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Social Networks 38 (2014) 115
Contents lists available at ScienceDirect
Social Networks
jo ur nal homepage: www.elsev ier .com
Assessing structural correlates to social capital innetwor
Brandon pea Department ob Oxford Internc School of Infod College
of Info
a r t i c l
Keywords:Social capitalFacebookEgo networksTransitivityCommunity
detection
muntively correlated with social capital. This research has
drawn primarily on Williams (2006) bridging andbonding scales as
well as behavioral attributes such as civic engagement. Yet, as
social capital is inher-ently a structural construct, it is
surprising that so little work has been done relating social
capital tosocial structure as captured by social network site (SNS)
Friendship networks. Facebook is particularlywell-suited to support
the examination of structure at the ego level since the networks
articulated on
1. Introdu
With mowidely use2013). Useexisting frienect with o(Ellison et
aon the site potential bethe notion tures both
CorresponCommunicatioTel.: +1 517 35
E-mail add
0378-8733/$ http://dx.doi.oFacebook tend to be large, dense, and
indicative of many ofine foci (e.g., coworkers, friends from
highschool). Assuming that each one of these foci only partially
overlap, we initially present two hypothe-ses related to Facebook
social networks and social capital: more foci are associated with
perceptions ofgreater bridging social capital and more closure is
associated with greater bonding social capital. Usinga study of 235
employees at a Midwestern American university, we test these
hypotheses alongsideself-reported measures of activity on the site.
Our results only partially conrm these hypotheses. Inparticular,
using a widely used measure of closure (transitivity) we observe a
strong and persistent neg-ative relationship to bonding social
capital. Although this nding is initially counter-intuitive it is
easilyexplained by considering the topology of Facebook personal
networks: networks with primarily closedtriads tend to be networks
with tightly bound foci (such as everyone from high school knowing
eachother) and few connections between foci. Networks with
primarily open triads signify many crosscuttingfriendships across
foci. Therefore, bonding social capital appears to be less tied to
local clustering thanto global cohesion.
2014 Elsevier B.V. All rights reserved.
ction
re than one billion active users, Facebook is the mostd social
network site (SNS) in the world (Facebook,rs employ Facebook to
maintain relationships withnds (Ellison et al., 2007; Hampton et
al., 2011), recon-ld friends (Smith, 2011), organize social
engagementsl., 2013), and seek information from their
connections(Lampe et al., 2012; Morris et al., 2010). To assess
thenets of Facebook use, researchers have regularly usedof social
capitala sociological framework which cap-the potential and actual
resources available from an
ding author at: Michigan State University, 404 Wilson Rd. Room
409,n, Arts, and Sciences, East Lansing, MI 48824, United States.5
8372; fax: +1 517 355 1292.ress: [email protected] (B. Brooks).
actors network (Bourdieu, 1986; Lin, 2001; Putnam, 2000). In
par-ticular, there is an expanding body of research that employs
thedistinction between bridging and bonding social capital
(Gittelland Vidal, 1998; Putnam, 2000) to characterize the
potential ben-ets of Facebook engagement. This distinction was
popularized byRobert Putnam, who argues that community
organizations work asengines of bonding social capital by bringing
together individualsfor shared events and group solidarity (2000).
Bridging social capi-tal can be traced to Granovetters (1973)
articulation of how weakties enable access to novel information
(and consequently greaterjob search success). Since Facebook houses
both dense clusters ofstrong ties (Gilbert and Karahalios, 2009)
and large swaths of weakties, it is plausible that Facebook can be
a site for the activation ofboth bonding and bridging social
capital.
Although social capital has its roots in structural analysis,
thebulk of social capital scholarship in computer-mediated
commu-nication concerning Facebook has focused on survey scales
thatrelate perceptions of social capital to individual-level
metrics such
see front matter 2014 Elsevier B.V. All rights
reserved.rg/10.1016/j.socnet.2014.01.002ks
Brooksa,, Bernie Hoganb, Nicole Ellisonc, Cliff Lamf Media and
Information, Michigan State University, United Stateset Institute,
University of Oxford, United Kingdomrmation, University of
Michigan, United Statesrmation Studies, University of Maryland,
United States
e i n f o a b s t r a c t
Research in computer-mediated com/ locate /socnet
Facebook ego
c, Jessica Vitakd
ication has consistently asserted that Facebook use is posi-
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2 B. Brooks et al. / Social Networks 38 (2014) 115
as self-esteem, messages sent, and attitudes toward Facebook.
Inparticular, many researchers have used Williams (2006)
InternetSocial Capital Scales (ISCS) to claim that specic
characteristics ofusers networks (e.g., Ellison et al., 2011;
Vitak, 2012) and usersbehaviors opositively ptake netwosize. On
thexplicitly exFacebook. Hsocial capitbroadly (Fri
In this sFacebook nand measurpast researas time on
structural-ltions of soFriggeri et a signicantherefore, tConsistent
Ellison et alInternet Soccapital.
One of tis that informatically. Testablishedtors, whichties
(McPhetend to focuconnectionare only apertheless inconnectionthat
the reofine relatbook is abland Karaha
As our Facebook nsequences obe obvious ego networand thus
su1984). Thusships as artthrough egoby considerthe cohesioFriggeri
et awe suggestmeasure oftriads (relatego networtightly knittions
acrossnetwork.
This papsocial capitasocial netwship examinas a personaresearch
qu
approach, variable conceptualizations, and descriptive data
aboutour participants. We then present the results of a series of
bivariateand multivariate analyses and conclude by discussing how
networkstructure can partially inuence the perception of social
capital in
tworo Faial ca, can
thised andinriads
frag
ratu
ncep
ial cces we or as beeldics, r
tendocialconcnalyoverlr, 200sidererenok isns onvely ve. Tinal
ses t
quesetwog soonneform).ert n betion al (1
zatiogageese e outhe poight ciateerneial cits cors ex
timeced srsonaliamethoh whn the site (e.g., Burke et al., 2011;
Ellison et al., in press)redict perceptions of social capital. When
these studiesrk composition into account they tend to use networke
other hand, there is a small body of research thatamines
ego-centric measures of network structure onowever, these studies
tend to take network structure asal (Brooks et al., 2011) or
examine social cohesion moreggeri et al., 2011).tudy we jointly
consider the structural properties ofetworks, scales of bridging
and bonding social capital,es of site engagement. In doing so, we
wish to extendch that has examined individual level variables,
suchthe site, while explicitly considering the potential forevel
metrics to have an independent effect on percep-cial capital.
Consistent with Brooks et al. (2011) andal. (2011), we assume that
dense clusters of ties havet bearing on the overall cohesion of the
network, andhe likelihood of resource provision from the
network.with other work in this vein (e.g., Burke et al., 2011;.,
in press), we use a modied version of Williams (2006)ial Capital
Scale (ISCS) to measure perceptions of social
he attractions of researching Facebook ego networksmation about
virtually all alters is available program-his allows us to operate
at a scale in between two
strategies for capturing ego networks: name genera- tend to
focus mainly on the small number of core socialrson et al., 2006),
and enumeration methods, whichs on estimating total network size
but forgo alteralters (McCarty et al., 2000). Although Facebook
networksproximations of ofine personal networks, they nev-clude
large swaths of weak ties and the alteralter
s between these weak ties. Further, past work has
shownlationships on Facebook tend to be characteristic ofionships
(Ellison et al., 2007), and that activity on Face-e to discriminate
ofine strong and weak ties (Gilbertlios, 2009; Jones et al.,
2013).ndings suggest, one of the further advantages of usingetworks
is that we can assess with high delity the con-f linkages across
social groups that may not necessarilyto ego, but still felt as a
form of social cohesion. In mostk analysis studies alteralter ties
are reported by ego,bject to a host of inaccuracies and biases
(Bernard et al.,, the network that is analyzed is not a list of
friend-iculated by the friends, but a list of friendships as seens
eyes. In this regard, we extend Friggeri et al. (2011),ing the
cohesion of the network as a whole, rather thann of distinct
clusters within the ego network. Whereasl. use closed triads to
signify distinct social groupings;
that the presence of open triads may in fact be a better global
cohesion, and that the presence of many closedive to open triads)
is in fact a strong indicator that thek is highly fragmented. Each
individual cluster might be, but the lack of open triads indicates
a lack of connec-
groups, and potentially a lack of social cohesion in the
er is organized as follows: First, we review the use ofl in
studies of computer-mediated communication andork analysis. Second,
we summarize current scholar-ing Facebook, both as a resource for
social capital andl network measurement tool. We then dene our
basicestions and hypotheses followed by our methodological
ego netudes tfor socclosureesting,assumless boopen tthat is
2. Lite
2.1. Co
Socresourof mor51)hdemic in politcapitalfrom sof the work abeing
Fischeto conto diffFaceboopinioeffectithe aboattitudproceson
thetheir nbondinwork cnew incapital
Robtinctiodistincand Vidorganiand enthat thpositivmore, that man
assothe Intfor socabout scholatakingenhanthe pe
Wilas a mthrougks on Facebook. In general, we assert that
individual atti-cebook usage remain the strongest explanatory
factorspital, but that structural measures, particularly
triadic
have a strong independent effect. Perhaps most inter- effect of
triadic closure is opposite to what would be
higher clustering coefcient is actually associated withg social
capital. We argue that this is the result of less
across groups and is experienced by ego as a networkmented
rather than globally cohesive.
re
tualization of social capital
apitalthe aggregate of the actual or potentialhich are linked to
possession of a durable network
less institutionalized relationships (Bourdieu, 1986, p.en
adapted and integrated into a large number of aca-s. Scholars have
explored the presence of social capitaleligion, education, family,
and culture. In all cases, socials to be a general stand-in for
positive social outcomes
interaction. The prominence (and perhaps the dilution)ept of
social capital has led members of the social net-sis community to
criticize the notion of the concept asy general, instrumental and
articial (Kadushin, 2004;5; Fine, 2010). However, there remains a
plausible need
how structural features and individual behaviors leadces in
perceptions and outcomes of social resources.
not solely a site for sharing music tastes, comparing current
affairs, or organizing social events. Rather, itfunctions as a
computer-mediated platform for all ofhus, our operationalization of
social capital emphasizessentiments, and any descriptions of these
resources ashat can be invested or traded are ancillary. We
focustion of whether individuals believe they can draw uponrk for
emotional and material resources (as a measure ofcial capital) and
whether individuals believe their net-cts them to the wider world
and provides them withation and experiences (as a measure of
bridging social
Putnam is widely regarded as popularizing the dis-tween bridging
and bonding social capital (even if theis often attributed to the
previously published Gittell998)). In Bowling Alone, Putnam argued
that communityns enabled individuals to converge in shared
locations
in activities that increase group solidarity. He
assertedorganizations were associated with a large number ofcomes,
such as greater health and lower crime. Further-stulated that
television was among a number of factorsbe responsible for the
decline in voluntary activity andd decline in social capital. At
the time of its publication,t was only beginning to emerge as an
object of studyapital in everyday life and Putnam remained
agnosticnsequences for public life. Subsequently, a number ofplored
whether the Internet impeded social capital, by
away from ofine activities (cf., Nie et al., 2002) orocial
capital, by providing increased connectivity withinl network
(Quan-Haase and Wellman, 2004).s (2006) addressed the growing
popularity of CMCd of communicationand thereby a separate outletich
social capital could be created and exchangedby
-
B. Brooks et al. / Social Networks 38 (2014) 115 3
constructing scales of social capital and examining online
andofine variants of it. Drawing on Putnams articulation of the
dis-tinction between bridging and bonding capital (2000) and
Resnicksextension toward sociotechnical capital (2001), Williams
(2006)developed sfocused on as a sense oitems assessomeone waccess
to aing social creported thdifferent kinot attempthey assess in a
speciconline an(e.g., Ellisoncontexts ha
Subsequlighted sigbonding soSNS-relatedesteem (BuMendelson
2.2. Facebo
In the lasuse of SNSssocial capittional and sEllison et alBrooks
et alerature reveoperational
Much ofcapital and perception-son and coestablishedtics of
Facebonding rescompositio2011)whiof a users how diversitively
predengagemenmaintenancwish a Frien
Moving by Burke acapitalaga(2006) scaltial study (Bpositively
cof directedsages a userelated to bwith a specof access tbridging
rethe authorsbonding socauthors sug
through a single good friend and is thus not reliant on
changesin Facebook-based communication. However, the authors did
ndthat a more sensitive measure of directed communication that
onlycaptured inbound messages (i.e., posts, messages, and Likes
sent by
s) poatasetiont alse byend whe opnot brk str, Face egorate.
Brombeial cary nmber
is thgo. Hunitilly a
et aic r
her a usedlled ok F. TheWouicipathe gted tred tohesmu
ohes 2). Tluateriggemuntogettweenetwus, thident
cebo
eboosuchonteeoplltistrtrand
(19ts pecontee. Fisemb
rks htivitontecales to capture bonding and bridging social
capital thattheoretical components associated with social capitalf
access to social resources. For bonding social capital,sed the
extent to which participants reported havingho could provide
emotional support and advice and
scarce resource, such as a nancial loan. For bridg-apital, items
assessed the extent to which participantsey had interactions that
are consistent with access tonds of people or diverse worldviews.
These scales dot to quantify the volume of resources available;
rather,respondents perceptions of the availability of resources
context; in Williams (2006) work, this was divided intod ofine
contexts, while in subsequent research, local
et al., 2007) and site-specic (e.g., Ellison et al., in press)ve
also been used when framing the items.ent research employing
Williams scales has high-nicant relationships between both bridging
andcial capital and a number of behavioral and attitudinal
factors such as various Facebook activities and self-rke et al.,
2010; Ellison et al., 2007; Papacharissi and, 2011; Steineld et
al., 2008; Valenzuela et al., 2009).
ok and social capital
t decade, researchers have explored the extent to whichand
specically Facebookis associated with variousal constructs,
including perceived access to informa-upport-based resources (e.g.,
Burke et al., 2010, 2011;., 2007, 2011, in press) and network
characteristics (e.g.,., 2011; Friggeri et al., 2011). As detailed
below, the lit-als a complex relationship between use and the
variousizations of social capital.
the research looking at the relationship between socialSNS use
has employed an adaption of Williams (2006)based social capital
measure. Notably, work by Elli-lleagues (e.g., Ellison et al.,
2007, 2011, in press) has
a positive relationship between various characteris-book use and
perceptions of access to bridging andources. For example,
characteristics of a users networkn, such as the number of actual
friends (Ellison et al.,ch was intended to capture a more
meaningful measurenetwork than total number of Facebook Friendsande
they perceive that network to be (Vitak, 2012) pos-ict users
perceptions of social capital, as does userst in communication
behaviors that support relationshipe, such as when they respond to
a request for advice ord happy birthday (Ellison et al., in
press).one step beyond strictly perceptual data, researchnd
colleagues has examined perceptions of socialin measured using an
adapted version of Williamse and server-level data of Facebook use.
In their ini-urke et al., 2010), they found that while Friend
countorrelated with both forms of social capital, ones level
communicationmeasured as the number of mes-r exchanged with
another Facebook Friendwas onlyonding social capital, such that
increases in interactionic Friend were associated with increased
perceptionso bonding resources from their network, but not
tosources. In a follow-up longitudinal study, however,
(Burke et al., 2011) found no relationship betweenial capital
and directed communication over time; thegested that bonding social
capital may be generated
Frienddinal dinteracient, bupurposthe Fri
At tsured netwocapitalanalyzto genecapitalthe nuing socfor
evethe nudegreewith eopportnormaBrookseconombut rat(2011)tion
caFaceborithm)them, If partname then racompathat cthe commore cof 1
orto evaever, Fof comTaken link besocial ual. Thabove
2.3. Fa
Factexts, each csame p(or mumultisFischercontexmany are rarwere
mnetwoconnecto all csitively predicted bridging social capital in
the longitu-t. Ellison et al. (in press) argue that these more
visible
s serve to signal ones relationship, not just to the recip-o to
the entire network, and can serve a social grooming
highlighting the relationship and potentially providingith a
needed resource.posite end of the spectrum, social capital may be
mea-y users perceptions of resources, but through theiructure.
While still rarely used in online studies of socialebook provides
an ideal environment to measure and
networks. Brooks et al. (2011) utilized Facebooks API personal
networks for the purposes of measuring socialoks et al.
conceptualized bridging social capital based onr of clusters within
an individuals network and bond-pital as the average degree of the
network. The degreeode in an undirected ego network can be
considered
of friends that alter shares with ego. Thus, the averagee mean
number of mutual friendships all alters haveaving many mutual
friends, on average, implies manyes for reciprocity, closure and
other structural featuresssociated with bonding social capital.
Findings froml. suggest that socioeconomic status or more
diverseesources was not associated with number of cliques,
larger and more dense network. Likewise, Friggeri et al. an
online experiment employing a Facebook applica-Fellows to present
users with a visualization of theirriends network (generated using
a simple greedy algo-
application showed users a group of Friends and askedld you say
that this list of friends forms a group for you?nts answered yes,
they were given the opportunity toroup and save it as a Friends
list on Facebook. Usershe quality of these suggested groups. When
researchershese ratings to the cohesion of the group, they foundion
is a strong indicator of users subjective perception ofnity-ness of
a set of people as indicated by the fact thative groups received
higher ratings (e.g., 4 stars insteadhis work suggests that
cohesion is a representative way
a community, at least at a correlational level. How-ri et al.
(2011) did not nd support for typical measuresity quality such as
density, clustering, or conductance.her, these two papers suggest
that there is some missingn personal network measurement and the
theoreticalork structure of social capital attached to the
individ-is current study examines the association between theied
two methods for conceptualizing social capital.
ok personal networks and structural social capital
k networks represent ties from a variety of social con- as
school, work, church or the neighborhood. Whilext may be
distinguishable from another, some of thee may exist in multiple
contexts, leading to multiplexanded) ties. Past work on personal
networks has foundedness to be a common occurrence. For example,
in82) Northern California study, the average number ofr alter was
1.6. At the same time, most ties do not occupyxts, since ties
bridging more than two social contextscher noted that on average
only 2.6 alters per networkers of three or more contexts. This
suggests personal
ave a structure that is characterized by high degrees ofy across
contexts, but not necessarily a core commonxts. It is not the case
that a core set of ties stand as a
-
4 B. Brooks et al. / Social Networks 38 (2014) 115
lynchpin linking all contexts, but different ties link the
multitudeof contexts into a cohesive personal network. This
assertion is alsoaligned with past analysis of personal networks,
such as Wellmanand Wortley (1990) and McCarty (2002).
Structurpotential tHoughton ain that socipresented tencourage
Facebook egroups, theand, in manexample, inFriend Listcontent
thecontexts ofone hand, suand interacThis consolthus social mation
acroto congregasuch contexfor examplesible to chuare made vthat
more increased pHoughton aviolations Fvate informmight consnote are
a ntal resourceit easier to on a new ptive or taboor family pr
We belidepending interacts wthat are hienables a spotentially
Other indivsure betwenetworks aor open (exist side-btion of the
sinformation
The struus toward great deal obook netwoQuercia et (Gilbert
ansis examiniFriggeri et existing undwork visuabelieve thetypifying
ththat capturidentify coh
the Louvain method (Blondel et al., 2008) and assess the
globalcohesion of the network across various clusters through
averagedegree and transitivity. We discuss our hypotheses and
researchquestions based on the above literature.
earc
ndin
earchs of, a ns amers.
s (2en nnetws reasonaigherial soen corahal highl. Sincork,
hesiscial c
andinetw
papetwo
meas higre wrk wtioninkager pren coore
riphe
hesiscial c
h tra the
fewpractsent tivit
he hiivityosed
idgin
ouge mure n
a net byok nal properties of personal networks on SNSs have theo
engender context collapse (Binder et al., 2009;nd Joinson, 2010;
Marwick and Boyd, 2010; Vitak, 2012)al connections from different
life contexts tend to beogether in the same stream and because
these sitesbroadcasting content to ones entire network. Whilenables
users to segment their connections into sub-
default settings typically reect ones full audiencey cases, few
users take advantage of these features; for
one study, just 14% of users reported using Facebooks feature to
recreate some of the ofine groups and ltery shared (Vitak, 2012).
The net effect of the collapsedten present in SNSs on social
capital is unclear. On thech context collapse provides an
opportunity to monitort with a diverse set of ties in one
convenient location.idation may enhance access to diverse resources
andcapital because individuals can learn about new infor-ss social
contexts in a single sitting, rather than havingte with each
individual cluster of ties. On the other hand,t collapse may
engender new privacy concerns, where,, pictures from the bowling
teams bar night are acces-rch friends, or political messages meant
for ones friendsisible to work colleagues. Binder et al. (2009)
founddiverse networks on Facebook were associated witherceptions of
tension in their social spheres whereasnd Joinson (2010) documented
a number of privacyacebook users experienced, many resulting from
pri-ation being shared across contexts. These situationstrain
disclosures on the site, which some researchersecessary requirement
to accessing specic social capi-s (Ellison et al., 2011). For
example, individuals may ndask for information about mundane topics
like advicehone or a vacation spot and harder to ask about sensi-o
topics like coming out, health issues, nancial woes,oblems (Newman
et al., 2011).eve context collapse may be experienced differentlyon
the arrangement of the various contexts that egoith on Facebook.
Some individuals may have networksghly fragmented, meaning that the
default newsfeedingle cross-cutting view of many diverse areas
andincongruous social circles of ones personal network.iduals may
have networks with a high amount of clo-en social contexts. Whether
individuals perceive theirs closed (with many linkages across
social contexts)having content from highly differentiated social
rolesy-side) ought to make a difference on their percep-ite as a
channel for garnering social support and social.ctural properties
of Facebook ego networks help guidereasonable social capital
metrics for analysis. While af work has focused on the number of
ties in a Face-rk, relating it to personality (Golbeck and Robles,
2011;al., 2012), brain size (Kanai et al., 2011) and closenessd
Karahalios, 2009), there has been much less analy-ng the topology
of personal Facebook networks (c.f.,al., 2011; Brooks et al.,
2011). As such, we draw uponerstandings of personal networks,
intuitions from net-
lizations, and past work where available. Insofar as wese
networks consist of multiple locally dense clusterse social
contexts of personal life, we employ measurese these features.
Specically, we employ a measure toesive subgroups embedded in a
personal network using
3. Res
3.1. Bo
Resfeelingwritesrelatioing othreturnbetwelarger For thiin
perwith hpotentalso beand Kaoveraloverala netw
Hypoting so
Expof the In thissonal nwe aretivity istructunetwoconnecmore
lwhethbetwethese cthe pe
Hypoting so
Botwithinmakesyet in be preconnecfrom ttransitis uncl
3.2. Br
Althincludthere atexts inattempFaceboh question and
hypotheses
g social capital
suggests that network density is strongly related to social
support and social capital. For example, Lindenser network with
more intimate and reciprocalong members may increase the likelihood
of mobiliz-
. .to defend and protect existing resources/expressive001, p.
20). Unfortunately, density scores vary widelyetworks of varying
sizes, placing undue demands onorks to have disproportionately more
ties per alter.son, we opt to include average degree. Average
degreel Facebook networks has previously been correlated
socioeconomic status, suggesting a baseline for highercial
resources (Brooks et al., 2011). Alters degree hasrrelated with
traditional measures of closeness (Gilbertlios, 2009), although the
latter nding did not imply thater average degree meant a presence
of more close tiese average degree implies more interconnections
withinwe follow Lin in proposing:
1: Average degree is positively related to perceived
bond-apital.
ng on average degree we use a measure of the cohesionork rather
than simply the density or average degree.er, we are interested in
the overall cohesion of a per-rk. By including a measure of
triangles over two-paths,suring the presence of sites of local
density. If transi-h, then either the network exhibits a
coreperipheryith many dense connections in the core, or a
multi-coreith few linkages between said cores, but many denses
within each core. In either case, transitivity meanses within a set
of nodes. Thus, the dense connections,esent in the core or multiple
cores with a few tiesres, are a measure of bonding social capital
assuminggroups represent stronger personal ties than those
onry.
2: High transitivity is positively related to perceived
bond-apital.
nsitivity and average degree signify greater
connectivitynetwork, but do so in different ways. Average degreeer
assumptions about how the network is connected,ice, we assert that
a higher average degree is likely toin networks characterized by
pockets of high internaly such as high school friends. In this
case, more linkagesgh school context to other contexts may actually
lower
as each new link between these separate contexts that would
lower transitivity.
g social capital
h it is now widely accepted that Facebook networksltiple social
contexts (Binder et al., 2009; Vitak, 2012),o established ways to
count the number of social con-twork programmatically. Friggeri et
al. (2011) make one
using overlapping clusters drawn from a respondentsetwork and
asked the respondent to rate the quality
-
B. Brooks et al. / Social Networks 38 (2014) 115 5
of these clusters. They use a novel cohesion measure based
ontriangles inside and outside a cluster. In that work, users
tendedto rate highly dense clusters as accurately signifying a
group, andless dense clusters as less accurately describing a
group. That said,the demandsmall groupof social consignify
diffetal. Consequalgorithm (to capture groups of sodistribution
We belisocial capitor bridgingsocial capitsense of conmizes
moduthere are feeach clusterleast inform
We thennetwork reters represethrough thethis measur
Hypothesispositively r
3.3. Beyond
Social cathat individhis scales, Wis to measuthe items inuser
behaviThis strategwork topolo(2005). Thisdeploy strusures separour
foremostructural cthe relationof social capaware of whwork. Put
bknow who Facebook rbetter at pasider indiviscales deveship
MainteInformation
ISB and with their Fscales attemtains, as wesuch, we hyon
bridgingformally we
Hypothesis 4a: Greater FRMB will be associated with both
bridgingand bonding capital.Hypothesis 4b: Greater ISB will be
associated with both bridging andbonding social capital.
thermese nshipt thake an
que.
hesisural nhesisural n
nsid
ed oned aging ips wlievefrienre frilso m
too bte reothest.
hat ook a
thod
datan onl
in iporte
samn universembeted ts foeted . Sprenterrvey ceboes an
incl Namtionrt ofacebe aprk tom theted t; ofed ths of that
algorithm lead to a large number of relativelys in a given ego
network rather than a coarse numbertexts such as work, family and
high school that wouldrent arenas for the distribution of bridging
social capi-ently, we employ a widely-used community detectionthe
Louvain method; Blondel et al., 2008) in orderthese larger group
structures that represent differingcial cohesion and thus different
sites for the potential
of bridging social capital.eve that our approach captures the
spirit of bridgingal, as it implies the spread of information
across groups
between pockets of dense ties. We consider bridgingal as a
metric of access to diverse social resources and anectedness to the
wider world. A technique that maxi-larity presents a count of the
number of groups wherew connections between the groups, thus
ensuring that
would uniquely contribute different information, or atation from
a substantively different set of alters.
assume that each unique cluster within a Facebookpresents a
specic context. The number of those clus-nts access to possible
diverse social resources captured
reported bridging social capital measure. Thus, usinge we
propose:
3: More clusters (found using community detection) iselated to
perceived bridging social capital.
structure: mobilizing social capital
pital encompasses both actual and potential resourcesuals have
access to through their network. In designing
illiams (2006) noted that the intent of these scalesre sentiment
as expected outcome. As such, none of
the scales measure either actual network topology orors that
would lead to particular structural outcomes.y of explicitly
distinguishing social resources from net-gy was also articulated by
Van Der Gaag and Snijders
emphasis on perceived outcomes makes it possible toctural,
behavioral, demographic and attitudinal mea-ately to assess their
relationship to the scales. Whilest emphasis in this paper is on
the relationship betweenovariates and social capital, we also wish
to addressship between the latent structure as a potential siteital
and the behaviors actors employ to become moreat potential social
resources are embedded in the net-luntly, what good is having a
network if you do nothas what resource? Based on previously
mentionedesearch, we pose the question, are some individualsying
attention to their network than others? We con-duals engagement
with their network using two recentloped specically for this
purpose: Facebook Relation-nance Behaviors (FRMB); Ellison et al.
(in press)) and-Seeking Behaviors (ISB; Lampe et al., 2012).FRMB
are ways of examining individuals engagementacebook network, while
Williams (2006) social capitalpt to elicit perceptions of resources
that network con-
ll as individuals perceived access to those resources.
Aspothesize that these measures have both a direct effect
and bonding social capital and a mediating effect. More
propose:
Furthat threlatiosuggeswho taing andcapital
HypotstructHypotstruct
3.4. Co
Basdescribof bridtionshWe beactual tal. Mocould aresentintimaof
hypinteres
RQ: WFaceb
4. Me
Thewith auniqueself-re
Thewesterthe unin Novcomplticipancomplhereinto be The suics,
Faattitudsurveysion ofinstructhis patheir Fvey. Thnetwo
Frocomplaccounaccessore, with regards to the mediating effect,
we suggestscales will attenuate but not eliminate any positive
between structural measures and social capital. Wet given a
specic network topology, those individuals
active interest in their network through social
groom-stion-asking are likely to report higher levels of social
5a: FRMB will partially mediate the association betweenetwork
properties and bridging and bonding capital.
5b: ISB will partially mediate the association betweenetwork
properties and bridging and bonding capital.
ering Facebook activity
previous work (Ellison et al., 2011), having more self-ctual
friends on Facebook should lead to higher levelscapital, because
these represent more meaningful rela-ith ego (as compared to very
weak, non actual Friends).
that there are compelling arguments for why moreds could lead to
either higher or lower bonding capi-ends could mean a denser set of
core ties for bonding. Itean that individuals feel their Facebook
networks rep-
road a set of ties to successfully capture the affective
andsources associated with bonding capital. Thus, insteadizing we
consider its effect to be a research question of
is the relationship between number of actual friends onnd
bridging and bonding social capital?
s
collection for this research combined an online surveyine
Facebook API network generator. This method wasts technique for
comparing ego network metrics andd behavioral data.ple for this
study was recruited through a large Mid-iversity within the United
States. An email was sent byity to a random sample of 3149
non-faculty staff (1000er 2010 and 2149 in February 2011). Fall
participantsa short screener survey which was used to select par-r
a more detailed lab session. Lab session participantsthe full
survey in addition to other tasks not discusseding survey
participants were invited to provide theired into a drawing for one
of ten Amazon gift cards.asked a series of questions including user
demograph-ok use, Williams (2006) social capital scales, privacyd
behaviors, and network characteristics. The online
uded a link to a Facebook application (a modied ver-eGenWeb
[Hogan, 2010]), that included study-specic
s and a consent statement. Participants who agreed to the study
had to (temporarily) add the application toook account while they
completed the rest of the sur-plication saved a copy of the
participants Facebook ego
a server maintained by the second author.e fall and spring data
collection efforts, 666 participantsa survey, with 534 reporting
having an active Facebook
that group, 238 added the Facebook application whicheir Facebook
network data; this latter group will be used
-
6 B. Brooks et al. / Social Networks 38 (2014) 115
in all analyses in this paper. Among the network-only
subsample,the average participant was female (66.8%), 45 years old
(SD = 10.8)and a college graduate (43.7% had a bachelors degree,
35.3% hadpostgraduate training). These numbers correspond closely
to thedemographic composition of the entire sample.
4.1. Measures
The two types of data collected for this
studyself-reportedperceptual measures and ego network
characteristicsare detailedbelow. Scale items were measured on
ve-point Likert-type scalesranging from Strongly Disagree to
Strongly Agree unless otherwisenoted. All non-network variables
used less than 5% missing val-ues, which were imputed using mean
replacement. Items, means,and standard deviations for each scale
are presented in AppendixA. Table 1 also provides full descriptive
characteristics for variablesused in the study.
4.2. Survey items
Gender was a self-report item with the option of male orfemale
with three missing values, which were excluded fromanalyses and
descriptive reporting. Age was self-reported in years.Education was
asked using an ordinal question with responsesless than high
school, high school degree, technical, tradeor vocationdegree,
cschool after
Faceboousing an ad(2006). For language wtinguish beFacebook
Fnetwork, wwho are nosider the Faasked to thFriends whaccess
vario
The bonincludes itein my Facebwith in myme. The b
includes questions such as, Interacting with people in my
Face-book network makes me want to try new things and
Interactingwith people in my Facebook network reminds me that
everyone inthe world is connected.
Facebook Relationship Maintenance Behaviors (FRMB) mea-sures the
extent to which Facebook users engage in social groomingand attempt
to respond to requests from their Facebook network,which may in
turn signal that ego is paying attention to alter (Ellisonet al.,
in press). The scale (Cronbachs = 0.90, M = 3.72, SD =
0.80)includes ve items, four of which reference users likelihood
torespond to requests from other members of their network and afth
that captures the common practice of signaling attention toa specic
Friend by writing Happy Birthday on their Wall. Pastresearch
employing this measure found signicant differences inperceptions of
bridging and bonding social capital across differ-ent levels of
engagement in FRMB, although measures of networkstructure were not
considered (Lampe et al., 2012).
Information-Seeking Behaviors (ISB) examines the extent towhich
individuals use Facebooks communication features to seeka range of
informational resources from their network (Lampe et al.,2012).
This scale (Cronbachs = 0.83, M = 2.34, SD = 0.83)
measuresparticipants use of Facebook for getting information or
adviceregarding purchases, health, business referrals, and other
specicquestions.
Self-esteem was measured using Rosenbergs (1989) seven-itemed
sinclue whual frson eany ? Atil exndiv
ofiux awit
ch asple mrks o
the appr frieon the nu
Table 1Sample descri
Nodes Average degTransitivity Clusters Modularity Giant
compo
Gender (FemAge Education (oSelf-esteem
Actual friendVisits per daFacebook enInfo-seekingFacebook
boFacebook br
N = 235.al school after high school, some college, no
4-yearollege graduate, post-graduate training/professional
college and I dont want to disclose.k bridging and bonding
social capital were measuredapted version of Williams bridging and
bonding scalesthis study, we replaced Williams (2006)
online/ofineith on Facebook and in my social network to dis-tween
social capital perceptions associated with theirriends and
perceptions associated with their full socialhich includes their
Facebook Friends as well as thoset on the site, respectively. In
this paper, we only con-cebook-specic responses, in which
participants wereink only about their interactions with their
Facebooken reporting the extent to which they felt they couldus
kinds of resources.ding scale (Cronbachs = 0.88, M = 3.40, SD =
0.73)ms such as, When I feel lonely, there are several peopleook
network I can talk to and The people I interact
Facebook network would share their last dollar withridging scale
(Cronbachs = 0.90, M = 3.47, SD = 0.66)
validatitems On th
Acton Ellihow mfriendslist unlater, itact. Inis in 1997)gies
suas peonetwopeoplerecentactualspent with th
ptive statistics.
Mean Median
217.74 153.00 ree 14.02 12.00
0.57 0.55 6.54 6.00 0.43 0.46
nent percentage 0.82 0.89
ale) 0.70 1.00 44.21 46.00
rdinal) 5.05 5.00 4.32 4.29
s on Facebook 80.90 45.00 y to Facebook 2.21 2.00 gagement
(FRMB) 3.73 4.00
on Facebook 2.35 2.25 nding capital 3.40 3.50 idging capital
3.47 3.60 cale (Cronbachs = 0.86, M = 4.32, SD = 0.50). Samplede, I
feel that I have a number of good qualities andole, I am satised
with myself.iends was measured using a self-report question, basedt
al. (2011), in which users were asked, Approximatelyof your TOTAL
Facebook Friends do you consider actualt present, Facebook Friends
remain in ones Friendsplicitly removed by one of the dyad. Thus
many yearsiduals may still be friends on the site despite no con-ne
personal networks, however, network membershipnd ties tend to fade
over time (Suitor and Keeton,
hout the ease of connection offered by social technolo-
Facebook, individuals may lose the ability to re-connectove away,
change jobs, etc. Thus, individuals may haven Facebook that do not
reect their active ties, or eveny would consider friends. To
address this, we followoaches that ask participants to report on
the number ofnds within their Facebook network as well as the timee
site. As noted in Table 2, actual friends is correlatedmber of
Facebook Friends (r = 0.49, p < 0.001). To note,
SD Min Max
227.61 14.00 1950.0010.67 1.00 70.000.12 0.33 0.953.45 1.00
22.000.17 0.00 0.770.17 0.24 0.99
0.46 0.00 1.0010.73 23.00 65.000.95 2.00 6.000.50 2.57 5.00
103.17 0.00 700.001.12 1.00 5.000.79 1.00 5.000.83 1.00 4.750.74
1.00 5.000.66 1.00 5.00
-
B. Brooks et al. / Social Networks 38 (2014) 115 7
Table 2Bivariate correlations between variables of interest.
Nodes Average degree Transitivity Clusters Modularity Actual
friendson Facebook
Self-esteem
Average degTransitivity Number of cModularity 0.40 **
Actual friend 0.37 ** 0.32 **
Self-esteem 0.0 *
Age 0.3Sex 0.1Education 0.0Visits per da 0.4Facebook en
0.3Info-seeking 0.3Facebook br 0.3Facebook bo 0.3
Visitday Face
Sex Education Visits per daFacebook en 0.42Info-seeking
0.41Facebook br 0.35Facebook bo 0.27
N = 235. p < 0.1.* p < 0.05.
** p < 0.01.***p < 0.001.
non-paramtionship (Spaccount forreasonably Individualstheir
netwoon the site.
Visits peusing a self-et al., 2010)measured bSuch a meakeep
Facebpercent of oand 5% said
4.3. Persona
The pera custom-blink in the on code frapplicationwork formato
downloapplicationloaded fromcalculated.
The netwnetwork onstatistics, th(Facebook, The mean nily
skewed
rks (367to th
withThat rageltersree 0.82 **
0.40 ** 0.21 **lusters 0.45 ** 0.31 ** 0.52 **
0.27 ** 0.04 0.46 **s on Facebook 0.49 ** 0.33 ** 0.38 **
0.04 0.01 0.04 0.37 ** 0.37 ** 0.29 **0.08 0.00 0.10 0.05 0.03
0.04
y to Facebook 0.47 ** 0.37 ** 0.38 **gagement (FRMB) 0.24 **
0.21 ** 0.23 **
on Facebook 0.34 ** 0.32 ** 0.28 **idging capital 0.26 ** 0.18
** 0.21 **nding capital 0.20 ** 0.12 0.37 **
Age Sex Education
0.080.11 0.09
y to Facebook 0.36 ** 0.12 0.00gagement (FRMB) 0.12 0.25 **
0.01
on Facebook 0.19 ** 0.25 ** 0.15 *idging capital 0.05 0.24 **
0.01 nding capital 0.26 ** 0.10 0.00
etric correlations indicate a substantially stronger
rela-earmans rho = 0.67, p < 0.001) suggesting that when we
the few individuals with very large networks, there is aclose,
if not perfect, relationship between these values.
report having a mean of 217.7 nodes (median 153) inrks, but
report a mean of 81 (median 45) actual friends
netwo1050, 1closer dense,0.57.1
on aveother ar day to Facebook measured time spent on
Facebookreported measure of visits per day. Past research
(Burke
has indicated that visits per day was more accuratelyy
participants than time spent on the site in minutes.sure also
accounts for instances where individuals willook available for
chatting on multiple devices. Thirtyur sample said they visit
Facebook once a day or less
they visit Facebook ve or more times per day.
l Facebook networks
sonal networks of participants were collected usinguilt Facebook
application accessed through a hyper-survey. The algorithm for this
application was basedom Hogans (2010) NameGenWeb, a
public-facing
for downloading ones friendship relations in a net-t, such as
GraphML (Brandes et al., 2002). In order
ad the network, respondents had to approve the from within their
Facebook accounts. Data down-
this project was cached while user statistics were
orks used in this study were larger than the average Facebook,
as reported by the site. According to the sitese average network
contains approximately 130 nodes2011), although this number varies
widely by country.umber of nodes in our survey was 217, but was
heav-by the presence of a few individuals with very large
Facebooconnected work compnodes in thremaining nof 99%
(meaponent, an igroups coexarate socialconnectivittiple
socialclusters. Thtechniquesber of compwhen visua
Transitiways, but ttimes the cof all two psecond is thtivity
arounthese resul
1 All networand Python 2.7
2 This gurewith clusters d2 0.03 0.132 ** 0.30 ** 0.23 ** 0.061
0.10 0.16 * 0.048 0.05 0.01 0.062 ** 0.28 ** 0.44 ** 0.067 ** 0.20
** 0.32 ** 0.104 ** 0.18 ** 0.40 ** 0.041 ** 0.19 ** 0.32 **
0.11
0 ** 0.27 ** 0.34 ** 0.17 *
s pertobook
FRMB IBS Facebookbridgingcapital
**
** 0.49 **** 0.59 ** 0.54 **** 0.44 ** 0.42 ** 0.51 **
the size of the ve largest networks were 843, 1026, and 1950).
The median network size was 153, which ise global average of 130.
These networks were also very
an average degree of 14 and an average transitivity ofis to say,
over half of all potential triads are closed and
each alter in the network is connected to at least 14.
k personal networks in our sample tended to be welloverall, in
that the giant component for any given net-rised most of the
network. The median percentage ofe giant component was 89 and the
mean was 82. Theodes tended to be isolates or isolated dyads. A
mediann of 93%) of all nodes was either part of the giant
com-solate, or a dyad. Thus, while it is likely that many socialist
in ones Facebook network, these groups are not sep-
islands. Rather, they are zones of either greater or lessery
where some alters will be highly connected to mul-
groups while others will be primarily tied to specicis also
reinforces the use of modularity maximization
for partitioning rather than simply counting the num-onents.
Fig. 1 is characteristic of the networks we sawlizing Facebook
personal networks.2
vity in a network can be measured in a multitude ofwo
conventional measures stand out. The rst is threeount of triangles
in the graph divided by the countaths (Davis, 1967; Holland and
Leinhardt, 1971). Thee global clustering coefcient, which measures
transi-d each node in the network and takes the average ofts (Watts
and Strogatz, 1998). Also, these calculations
k metrics were calculated using iGraph 0.6 (Csrdi and Nepusz,
2006).
does not come from the study, but was created by one of the
authors,etected using the same algorithms as done for
participants.
-
8 B. Brooks et al. / Social Networks 38 (2014) 115
Fig. 1. Faceboowner has labeswatches deno
exclude egosent 4-cliquinterest herobject, andson for
exclisolates, whscore.
Clustersdetection resent distiLouvain mnetwork inmodularitywork
that iacross conting to bothalternate mmodularityok network with
clusters found through multilevel community detection (the Louvain
meled the clusters and veried their accuracy as distinct subgroups.
Network captured usingte separate subgroup membership.
, so in the complete graph, triangles actually repre-es, as ego
is connected to all three alters. However, oure is in the network
that ego perceives as an external
so ego is excluded from calculations. The other rea-uding ego is
that it simplies calculations dealing witho would otherwise
extremely skew any transitivity
were measured using an automated communityalgorithm. We loosely
consider these clusters to rep-nct social groups or contexts.
Specically, we used theethod, a highly efcient technique for
decomposing ato mutually exclusive clusters that seek to
maximize
(Blondel et al., 2008). We acknowledge that in a net-s not
completely disconnected some individuals linkexts, and ego may
consider this individual as belong-
contexts. We opt for the Louvain method rather thanethods for
its efciency and its capacity to maximize
relative to other methods, such as Girvan-Newman
(Girvan andet al., 2004)are identielocal clusteit reaches
thcluster hasmost withinassigned a gponent. Wethe degree (2010)
suggbe unrealisany node tbetween anFacebook pin
commonmodularitydistinctiventhod). Node size corresponds to a log
scale of betweenness. Network NameGenWeb and rendered using GUESS
1.04 (Adar, 2006). Different
Newman, 2002), Greedy community detection (Clauset and spectral
partitioning (Newman, 2006). The groupsd using a greedy
optimization technique that seeks outrs of high density. This
iterative process continues untile highest modularity values,
thereby ensuring that each
the fewest number of links between each group, the each group,
relative to a null model and every node isroup. We only calculate
clusters within the giant com-
use the standard conguration null model that xesdistribution
while randomizing edges. While Fortunatoests that in some cases the
conguration model maytic as a benchmark since it assumes the
potential foro connect to any other node. We believe connectionsy
two nodes is actually a legitimate assumption withinersonal
networks since all alters have at least one friendego. Along with
the number of groups, we include the
(or quality) score as a supplementary measure of theess of the
groups.
-
B. Brooks et al. / Social Networks 38 (2014) 115 9
5. Models and analyses
The correlation matrix of our variables of interest indicates
amultitude of strong relationships. Results in Table 2 suggest
thatmany of the measures discussed above can at least partially
explainvariations in bridging and bonding social capital, given the
manycorrelations over 0.2. We rst review some of the most
notablecorrelations and then proceed to discuss nested OLS
regression andmultiple m
5.1. Correla
Bivariatethe topologated with hia higher mo(where p < 0ables.
Mostwith more informationThis suggestion sourceproviding aexplore
this
Averagecapital as hboth (weakTransitivityboth forms that more
cgreater persocial capitbelow and t
Only onticollinearitIn our subseVIF and Tolshowed VIFfrom
conceinterestingincreases. Italters are lreturn to
thtransitivity
5.2. Multiva
Althoughkey structudard OLS recapital we models whrst as
wellmodels [Monumber of and transitivious workexpanded
mengagemenFriends, FRM
When coaverage de
3 Despite thof either does
non-signicant. Using only node or average degree (not
shown)instead of both does not change this outcome. Thus, we have
evi-dence to reject Hypothesis 1. In line with the earlier
correlations,transitivity is negatively related to both perceived
bridging andbonding casocial capitpersists wit
odeignignic
will ationmenodeica
Hypong a rk mndinters eivityre mome
sultinode
dels y thacapitce. Tral vcanny, we
ediat
xplond s
Preaoder simhiched b
modof theg to dthe rme oyed m
aggr. For tf en
ed soed oncallynding so
e visentiaial toltiple
intependariabdepef theport ediation models.
tions
correlations reinforce our earlier characterization ofy of
Facebook ego networks: larger networks are associ-gher average
degree, less transitivity, more clusters anddularity score. The
network metrics also signicantly.05) relate to the demographic and
engagement vari-
notably, networks with more nodes strongly correlateactual
friends as well as more visits to Facebook. Also,-seeking is
strongly related to the number of clusters.ts individuals who
consider Facebook as an informa-
tend to have more social contexts from which to draw,
preliminary validation of hypothesis 4b above. We will
relationship further in a multivariate model below. degree is
(weakly) positively related to bonding socialypothesized. More
clusters and higher modularity arely) related to bridging social
capital as hypothesized.
actually shows a signicant negative relationship toof social
capital. This goes against the basic hypothesisonnectivity in the
network would be associated withception of inclusiveness as
suggested by a traditionalal approach. This nding will be explored
in the modelshe subsequent discussion.e correlation is high enough
to suggest potential mul-y issues: average degree and nodes (r =
0.82, p < 0.001).quent regressions we check for
multicollinearity usingerance parameters within SPSS version 19. No
models
scores above 10 or Tolerance scores below 0.20. Apartrns with
multicollinearity, this correlation in itself isas nodes come into
the network, the average degree
would suggest that when people add new alters, theseikely to
share friends in common with ego. We willis nding in the discussion
as it helps to explain howand bonding social capital can be
negatively related.
riate analysis
we wish to test for the independent effects of ourral metrics,
we rst consider these metrics in a stan-gression framework. For
bonding and bridging socialinclude two models each. These are
standard nestedere the second model includes all variables from
the
as additional social engagement variables. The smallerdels 1 and
3 in Table 3] include ve structural metrics:nodes, average degree,
modularity, number of clustersvity. These models also include
controls based on pre-: age, education (ordinal), gender and
self-esteem. Theodels [Models 2 and 4 in Table 3] append four
Facebookt metrics: visits per day to Facebook, actual FacebookB,
and ISB.3
ntrolling for other structural variables, the effects ofgree,
modularity and number of nodes is rendered
e high correlation between nodes and average degree, the
exclusionlittle to change the model t or the signicance of the
variables.
In Monly snon-si4). Wethe relengage
In mno signlation.in havinetwoand boof clustransitsures awith
wThe re0.17. M
Monamelsocial varianstructueffect quentl
5.3. M
To ement a(MML;ation mmannemine wexplainoverallcance seekinior
on and soemploverbalpowersures oreport
Basspeciand bobridginincludas potpotent
Muable ofthe dedent vto the effect oalso repital. However, it is
only signicant where bondingal is used as the dependent variable.
This signicanceh the inclusion of engagement variables (model 2).l
3 (bridging social capital), number of clusters is thecant
variable, although this relationship is renderedant by the
inclusion of engagement variables (Modelexplore this further in a
mediation model to help clarifyship between number of clusters
(i.e., social contexts),t and bridging social capital.l 1,
Hypothesis 1 is not supported; average degree hasnt effect, which
was expected based on the weak corre-thesis 2 was also not
supported as transitivity persistednegative effect on bonding
social capital. Thus, while alleasures were signicantly correlated
with the bridgingg scores in the bivariate correlations, only the
numbermerges as a signicant predictor for bridging and only
was a signicant predictor for bonding when the mea-odeled
jointly. Gender is also a signicant predictor,n reporting greater
bridging social capital than men.g Model 1 has a moderate t, with
an adjusted R2 of
l 2 has an adjusted R2 of 0.32.2 and 4 indicate support for
Hypotheses 4a and 4b,t FRMB and ISB positively predict bridging and
bondingal while leading to substantial increases in explainedhese
variables also alter the relationship between theariables and
social capital. The precise nature of thisot be determined in the
current OLS models. Conse-
turn to mediation models.
ion modeling of structural effects
re the relationship between network structure, engage-ocial
capital, we employ multiple mediation modelscher and Hayes, 2008),
as an extension to classical medi-ls (Baron and Kenny, 1986). These
models operate in ailar to structural equation models in that they
deter-
part of a variables effect on a dependent variable isy an
intermediary variable. That said, they provide anel R2 and employ
bootstrapping to assess the signi-
effect of the mediators. Such models are useful whenisentangle
the effect of individual perception or behav-elationship between
some objectively measured valueutcome variable. For example,
Vanbrabant et al. (2012)ediation models in personal networks to
assess how
ession was mediated by status and subjective sense ofhis study,
we are interested in the extent to which mea-gagement mediate the
effects of network structure oncial capital.
the regression models described in Table 3, we focus on
disentangling the relationship between transitivityg social capital
(Model 5) and number of clusters andcial capital (Model 6). In
addition to FRMB and ISB, weits per day to Facebook and number of
actual friendsl mediators since these latter variables indicate
egos
activate social capital. mediation models articulate three paths
from the vari-rest, rather than one single path from all variables
toent variable (Fig. 2): the a path, showing the indepen-le to the
mediators; the b path, showing the mediatorsndent variable; and the
c path, showing the residual
independent variable accounting for the mediators. Wethe c path,
which is the total effect of the independent
-
10 B. Brooks et al. / Social Networks 38 (2014) 115
Table 3OLS regression predicting to the Williams scale of
bonding and bridging capital.
DV: Bonding social capital DV: Bridging social capital
Model 1: Networkvariables
Model 2:Network + engagementvariables
Model 3: Network Model 4:
DemographicGender (WAge **
EducationSelf-estee *
Network varNodes Average dModularitNumber oTransitivit **
Engagement Actual frieVisits per ISB ***
FRMB ***
Constant (un ***
Adjusted R2
Standardized cN = 235.
* p < 0.05.** p < 0.01.
*** p < 0.001.
Fig. 2. Exampand mediating
variable onthere is no mc path is sig
Model 5capital andthat transittransitivityFRMB and (p <
0.001), not signicsignicancebelieve tha5b for bondsignicant
stherefore nrole in the
4 We emplosomen) 0.06 0.05
0.17 * 0.17 0.02 0.02 m 0.16 ** 0.12iables
0.02 0.03egree 0.01 0.10y 0.07 0.03 f clusters 0.10 0.03 y 0.24
** 0.22variablesnds on Facebook 0.11 day on Facebook 0.08
0.220.29
standardized) 3.50 *** 2.55F(225) = 6.46*** F(221) = 9.50***
0.17 0.32
oefcients are reported for all numbers unless otherwise noted.le
mediation model schema denoting specic paths for independent
variables. Control variables are not shown.
the dependent variable. If this c path is
non-signicant,eaningful relationship to test in the rst place.4 If
this
nicant, we can consider the mediating paths. examines the
relationship between bonding social
transitivity (Table 4). The signicant c path suggestsivity has
an effect. The signicant c path suggests that
has a direct effect independent of the mediators. TheISB b paths
to bonding social capital are signicantbut the a paths from
transitivity to FRMB and ISB areant at the p < 0.05 level.
However, given that their
values are close to the critical value (p 0.06), wet we can
neither reject nor accept Hypotheses 5a anding social capital. Most
importantly, the fact that c isuggests that transitivity has an
independent effect andetwork structure can play a signicant
independent
perceived experience of bonding social capital. The
y the INDIRECT algorithm (version 4.1) written by Hayes in SPSS
18.
coefcient further in t
Model 6ital and numsignicant cHowever, this indirect. ters to
FRMto bridgingmediated, pconsideredated. Consesocial capit
In both of actual frthere is no nicantly p
6. Discussi
Results social capitengagemenpredictor inrecent nd2011, in
prthat Faceboof more socmaintaininalso reinforlater scale dbook as
a sFacebook aa site for msive bondinsocial capitsible to
posvariables Network + engagementvariables
0.19 ** 0.050.08 0.100.00 0.060.08 0.04
0.11 0.120.05 0.090.07 0.030.20 ** 0.020.02 0.01
0.010.040.33 ***
0.38 ***
2.21 *** 0.79F(225) = 5.07*** F(221) = 14.040.14 0.42
for transitivity remains negative. We consider thishe discussion
section.
examines the relationship between bridging social cap-ber of
clusters found using community detection. The
path suggests that the number of clusters has an effect.e
non-signicant c path indicates that this relationship
Since there are signicant a paths from number of clus-B and ISB
and signicant b paths from FRMB and ISB
social capital we consider this to be an indirect, fullyath. If
the a paths were not signicant we would have
this relationship to be spurious rather than fully medi-quently,
we accept Hypotheses 5a and 5b for bridgingal.models 5 and 6 there
are signicant a paths to number
iends and daily number of visits to Facebook, althoughevidence
to support the notion that these variables sig-redict perceived
social capital in these models.
on
from the analyses demonstrate that the perception ofal is
related to both social structure and patterns oft in complex ways.
The fact that FRMB was the strongest
most models, both bridging and bonding, reinforcesings in this
area (Burke et al., 2010, 2011; Ellison et al.,ess; Vitak, 2012).
In particular, these results indicateok use in itself is not a
guaranteed path to perceptionsial capital, but that specic
attitudes and strategies forg relationships (FRMB) play a large
part as well. This isced by the consistently signicant results for
ISB. Thisescribes the extent to which individuals perceive Face-ite
for information seeking needs. Those who considers a site for
information seeking also tend to report it asore general social
capital, both the emotional and inclu-g capital and the more
instrumental and broad bridgingal. It is important to note that in
this study it is not pos-it a causal direction. As such, we cannot
tell if attitudes
-
B. Brooks et al. / Social Networks 38 (2014) 115 11
Table 4Mediation models for bonding and bridging social
capital.
Model 5 Model 6
Bonding (IV: Transitivity) Bridging (IV: Clusters)
p
IV to mediatActual frie 0.0Visits per 0.0Info-seeki 0.0Facebook
0.0
Direct effectActual frie 0.0Visits per 0.2Info-seeki 0.0Facebook
0.0
Total effect oTransitivit 0.0Direct effeTransitivit 0.0
Partial effectGender (w 0.4Age 0.0Education 0.7Self-esteem
0.0Nodes 0.0
Bonding: N = 2 , F(224
lead to behathe accrual
While wated effect of clusters. on Facebooet al. (2011nication
thain the case individual tthem to a bonding socmost likely ing
close refrom netwobonding socing with fewtheir mass c
The relathas face valtion seekingwho considfound in thsow.
Howevmay potenttured in siga clear pattemere handfa network
wversity, a fustill be a smsuch as onedistinct clusare closed,
closed trianoverlappingone went tothen got a from ones from high
sand co-wor
ouldpeopknow
ironal cloon. In
in ea opeivitymorelosurwo petwen tralosenshipnalyn deon ark
coCoefcient SE
ors (a paths)nds on Facebook 177.22 52.91 day on Facebook 1.74
0.57 ng on Facebook 0.86 0.45engagement (FRMB) 0.83 0.45 s of
mediators on DV (b paths)nds on Facebook 0.00 0.00 day on Facebook
0.05 0.05 ng on Facebook 0.19 0.06 engagement (FRMB) 0.27 0.06f IV
on DV (c path)y/number of clusters 1.87 0.41 ct of IV on DV (c
path)y/number of clusters 1.42 0.38
of control variables on DVomen) 0.07 0.09
0.01 0.00 0.02 0.04
0.18 0.08 0.00 0.00
35, Adjusted R2 = (0.325), F(224) = 12.29***; bridging: N = 235,
Adjusted R2 = (0.424)
viors that reinforce the perceptions of social capital orof
social capital changes attitudes and behaviors.e fully expected
structural measures to have a medi-on FRMB and ISB, we found no
mediation for numberThis may be due to the ways in which people
interactk, but it is likely the result of a nding within Burke),
who nd that it is direct person-to-person commu-t leads to an
increase in bridging social capital. Thus,of bridging social
capital, it is more important for ano know of an available job or
someone who can linkresource. Further, we agree with Burke et al.
in thatial capital generation and maintenance on Facebook isdue to
individuals utilizing other methods for maintain-lationships. Our
ndings suggest that those individualsrks with low transitivity will
be able to experience moreial capital on Facebook because they are
communicat-er contexts and do not have to limit the exposure of
ommunication to the least close relationship.ionship between
attitudes, behaviors and social capitalidity. People who consider
Facebook a site for informa-
there wa few might
Theing loccohesipeopleare fewtransitmean local cmore tping
bbetweemore crelatiowork abetweebased netwo and a site for small
symbolic practices tend to be peopleer Facebook a place for the
positive social resourcese social capital scales. That is, people
reap what theyer, a focus on individual attitudes and behaviors
aloneially be reductionist. Facebook ego networks are
struc-nicantly different ways. Some of these networks showrn of
dense pockets of ties from separate contexts with aul that link the
network together. For example, considerith separate friendship
groups from high school, uni-
ll time job and a neighborhood association. There mayall number
of people who link these groups together,
s signicant other or best friend, but the groups remainters.
Transitivity would be high because most trianglesbut there are few
links between the dense clusters ofgles. Other networks are very
diffuse with many ties
between multiple clusters. This might be the case if a local
college alongside many people from high schooljob in the same town
while living a few blocks downparents. In such a case, it is
plausible that many peoplechool who know ones college friends,
family memberskers. While the entire network would be very
cohesive,
trast, Facebinclusion of
Networkwithin the groups knocially largeThis suggesother.
Whebook user cown demanwhere sepasides of theas many nothe graph
social capiting ties witbonding soeven if locaet al. (2009because
thefore fewer dCoefcient SE p
0 5.06 1.91 0.010 0.07 0.02 0.006 0.05 0.02 0.007 0.07 0.02
0.00
7 0.00 0.00 0.674 0.02 0.04 0.530 0.25 0.05 0.000 0.32 0.05
0.00
0 0.04 0.01 0.00
0 0.01 0.01 0.58
6 0.07 0.08 0.321 0.01 0.00 0.082 0.05 0.04 0.203 0.05 0.07
0.478 0.00 0.00 0.53
) = 18.23***.
be fewer closed triangles. The co-workers might knowle from high
school, but not all. The family members
a few friends from college, but not all.y of these two kinds of
networks is that by measur-sure, transitivity could be evidence of
a lack of global
the rst case, transitivity would be high because mostch of the
separate groups knows each other while theren paths between the
separate groups. In the second case,
might be lower because the overlapping social circles open two
paths between different groups. So whilee is lower, this is due to
the fact that there are simplyaths to account for since there are
so many ties overlap-en the groups. We initially hypothesized a
relationshipnsitivity and bonding social capital since we
consideredd paths to be indicative of dense pockets of reciprocals.
It is an assumption that is long held in social net-
sis (Feld, 1981; Louch, 2000). However, this relationshipnse
reciprocal relationships and social capital may ben untenable
assumption in Facebook ego networksamprised primarily of a single
cohesive group. By con-
ook networks are almost always characterized by the
multiple social groups that only ever partially overlap.s with
lower transitivity mean more open two pathsnetwork. This is
evidence that people from separatew each other. Networks with
higher transitivity (espe-
networks) tend to have groups that are very distinct.ts that
people from separate groups do not know eachn people do not know
each other, it suggests that a Face-an be torn between multiple
social worlds, each with itsds and expectations. It may be like an
awkward partyrate groups unknown to each other stay on opposite
room. In such a case, it is plausible that despite havingdes and
edges as a graph with more open two pathsfeels different concerning
the perception of bondingal. Thus, somewhat surprisingly we suggest
that bridg-hin a Facebook ego network are associated with
greatercial capital by making the overall graph more cohesivel
network structures are less dense. Similarly to Binder), we suspect
individuals will feel less context collapseir network is still
relatively well connected and there-istinct subgroups, whereas
those individuals with high
-
12 B. Brooks et al. / Social Networks 38 (2014) 115
transitivity will experience lower amounts of trust and
engagementbecause of the lack of privacy (Houghton and Joinson,
2010).
Interpreting these results requires us to make assumptionsabout
the sort of relationships that comprise a group (or a clusterfound
usingclusters in based on liftion of grouof clusters greater
seninformationemerge necgroups repowho have anot considetional
mainbridging so
There istionships rawell as a recertain netwclude from
relationshipfocus on bastrate that gin bridging However, ndyadic
leveoverlap beteffect indep
We haveital can be gnetworks casuch as namabout transsmall
netwresearch asindividualsthe wider nbe discoverwhich sociaeach
other rGranted, thnovel. For emean path ograms oft-cand Time
dracial homodata set haslevel reinfoby major, c2011). Howcan have
thsible to stunodes (indiconnectionthat access ethically chanalysis
of practical alt
7. Limitati
Facebooindividuals
of the personal network, nor a network that necessarily
matchesegos biases, since some ties may exist between alters
without egosknowledge. We assert that bonding is related to a sense
of the net-work as being globally cohesive or fragmented. It is
possible that
y noly. Nrks (suchin hpubliies th
Face to pldetaed incomms.cernunity
theion m
the ds exuvainre cof limunt ere ansidolutny tthe eusiby pring
m
but
ond g ad
There this . Nevd sos ea
igheith a ergrr picFurth.S. s
ally, olvitheretwo. Ourntannshipeld e
wled
ackle. Thation community detection). In general, we maintain
thatFacebook ego networks represent broad assemblagese course
stages and shared activities. This characteriza-ps also helps to
explain the full mediation of number
on bridging social capital. More groups should mean ase of
connectivity to the wider world and the diverse
resources. Yet this sense of connectedness does notessarily.
Those individuals who actively attend to thesert that they draw
novel information from them. Those
Facebook network but remain indifferent to it (i.e. dor it a
site of information seeking or site for small rela-tenance
practices) do not report as high a perception ofcial capital.
a present turn toward considering the quality of rela-ther than
mere structure (Aral and Alstyne, 2011) asnewed focus on individual
traits that can give rise toork structures (Burt, 2010, 2012). What
we can con-
this analysis is that this focus on individual and dyadics makes
sense in some arenas. Like Aral and Alsytnesndwidth over diversity,
our mediation models demon-reater engagement (i.e., bandwidth)
plays a larger role
social capital than multiple social groups (i.e., diversity).ot
all structural factors can be reduced to individual orl covariates.
Lower transitivity, as evidence of greaterween groups and thus
greater global cohesion, has anendent of how individuals approach
their networks.
also demonstrated that new insights about social cap-leaned from
Facebook ego networks compared to egoptured using traditional
respondent driven techniquese generators or enumeration methods.
Our ndings
itivity would not have been discovered in the veryorks
traditionally employed in core discussion network
it is not an insight about the most closely connected to ego,
but about how these individuals are situated inetworks of personal
afliation. Similarly, it could noted by merely articulating which
individuals belong tol groups since it is about how these groups
connect toather than how many individuals exist in which group.e
use of Facebook as a social network is not in itselfxample, very
large scale analysis has indicated how thef Facebook is
approximately 3.74, much lower than Mil-ited six degrees (Backstrm
et al., 2012). The Taste, Tiesataset has indicated how students
reinforce patterns ofphily (Wimmer and Lewis, 2010). The Facebook
100
revealed how community structure at the university-rces existing
networking patterns such as homophilyohort or dorm depending on the
school (Traud et al.,ever, this work highlights how Facebook ego
networksemselves substantial explanatory power. Thus, it is pos-dy
Facebook as a network of networks, consideringviduals), their
connections on the site, and how theses and the groups they
represent are connected. Givento the total Facebook graph is both
extremely limited,allenging and technically formidable, we believe
thatsampled ego-centered networks can be a germane andernative in
some circumstances.
ons
k personal networks offer a remarkable view into an personal
network, but it is neither a complete view
ego maunlikenetwograms ascertathese about tby ourabilityalters
includis not nding
Concommrst isdetectond ismethothe Lobe moment oin a cokers wmay
cothe resing maGiven it is plaods maprovidbetter,ing.
Beyworkinwest. withinlationssites anconrmwith htion wby unda
fullelarge. ing a U2010).
Finhow evor wheboth ncapitalto diserelatioStein
Ackno
WeMelvilFoundt perceive this cohesion. We consider this
plausible butevertheless, future research should compare
Facebooklike those drawn from NameGenWeb, or similar pro-
as NetVizz) to personal network name generators toow
sociocognitive networks on Facebook differ fromcly articulated
networks. Conversely, ego may knowat exist in the Facebook network,
but were not captured
book app. This is because Facebook offers individuals theace
friends on limited prole, meaning ego cannot seeils, but is still
alters friend. In this case, alter will not be
the Facebook network as downloaded. We believe thison practice,
and thus does not signicantly bias our
ing our methods, we acknowledge several criticisms of detection
methods. Two in particular stand out: The
resolution limits of modularity-oriented communityethods
(Fortunato and Barthlemy, 2007). The sec-
recent demonstration that most community detectionperience
degeneracy near optimal solutions (including
method, cf., Good et al., 2010). These concerns wouldrrosive if
this analysis hinged on the correct assign-inal nodes to clusters.
However, we were interested
of clusters, without concern for whether certain bro-ssigned to
one group or the other (when in reality egoer such brokers as
members of both groups). Moreover,ion limit may even work in our
favor by not present-iny (arguably trivial) clusters in our larger
networks.xplanatory power of the Louvain method in this paperle
that other slower but potentially more precise meth-ovide a better
t as well as more explanatory power byore accurate results. No
method we tested performednew methods for partitioning are
continually emerg-
this, we acknowledge the limitations of our sample asults and
university employees from the American Mid-
may be cultural norms within this population andgeographical
area that do not generalize to larger popu-ertheless, much of the
current work on social networkcial capital employs undergraduate
samples. Our studyrlier ndings that greater Facebook use is
associated
r levels of social capital and expands this to a popula-wider
range of ages and life histories than representedaduate students.
We believe this enables us to presentture of how Facebook operates
in the population ater, the non-student sample is valuable, but
only hav-ample poses limitations on the work (Henrich et al.,
we encourage longitudinal work that can disentangleng networks
would lead to differences in social capital,
persistent demographic and personality factors driverk structure
and the sentiments associated with social
work points toward future research that would attemptgle this
research, but does not implicitly inform the
between the works of Burke et al. (2010, 2011) andt al.
(2008).
gements
nowledge the advice of Rebecca Gray and Joshuais work was
supported in part by the National Science
(HCC 0916019).
-
B. Brooks et al. / Social Networks 38 (2014) 115 13
Appendix A. Scales
The scales presented in Tables A1 and A2 below are a modi-cation
of Williams nal scales, as pruned from a larger questionbank. As in
the original paper and subsequent applications, thescales have a
tolerable Cronbachs alpha above 0.8. Readers famil-iar with the
development of social capital within sociological andsocial network
literatures may nd the inclusion of job informationin the bonding
social capital scale as somewhat surprising, since jobinformation
is presumed to be accessed through weak ties. It inclu-sion in
bonding social capital is based on a principal componentsanalysis
in Williams (2006). The means refer to a Likert scale fromstrongly
disagree (1) to strongly agree (5). Items were standardizedbefore
inclusion in the scale, but not weighted by factor loadings.
Tables A3 and A4 are the engagement scales discussed.The nal
Table A5 below represents the self-esteem scale dis-
cussed.
Table A1Facebook specic bridging social capital scale.
Items Mean SD
Interacting with people in my Facebook networkmakes me
interested in things that happenoutside of my town.
3.75 0.82
Interacting with people in my Facebook networkmakes me want to
try new things.
3.59 0.83
Interacting with people in my Facebook networkmakes me
interested in what people unlike meare thinking.
3.58 0.79
Talking with people in my Facebook networkmakes me curious about
other places in theworld.
3.71 0.86
Interacting with people in my Facebook networkmakes me feel like
part of a larger community.
3.68 0.98
Interacting with people in my Facebook networkmakes me feel
connected to the bigger picture.
3.54 0.89
Interacting with people in my Facebook networkreminds me that
everyone in the world isconnected.
3.71 0.91
I am willing to spend time to support generalFacebook community
activities.
3.02 0.92
Interacting with people in my Facebook networkgives me new
people to talk to.
3.17 1.01
Through my Facebook network, I come in contactwith new people
all the time.
2.96 1.03
Adapted from ).Full scale: M = 3.47, SD = 0.66 ( = 0.90).
Table A2Facebook specic bonding social capital scale.
Items Mean SD
There are several people in my Facebook network Itrust to help
solve my problems.
3.30 1.15
There is somturn to fordecisions.
When I feel Facebook
If I needed asomeone i
The people Iwould put
The people Iwould be
The people Iwould sha
The people Iwould hel
Adapted from Full scale: M =
Table A3Facebook Relationship Maintenance Behavior scale
(Ellison et al., in press).
Items
When I see anews on F
When I see anews on F
When I see sFacebook,
When a Facepost some
When I see sthat I know
Full scale: M =
Table A4Information-s
Items
I use Facebowant to bu
I use FaceboI use FaceboI use Facebo
Full scale: M =
Table A5Self-esteem sc
Items Mean SD
I feel that Im a person of worth, at least on anequal plane with
others.
4.50 0.60
I feel that I have a number of good qualities. 4.50 0.63All in
all, I am inclined to feel that I am a failure
(reversed).4.47 0.67
I am able to do things as well as most other people. 4.22 0.65I
feel I do not have much to be proud of (reversed). 4.44 0.72I take
a positive attitude toward myself. 4.08 0.75On the whole, I am
satised with myself. 4.04 0.76
Adapted from Rosenberg (1989).Full scale: M = 4.32, SD = 0.50 (
= 0.86).
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