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CITATION: McDaniel, B. T., Drouin, M., & Cravens, J. (2017). Do you have anything to hide?
Infidelity-related behaviors on social media sites and marital satisfaction.
Computers in Human Behavior, 66, 88-95. doi: 10.1016/j.chb.2016.09.031
Link to published article:
Do You Have Anything to Hide?
Infidelity-Related Behaviors on Social Media Sites and Marital Satisfaction
Brandon T. McDaniel, Ph.D.
Illinois State University
Michelle Drouin, Ph.D.
Indiana University-Purdue University Fort Wayne
Jaclyn D. Cravens, Ph.D.
Texas Tech University
*Corresponding Author: Brandon T. McDaniel, Campus Box 5060, Normal, IL 61790. Email:
email@example.com. Phone: 309-438-5802.
*The authors declare no conflict of interest.
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Social media provides one route to behaviors that may be potentially harmful to romantic
relationships, such as communicating with alternative partners, which can sometimes create
relationship conflict, breakups, or divorce. Limited empirical evidence exists concerning social
media infidelity-related behaviors and marital relationships. This study examined whether
married/cohabiting individuals are using social media sites to engage in online infidelity-related
behaviors and to what extent this related to relationship satisfaction, ambivalence, and relational
attachment characteristics as reported by 338 married/cohabiting individuals from 176 families.
Only a small percentage of married/cohabiting couples reported engaging in social media
infidelity-related behaviors; however, more engagement in infidelity-related behaviors on social
media was significantly related to lower relationship satisfaction, higher relationship
ambivalence, and greater attachment avoidance and anxiety in both women and men.
Additionally, attachment anxiety and gender interacted with relationship satisfaction in
predicting online infidelity-related behaviors when controlling for other variables. Implications
Keywords: social media use; social networking; infidelity behaviors; relationship
satisfaction; relationship ambivalence; attachment
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We would like to thank the families who participated in this research, as well as the research
assistants who made all of this recruitment and data collection possible.
We would also like to acknowledge the College of Health and Human Development, the
Department of Human Development and Family Studies, as well as the Bennett Pierce
Prevention Research Center at The Pennsylvania State University which awarded research funds
to the first author to complete this research.
This research was also supported by the National Institute on Drug Abuse (T32DA017629) and
the National Institute of Child Health and Human Development (F31HD084118). The content is
solely the responsibility of the authors and does not necessarily represent the official views of the
university or the National Institutes of Health.
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Do You Have Anything to Hide?
Infidelity-Related Behaviors on Social Media Sites and Marital Satisfaction
According to Pew statistics, 65% of American adults use social media, and this has risen
substantially over the last decade (Perrin, 2015). Alongside this rapid growth, relationship
researchers began investigating how social media is used within relationships, especially
romantic relationships. Much of this research has portrayed social networking as a potential
threat to existing romantic relationships, as it provides a vehicle for communicating with
alternative partners through friend requests, commenting on others’ posts or pictures, covert
communication, or even engaging in cybersex (Cravens & Whiting, 2014; Dibble & Drouin,
2014; Dibble, Drouin, Aune, & Boller, 2015; Drouin, Miller, & Dibble, 2014; Drouin, Miller, &
Dibble, 2015). Accordingly, researchers have shown that social media and/or the conflict and
jealousy that arises from social media use is associated with relationship conflict, breakups, and
even divorce (Clayton, 2014; Clayton, Nagurney, & Smith, 2013; Cravens, Leckie, & Whiting,
2013; Fox, Osborn, & Warber, 2014; Ridgway & Clayton, 2016; Valenzuela, Halpern, & Katz,
Although the empirical literature related to social media and relationships has expanded
greatly over the past few years, much of this research has been conducted with young adults;
research on problematic social networking behaviors within married couples is sparse. Although
media sources report that Facebook has been cited in one third of U.S. divorces (Lupkin, 2012),
only a limited number of studies have examined problematic online infidelity-related (IR)
behaviors (e.g., engaging in cybersex, befriending romantic interests or attractive alternative
partners) among couples. The few empirical studies that have examined IR behaviors have
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focused on accounts of those who found their partners cheating (Cravens et al., 2013) or
characteristics of individuals who have sought extra-marital relationships via chat rooms (Dew,
Brubaker, & Hays, 2006). Together, these studies suggest that online environments may provide
a ripe venue for online IR behaviors. There is also some evidence that technology usage
generally can interfere with relationships, potentially causing conflict and lower relationship
satisfaction, even among married couples (McDaniel & Coyne, 2016; Roberts & David, 2016).
Moreover, a recent study involving couples showed that a greater amount of social networking
use (more specifically, Facebook maintenance behaviors) was related to lower levels of partner
love (Northrup & Smith, 2016). In this exploratory study, we extended these inquiries to
examine whether married/cohabiting individuals are using social networking to engage in online
IR behaviors, and to what extent this relates to relationship satisfaction, ambivalence, and
relational attachment characteristics. More specifically, our goals were to conceptualize and
measure social media IR behaviors among primarily married couples, examine these behaviors
as an outcome of relationship satisfaction and ambivalence, and examine whether attachment
anxiety moderates the relationship between relationship satisfaction and ambivalence and
engagement in social media IR behaviors.
1.1. Social Networking Usage and Romantic Relationships
A growing body of research has examined the potential negative effects of social media
usage on romantic relationships. In a seminal study on the topic, Clayton et al. (2013) found that
Facebook usage predicted negative relationship outcomes (e.g., cheating, breakup, and divorce),
but that this association was mediated by Facebook-related conflict and moderated by
relationship length. In other words, Facebook usage predicted negative relationship outcomes
especially when there was conflict surrounding this usage, but only among those who had been
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in their relationships for three years or less. Clayton (2014) found similar results with regard to
Twitter use: Higher usage was related to negative relationship outcomes, and this association was
mediated by Twitter-use conflict. However, in this case, relationship length did not moderate the
indirect effect of social media usage on negative relationship outcomes. Regardless of
relationship length, those who used Twitter more often were more likely to have Twitter-related
conflict, and this predicted negative relationship outcomes. More recently, Ridgway and Clayton
(2016) extended this inquiry to yet another social networking venue and found that posting
selfies on Instagram was related to Instagram-related conflict, which in turn was related to
negative relationship outcomes. This link between social networking usage and negative
relationship outcomes was also supported by a recent, national survey (Valenzuela et al., 2014).
Valenzuela et al. (2014) found that Facebook penetration rate predicted higher rates of divorce
across 43 U.S. states, even after controlling for other potential divorce factors (e.g., income and
unemployment). Moreover, social networking use predicted lower marital quality, marital
dissatisfaction, and marital trouble (Valenzuela et al., 2014).
Another avenue of research has focused more specifically on the potential sources of
online and/or social-networking-related relationship conflict. For example, 920 married couples
in Helsper and Whitty’s (2010) study reported that falling in love, engaging in cybersex, flirting,
and revealing personal details to other parties were the most agreed-upon online infidelity
behaviors. More specific to social networking, Cravens et al. (2013) found the following
Facebook-related infidelity behaviors most consistently reported: friending one’s ex-partner,
private messaging, commenting on attractive user’s pictures, and posting an inaccurate
relationship status. Additionally, other recent studies examined two potential sources of conflict
(i.e., befriending romantic interests and attractive alternatives within Facebook friends lists) and
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their associations with relationship investment characteristics (Drouin et al., 2014; Drouin et al.,
2015). Drouin et al. (2014) found that the frequency of friending attractive alternatives during the
relationship, but not simply the number of attractive alternatives contained in one’s friends list,
related to lower levels of relationship commitment. In a follow-up experiment, Drouin et al.
(2015) found that Facebook friends lists served as memory primers for sexual and committed
relationship alternatives: Those who used Facebook (as opposed to memory) to identify potential
relationship partners identified more alternatives, specifically sexual alternatives.
Combined, these studies present empirical evidence that there are multiple avenues
through which individuals can communicate with others online in ways that are perceived to be
infidelity-related or problematic to relationships. More specifically, the elements of social
network communication that are most consistently labeled as problematic include befriending
past partners (or alternative partners), flirtation, secrecy, and engaging in deep or sexual
conversations with others online. However, although these online behaviors have been identified
as potential threats to fidelity and researchers have begun to link these behaviors to aspects of
relationship investment, no known research has examined whether engagement in online IR
behaviors is related to marital dissatisfaction or ambivalence.
1.2. Infidelity-Related Online Behaviors and Relationship Outcomes
For decades, researchers have been exploring the role of relationship satisfaction in
infidelity. Within cross-sectional studies, the results have been rather consistent: Relationship
dissatisfaction is related to a range of IR behaviors, including both emotional and sexual
extradyadic interactions (e.g., Drigotas, Safstrom, & Gentilia, 1999; Roscoe, Cavanaugh, &
Kennedy, 1998; Shaw, Rhoades, Allen, Stanley, & Markman, 2013; Whisman, Gordon, &
Chatav, 2007). Meanwhile, relationship ambivalence, or the experience of both positive and
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negative sentiment about the same relationship (Luescher & Pilemer, 1998), has been little
explored as a correlate of IR behaviors. Relationship ambivalence may develop in response to
past relationship conflict or transgressions in the relationship, such as disagreements or acts of
betrayal (Birditt, Miller, Fingerman, & Lefkowitz , 2009). These acts of betrayal could include
suspicions or confirmations of a partner’s infidelity, which has been shown to be predictive of
one’s own infidelity behaviors (Whisman et al., 2007). In sum, when individuals feel ambivalent
about their committed partner for any reason, they may be more likely to engage in infidelity
behaviors. Extending these findings to an online environment, we expected that both of these
relationship characteristics—dissatisfaction and ambivalence—may be related to engagement in
online IR behaviors. More specifically, we expected:
H1: Those with lower levels of relationship satisfaction and higher levels of ambivalence
would engage in more social media IR behaviors.
Additionally, we wanted to explore attachment orientation as a predictor of engagement
in social media IR behaviors. Attachment research was originally based on observations of
infants’ attachments to their caregivers (e.g., Ainsworth, Blehar, Waters, & Wall, 1978);
however, a number of researchers in the last few decades have suggested that attachment
characteristics influence adults’ relationship interactions (Bartholomew & Horowitz, 1991;
Brennan, Clark, & Shaver, 1998; Hazan & Shaver, 1987; Mikulincer & Shaver, 2003; 2007;
DeWall et al., 2011). According to these researchers, those who display secure attachment
patterns are comfortable depending on others and having others depend on them, and they
typically build close, intimate relationships with romantic partners. Meanwhile, those who
display insecure attachment patterns exhibit high levels of attachment avoidance or attachment
anxiety (Brennan et al., 1998).
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Those with high levels of attachment avoidance often display an air of detachment and
need for independence within their romantic relationships. In accordance with this, researchers
have shown that those who are high in avoidance keep an emotional distance from their partners,
and they are also more likely to engage in casual sex, where physical and emotional intimacy are
not necessarily intertwined (Brennan & Shaver, 1995; Feeney & Noller, 1990; Gentzler & Kerns,
2004; Schmitt, 2005). Additionally, those high in attachment avoidance express less commitment
to their romantic partners (DeWall et al., 2011), and attachment avoidance, in addition to lower
levels of commitment, predicts both emotional and sexual infidelity (DeWall et al., 2011;
Drigotas et al., 1999).
Meanwhile, those with high levels of attachment anxiety have an intense need for
closeness and fear of losing their romantic partner. In order to keep their partners, those high in
anxiety often use hyperactivating strategies—or behavioral attempts to draw their partners closer
(Mikulincer & Shaver, 2003; 2007). These hyperactivating strategies may include engaging in IR
behaviors to incite jealousy in their romantic partners (e.g., Guerrero, Andersen, Jorgensen,
Spitzberg, & Eloy, 1995). Alternatively, those with high levels of attachment anxiety might seek
intimacy outside of their relationship when they feel that their (high) needs for intimacy are not
being met by a current partner (Drigotas et al., 1999; Mikulincer & Shaver, 2013) or when they
feel that they might lose their partner and try to compensate by establishing a relationship with a
new potential partner (Drouin et al., 2015).
Thus, both attachment avoidance and attachment anxiety have been linked with IR
behaviors in empirical studies, however, much of this research (e.g., DeWall et al., 2011;
Drigotas et al., 1999) has focused on unmarried dating adults, whose relationship dynamics may
differ substantially from those who are in more committed relationships. In a more recent study
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of married individuals, Russel, Baker, and McNulty (2013) found that attachment anxiety, but
not attachment avoidance, predicted infidelity among married couples. Thus, in our study, we
sought to further examine the links between attachment characteristics and IR behaviors among
married/cohabiting couples. In accordance with the findings from Russell et al. (2013), we
H2: Attachment anxiety would predict social media IR behaviors in this married sample.
Finally, we also examined attachment characteristics and gender as moderators in the
relationships between relationship satisfaction, ambivalence, and engagement in social media IR
behaviors. Several researchers have noted that there are sex differences in the ways in which
attachment characteristics interact with infidelity behaviors. As an example, Allen and Baucom
(2004) found that among women, an anxious attachment style was predictive of engaging in
infidelity behaviors, but among men, an avoidant attachment style was predictive of engaging in
infidelity. Moreover, Drigota et al. (1999) found that women who have an anxious attachment
style may engage in infidelity behaviors if they believe their emotional needs are being
unfulfilled by their committed partner. Thus, both attachment characteristics and sex were
explored as potential moderators in the relationship between satisfaction, ambivalence and
engaging in social media IR behaviors.
1.3. Current Study
In sum, the goals of the current, exploratory study were to: (1) develop a brief measure of
social media IR behaviors, (2) explore the prevalence of social media IR behaviors among
individuals in a married/cohabiting sample, and (3) examine the relationships between engaging
in social media IR behaviors, relationship satisfaction and ambivalence, and attachment anxiety
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2.1. Participants & Procedure
The data in this study were collected as part of a larger project on parenting and daily
family life (Daily Family Life Project; McDaniel, 2016). We recruited both parents (mother and
father) from families who had at least one young child via a database of families in a
Northeastern U.S. state, announcements on parenting websites and listservs, and announcements
in the local community. This multi-pronged recruitment strategy was utilized to obtain a sample
of families throughout the U.S. After completing informed consent, participation then consisted
of an initial online survey and subsequent follow-up online surveys at 1, 3, and 6 months.
Participants who completed their survey were entered into a drawing for one of three $100 gift
cards at each time point. At baseline, 183 heterosexual couples (including both partners/spouses)
were recruited into the study, exceeding our original goal of 150 couples based on a priori power
analyses for our planned between-person and within-person analyses. In the present study, our
analytic sample consisted of 338 individuals (173 wives and 165 husbands) from 176 families
(due to missing data on 10 wives and 18 husbands). Couples were currently living together in the
United States and had a child age 5 or younger. Due to a slight modification of the focus of the
study after its inception, measures pertaining to online IR behaviors were added part way
through the study. We therefore utilized data for each family from the first time they received the
items. Thus, 65% (n = 220) of the data came from families at baseline, 14% (n = 46) from month
1, and 21% (n = 72) from month 3.
Our analytic sample resided in these U.S. regions: 55% Northeast, 17% West, 14%
Midwest, and 14% South. The majority of participants were Caucasian (92%) and married
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(96%1), and 73% had a college degree. The mean age of wives was 31.59 years old (SD = 4.44;
Range = 20 to 42), and the mean age of husbands was 33.26 (SD = 5.05; Range = 22 to 52).
Participants self-reported their yearly household income, with the median income being $69,500
(SD = $39,500; Range = $0 to $250,000) with 20% indicating some form of state or federal
assistance (e.g., medical assistance, food stamps). The participants had been in relationships with
their current partners for 10.02 years on average (SD = 4.05; Range = 2 to 23 years). The
participants in our final analytic sample as compared with our baseline recruited sample were
more likely to be Caucasian (χ2 (1) = 23.72, p < .001), married (χ2 (1) = 33.25, p < .001) and to
have received at least some college education (χ2 (1) = 14.12, p < .001).
2.2.1. Social Media Infidelity-Related Behaviors (SMIRB). IR behaviors on social
networking sites were measured with a series of questions we created specifically for this study
based on a review of the relevant literature (e.g., Cravens et al., 2013; Drouin et al., 2014; Drouin
et al., 2015; Helsper & Whitty, 2010; Hertlein, 2012). In creating this measure, we included the
types of behaviors in which those who are unfaithful might engage (such as feeling
uncomfortable, hiding information/being secretive, forming emotional connections with others
instead of one’s partner, messaging past significant others, and getting defensive). Similar
behaviors have also been measured in other studies of online and offline infidelity (e.g., Cravens
& Whiting, 2014; Dibble & Drouin, 2014; Dibble, Drouin, Aune, & Boller, 2015; Drouin,
Miller, & Dibble, 2014; Drouin, Miller, & Dibble, 2015; Helsper & Whitty, 2010). These
questions form the Social Media Infidelity-Related Behaviors (SMIRB) scale, which contains 7
items (e.g., “If my spouse/partner asked me about my chats, comments, and messages to others
1 Although 4% of the couples were not legally married, they were in long-standing, cohabiting partnerships (average
relationship length = 5.88 years), were raising at least one child together, and reported similar levels of relationship
satisfaction and ambivalence to married couples. Thus, for parsimony, we henceforth refer to them as married.
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on social networking sites, there are some messages I would like to hide from him/her”). [See
Table 1 for all 7 items.] Participants rated their agreement on a 6-point scale (1 = strongly
disagree, 6 = strongly agree). Items were averaged to create an overall IR behavior score with
higher scores representing greater tendency to engage in these behaviors (α = .90 for women, .85
for men). We provide other relevant statistics for this measure in the Results section.
2.2.2. Relationship Satisfaction. Participants completed the Quality of Marriage Index
(QMI; Norton, 1983) to measure their relationship satisfaction. For inclusivity across marital
status, we changed the wording from “spouse” to “partner” and from “marriage” to
“relationship.” The QMI, although having the word “quality” in its name, is generally considered
a global assessment of relationship satisfaction, which includes five satisfaction items (e.g., “My
relationship with my partner makes me happy”) on a 7-point scale (1 = very strongly disagree, 7
= very strongly agree) and one overall happiness item on 10-point scale (1 = unhappy, 10 =
perfectly happy). Additionally, the QMI has been shown to correlate highly with other measures
of relationship satisfaction, such as the Couple Satisfaction Index (CSI; Funk & Rogge, 2007).
Higher scores indicate greater relationship satisfaction. The QMI had high internal consistency
(α = .96 for women, .95 for men) and functioned well for both married (α = .95) and cohabiting
individuals (α = .95) in our sample. The QMI has been successfully used in prior relationship
research with mixed marital status samples (e.g., Cowan et al., 2009; Feinberg et al., 2010).
2.2.3. Relationship Ambivalence. To measure relationship ambivalence, 3 items (e.g.,
“How ambivalent or unsure are you about continuing in the relationship with your partner?”)
from Braiker and Kelley's (1979) ambivalence subscale were rated by participants. The
ambivalence subscale uses a 7-point scale (1 = not very much or just a little, 7 = very much or a
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lot), with higher scores indicating greater relationship ambivalence or uncertainty. These items
showed good internal consistency (α = .85 for women, .88 for men).
2.2.4. Attachment in Romantic Relationships. To measure adult romantic attachment,
participants completed the Experiences in Close Relationship Scale-Short Form (ECR-S; Wei,
Russell, Mallinckrodt, & Vogel, 2007). The ECR-S asks participants to rate 12 statements on a
7-point scale (1 = disagree strongly, 7 = agree strongly) concerning how they feel in romantic
relationships. Six statements measured attachment anxiety (e.g., “I worry that romantic partners
won’t care about me as much as I care about them”) and six measured attachment avoidance
(e.g., ‘‘I am nervous when partners get too close’’). As a result of low correlation with the other
5 anxiety items, the item "I do not often worry about being abandoned" was dropped (similar to
Ruppel & Curran, 2012). A higher score indicates greater anxiety or greater avoidance (Anxiety
α = .72 for women and .78 for men; Avoidance α = .83 for women and .78 for men).
2.2.5. Control Variables. We included the following controls: participant age, education
(not college graduate = 1), family income, race/ethnicity (not Caucasian = 1), number of children
(more than one child = 1), relationship length in years, and marital status (not married = 1).
3.1. Measure of Social Media IR Behaviors
As explained in the measures section, we created 7 items (see Table 1) from a review of
the relevant literature on unfaithfulness in relationship. We then explored whether these items
loaded together by performing a principal components analysis. This revealed one factor that
accounted for 62% of the variance in the entire sample, 67% of the variance for women, and
58% of the variance for men. Individual factor loadings for the entire sample and factor loadings
and descriptives for women and men are listed in Table 1. All loadings were above .53.
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3.2. Prevalence of Online IR Behaviors
Paired samples t-tests showed no significant differences between men’s and women’s
engagement in the various IR behaviors (see Table 1); therefore, we report combined prevalence
statistics (i.e., participants who indicated somewhat agree, agree, or strongly agree) for each
item. Overall, 12% (n = 42) would feel uncomfortable if spouse/partner read their messages, 5%
(n = 17) sometimes wonder whether spouse/partner would be upset if read messages, 6% (n =
20) say there are some messages they want to hide, 7% (n = 24) sometimes share emotional or
intimate information with others instead of spouse/partner, 6% (n = 19) sometimes like to chat or
message old romantic partners, 6% (n = 20) get defensive or angry if disturbed while online, and
5% (n = 16) sometimes hide the things they say to others online.
3.3. Associations Between IR Behaviors and Relationship Satisfaction, Ambivalence, and
Greater IR behavior on social networking was significantly related to lower relationship
satisfaction and greater ambivalence as well as greater attachment avoidance and anxiety in both
women and men (see Table 2). To examine our hypotheses further, we used multilevel modeling
(SAS Proc Mixed) to account for the nested nature of our data (spouses/partners within families).
We ran two models predicting online IR behavior: Model 1 with relationship satisfaction as the
predictor, and Model 2 with relationship ambivalence as the predictor (see unstandardized
estimates in Table 3). Both attachment anxiety and avoidance were included as predictors and
moderators. Gender was entered as a moderator (coded 1 = male, 0 = female) to test for
differences in predictions for males and females. Controls (e.g., participant age, household
income, ethnicity, etc.) were also included, and we ultimately removed nonsignificant
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interactions from the final models to increase parsimony and the interpretability of lower order
terms that were significant.
3.3.1. Relationship Satisfaction and Ambivalence. In support of H1 and as shown in
Table 3, lower levels of relationship satisfaction (Model 1; b = -0.03, p < .001; Cohen’s f 2 = .08)
and higher levels of ambivalence (Model 2; b = 0.26, p < .001; Cohen’s f 2 = .15) predicted
greater social media IR behavior.
3.3.2. Attachment avoidance and anxiety. In support of H2 and as shown in Table 3,
greater attachment anxiety predicted greater social media IR behavior (in Model 1 and Model 2;
bs = 0.11 and 0.10, ps < .01; Cohen’s f 2 = .05). Moreover, attachment avoidance did not predict
3.3.3. Moderation by Attachment Anxiety and Gender. As shown in Table 3, attachment
anxiety and gender significantly interacted with relationship satisfaction in predicting IR
behavior (b = 0.02, p = .02; Cohen’s f 2 = .02). We plotted this interaction in Figure 1. We
explored this three-way interaction using the PROCESS macro (Hayes, 2013) and obtained
Johnson-Neyman regions of significance. For men with average or lower levels of attachment
anxiety, higher relationship satisfaction related to less IR behavior, and within this group of men
(54% of men) the strength of the relation between relationship satisfaction and IR behavior
became stronger the lower their anxiety. For men with above average levels of attachment
anxiety (46% of men), there was no association between relationship satisfaction and IR
behavior. In contrast, relationship satisfaction was not associated with IR behavior in women
whose anxiety levels were very low (i.e., lower than 1 standard deviation below the average
anxiety level; 16% of women). However, for most women (whose anxiety levels were 1 standard
deviation below average and higher; 84% of women), higher relationship satisfaction predicted
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less IR behavior. Additionally, within these women (and in contrast to men) the strength of the
association between relationship satisfaction and IR behavior becomes stronger as anxiety levels
Social networking behaviors have been a subject of recent inquiry as a potential source of
relationship dissatisfaction, conflict, and dissolution (Clayton, 2014; Clayton et al., 2013;
Cravens et al., 2013; Fox et al., 2014; Ridgway & Clayton, 2016; Valenzuela et al., 2014).
However, the existing research has focused mainly on general social networking use, and few
studies have examined the specific social networking behaviors that may be problematic in
romantic relationships. Therefore, we examined the prevalence of specific types of potentially
problematic IR social networking behaviors among married/cohabiting couples, and whether
engaging in online IR behaviors related to relationship satisfaction, ambivalence, and attachment.
In our sample, only a small percentage of partners reported engaging in social media IR
behaviors. Although 12% indicated that they would be uncomfortable if their partner read their
messages, fewer than 10% of partners stated that they had: shared intimate information with
others online, chatted with ex-relationship partners, engaged in behaviors online that they would
hide from their partner, hidden their chats from their partners, gotten defensive or angry when
their partner interrupted their online behavior, or thought that their partners might be upset if
they read through their online correspondence. These results suggest that few married/cohabiting
individuals engage in online IR behaviors. As research has shown that social media use is
associated with relational conflict and dissolution (Clayton, 2014; Clayton et al., 2013; Cravens
et al., 2013; Fox et al., 2014; Ridgway & Clayton, 2016; Valenzuela et al., 2014) and lower
levels of love (Northrup & Smith, 2016), these low prevalence statistics were somewhat
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surprising as presumably, IR behaviors are the source of some of this conflict. However, there
are a few potential explanations for these findings.
First, most of these previous studies on social media use and relationship
conflict/dissolution were conducted with single college students, who may have experienced
SNS-related conflict and negative outcomes in a past or current relationship. In contrast, this
study specifically examined primarily married couples who had volunteered to participate in a
longitudinal study of family life. Consequently, negative relationship dynamics (e.g., IR
behaviors) and outcomes (i.e., conflict and dissolution) may be less likely to exist among these
couples, who chose together to participate in this study. Second, their willingness to participate
in this study may be reflective of a greater level of openness and commitment than a couple who
would not choose to participate in such a study, and the study’s duration and intensity may have
lessened the likelihood that people would admit to IR behaviors in self-reports (e.g., social
desirability). Therefore, our prevalence statistics likely represent a conservative estimate of these
types of behaviors within married/cohabiting couples. Finally, our results suggest that there are
components of social media usage that are not infidelity related that might be contributing to
relational conflict among married couples. In fact, some researchers have suggested that social
media and technology conflict may exist among couples simply because one is choosing to
engage with technology over engaging with one’s partner (McDaniel & Coyne, 2016; Roberts &
David, 2016). As such, the relationship between technology and/or social media use and marital
conflict is likely nuanced, comprised of both IR behaviors and general usage patterns that
interfere with couple satisfaction.
More importantly, our analyses showed that married/cohabiting individuals who were
less satisfied and more ambivalent in their relationship were more likely to engage in IR social
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media behaviors. Valenzuela et al. (2014) found that higher overall Facebook usage predicted
lower levels of marital satisfaction and greater incidence of divorce, postulating that social media
may provide social support for those in unhappy marriages, offering opportunities for cheating
behaviors that may cause conflict and erode marital quality. In this study, we examined
relationship satisfaction and ambivalence as predictors of IR behaviors, on the assumption that
engagement in IR behaviors may be the problematic aspect of social media usage, that
relationship satisfaction and ambivalence are more stable traits, and that social media IR
behaviors may be more transient behaviors. With consideration for Valenzuela et al. (2014) we
suggest that this relationship is likely bi-directional; those in less satisfied relationships likely
seek out these types of online interactions with others, and these interactions, in turn, may cause
lower levels of satisfaction. In the future, we intend to fill gaps in the literature with analyses of
Finally, although both attachment anxiety and avoidance were positively related to social
media IR behaviors, only attachment anxiety emerged as a unique, significant predictor once
other variables (e.g., relationship satisfaction or ambivalence) were controlled. These findings
align with Russell et al. (2013), who found that among married couples, attachment anxiety but
not attachment avoidance, predicts infidelity. This study offers an extension to prior work,
showing that similar relationship characteristics might influence both offline and online IR
behaviors. However, the results from our study do not elucidate whether those who are anxiously
attached are engaging in IR behaviors as hyperactivating strategies to incite jealousy in their
partners (Guerrero, et al., 1995; Mikulincer & Shaver, 2003; 2007) or to line up a potential
partner in case their current relationship fails (Drouin et al., 2015). Thus, future research should
more directly address the motivations behind engagement in these IR behaviors, especially
INFIDELITY ON SOCIAL MEDIA 20
among those with insecure attachment patterns. Additionally, attachment anxiety and gender
were moderators in the relationship between relationship satisfaction and social media IR
behaviors. For men with lower attachment anxiety (i.e., more secure attachment styles), higher
levels of satisfaction predicted lower levels of social media IR behaviors. On the contrary, most
of the women in our sample and especially those with higher attachment anxiety demonstrated
this pattern. Perhaps, for men, there is more linear alignment between secure attachment, marital
satisfaction, and fidelity, but for women, the relationship is more complex. It is possible, for
women, that fear of losing one’s partner is greater for those who are highly satisfied in their
relationships, and this fear may keep them from engaging in online IR behavior. Again, this is a
direction for future inquiry.
4.1. Limitations and Conclusion
As mentioned, the participants from this study were volunteers from a longitudinal study
of family life who were fairly well-educated and in stable relationships, and these individuals
may be less likely to have engaged in or reported online IR behaviors. However, there was
enough variance in online IR behaviors that we were able to examine relations between
relationship quality and IR behaviors. In general, our effect sizes were small to medium (as
indicated by the f 2 statistics; Cohen, 1988), which suggests that there are other factors that also
predict online IR behavior that should be explored. Additionally, our measure of social media IR
behaviors was limited to seven items. There are likely other online behaviors that might indicate
or facilitate infidelity, and we look to future studies to help elucidate those behaviors.
Despite these limitations, our study adds to a growing body of literature on social media
and relationships. Overall, few married/cohabiting individuals reported engaging in the social
media infidelity-related (IR) behaviors we measured. However, those who were less satisfied and
INFIDELITY ON SOCIAL MEDIA 21
more ambivalent in their relationships engaged in them more often. Moreover, attachment
anxiety interacted in a complex way, with the strength of the association between IR behavior
and relationship satisfaction becoming stronger for men low in anxiety but for women high in
anxiety. In sum, similar characteristics appear to influence both offline and online IR behaviors,
and our study offers an important initial inquiry into the nature of those characteristics and
behaviors among stable married/cohabiting couples.
INFIDELITY ON SOCIAL MEDIA 22
Allen, E. S., & Baucom, D. H. (2004). Adult attachment and patterns of extradyadic
involvements: How do they compare? Journal of Sex Research, 43, 307-317.
Bartholomew, K. & Horowitz, L. M. (1991). Attachment styles among young adults: A test of a
four-category model. Journal of Personality and Social Psychology, 61, 226–244.
Birditt, K. S., Miller, L. M., Fingerman, K. L., & Lwefkowitz, E. S. (2009). Tensions in the
parent and adult child relationship: Links to solidarity and ambivalence. Psychology and
Aging, 24(2), 287-295.
Braiker, H. B., & Kelley, H. H. (1979). Conflict in the Development of Close
Relationships. Social Exchange in Developing Relationships, 135-168.
Brennan, K. A., Clark, C. L, & Shaver, P. R. (1998). Self-report measurement of adult
attachment: An integrative overview. In J. A. Simpson & W. S. Rholes (Eds.),
Attachment Theory and Close Relationships (pp. 46–76). New York: Guilford.
Brennan, K. A., & Shaver, P. R. (1995). Dimensions of adult attachment, affect regulation and
romantic relationship functioning. Personality and Soc Psychology Bulletin, 21, 267–283.
Clayton, R. B., Nagurney, A., & Smith, J. R. (2013). Cheating, Breakup, and Divorce: Is
Facebook Use to Blame? Cyberpsychology, Behavior, and Social Networking, 16(10),
Clayton, R. B. (2014). The Third Wheel: The Impact of Twitter Use on Relationship Infidelity
and Divorce. Cyberpsychology, Behavior, and Social Networking, 17(7), 425-430.
Cohen, J. E. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ:
Lawrence Erlbaum Associates.
INFIDELITY ON SOCIAL MEDIA 23
Cowan, P. A., Cowan, C. P., Pruett, M. K., Pruett, K., & Wong, J. J. (2009). Promoting fathers'
engagement with children: Preventive interventions for low-income families. Journal of
Marriage and Family, 71(3), 663-679.
Cravens, J. D., Leckie, K. R., & Whiting, J. B. (2013). Facebook Infidelity: When Poking
Becomes Problematic. Contemporary Family Therapy, 35(1), 74-90.
Cravens, J. D., & Whiting, J. B. (2014). Clinical Implications of Internet Infidelity: Where
Facebook Fits In. The American Journal of Family Therapy, 42(4), 325-339.
Dew, B., Brubaker, M., & Hays, D. (2006). From the Altar to the Internet: Married Men and
their Online Sexual Behavior. Sexual Addiction & Compulsivity, 13(2-3), 195-207.
Dewall, C. N., Lambert, N. M., Slotter, E. B., Pond, R. S., Deckman, T., Finkel, E. J., . . .
Fincham, F. D. (2011). So far away from one's partner, yet so close to romantic
alternatives: Avoidant attachment, interest in alternatives, and infidelity.Journal of
Personality and Social Psychology, 101(6), 1302-1316. doi:10.1037/a0025497
Dibble, J. L., & Drouin, M. (2014). Using modern technology to keep in touch with back
burners: An investment model analysis. Computers in Human Behavior, 34, 96-100.
Dibble, J. L., Drouin, M., Aune, K. S., & Boller, R. R. (2015). Simmering on the Back Burner:
Communication with and Disclosure of Relationship Alternatives. Communication
Quarterly, 63(3), 329-344. doi:10.1080/01463373.2015.1039719
Drigotas, S. M., Safstrom, C. A., & Gentilia, T. (1999). An investment model prediction of
dating infidelity. Journal of Personality and Social Psychology, 77, 509-524.
Drouin, M., Miller, D. A., & Dibble, J. L. (2014). Ignore your partners’ current Facebook
friends; beware the ones they add! Computers in Human Behavior, 35, 483-488.
INFIDELITY ON SOCIAL MEDIA 24
Drouin, M., Miller, D. A., & Dibble, J. L. (2015). Facebook or Memory: Which Is the Real
Threat to Your Relationship? Cyberpsychology, Behavior, and Social
Networking, 18(10), 561-566. doi:10.1089/cyber.2015.0259
Feeney, J. A., & Noller, P. (1990). Attachment style as a predictor of adult romantic
relationships. Journal of Personality and Social Psychology, 58, 281–291.
Feinberg, M. E., Jones, D. E., Kan, M. L., & Goslin, M. C. (2010). Effects of Family
Foundations on parents and children: 3.5 years after baseline. Journal of Family
Psychology, 24(5), 532.
Fox, J., Osborn, J. L., & Warber, K. M. (2014). Relational dialectics and social networking sites:
The role of Facebook in romantic relationship escalation, maintenance, conflict, and
dissolution. Computers in Human Behavior, 35, 527-534. doi:10.1016/j.chb.2014.02.031
Funk, J. L., & Rogge, R. D. (2007). Testing the ruler with item response theory: Increasing
precision of measurement for relationship satisfaction with the Couples Satisfaction
Index. Journal of Family Psychology, 21(4), 572-583.
Gentzler, A. L., & Kerns, K. A. (2004). Associations between insecure attachment and sexual
experiences. Personal Relationships, 11, 249–265.
Guerrero, L. K., Andersen, P. A., Jorgensen, P. F., Spitzberg, B. H., & Eloy, S. V. (1995).
Coping with the green-eyed monster: Conceptualizing and measuring communicative
responses to romantic jealousy. Western Journal of Communication, 59, 270–304.
Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis:
A Regression-Based Approach. New York, NY: The Guilford Press.
Hazan, C., & Shaver, P. (1987). Romantic love conceptualized as an attachment process. Journal
of Personality and Social Psychology, 52(3), 511-524. doi:10.1037/0022-35220.127.116.111
INFIDELITY ON SOCIAL MEDIA 25
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(5), 916-926. doi:10.1016/j.chb.2010.02.006
Hertlein, K. M. (2012). Digital Dwelling: Technology in Couple and Family Relationships.
Family Relations, 61(3), 374-387. doi:10.1111/j.1741-3729.2012.00702.x
Luescher, K. & Pillemer, K. (1998) Intergenerational ambivalence: A new approach to the study
of parent-child relations in later-life. Journal of Marriage and the Family, 60, 413-425.
Lupkin, S. (2012). Can Facebook ruin your marriage? ABC News. Retrieved from
Martins, A., Pereira, M., Andrade, R., Dattilio, F. M., Narciso, I., & Canavarro, M. C. (2016).
Infidelity in dating relationships: Gender-specific correlates of face-to-face and online
extradyadic involvement. Archives of Sexual Behavior, 45, 193–205.
McDaniel, B. T. (2016). Understanding stability and change in daily coparenting: Predictors
and outcomes in families with young children (Doctoral dissertation, The Pennsylvania
McDaniel, B. T., & Coyne, S. M. (2016). “Technoference”: The interference of technology in
couple relationships and implications for women’s personal and relational well-
being. Psychology of Popular Media Culture, 5, 85–98. doi:10.1037/ppm0000065
Mikulincer, M., & Shaver, P. R. (2003). The attachment behavioral system in adulthood:
Activation, psychodynamics, and interpersonal processes. Advances in Experimental
Social Psychology, 53-152. doi:10.1016/s0065-2601(03)01002-5
Mikulincer, M., & Shaver, P. R. (2007). Attachment in adulthood: Structure, dynamics, and
change. New York, NY: Guilford Press.
INFIDELITY ON SOCIAL MEDIA 26
Mikulincer, M., & Shaver, P. R. (2013). The role of attachment security in adolescent and adult
close relationships. In J. A. Simpson, L. Campbell, J. A. Simpson, L. Campbell (Eds.) ,
The Oxford handbook of close relationships (pp. 66–89). New York, NY, US: Oxford
Northrup, J., & Smith, J. (2016). Effects of Facebook maintenance behaviors on partners’
experience of love. Contemporary Family Therapy: An International Journal, 38, 245–
Norton, R. (1983). Measuring Marital Quality: A Critical Look at the Dependent Variable.
Journal of Marriage and the Family, 45(1), 141. doi:10.2307/351302
Perrin A. (2015). Social Networking Usage: 2005-2015. Pew Research Center. Retrieved
Ridgway, J. L., & Clayton, R. B. (2016). Instagram unfiltered: Exploring associations of body
image satisfaction, Instagram #selfie posting, and negative romantic relationship outcomes.
Cyberpsychology, Behavior & Social Networking, 19, 2–7. doi:10.1089/cyber.2015.0433
Roberts, J. A. & David, M. E. (2016). My life has become a major distraction from my cell phone:
Partner phubbing and relationship satisfaction among romantic partners. Computers in
Human Behavior, 54, 134 – 141. doi: 10.1016/j.chb.2015.07.058
Roscoe, B., Cavanaugh, L. E., & Kennedy, D. R. (1988). Dating infidelity: Behaviors, reasons
and consequences. Adolescence, 23, 35–43.
Ruppel, E. K., & Curran, M. A. (2012). Relational sacrifices in romantic relationships:
Satisfaction and the moderating role of attachment. Journal of Social and Personal
Relationships, 29(4), 508-529. doi:10.1177/0265407511431190
INFIDELITY ON SOCIAL MEDIA 27
Russell, V. M., Baker, L. R., & Mcnulty, J. K. (2013). Attachment insecurity and infidelity in
marriage: Do studies of dating relationships really inform us about marriage? Journal of
Family Psychology, 27(2), 242-251. doi:10.1037/a0032118
Schmitt, D. P. (2005). Is short-term mating the maladaptive result of insecure attachment? A test
of competing evolutionary perspectives. Personality and Social Psychology Bulletin, 31,
Shaw, A. M., Rhoades, G. K., Allen, E. S., Stanley, S. M., & Markman, H. J. (2013). Predictors
of extradyadic sexual involvement in unmarried opposite-sex relationships. Journal of
Sex Research, 50, 598–610. doi:10.1080/00224499.2012.666816.
Valenzuela, S., Halpern, D., & Katz, J. E. (2014). Social network sites, marriage well-being and
divorce: Survey and state-level evidence from the United States. Computers in Human
Behavior, 36, 94-101. doi:10.1016/j.chb.2014.03.034
Wei, M., Russell, D. W., Mallinckrodt, B., & Vogel, D. L. (2007). The Experiences in Close
Relationship Scale (ECR)-Short Form: Reliability, Validity, and Factor Structure.
Journal of Personality Assessment, 88(2), 187-204. doi:10.1080/00223890701268041
Whisman, M. A., Gordon, K. C., & Chatav, Y. (2007). Predicting sexual infidelity in a
population-based sample of married individuals. Journal of Family Psychology, 21, 320–
INFIDELITY ON SOCIAL MEDIA 28
Table 1. Items, Factor Loadings, and Descriptives for Women and Men on the Social Media Infidelity-Related Behaviors (SMIRB) Measure.
Women and Men
I would feel uncomfortable if my spouse/partner read my
chats, comments, and messages to others on social
I sometimes wonder whether my spouse/partner would
be upset if he/she read my chats, comments, or messages
to others on social networking sites.
If my spouse/partner asked me about my chats,
comments, and messages to others on social networking
sites, there are some messages I would like to hide from
Sometimes, instead of going to my spouse/partner, I
share deep emotional or intimate information with others
I sometimes like to chat or message old romantic
partners online or on social networking sites.
If my spouse/partner disturbs or interrupts me while I am
online, I sometimes get defensive or angry.
I sometimes hide the things I say to others online from
Note. Items were answered on a 6-point scale: (1) Strongly disagree, (2) disagree, (3) somewhat disagree, (4) somewhat agree, (5) agree, and (6) strongly agree.
Mean differences between women and men were tested using pairwise t-tests. No significant mean differences were found.
INFIDELITY ON SOCIAL MEDIA 29
Descriptives and bivariate correlations between study variables
Note. *p < .05, **p < .01, ***p < .001. N = 173 women and 165 men (from 176 families). Cronbach's alphas are presented on the diagonal.
INFIDELITY ON SOCIAL MEDIA 30
Table 3. Multilevel models of social media infidelity-related behaviors predicted by relationship satisfaction,
ambivalence, and attachment avoidance and anxiety
Not college graduate
Relationship satisfaction or ambivalence, attachment avoidance and anxiety, and interactions with gender
RQ (Satisfaction or ambivalence)
RQ X Gender
Avoidance X Gender
Anxiety X Gender
RQ X Avoidance
RQ X Anxiety
RQ X Avoidance X Gender
RQ X Anxiety X Gender
Note: ***p < .001, **p < .01, *p < .05. RQ = Relationship satisfaction in Model 1 and relationship ambivalence in Model 2. Gender is coded 0 = female
and 1 = male; for interactions, the main effect is for women, and the interaction is the value to add to the main effect in order to get the effect for men.
Non-significant interactions were trimmed and are marked with a "--". Control variables were coded as follows: Gender (1 = male, 0 = female), Not
Caucasian (0 = Caucasian, 1 = other race), Not college graduate (1 = college grad., 0 = less education than college grad.), Multiple children (1 = multiple
children, 0 = only one child in family), and marital status (1 = living together, not married, 0 = married). Except for the above mentioned controls, all other
variables were grand mean centered. Family income was in $1,000 units.
INFIDELITY ON SOCIAL MEDIA 31
Low Rel. Sat.
High Rel. Sat.
Low Rel. Sat.
High Rel. Sat.
Figure 1. Predicted values of social media infidelity-related behavior at high and low (1 SD
above and 1 SD below mean) values of relationship satisfaction, moderated by attachment
anxiety and gender. High anxiety is 1 SD above mean (black line) and low anxiety is 1 SD below
mean (gray dashed line).
INFIDELITY ON SOCIAL MEDIA 32
Social Media Infidelity-Related Behaviors (SMIRB)
Please rate how much you agree with the following statements.
1. I would feel uncomfortable if my spouse/partner read my chats, comments, and messages
to others on social networking sites.
2. I sometimes wonder whether my spouse/partner would be upset if he/she read my chats,
comments, or messages to others on social networking sites.
3. If my spouse/partner asked me about my chats, comments, and messages to others on
social networking sites, there are some messages I would like to hide from him/her.
4. Sometimes, instead of going to my spouse/partner, I share deep emotional or intimate
information with others online.
5. I sometimes like to chat or message old romantic partners online or on social networking
6. If my spouse/partner disturbs or interrupts me while I am online, I sometimes get
defensive or angry.
7. I sometimes hide the things I say to others online from my spouse/partner.
1 – Strongly disagree
2 – Disagree
3 – Somewhat disagree
4 – Somewhat agree
5 – Agree
6 – Strongly agree