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Social network sites, marriage well-being and divorce:
Survey and state-level evidence from the United States
Sebastián Valenzuela
a,
⇑
, Daniel Halpern
a
, James E. Katz
b
a
Pontificia Universidad Católica de Chile, School of Communications, Alameda 340, Santiago 8331150, Chile
b
Boston University, College of Communication, Division of Emerging Media Studies, Boston, MA 02215, United States
article info
Article history:
Available online 14 April 2014
Keywords:
Social network sites
Facebook
Marriage well-being
Divorce
abstract
This study explores the relationship between using social networks sites (SNS), marriage satisfaction and
divorce rates using survey data of married individuals and state-level data from the United States. Results
show that using SNS is negatively correlated with marriage quality and happiness, and positively corre-
lated with experiencing a troubled relationship and thinking about divorce. These correlations hold after
a variety of economic, demographic, and psychological variables related to marriage well-being are taken
into account. Further, the findings of this individual-level analysis are consistent with a state-level anal-
ysis of the most popular SNS to date: across the U.S., the diffusion of Facebook between 2008 and 2010 is
positively correlated with increasing divorce rates during the same time period after controlling for all
time-invariant factors of each state (fixed effects), and continues to hold when time-varying economic
and socio-demographic factors that might affect divorce rates are also controlled. Possible explanations
for these associations are discussed, particularly in the context of pro- and anti-social perspectives
towards SNS and Facebook in particular.
Ó2014 Elsevier Ltd. All rights reserved.
1. Introduction
On May 17, 2012 Facebook became the first social network site
to hold a public offering, valuing the company at $104 billion, the
largest valuation to date for a newly listed public company (Tangel
& Hamilton, 2012). Few months later, Facebook—created with the
vision to make the world more connected and help users to dis-
cover what’s going on in their world—announced to have more
than one billion active users (Facebook, 2013). Despite its pro-
fessed mission to help people to connect each other, the company
has been accused of damaging the relationship of thousands of
couples. Circumstantial evidence, including information described
in the popular media and law firms, suggests that Facebook may be
responsible for causing divorce in one out of five divorces in the
U.S. (Gardner, 2013).
The first report was in 2009, when an executive of Divorce-
Online in the U.K., Mark Keenan, found that the word ‘‘Facebook’’
appeared in 989 of the company’s 5000 most recent divorce peti-
tions (Keenan, 2009). Similarly, a 2010 survey by the American
Academy of Matrimonial Lawyers (AAML) found that four out of
five lawyers reported an increasing number of divorce cases citing
‘‘evidence’’ derived from Facebook (AAML, 2010). Further, websites
have been developed to aid in detection of cheating on the social
network site. FacebookCheating.com, for instance, provides tips
on how to catch a spouse having an extramarital affair using the
social network site.
Anecdotal evidence notwithstanding, the issue of whether Face-
book affects negatively marriage satisfaction and increases the
likelihood of divorce is an empirical claim that to our best knowl-
edge has not been put to test yet. This study is a first step towards
meeting that goal. It does so in two ways. First, it analyzes the
aggregate-level relationship between Facebook penetration and di-
vorce rates across 43 U.S. states between 2008 and 2010 control-
ling for a host of variables, including stable differences across
states. Second, using individual-level survey data of a representa-
tive sample of married individuals in the U.S., it examines the rela-
tionships between using social network sites (SNS) such as
Facebook and indicators of relationship satisfaction, also control-
ling for potential confounds.
To be clear, both data sets can provide solid evidence of the
existence of an association between Facebook use and marriage
quality and, in the case of the state-level data, some evidence of
the temporal ordering of the variables. However, the study does
not establish a cause-and-effect relationship because that would
require longitudinal and/or experimental data. In fact, as we
explain below, a negative correlation between Facebook and
http://dx.doi.org/10.1016/j.chb.2014.03.034
0747-5632/Ó2014 Elsevier Ltd. All rights reserved.
⇑
Corresponding author. Tel.: +56 2 2354 1959.
E-mail addresses: savalenz@uc.cl (S. Valenzuela), dmhalper@uc.cl (D. Halpern),
Katz2020@bu.edu (J.E. Katz).
Computers in Human Behavior 36 (2014) 94–101
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
marriage well-being can be explained by either a causal link or
through self-selection. Therefore, the current study is a necessary,
though not sufficient, step towards understanding the role of SNS,
especially Facebook, in marriage well-being.
Following, we review extant claims on the relationship between
SNS and marriage quality. Afterward, we present the data and
methods employed to test this association. Further, we report the
findings of the individual- and aggregate-level statistical analyses.
Finally, we discuss the implications and limitations of the study,
and present directions for future research.
1.1. Theoretical overview
A negative relationship between SNS use and marriage well-
being could be explained by two, very different, perspectives: (1)
Using SNS weakens marriage and causes divorce (the negative ef-
fect hypothesis); or (2) divorcees and people in troubled relation-
ships use SNS such as Facebook more often (the self-selection
hypothesis). Whereas both views predict an association between
using the social networks site and marriage dissatisfaction, their
implications for the social effects of Facebook and other SNS are
opposite. In the first case, SNS are conceived as an anti-social med-
ia. In the second, SNS are perceived as a pro-social force that helps
people with a bad marriage experience to find social support. In the
next two sections, we review the rationale of each perspective.
1.2. The case for negative effects
Before trying to explain why using sites like Facebook may be
negatively associated to marriage satisfaction, it is relevant to sum-
marize prior research on the basis of strong, satisfying marriages
(Bergner & Bridges, 2002; Davis, 1985; Roberts, 1982). Manning
(2006) has noted that a long-term romantic relationship entails se-
ven characteristics: (a) investment in the well-being of the be-
loved, (b) respect, (c) admiration, (d) sexual desire, (e) intimacy,
(f) commitment, (g) exclusivity, and (h) understanding. Bergner
and Bridges (2002) hold that when one or more of these aspects
are violated by a romantic partner, the other partner is likely to feel
unloved, causing that spouse to reevaluate the relationship.
There are many reasons why Facebook and SNS in general
might be negatively affecting one or more of these characteristics
and, consequently, marriage quality. First, excessive use of social
media has been associated with ‘‘dependency’’ or compulsive use
(Raacke & Bonds-Raacke, 2008), creating psychological, social,
school and/or work difficulties in a person’s life (Kuss & Griffiths,
2011). Lee, Cheung, and Thadani (2012) describe social features
that could be highly problematic. Citing research by Sickface-
book.com, an anti-Facebook blog, the authors argue that over 350
million users suffer of Facebook Addiction Disorder (FAD). The
term, introduced by American psychologists, has been considered
by some an addiction since individuals who use social network
sites (SNS) excessively present several addictive symptoms such
as neglect of personal life, mental preoccupation, escapism, mood
modifying experiences, tolerance, and concealing the addictive
behavior (Kuss & Griffiths, 2011).
Second, SNS create an environment with potential situations
that may evoke feelings of jealousy between partners, harming
the quality of their relationship (Elphinston & Noller, 2011). More-
over, SNS facilitate users reconnecting with a variety of people
with whom they have had a past relationship (Ellison, Steinfeld,
& Lampe, 2007), creating the potential for jealousy in current rela-
tionships. Similarly, SNS also support users’ maintenance of rela-
tionships that may otherwise be only transitory, but could
become problematic when juxtaposed to the marital relationship.
Elphinston and Noller (2011) explain that exposing one’s partner
to all of these individuals, many of whom may be unknown to
the partner, may increase the potential for jealousy and suspicion.
Further, Muise, Christofides, and Desmarais (2009) corroborated
empirically this jealousy-provoking information between partners
using Facebook, which instead creates a feedback loop whereby
heightened jealousy leads to increased surveillance of a partner’s
Facebook page, causing even more suspicion between the partners,
which ultimately affects negatively the relationship (for online sur-
veillance behaviors within married couples, see also Helsper &
Whitty, 2010).
Third, previous research has noted that substantial decline in
partner search costs could lead to higher levels of divorce (Kendall,
2011). Kendall explains that when people manage more informa-
tion about others and it is easy to search for partners after mar-
riage, the expected benefit from a new match may outweigh the
cost of dissolving the old one, fuelling divorce rates. Facebook in
particular has a series of unique affordances that has helped to re-
duce these searching costs and consequently may contribute to
cheating. First, Facebook’s search options and capabilities make
cheating easier. If someone is trying to find another person, it is
possible to search by name, email address, company/workplace,
or common friends, and even get narrow results by indicating only
hometown or school, making it still easier to find, for example, an
ex. Similarly, with the ‘‘event’’ invitations feature, it is easy for
users to monitor and determine if a certain person of potential
interest will be attending a particular event.
The mutual and suggested friends features may also facilitate
potential cheating since users can search through their friends’
friends to find someone in whom they may be interested. Facebook
also suggests friend based on mutual friends and interests, conse-
quently, if the user is already predisposed to being interested in
someone else, it is more likely they will become close friends with
them and open a venue for an extramarital or separation.
In addition, Facebook allows users to have multiple profiles: a
person could have a profile for family and friends which lists them
as married but also a secondary profile which lists them as single
and being interested in forming relationships. Consequently, Face-
book and other SNS make easier finding another romantic partner
for those so inclined to do so.
1.3. The case for self-selection
The discussion summarized earlier is consistent with a causal
relationship between SNS and marriage quality. But there is a
counterargument: individuals in unhappy marriages may use SNS
such as Facebook more often because it proves beneficial to them
and, thus, it is self-selection what would explain the negative cor-
relation between SNS use and marriage well-being. In this in-
stance, individuals may turn to services like Facebook more
frequently after they get divorced for social support and/or to en-
hance their (newly single) social lives. Thoits (1982) defines social
support as the degree to which a person’s basic social needs are
gratified through interaction with others. Consistent with this def-
inition, several studies suggest that groups formed in Facebook are
acting as these ‘‘support’’ places where users go to find emotional
support (Ellison, Steinfield, & Lampe, 2011), sense of belonging
(Bender, Jimenez-Marroquin, & Jadad, 2011), and encouragement
(Greene, Choudhry, Kilabuk, & Shrank, 2011), in addition to instru-
mental aid (Newman, Lauterbach, Munson, Resnick, & Morris,
2011). Other research suggests that online services, in general,
can provide social support when a personal or family transition oc-
curs (Mikal, Rice, Abeyta, & DeVilbiss, 2013).
Previous research has also associated SNS use with bonding so-
cial capital (i.e., emotional support from close friends). Ellison and
her colleagues (2007) argue that given the searching capabilities
afforded by SNS to form groups based on individuals with related
interests and needs, Facebook makes it easier to find others in
S. Valenzuela et al. / Computers in Human Behavior 36 (2014) 94–101 95
similar situations and get emotional support, social support, a
sense of belonging and companionship. In fact, compared with
other Internet users, Facebook users report significantly higher lev-
els of social support (Hampton, Sessions Goulet, Rainie, & Purcell,
2011). Additionally, SNS may reduce the coordination costs associ-
ated with interacting both directly and indirectly with individuals
and groups of users, supporting relationship maintenance behav-
iors among close friends, which, in turn, could enable individuals
to increase the social and emotional support they may receive in
case of need (Vitak, Ellison, & Steinfield, 2011). For instance, Face-
book’s numerous communication channels (e.g., status updates,
wall posts, inbox messages, chat) are helpful for individuals look-
ing for some forms of support and for engaging in generalized rec-
iprocity by responding to others’ requests. As Hampton et al.
(2011) explained to put the finding that Facebook users get more
support into perspective, ‘‘someone who uses Facebook multiple
times per day gets about half the boost in total support that some-
one receives from being married or living with a partner’’ [p. 35].
In conclusion, both the negative effect perspective and the self-
selection perspective predict that there is a negative relationship
between Facebook use and marriage well-being. This study seeks
to empirically test the existence, size, and sign of this relationship
as a first step towards understanding the role played by SNS on
marriage. Absent a statistically discernible association between
SNS use, marriage well-being and divorce rates, any claim about
the socially negative effects of Facebook on marriages is un-
founded. On the other hand, if an association is established, then
future research can address the causality quandary.
2. Methods
Borrowing from the work of Kendall (2011) on Internet access
and divorce rates, we followed a two-pronged approach of con-
ducting aggregate- and individual-level data analyses of SNS and
Facebook use, marriage quality indicators and divorce rates in
the United States. Whereas cross-sectional survey data allows us
to control for a larger variety of confounds and to create a richer
battery of psychological variables related to marriage well-being,
state-level data can be used to specify statistical models that incor-
porate the time ordering of the variables of interest.
2.1. State-level data
Considering that Facebook became available to any person
worldwide in 2006, all state-level data was compiled annually
from 2008 to 2010 (the most current year with official state-level
divorce data available). The key dependent variable was the
state-level divorce rate as measured by Bitler, Gelbach, Hoynes,
and Zavodny (2004) and Kendall (2011), which is calculated as
the count of new divorces in a state accumulated between January
and December of each year as measured by the National Center for
Health Statistics (NHCS) divided by the number of married women
in a state at the same year as measured by the Annual Demo-
graphic Supplement of the Current Population Survey (CPS). Unfor-
tunately, six states (California, Georgia, Hawaii, Indiana, Louisiana
and Minnesota) do not report divorce data to the NHCS and thus
had to be excluded from the analysis. Nevada and Washington,
D.C., were excluded, too, because they were extreme outliers (Ne-
vada doubled the average state-level divorce rates, whereas in D.C.
the population of Facebook users almost doubled the population of
residents). In addition, considering that divorce rates are positively
skewed, in the estimations the variable was transformed using the
natural logarithm.
Facebook penetration rate was a composite of two measures.
For each state, the total number of Facebook accounts in each state
as registered by the World Internet Stats website (www.internet-
worldstats.com) in 2008, 2009 and 2010 was divided by the states’
U.S. Census population estimates for the same years. This measure
does not, of course, provide information on which individuals
(married or not) use Facebook. Therefore, this measure captures
the availability of Facebook within the population, rather than
Facebook access of married individuals only. While this may be
thought as a limitation of the study, it has the advantage of being
a more exogenous measure than married individuals’ Facebook
usage.
In addition, a number of covariates related to both divorce rates
and Facebook penetration were used in the statistical models.
Internet access was measured as the proportion of people in a state
relative to the state’s population that use the Internet at any loca-
tion, as measured by the Internet Use Supplement of the CPS. In-
come was measured as the real per capita personal income of a
state in a given year measured in 2005 U.S. dollars, as reported
by the U.S. Bureau of Economic Analysis. The unemployment rate
was the state’s annual average unemployment rate, obtained from
the Bureau of Labor Statistics. The rest of the control variables were
all taken from the American Community Survey conducted by the
Census Bureau. Level of education was measured as the share of
the state population in a given year with a Bachelor’s degree or
higher. Race and ethnicity was measured as the share of the state
population at a given year that self-identified as African American
and Hispanic. The mean household size was the average number of
people living in a single household located within the state, and
age distribution was gauged as the proportion of the states’ adult
population (aged 18–64) relative to the states’ total population. Ta-
ble 1 shows descriptive statistics of all state-level variables
analyzed.
2.2. Individual-level data
A secondary analysis of survey data was employed to study the
individual-level relationship between SNS use and marriage well-
being. The survey was conducted online on a probability sample
of adults aged 18 to 39 by Knowledge Networks (KN) on behalf
of the University of Texas at Austin’s Population Research Center
(http://www.prc.utexas.edu/nfss/index.html), which is not respon-
sible for the analyses and interpretations presented here. To our
knowledge, this is the only publicly available representative survey
in the U.S. that contains questions about both SNS use and indica-
tors of marriage well-being. Participants were recruited via ran-
dom-digit-dialing and address-based sampling methods. Data
were collected between July 2011 and February 2012. Due to the
objectives of the current study, only data from the subsample of
married individuals (N= 1160) were analyzed. To make the results
generalizable to the population of married individuals in the U.S.,
all calculations presented here employ KN’s postratification
weights.
Four dependent variables gauging different aspects of marriage
satisfaction were created. Current marriage quality was an additive
scale (
a
= .96) of responses to six items asking the respondent’s
level of agreement with statements such as ‘‘We have a good
relationship’’ and ‘‘My relationship with my partner is very
healthy.’’ Happiness in current marriage was measured asking
the respondent to rate on a scale from 1 to 10 the degree of
happiness in his/her marriage. A scale of trouble in the current
marriage (
a
= .75) was computed by averaging three items on
the frequency with which the respondent has thought the
relationship is on trouble. Lastly, the likelihood of divorce was
gauged with a dichotomic variable asking whether in the past year
the respondent has thought about leaving his/her spouse.
The key independent variable, SNS use, was measured with a
question on how much time the respondent spends on a typical
96 S. Valenzuela et al. / Computers in Human Behavior 36 (2014) 94–101
weekday ‘‘on social networking sites like Facebook, Twitter, or
MySpace.’’ Responses were measured on a 7-point scale ranging
from ‘‘not at all’’ to ‘‘four hours or more.’’ At the time of the
survey, Facebook had 145.5 million unique visitors in the U.S.,
compared to Twitter’s 32.8 million and MySpace’s 33.1 million
(Martin, 2013).
As was the case with the state-level data, and based on prior re-
search (Bitler et al., 2004; Kendall, 2011; Lehrer, 2008) a battery of
control variables were measured to account for potential con-
founds: whether the respondent ever had extramarital sex,
whether he or she lived together with both parents while growing
up, unemployment, total household income, educational attain-
ment, religiosity, and number of children. In order to control for
respondents who use SNS more frequently because they happen
to have more spare time, time spent on television and Internet ac-
cess at home were gauged with separate variables. Lastly, a set of
demographic variables were included as covariates: age (in years),
race and ethnicity. Descriptive statistics of all variables are
displayed in Table 2.
3. Results
3.1. State-level data findings
For the analysis we pooled the data to form a panel and then ran
a set of fixed-effects regression models. This type of estimator,
contrary to pooled OLS regression, allows us to control for all
time-invariant differences across the states that may be related
to divorce rates and Facebook usage, which are not controlled for
in the models, through the inclusion of a state-specific constant.
Thus, regression coefficients are obtained from variance in
Facebook penetration within states over time.
Table 3 shows the results of four different models. The first
model shows that Facebook penetration is a positive predictor
of divorce rates. In substantive terms, a 20% annual increase in
the share of a state’s population with a Facebook account (i.e.,
the median growth rate of Facebook in the period under study)
is associated with a 2.18% increase in the divorce rate. This rela-
tionship becomes more robust when a series of time-varying
variables related to divorce rates are added, as shown in model
2. In this case, the model predicts that a 20% annual increase in
Facebook penetration rates is associated with an average 4.32%
growth in divorce rates. In model 3, we take advantage of the
longitudinal nature of the state-level data and include a lagged
term of the dependent variable as an explanatory variable. In
this case, the model still controls for all unobserved time-con-
stant differences among states but adds past factors not directly
observed that shape current divorce rates (i.e., factors filtered
through the lagged term). Again, the correlation between Face-
book penetration and divorce rates is positive and statistically
significant. In this case, a 20% increase in the share of Facebook
users in a given state is associated with a 4.00% increase in the
divorce rate in the following year. This relationship holds in
Model 4, which adds a set of covariates. Thus, under diverse
specifications, the state-level data shows that Facebook penetra-
tion rates and divorce rates across the states have a positive,
statistically significant relationship. As noted before, this should
not be interpreted as a causal effect necessarily. It does,
however, suggest the need to further probe the relationship
using individual-level data.
Table 1
Descriptive statistics for state-level data.
MMdn SD Min Max Valid N
Divorces per 100 married couples 1.777 1.762 0.370 0.963 2.716 129
Ln (divorces per 100 married couples) 0.553 0.567 0.215 -0.037 0.999 129
Facebook penetration rate 0.236 0.203 0.146 0.028 0.594 129
Share with Internet access 0.697 0.700 0.057 0.550 0.803 129
Real per capita personal income ($2005) 35,771.061 35,182.404 5,348.820 27,671.532 52,048.401 129
% With Bachelor’s degree or higher 0.200 0.193 0.040 0.112 0.310 129
% Of unemployment 0.074 0.074 0.023 0.030 0.134 129
% Who are African American 0.099 0.069 0.090 0.005 0.374 129
% Who are Hispanic 0.091 0.065 0.093 0.004 0.437 129
Average household size 3.075 3.040 0.179 2.700 3.880 129
% Aged 18–64 years 0.627 0.627 0.015 0.589 0.668 129
Table 2
Descriptive statistics for individual-level data.
MMdn SD Min Max Cronbach’s
a
Valid N
Scale of current marriage quality 4.113 4.333 0.943 1.000 5.000 0.962 1109
Degree of happiness in current marriage 7.604 8.000 2.147 1.000 10.000 1153
Scale of trouble in current marriage 1.592 1.333 0.548 1.000 3.000 0.753 1134
In the past year, respondent has thought about leaving spouse 0.238 0.000 0.426 0.000 1.000 1156
Frequency of SNS use 2.730 2.000 1.656 1.000 7.000 1144
Internet access at home (yes) 0.880 1.000 0.324 0.000 1.000 1160
Household Income 12.610 13.000 3.823 1.000 19.000 1158
Educational attainment 3.025 3.000 0.923 1.000 4.000 1160
Currently unemployed (yes) 0.070 0.000 0.255 0.000 1.000 1160
Race/ethnicity (African American) 0.083 0.000 0.276 0.000 1.000 1160
Race/ethnicity (Hispanic) 0.174 0.000 0.379 0.000 1.000 1160
Number of children 1.383 1.000 1.430 0.000 5.000 1119
Age 31.726 32.000 4.917 18.000 39.000 1160
Religious attendance 2.290 2.000 2.027 0.000 5.000 1152
Lived with both parents entire time until age 18 (yes) 0.441 0.000 0.497 0.000 1.000 1160
Time spent watching TV 4.280 4.000 1.590 1.000 7.000 1147
Ever had extramarital sex (yes) 0.186 0.000 0.389 0.000 1.000 1147
S. Valenzuela et al. / Computers in Human Behavior 36 (2014) 94–101 97
3.2. Individual-level data findings
Following, we present the results of the analysis of the survey
sample of married individuals aged 18–39 years. Contrary to the
state-level data, the survey data allows us to measure the associa-
tion between SNS use and a host of psychological variables related
to marriage well-being. Also, the statistical power is greater, which
allows for the inclusion of additional covariates.
Table 4 shows the estimates of four regression models, one for
each dependent variable studied, along with a host of demo-
graphic, economic, and social factors that prior research and theory
suggest are related to marriage well-being, SNS and Facebook use.
The expectation was that the key predictor variable, frequency of
SNS use, would be negatively related to marriage quality and
marriage happiness and, conversely, positively associated to
experiencing trouble in the marriage and to having thought about
separating.
The first model shows that frequency of SNS use is a negative,
statistically significant predictor of the index of marriage quality,
although the relationship is rather weak. Holding all other vari-
ables constant at their means, a respondent that does not use
SNS scores 4.22 in the scale of current marriage quality—a 6.96%
difference with a respondent who uses SNS four hours or more
(score = 3.87). More robust is the negative relationship between
the SNS use and degree of happiness in current marriage (second
model). In this case, the model predicts that a nonuser is 11.40%
more happy with his/her marriage than a heavy user (predicted
score of 7.99 vs. predicted score of 6.85 on a 1 to 10 scale). The
nature of these associations—that using sites like Facebook more
frequently is associated with diminished marriage wellbeing—is
Table 3
Regression analysis of divorce rates in 43 U.S. states (2008–2010).
Dependent variable: ln (divorces per 100 married couples)
Model 1 fixed effects Model 2 fixed effects
with covariates
Model 3 fixed effects
with lagged DV
Model 4 fixed effects with
lagged DV and covariates
Facebook penetration rate 0.109
***
(0.033) 0.216
***
(0.064) 0.199
***
(0.049) 0.238
***
(0.087)
Internet access 0.272 (0.215) 0.704
**
(0.274)
Ln (real per capita personal income) [$2005] 0.685 (0.627) 0.678 (1.447)
Share with Bachelor’s degree or higher 1.108 (0.752) 1.210 (0.853)
Unemployment rate 1.053 (0.688) 3.898
*
(2.178)
Share who are African American 2.021 (3.860) 1.456 (4.043)
Share who are Hispanic 0.301 (0.856) 0.929 (1.029)
Mean household size 0.373
***
(0.132) 0.445
**
(0.179)
Share aged 18–64 years 0.063 (0.857) 0.448 (1.318)
Ln (divorces per 100 married couples) [lagged] 0.547
***
(0.160) 0.449
***
(0.139)
(constant) 0.527
***
(0.008) 5.625 (6.681) 0.785
***
(0.093) 9.599 (14.853)
Adjusted R
2
0.916 0.930 0.949 0.959
(Observations) (129) (129) (86) (86)
Notes: The data set is comprised of 129 state-level (excluding District of Columbia) observations for 2008, 2009, and 2010. Of the possible 153 observations, 24 are unusable
due to missing data on the number of divorces. Data from Nevada and Washington, D.C., extreme outliers in marriage patterns and Facebook penetration, are also excluded.
Entries report regression coefficients with robust (HAC) standard errors in parentheses.
*
p< 0.10.
**
p< 0.05.
***
p< 0.01 (two-tailed).
Table 4
Regression analysis of indicators of Marriage Satisfaction among U.S. married individuals (2011).
Dependent variables
Scale of current marriage
quality (range: 1–5)
Degree of happiness in current
marriage (range: 1–10)
Scale of trouble in current
marriage (range: 1–3)
In past year respondent has thought
about leaving spouse (range: 0–1)
OLS regression OLS regression OLS regression Logistic regression
Frequency of SNS use 0.058
**
(0.018) 0.190
***
(0.040) 0.035
***
(0.010) 0.146
**
(0.048)
Internet access at home (yes) 0.069 (0.095) 0.306 (0.207) 0.086 (0.052) 0.731
*
(0.284)
Household Income 0.009 (0.009) 0.028 (0.020) 0.004 (0.005) 0.037 (0.025)
Educational attainment 0.007 (0.038) 0.066 (0.082) 0.049
*
(0.021) 0.237
*
(0.101)
Currently unemployed (yes) 0.185 (0.122) 0.342 (0.261) 0.013 (0.064) 0.009 (0.321)
Race/ethnicity (African
American)
0.061 (0.109) 0.023 (0.238) 0.052 (0.060) 0.357 (0.291)
Race/ethnicity (Hispanic) 0.119 (0.076) 0.468
**
(0.166) 0.093
*
(0.041) 0.751
***
(0.195)
Number of children 0.055
*
(0.022) 0.047 (0.047) 0.026
*
(0.012) 0.017 (0.058)
Age 0.012 (0.006) 0.032
*
(0.013) 0.002 (0.003) 0.010 (0.017)
Religious attendance 0.009 (0.015) 0.076
*
(0.032) 0.014 (0.008) 0.007 (0.042)
Lived with both parents entire
time until age 18 (yes)
0.093 (0.061) 0.155 (0.131) 0.099
**
(0.032) 0.356
*
(0.171)
Time spent watching TV 0.001 (0.019) 0.033 (0.041) 0.007 (0.010) 0.016 (0.053)
Ever had extramarital sex (yes) 0.387
***
(0.076) 1.030
***
(0.164) 0.338
***
(0.041) 1.522
***
(0.183)
(Constant) 4.704
***
(0.227) 9.286
***
(0.492) 1.463
***
(0.121) 1.767
**
(0.617)
Adjusted R
2
0.059 0.084 0.148 0.136
(Observations) (1047) (1084) (1070) (1083)
Notes: Entries report regression coefficients with standard errors in parentheses. Data is about married individuals aged 18–39 years. Adjusted R
2
for Model 4 is Cox and Snell.
*
p< 0.10.
**
p< 0.05.
***
p< 0.001 (two-tailed).
98 S. Valenzuela et al. / Computers in Human Behavior 36 (2014) 94–101
confirmed by the last two models. Regressing the index of trouble
in the current marriage on SNS use and a battery of control vari-
ables yields that a one-unit increase in SNS use is associated with
a .035 increase in marriage trouble. In substantive terms, moving
from the lowest to the highest score on the SNS use measure—
and holding all other variables constant—is associated with a 7%
increase in the index of trouble in current marriage.
The last model is a logistic regression predicting the likelihood
that the respondent has thought about leaving his or her spouse in
the last 12 months. The model shows that the SNS measure is a
strong, positive predictor of thinking about such course of action.
The predicted probabilities of average respondents who differ only
in SNS use show that the likelihood of thinking about separating is
16.34% for a nonuser and 31.93% for a heavy user. The individual-
level analysis, thus, is consistent with the results of the state-level
analysis: use of SNS such as Facebook is associated with lower
marriage satisfaction and a higher likelihood of divorce or, con-
versely, respondents in troubled relationships use SNS, including
Facebook, more often.
4. Discussion
We explored the relationship in the U.S. between SNS use and
relationship satisfaction among a nationally representative sample
of married individuals, as well as the association between Face-
book penetration and divorce rates at a state-level. Survey results
revealed a positive correlation between more frequent use of SNS
and the variables that reflected lower marriage quality, marriage
unhappiness, experiencing a troubled relationship, and thinking
about separating. This was consistent with state-level analyses,
in which we found that across the U.S., the diffusion of Facebook
between 2008 and 2010 is positively correlated with divorce rates,
a relationship that held in the presence of numerous control
variables.
Two possible explanations may account for this negative corre-
lation. From a self-selection perspective, this phenomenon can be
understood by the fact that it is not so much that social network
services such as Facebook causes problematic relationships be-
tween couples or cause divorce, but that divorcees and individuals
in unhappy marriages use Facebook and SNS more often because it
proves beneficial to them by providing emotional support. Accord-
ing to this first view, Facebook would be fulfilling its raison d’être
(or at least its vision) as it connects people with friends, family,
and other strong ties. That means that divorcees or people going
through difficult moments in their marriage would choose this so-
cial network site to communicate with their close contacts, trying
to found the psychological well-being that often flows from bond-
ing social capital.
There are various reasons to explain why SNS, particularly Face-
book, would be useful to facilitate reciprocity, emotional support,
and companionship from close contacts. Online services such as
Facebook have several features that support relationship mainte-
nance among close friends, which, in turn, could enable individuals
to accrue bonding social capital. The multiple communication
channels reduce the coordination costs and simplify the process
through which individuals can request some form of support (Vitak
et al., 2011). Similarly—and based in our results—it is possible to
argue that also SNS present several affordances that might lead
users to connect with people that are going through similar prob-
lems in order to receive emotional support.
Importantly, Facebook has exploited the use of collaborative
filtering systems (user-to-user interactions) to increase the
amount of social interaction between users. Unlike content-based
recommendation methods, collaborative recommender systems
try to match people with similar interests and then make
recommendations on this basis. Under these collaborative models,
the system automatically retrieves and filters data by considering
the feedback given by other users to the documents. On the other
hand, social media in general and Facebook in particular have facil-
itated the creation of a ‘‘profile culture’’ where millions of users
have generated their own persona on the web (Utz, 2010). Home-
town, phone number, email, language, football team, favorite book,
best movie, political affiliation, religion, marital status, type and
number of friends, job, preferred networks, photos, and videos
are but a fraction of the huge amount of data that users may utilize
to define themselves. In this way—and powered by collaborative
systems—, SNS can today tie millions of geographically dispersed
users who have common elements in their profiles. Consequently,
individuals are grouped based on commonalities and it is possible
to expect that divorcees may use the same recommendations
developed by these platforms in order to look for people in similar
situations and get support from them. Such a mechanism afforded
by social network sites could also explain why individuals may
turn to Facebook more frequently after they get divorced for social
support and/or to enhance their (newly single) social lives.
Another explanation for the positive relationship between
experiencing a troubled relationship or divorce and SNS use, fol-
lowing the self-selection perspective, is that this social network
site may be useful for reducing uncertainty following the termina-
tion of a romantic relationship. In contrast to traditional communi-
cation, SNS such as Facebook allow people to reduce uncertainty
covertly. Tong and Walther (2011) explain that Facebook obviates
direct communication between ex-partners or mutual friends by
allowing individuals to gather information through social search
of profiles and newsfeeds. This according to the authors, allows
ex-partners to avoid the social disapproval from friends in their
network that could arise from direct question-and-answer strate-
gies, while still being able to reduce their own uncertainty in a less
detectable way. In fact, Tong and Walther (2011) found that after
breaking up, ex-partners rely on Facebook to monitor their ex’s so-
cial activities, to detect if their ex has a new romantic partner, and
to communicate directly with their ex. Thus, the termination of a
relationship may lead users to spend more time in Facebook in or-
der to monitor their ex-partner’s behavior.
From a cause-and-effect perspective, SNS may reduce marriage
well-being through habituation or addiction, sparking feelings of
jealousy between partners, or facilitating having extramarital af-
fairs. We offered three different reasons to explain this relation-
ship. First, excessive use of social media has been associated with
compulsive use, which may create psychological, social, school
and/or work difficulties in a person’s life. These phenomena, in
turn, may trigger marriage unhappiness and, ultimately, divorce.
Second, Facebook in particular creates an environment with poten-
tial situations that may evoke feelings of jealousy between part-
ners, harming the quality of their relationship. And third, we
noted that services like Facebook have unique affordances that
may help partners to reduce searching costs for extra-matrimonial
affairs and consequently may contribute to cheating. Conse-
quently, it is important to note that based on the data we analyzed
it is also plausible to argue that the capabilities of Facebook also
enable certain negative social consequences such as cheating or
deteriorating a marriage. In fact, previous research has shown that
pervasive technology often leads to unintended consequences,
such as infidelity and threats to privacy (Iachello & Hong, 2007;
Hand et al., 2013; Mileham, 2007; Pankoke-Babatz & Jeffrey,
2002). As Debatin, Lovejoy, Horn, and Hughes (2009) explain, spe-
cific privacy concerns of online social networking include inadver-
tent disclosure of personal information, damaged reputation due to
rumors and gossip, unwanted contact and harassment or stalking,
surveillance-like structures due to backtracking functions, use of
personal data by third-parties, and hacking and identity theft.
S. Valenzuela et al. / Computers in Human Behavior 36 (2014) 94–101 99
However, absent longitudinal and experimental data, the cur-
rent study cannot determine the causal direction of these associa-
tions. It may well be that for some individuals Facebook and SNS
use in general creates opportunities that may end up in divorce
whereas for individuals who have recently divorced or are experi-
encing a bad marriage, Facebook provides social and emotional
support. The media effects perspective and the self-selection per-
spective are not mutually exclusive, thus, future research needs
to address the possibility of reinforcing spirals. Furthermore, the
key theoretical issue would not be the relationship between partic-
ular features and particular outcomes, but rather how people use
this social network site and what they try to accomplish with it.
This approach would be consistent with what the literature in
communication has called the ‘‘rational actor’’ perspective
(Markus, 1994), which holds that impacts result not from the tech-
nology itself but from the choices individuals make about how to
use it. According to this perspective, there would be ‘‘good uses’’
that result in positive social outcomes, within the constraints im-
posed by technological characteristics, and ‘‘bad uses’’ that result
in negative social outcomes.
Markus (1994) argues that this perspective should lead
researchers to understand under what conditions it would be ra-
tional for users to behave in ways that result in negative effects.
Following this logic, the approach suggests two broad answers.
First, ‘‘bad uses’’ of electronic communication technology might
be rational when users want to achieve ‘‘negative social impacts’’
using it. For instance, if users in this particular case would not have
a good marriage and they would want to ‘‘escape’’ from their
spouses and achieve some social distance in their relationships
with them, Facebook would offer them a good alternative for that.
Following this logic, partners may spend more time in this social
network connecting with friends in order to avoid communicating
with their respective spouses.
Markus (1994) explains that ‘‘bad uses’’ might be also rational
when, despite the fact that they generate negative social effects,
they also produce benefits that outweigh these negative effects.
One insight of this perspective is that even though partners may
be aware of the negative social effects of Facebook, such as experi-
encing a troubled relationship, this may be the outcomes deliber-
ately intended by the social media users since they are really
thinking about cheating. That means that users may continue to
engage in a behavior that has the potential for negative impacts,
because it has other, positive effects that users desire to achieve.
As rational actors, spouses may know that particular uses of SNS
and Facebook, such as lurking their ex partners behaviors and
activities, entail risks and potential problems at home. However,
they may also value the benefits of using social media in these
ways, such as the ability to escape from their marriages in order
to feed their fantasies. In such cases, the rational actor perspective
suggests that users, who are aware of the potential negative conse-
quences that their actions may have, will take active measures to
minimize them. Markus (1994) explains that humans have a un-
ique ability to anticipate that an action they plan to take has the
possibility of negative consequences, and accordingly they employ
measures to counteract, or compensate, for these consequences.
5. Limitations
There are a variety of limitations that affect the validity and
generalizability of the data. One of these is that the data on individ-
ual actions is taken from self-reports. Although effort was made to
be sure the sample was representative, doubtless there are sources
of distortion due to social desirability biases and other forms of
inaccuracies endemic to this methodology. Also, the survey is
cross-sectional, which cannot address issues of temporal order,
whereas the state-level analysis is based on a coarse measure of
Facebook use, assesses marriage quality using divorce rates only,
and covers three years only. As more publicly available data on
divorce rates becomes available, the current study should be repli-
cated in order to test the robustness and generalizability of its
findings.
6. Conclusion
The data presented in this study provide evidence that Face-
book use is correlated with reduced marital satisfaction and di-
vorce rates. Although it may seem surprising that a Facebook
profile, a relatively small factor compared to other drivers of hu-
man behavior, could have a significant statistical relationship with
divorce rates and marital satisfaction, it nonetheless seems to be
the case. This relationship holds up at both the individual and state
levels. If the preliminary findings in this study are sustained, it
would represent an important step forward in the study of SNS
and human behavior. It would also raise profound questions about
the role of social media in daily lives. Finally, it would spur new
lines of research in understanding the role of Facebook in divorce
and marital satisfaction, prompting a host of policy-oriented re-
search endeavors by social scientists.
References
American Academy of Matrimonial Lawyers (AAML). (2013). Big surge in social
networking evidence says survey of nation’s top divorce lawyers.<www.aaml.org/
about-the-academy/press/press-releases/e-discovery/big-surge-social-network-
ing-evidence-says-survey>. Accessed 03.03.13.
Bender, J. L., Jimenez-Marroquin, M. C., & Jadad, A. R. (2011). Seeking support on
Facebook: A content analysis of breast cancer groups. Journal of Medical Internet
Research, 13(1), e16.
Bergner, R. M., & Bridges, A. J. (2002). The significance of heavy pornography
involvement for romantic partners: Research and clinical implications. Journal
of Sex & Marital Therapy, 28, 193–206.
Bitler, M. P., Gelbach, J. B., Hoynes, H. W., & Zavodny, M. (2004). The impact of
welfare reform on marriage and divorce. Demography, 41, 213–236.
Davis, K. (1985). Near and dear: Friendship and love compared. Psychology Today,
19, 22–30.
Debatin, B., Lovejoy, J. P., Horn, A. K., & Hughes, B. N. (2009). Facebook and online
privacy: Attitudes, behaviors, and unintended consequences. Journal of
Computer-Mediated Communication, 15(1), 83–108.
Ellison, N. B., Steinfeld, C., & Lampe, C. (2007). The benefits of Facebook ‘‘friends’’:
Social capital and college students’ use of online social network sites. Journal of
Computer-Mediated Communication, 12, 1143–1168.
Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital
implications of Facebook-enabled communication practices. New Media &
Society, 13, 873–892.
Elphinston, R. A., & Noller, P. (2011). Time to face it! Facebook intrusion and the
implications for romantic jealousy and relationship satisfaction.
Cyberpsychology, Behavior, and Social Networking, 14, 631–635.
Facebook (2013). Facebook’s mission.<www.facebook.com/facebook?v=info>
Accessed 04.04.13.
Gardner, D. (2013). The marriage killer: One in five American divorces now involve
Facebook.<www.dailymail.co.uk/news/article-1334482/The-marriage-killer-
One-American-divorces-involve-Facebook.html> Accessed 03.03.13.
Greene, J. A., Choudhry, N. K., Kilabuk, E., & Shrank, W. H. (2011). Online social
networking by patients with diabetes: A qualitative evaluation of
communication with Facebook. Journal of General Internal Medicine, 26,
287–292.
Hampton, K., Sessions Goulet, L., Rainie, L., & Purcell, K. (2011). Social networking
sites and our lives. pewinternet.org/Reports/2011/Technology-and-social-
networks.aspx Accessed 15.03.13.
Hand, M. M., Thomas, D., Buboltz, W. C., Deemer, E. D., & Buyanjargal, M. (2013).
Facebook and romantic relationships: Intimacy and couple satisfaction
associated with online social network use. Cyberpsychology, Behavior, and
Social Networking, 16, 8–13.
Helsper, E. J., & Whitty, M. T. (2010). Netiquette within married couples: Agreement
about acceptable online behavior and surveillance between partners. Computers
in Human Behavior, 26, 916–926.
Iachello, G., & Hong, J. (2007). End-user privacy in human–computer interaction.
Foundations and Trends in Human–Computer Interaction, 1(1), 1–137.
Keenan, M. (2009). Facebook is bad for your marriage – research finds.<http://
www.journalism.co.uk/press-releases/facebook-is-bad-for-your-marriage—
research-finds/s66/a536960/> Accessed 03.03.13.
Kendall, T. D. (2011). The relationship between Internet access and divorce rate.
Journal of Family and Economic Issues, 32, 449–460.
100 S. Valenzuela et al. / Computers in Human Behavior 36 (2014) 94–101
Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction: A
review of the psychological literature. International Journal of Environmental
Research and Public Health, 8, 3528–3552.
Lee, Z. W., Cheung, C. M., & Thadani, D. R. (2012). An investigation into the
problematic use of Facebook. In Proceedings of the 45th Hawaii International
Conference on System Sciences (HICSS) (pp. 1768–1776).
Lehrer, E. (2008). Age at marriage and marital instability: The Becker–Landes–
Michael hypothesis revisited. Journal of Population Economics, 21, 463–484.
Manning, J. C. (2006). The impact of internet pornography on marriage and the
family: A review of the research. Sexual Addiction & Compulsivity, 13, 131–165.
Markus, M. L. (1994). Finding a happy medium: Explaining the negative effects of
electronic communication on social life at work. ACM Transactions on
Information Systems (TOIS), 12(2), 119–149.
Martin, S. (2013). Facebook, Twitter up despite Google+. content.usatoday.com/
communities/technologylive/post/2011/08/facebook-twitter-users-up-despite-
google/1#.UUsA0Rw3uuI Accessed 21.03.13.
Mikal, J. P., Rice, R. E., Abeyta, A., & DeVilbiss, J. (2013). Transition, stress and
computer-mediated social support. Computers in Human Behavior, 29, A40–A53.
Mileham, B. L. A. (2007). Online infidelity in Internet chat rooms: An ethnographic
exploration. Computers in Human Behavior, 23, 11–31.
Muise, A., Christofides, E., & Desmarais, S. (2009). More information than you ever
wanted: Does Facebook bring out the green-eyed monster of jealousy?
Cyberpsychology, Behavior, and Social Networking, 12, 441–444.
Newman, M. W., Lauterbach, D., Munson, S. A., Resnick, P., & Morris, M. E. (2011). It’s
not that I don’t have problems, I’m just not putting them on Facebook:
Challenges and opportunities in using online social networks for health. In
Proceedings of the ACM 2011 conference on Computer Supported Cooperative Work
(CSCW) (pp. 341–350).
Pankoke-Babatz, U., & Jeffrey, P. (2002). Documented norms and conventions on
the Internet. International Journal of Human–Computer Interaction, 14(2),
219–235.
Raacke, J., & Bonds-Raacke, J. (2008). MySpace and Facebook: Applying the uses and
gratifications theory to exploring friend-networking sites. Cyberpsychology,
Behavior, and Social Networking, 11, 169–174.
Roberts, M. (1982). Men and women: Partners, lovers and friends. Advances in
Descriptive Psychology, 2, 57–78.
Tangel, A., & Hamilton, W. (May 17, 2012). Stakes are high on Facebook’s first day of
trading. The Los Angeles Times. Retrieved 17.05.12.
Thoits, P. A. (1982). Conceptual, methodological, and theoretical problems in
studying social support as a buffer against life stress. Journal of Health and Social
behavior, 23, 145–159.
Tong, S. T., & Walther, J. B. (2011). Facebook and relational termination: Connection,
isolation, and stalking. Symposium on relationships and the Internet, Oxford
Internet Institute, Oxford, UK, 2011.<www.msu.edu/~stong/files/oxford_2011.
pdf> Accessed 07.07.13.
Utz, S. (2010). Show me your friends and I will tell you what type of person you are:
How one’s profile, number of friends, and type of friends influence impression
formation on social network sites. Journal of Computer-Mediated Communication,
15, 314–335.
Vitak, J., Ellison, N. B., & Steinfield, C. (2011). The ties that bond: Re-examining the
relationship between Facebook use and bonding social capital. In Proceedings of
the 44th Hawaii International Conference on System Sciences (HICSS) (pp. 1–10).
S. Valenzuela et al. / Computers in Human Behavior 36 (2014) 94–101 101
