bad

things, to obtain new information) interacted with Facebook use to

predict changes in affective or cognitive well-being (Text S6). In no

case did we observe any significant interactions (ps..16).

Exploratory analyses. Although we did not have a priori

predictions about whether Facebook use and direct social contact

would interact to predict changes in affective and cognitive well-

being, we nevertheless explored this issue in our final set of

analyses. The results of these analyses indicated that Facebook use

and direct social contact interacted significantly to predict changes

in affective well-being, B = .002, x 2

= 19.55, p,.0001, but not

changes in cognitive well-being, B = .000, b = .129, t(71) = .39, p = .70. To understand the meaning of the former interaction, we

performed simple slope analyses. These analyses indicated that the

relationship between Facebook use and declines in affective well-

being increased linearly with direct social contact. Specifically,

whereas Facebook use did not predict significant declines in

affective well-being when participants experienced low levels of

direct social contact (i.e., 1 standard deviation below the sample

mean for direct social contact; B = .00, x 2

= .04, p = .84), it did

predict significant declines in well-being when participants

experienced moderate levels of direct social contact (i.e., at the

sample mean for direct social contact; B = .05, x 2

= 11.21, p,.001)

and high levels of direct social contact (i.e., 1 standard deviation

above the sample mean for direct social contact; B = .10,

x 2

= 28.82, p,.0001).

Discussion

Within a relatively short timespan, Facebook has revolutionized

the way people interact. Yet, whether using Facebook predicts

changes in subjective well-being over time is unknown. We

addressed this issue by performing lagged analyses on experience

sampled data, an approach that allowed us to take advantage of

the relative timing of participants’ naturally occurring behaviors

and psychological states to draw inferences about their likely

causal sequence [17,18]. These analyses indicated that Facebook

use predicts declines in the two components of subjective well-

being: how people feel moment to moment and how satisfied they

are with their lives.

Critically, we found no evidence to support two plausible

alternative interpretations of these results. First, interacting with

other people ‘‘directly’’ did not predict declines in well-being. In

fact, direct social network interactions led people to feel better over

time. This suggests that Facebook use may constitute a unique

form of social network interaction that predicts impoverished well-

being. Second, multiple types of evidence indicated that it was not

the case that Facebook use led to declines in well-being because

people are more likely to use Facebook when they feel bad—

neither affect nor worry predicted Facebook use and Facebook use

continued to predict significant declines in well-being when

controlling for loneliness (which did predict increases in Facebook

use and reductions in emotional well-being).

Would engaging in any solitary activity similarly predict declines

in well-being? We suspect that they would not because people

often derive pleasure from engaging in some solitary activities (e.g.,

exercising, reading). Supporting this view, a number of recent

studies indicate that people’s perceptions of social isolation (i.e., how

lonely they feel)—a variable that we assessed in this study, which

did not influence our results—are a more powerful determinant of

well-being than objective social isolation [25]. A related question

concerns whether engaging in any Internet activity (e.g., email,

web surfing) would likewise predict well-being declines. Here too

prior research suggests that it would not. A number of studies

indicate that whether interacting with the Internet predicts

changes in well-being depends on how you use it (i.e., what sites

you visit) and who you interact with [26].

Future research Although these findings raise numerous future research

questions, four stand out as most pressing. First, do these findings

generalize? We concentrated on young adults in this study because

they represent a core Facebook user demographic. However,

examining whether these findings generalize to additional age

groups is important. Future research should also examine whether

these findings generalize to other online social networks. As a

recent review of the Facebook literature indicated [2] ‘‘[different

online social networks] have varied histories and are associated

with different patterns of use, user characteristics, and social

functions (p. 205).’’ Therefore, it is possible that the current

findings may not neatly generalize to other online social networks.

Second, what mechanisms underlie the deleterious effects of

Facebook usage on well-being? Some researchers have speculated

that online social networking may interfere with physical activity,

which has cognitive and emotional replenishing effects [27] or

trigger damaging social comparisons [8,28]. The latter idea is

particularly interesting in light of the significant interaction we

observed between direct social contact and Facebook use in this

study—i.e., the more people interacted with other people directly,

the more strongly Facebook use predicted declines in their

affective well-being. If harmful social comparisons explain how

Facebook use predicts declines in affective well-being, it is possible

that interacting with other people directly either enhances the

frequency of such comparisons or magnifies their emotional

impact. Examining whether these or other mechanisms explain

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the relationship between Facebook usage and well-being is

important both from a basic science and practical perspective.

Finally, although the analytic approach we used in this study is

useful for drawing inferences about the likely causal ordering of

associations between naturally occurring variables, experiments

that manipulate Facebook use in daily life are needed to

corroborate these findings and establish definitive causal relations.

Though potentially challenging to perform—Facebook use prev-

alence, its centrality to young adult daily social interactions, and

addictive properties may make it a difficult intervention target—

such studies are important for extending this work and informing

future interventions.

Caveats Two caveats are in order before concluding. First, although we

observed statistically significant associations between Facebook

usage and well-being, the sizes of these effects were relatively

‘‘small.’’ This should not, however, undermine their practical

significance [29]. Subjective well-being is a multiply determined

outcome—it is unrealistic to expect any single factor to powerfully

influence it. Moreover, in addition to being consequential in its

own right, subjective well-being predicts an array of mental and

physical health consequences. Therefore, identifying any factor

that systematically influences it is important, especially when that

factor is likely to accumulate over time among large numbers of

people. Facebook usage would seem to fit both of these criteria.

Second, some research suggests that asking people to indicate

how good or bad they feel using a single bipolar scale, as we did in

this study, can obscure interesting differences regarding whether a

variable leads people to feel less positive, more negative or both

less positive and more negative. Future research should administer

two unipolar affect questions to assess positive and negative affect

separately to address this issue.

Concluding Comment

The human need for social connection is well established, as are

the benefits that people derive from such connections [30–34]. On

the surface, Facebook provides an invaluable resource for fulfilling

such needs by allowing people to instantly connect. Rather than

enhancing well-being, as frequent interactions with supportive

‘‘offline’’ social networks powerfully do, the current findings

demonstrate that interacting with Facebook may predict the

opposite result for young adults—it may undermine it.

Supporting Information

Text S1

(DOCX)

Text S2

(DOCX)

Text S3

(DOCX)

Text S4

(DOCX)

Text S5

(DOCX)

Text S6.

(DOCX)

Acknowledgments

We thank Emily Kean for her assistance running the study and Ozlem

Ayduk and Phoebe Ellsworth for their feedback.

Author Contributions

Conceived and designed the experiments: EK ED JP DSL NL JJ OY.

Performed the experiments: HS NL. Analyzed the data: PV ED. Wrote the

paper: EK ED PV JJ OY. Discussed the results and commented on the

manuscript: EK PV ED JP DSL NL HS JJ OY.

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PLOS ONE | www.plosone.org 6 August 2013 | Volume 8 | Issue 8 | e69841