Platform Characteristics, Multi-Homing, and Homophily in Online
Social Networks
Hyeokkoo Eric Kwon
Korea Advanced Institute of
Science and Technology
hkkwon7@business.kaist.ac.kr
Wonseok Oh
Korea Advanced Institute of
Science and Technology
wonseok.oh@kaist.ac.kr
Abstract
This study examines how the “openness” of SNS platforms and the number of SNS
platforms users adopt moderates online homophily. The analyses—based on panel
data on recorded SNS usage behaviors of 9,484 individual users— reveal that age
homophily is pervasive in online social lives. However, the patterns of homophily
differ significantly across diverse demographic groups, platform type, and users’
channel adoption behaviors. For example, teenagers exhibit a strong age
homophily behavior, regardless of platform type. However, all users who adopt
both open and closed SNS channels, rather than only one SNS channel, do not show
strong age homophily propensities. In addition, online SNS users in their 30s and
40s do not show age homophily. In fact, users over 40s exhibit age heterophily in
that they react more positively when new members from a dissimilar age group
enter their social network than when new members from their own age cluster join
this network. Men tend to form strong homophilous ties with other men on online
SNSs, but women prefer socializing with men. However, whether an SNS is open or
closed significantly moderates the homophily or heterophily propensities of
individual users across diverse cohorts.
Keywords: age homophily, gender homophily, online social networking service
(SNS), platform type, multi-homing, panel data, negative binomial
regression
Platform Characteristics, Multi-Homing, and Homophily in Online
Social Networks
“For it often happens that some of us elders of about the same age come together and verify
the old saw of like to like." (Cephalus in Plato’s Republic, c. 380 BC)
INTRODUCTION
Homophily— an individual’s inexorable tendency to associate disproportionately with similar
others—is one of the most pervasive features of our social lives (Lazarsfeld and Merton, 1954;
McPherson and Smith-Lovin 1987). This pattern of “love of the same” and a variety of acquired
attributes (e.g., religion, education, occupation, and geographical propinquity) are widely
observed across diverse sociodemographic dimensions (McPherson et al. 2001). Homophilous
relationships may develop as a result of structural proximity, opportunities for interaction (i.e.
induced homophily), or as a consequence of individual preference (i.e. choice homophily)
(McPherson and Smith-Lovin 1987). Homophily shapes and often regulates individuals’ social
worlds and has a profound impact on the information they receive, the attitudes and norms they
form, and the interpersonal association they experience (McPherson et al. 2001).
Although homophily is a key catalyst in positive interpersonal contacts, it is often criticized
as a root cause of social, political, and economic stratification, such as racial segregation and
political polarization (Blau 1977; Marsden 1988), social divide (DiMaggio and Garip 2012),
cultural boundaries (Smith et al., 2014), and economic inequality (Torche 2010). Researchers
have recently identified an interesting pattern of homophily-driven segregation in major online
social networking service (SNS) platforms, despite the widespread belief that SNS use
homogenizes social interactions globally and strengthens family ties. For example, one media
1
impact study1 of European teenagers’ Facebook use discovered adolescent users were fleeing to
alternative, more private SNS platforms mainly because their parents are starting to use popular
networks and send them friend requests. Another study showed the age structure of Facebook
has altered drastically in recent years2. While approximately 25% of teenagers have joined the
exodus to alternative sites (e.g., Twitter and Instagram) over the past three years, Facebook users
aged 50 and older have increased by over 80% during the same period. Alternative social
networks allow children and teenagers to use social media with limited parental monitoring.
Obviously adolescent users prefer to keep their parents out of their private social lives and enjoy
the freedom that they effectively had before their parents “invaded” their online social territories.
During Facebook’s rise, parents may have worried about their teenagers joining the site, but now
teenagers are concerned about their parents flocking en masse to the SNS.
Although these examples offer intriguing insights into how online homophily affects
intergroup dynamics on major SNS platforms such as Facebook, the extent of these tendencies,
or how frequently users interact across different social groups, remains unclear for SNS sites.
More importantly, while teenage social media migration behaviors have been highly publicized,
scant attention has been paid to the reactions of other age groups (e.g., users in their 20s, 30s,
and 40s) to the entry of new members of different demographic characteristics into their social
networks. In addition, although extensive literature (see Kalleberg et al. 1996 for a detailed
review) exists regarding gender homophily in professional, task-oriented settings (e.g., work
1
2
http://www.theguardian.com/technology/2013/dec/27/facebook-dead-and-buried-to-teens-research-finds
http://national.deseretnews.com/article/904/Teenagers-unfriend-Facebook-as-it-ages.html
2
establishments), there is a lack of research on the extent of gender homophily in online social
environments.
In addition, our understanding is limited regarding the ways in which SNS platforms— open
or closed— moderate the influence of homophily on user behaviors across diverse social
segments. While open platforms (e.g., Facebook) allow “strangers” to form new friendships and
interact online, closed platforms (e.g., Path) permit only acquaintances who share mobile
numbers to engage in personal dialog. Finally, little is known about how multi-homing users who
adopt multiple SNS platforms to maximize network effects differ from those who patronize a
single platform in terms of sensitivity to new members entering their social circles.
This study seeks to fill this void and to explore the diverse aspects of “flow dynamics”
created by age and gender homophily on two major mobile SNS platforms with varying degrees
of openness. Based on panel data on the actual SNS usage behaviors of 9,484 individual users
over time, this paper investigates how online SNS users in diverse demographic groups react to
new members who are from the same or dissimilar social group. In addition, we pay close
attention to open mobile SNS platforms users’ sensitivity to homogeneity in comparison to users
of app-based, more private message-sharing SNS platforms. Furthermore, we compare multihoming users with single-homing counterparts with respect to their reactions to homophily.
Understanding the pattern and magnitude of online homophily across diverse sociodemographic groups and their association with platform characteristics and multi-homing is
important for many constituencies of online SNS, such as advertisers, platform providers, and
policy-makers. For example, by recognizing social interaction patterns among different gender or
age groups, advertisers can more precisely identify the flow of peer influence and the dynamics
of social contagion (i.e., word of mouth), which can be vital for advertisers when formulating an
3
effective targeting campaign. Homophily can be an important resource for targeted advertising in
online social networks as it may induce behavioral isomorphism and collective rationality. The
Facebook “Like” button exemplifies behavioral isomorphism driven by homophily, denoting the
collective rationality and preference of those who share similarities.
For platform providers, the findings of our study may sensitize them to the shift occurring in
the online social landscape due to the rapid changes in online social structures, revealing how
users distribute and manage their SNS consumption across diverse demographic categories. The
observed empirical regularities may shed important light on how much platform owners should
“nurture” homophily and how much they should mix it up. Furthermore, the pattern of online
homophily or heterophily (i.e., “opposites” attract) may have different ramifications for different
types of platforms. In other words, online homophily may have a limited impact on some
platform providers, but may create a slew of challenges for others depending on the
characteristics of platforms (e.g., open versus closed).
The present study also seeks to enhance the methodological precision for the notion of online
homophily. Extant studies on online homophily define and characterize individuals’ social
relationships and homophily propensity based solely on SNS links and connections rather than
the intensity of interaction. This approach can be inaccurate and unreliable since numerous social
relationships formed through SNS forums are inactive and exist only in a friend list. For
example, many Facebook account holders have several thousand Facebook friends, but most of
them cannot be considered friends in the conventional sense as social interaction rarely takes
place between them (Trusov et al. 2010). Instead of using nominal relationships manifested in
SNS friend lists, we utilize mobile SNS app consumption and log-in activity at the aggregated
level to refine the operational notion of homophily.
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The rest of the paper is organized as follows: Section 2 discusses the theoretical background
and presents the hypotheses. Section 3 describes the data and model estimations. Section 4
provides the results of the analyses. Section 5 discusses managerial implications along with
possible future research directions.
THEORETICAL BACKGROUND AND HYPOTHESES
Research on homophily abounds with numerous theoretical frameworks and empirical
manifestations, exploring a variety of dimensions, including race, ethnicity, age, gender, and
religion. In this study, we focus primarily on two attributes of homophily—age and gender—
since they represent strong and pervasive phenomena across all global communities. Although
race and ethnicity may have the strongest impact on social structures and political segregations in
certain regions of the world, their effects are more or less limited in many countries as well as in
online social network platforms (Thelwall 2009).
Age homophily has been validated empirically as one of the strongest homophilous traits,
shaping the creation of close friendships and confiding relationships (Verbrugge 1977; Fischer et
al. 1977; Feld 1982; McPherson et al. 2001). For example, Fischer et al. (1977) discovered that
72 % of Detroit men’s close friends were within eight years of their own age. Similarly, Feld
(1984) —based on the analysis of northern California data— found that nearly half of non-kin
associates with whom the survey respo.ndents formed confidant relationships were within five
years of their age. Similar life course patterns and institutional settings (e.g., schools) stimulate
age homophily (Kalmijn and Vermunt 2007)
5
According to Fischer (1982), age homophilous ties persist longer, with more personal,
frequent exchanges, than relationships formed by dissimilar ages. Individuals in similar age
cohorts are likely to have similar life course experiences that influence their values, norms, and
interests because they have grown up within the same socio-historical context (Kohli 1988). In
fact, age homophily is naturally formed early in life due to a structural age segregation
implemented by educational institutions, such as nurseries and day care centers (Uhlenberg and
Gierveld 2004). These institutions use single years of age to cluster children throughout their
childhood, offering similarly organized sports and music programs. The age homophily pattern is
further reinforced as age-homogeneous groups are exposed to television and movies that target
the children of particular age groups. Further, social influence promotes individuals to adopt the
behaviors of others, with assimilations occurring frequently between individuals in similar age
cohorts (Friedkin 2006). These structural and institutional forces stimulate a culture that
encourages age homophily (Uhlenberg and Gierveld 2004). Consequently, individuals of a
similar age can establish trust and solidarity more easily than those who are of a dissimilar age
(Kossinets and Watts, 2009).
Verbrugge (1977) reveals that only about 20% of people name someone of the opposite sex
as their closest friend. Likewise, based on the General Social Survey conducted across the U.S.
in 1985, Marsden (1988) finds a strong preference for social ties and friendship relationship
between same sex individuals. Interestingly, Ibarra (1992) shows that men tend to form more
gender-driven homophilous networks than women, particularly in work establishments where
males represent a strong majority. According to Aldrich et al. (1989), both men and women tend
to rely on men as networking routes through which they accomplish tasks and obtain key
information in fields other than their own.
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Homophily in Mobile Social Network Platforms
An online SNS platform, such as Facebook, is a social place where generations of people interact
and converse with their close friends, curate their life story, and share moments and memories. In
addition, these SNS sites allow users to reconnect with old acquaintances (e.g., campus buddies,
high school sweethearts, and former co-workers), find jobs and mates, play social games, and
recommend favorite products or services. In contrast to physical, offline social networks, online
social networks enable users to form new ties beyond geographical barriers and outside their
social boundaries (e.g., school and workplace).
Recently, Singla and Richardson (2008) found that friends who communicate through instant
messaging services share similar demographic characteristics. Similarly, based on a survey of 35
Facebook users—primarily students and working staff at a suburban college in the U.S— Gilbert
and Karahalios (2009) predict the strength of a particular tie formed through online social
networks. Their findings suggest that while thread depth negatively affects tie strength,
emotional closeness has a positive effect on relational strength. Although interesting and
informing, these results are difficult to generalize because the data represent the behaviors of
only 35 individuals associated with one particular university. A research team at Facebook has
also discovered the principle of “birds of a feather surf together,” which indicates a homophilous
tendency that online SNS users who interact frequently share similarities and consume similar
information3. According to Fiore and Donath (2005), despite no physical, face-to-face
interaction, users of an online dating system prefer partnering with people like themselves in
3
https://www.facebook.com/notes/facebook-data-team/rethinking-information-diversity-innetworks/10150503499618859
7
terms of race, lifestyle, religion, and educational level, among other characteristics. These
findings provide evidence that homophily is an apparent universal phenomenon, transcending the
online and offline social worlds.
Regarding the homophily phenomenon in online social environments, SNS users are
expected to welcome new members who share similarities in terms of age and gender entering
their social networks because of the positive network externalities (Katz and Shapiro 1994). The
value of social exchange increases as more peers with similar attributes enter the circle. In
addition, as similar others enter the network, additional social learning opportunities arise, which
induces users to utilize their SNS sites more frequently (DiMaggio and Garip 2012). Social
learning effects will be reinforced with the addition of cohort peers who enhance the utility of
social, relational resources. Conversely, users may feel less comfortable when new members
with different characteristics wish to get connected. Many users reluctantly accept a friend
request from people in dissimilar groups (e.g., parents, uncle, teacher, and boss) because refusing
the invitation can be viewed as impolite and anti-social. Consequently, users become less active
when new members from dissimilar groups join the network. Therefore, we posit the following:
Hypothesis 1: Mobile SNS users increase their usage frequency when new members of a
similar demographic characteristics join the network. Conversely, mobile SNS
users decrease their usage frequency when new members of dissimilar groups
join the network.
Multi-Homing and Online Homophily
Today, consumers increasingly own and subscribe to multiple services with comparable features
and functionalities. For example, many consumers own multiple credit cards to optimize the
rewards and incentives card providers offer, or they use one as a complement to the other.
8
Similarly, numerous Internet users download and activate multiple browsers on their PC or
mobile platforms to cope with compatibility issues. Web surfers seek knowledge by adopting
more than one search engines. Many readers regularly subscribe to multiple newspapers or paid
online news services. A user’s propensity to subscribe to multiple services is often dubbed
“multi-homing” (Rochet and Tirole 2003). The concept originates from Internet technology, the
connection of computing devices to multiple networks or platforms to enhance reliability. Users
are more inclined to exhibit multi-homing-behaviors when incompatible services prevent them
from maximizing the benefits each network or platform can provide (Doganoglu and Wright
2006; Mital and Sarkar 2011).
Incompatibility and differing scopes and modalities across diverse mobile SNS platforms
encourage multi-homing behaviors. These multi-homing individuals may leverage multiple
SNS’s in chorus to expand their social circles and enhance the network benefits that accrue from
the breadth of their interpersonal structures. Conversely, users of a single SNS platform may
attend to the depth of social connections and minimize the costs associated with managing
several social venues. Such single-homing users may prioritize the intimacy and familiarity of
social interactions rather than their breadth or network size.
From a socio-behavioral perspective, how many SNS platforms a user adopts simultaneously
may have an important implication for his or her propensity to homophily. For example, because
multi-homing SNS users desire to expand the breadth and horizons of their social relationships,
they would be more proactive in getting to know people who have heterophilious attributes than
users who maintain a single SNS channel. We expect that the dynamics of the relationships
between the number of SNS’s a user adopts and his or her homophilous tendencies will vary
across diverse groups. Therefore, we posit the following:
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Hypothesis 2: Users of single-homing mobile SNS exhibit stronger homophily than users of
multi-homing mobile SNS.
Platform Openness and Online Homophily
Mobile SNS platforms differ in many aspects, including their degree of openness. Some SNS
platforms (e.g., FB and Twitter) are relatively more open and public in that even “strangers”
(e.g., a friend’s friend’s friend) can easily enter into one’s social circles. SNS users in the open
platform often find it difficult to refuse or ignore a friend request for social and personal reasons.
For its own expansion, the FB company uses its own match algorithm to send the request and
structurally encourages users to accept it4. As a consequence, users are often left with no option
to refuse it, but instead are induced to get connected with “strangers.” In contrast, other mobile
SNS platforms (e.g., Path) are far more personal and protective, allowing users to preserve their
choice and block strangers from crossing into their social boundaries. For example, users of these
“closed” SNS platforms can become connected only when they exchange their phone numbers or
identities.
Open platforms may lead to “induced homophily”, which is formed as a result of
structural and systematical constraints (e.g., compulsive friend requests) (McPherson and SmithLovin 1987). On the other hand, closed platforms, such as Whatsapp and Path, may embrace
“choice homophily,” which is established and maintained by individuals’ own choice (e.g.,
exchanges of phone numbers). Users of closed platforms are likely to be already homogeneous
as they choose their own friends and have regular contact each other. Consequently, homophily
4
http://techcrunch.com/2010/09/20/facebook-not-now-follow
10
will be more pronounced on closed platforms compared to open platforms. Relationships that are
developed and cultivated via closed platforms are characterized as strong ties, which require
similarities and homogeneities, including age and gender. In contrast, relatively less
homogeneity is necessitated on open platforms since many of the ties and bonds are forcefully
created by the open structure (e.g., weak-tie strangers and algorithm-induced requests).
Therefore, we posit the following:
Hypothesis 3: Users of closed mobile SNS exhibit stronger homophily than users of open
mobile SNS users.
DATA
We collected panel data on individual users’ daily activity frequency on two major mobile SNS
apps— open SNS (OS) and closed SNS (CS). Although these mobile apps have been
consistently ranked among the top social network platforms, they vary in degree of openness. OS
is classified as an open SNS platform because its users can connect and socialize with all OS
users in the open. In contrast, CS is considered a relatively closed and private SNS site because
the process of becoming CS friends is far more complicated and restrictive. For example, CS
users can become friends only when they know each other’s identity or share phone numbers.
The data are provided by Nielsen Korean Click —a global marketing research company that
collects data on mobile and PC usage through self-installed tracking apps embedded in
smartphones.
The tracking programs count how frequently the participating panels activate the mobile SNS
apps on a daily basis. To represent the population more precisely, 9,484 panels were initially
chosen by virtue of stratified random sampling that utilized a proportionate allocation strategy
11
based on diverse demographic profiles (e.g., age, gender, and geographical region). The data
covers usage frequency for 134 days from June 20, 2013 to October 31, 2013. Of the 9,484
participating panels, 3,179 and 6,305 users utilized OS and CS apps, respectively, at least once
during the sampling period. The panels were divided into two gender groups and four age
groups: 1) under–18, 2) 19–29, 3) 30–39, and 4) over–40.
Table 1 provides the descriptive statistics. The second column indicates the proportion of the
four age categories as well as the two gender groups that consist our sample. The 19–29 age
group represents the largest proportion on OS, but the over–40 cohort makes up the largest
proportion on CS. However, female groups and male groups are evenly distributed on both SNS
sites. The third column shows the average frequency of activation per day for OS and CS
platforms. The under—18 age group uses both SNS sites most frequently. Women activate more
frequently both SNS sites than do men. The last column exhibits the percentage of users adopting
both SNS platforms (i.e. multi-homing)5. While the 19– 29 age group has the highest tendency to
use both SNS apps, the over–40 group seems to rely on a single SNS app. Overall, more users
adopt CS, but OS is used more frequently than CS.
5
A user is considered to have adopted both SNS platforms (i.e. multi-homing) if he or she utilizes another
SNS channel at least once a week, during the sample period. As we will discuss, we replicate the results
with different cutoff value which is used to classify an individual as multi-homing.
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Table 1. Descriptive Statistics for Data
OS
(n=3,179)
Demographic Group
Proportion
Average # of
Running App (Daily)
under—18
19—29
Age group
30—39
over—40
Female
Gender group
Male
Total
0.115
0.367
0.240
0.278
0.450
0.550
1
17.85
11.61
2.82
2.30
8.61
6.83
7.63
Demographic Group
Proportion
Average # of
Running App (Daily)
0.097
0.215
0.332
0.356
0.510
0.490
1
13.63
4.43
4.24
2.55
5.79
3.33
4.59
under—18
19—29
CS
Age group
30—39
(n=6,305)
over—40
Female
Gender group
Male
Total
Proportion of
using both SNS
platforms
0.16
0.21
0.30
0.19
0.25
0.19
0.22
Proportion of
using both SNS
platforms
0.09
0.22
0.06
0.05
0.09
0.10
0.09
MODEL DEVELOPMENT AND ESTIMATION
Model Development
To investigate the extent of homophily on mobile SNS sites and its association with platform
characteristics and multi-homing, we constructed a model with the following dependent and
independent variables. In keeping with Trusov et al. (2010), the dependent variable, 𝑌𝑖,𝑡 ,
represents the number of each individual i’s frequency of SNS app usage at time t, which denotes
the number of log-ins. The independent variables include (1) the number of active users who are
in the same group of a specific demographic factor X (e.g. age and gender) with i at time t-1 in
the same SNS site (𝑁𝑆𝑋𝑖,𝑡−1 ) and (2) the number of active users who are in a different group of
the specific demographic factor X with i at time t-1 in the same SNS site (𝑁𝐷𝑋𝑖,𝑡−1). Lagged
values of these two covariates are utilized to identify their effects on the dependent variable. It
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should be noted that 𝑁𝑆𝑋𝑖,𝑡−1 and 𝑁𝐷𝑋𝑖,𝑡−1 are computed by counting the active users in the
sample population.
We examine whether homophily exists in SNS platforms by comparing the estimated
parameters of 𝑁𝑆𝑋𝑖,𝑡−1 and 𝑁𝐷𝑋𝑖,𝑡−1. More specifically, the presence of homophily can be
validated if (1) the estimated parameters of 𝑁𝑆𝑋𝑖,𝑡−1 and 𝑁𝐷𝑋𝑖,𝑡−1 are both positive and (2) the
parameter of 𝑁𝑆𝑋𝑖,𝑡−1 is greater than that of 𝑁𝐷𝑋𝑖,𝑡−1 . A strong homophily exists when
individual users respond negatively to 𝑁𝐷𝑋𝑖,𝑡−1 . Separate models are constructed to identify the
extent of age and gender homophily.6 In the first model, we use age as a demographic factor X,
then include two variables, 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 and 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 , which denote the number of active
users in the same or different age group with i at time t-1, respectively. Similarly, the second
model incorporates 𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 and 𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1.
In addition to these analyses, we divide the samples into demographic clusters and examine
how the two main covariates distribute across these diverse demographic groups. This analysis is
designed to validate whether individuals become less age homophilous as they mature (Shrum et
al. 1988), and whether men exhibit a higher level of gender homophily than women (Huckfeldt
and Spragu 1995). The first model is established by incorporating the interactions of 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
and 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 for three different age groups: (1) under—18, (2) 19—29, and (3) 30—39. The
second model incorporates the interactions of 𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 and 𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 for the female
group. It should be noted that the over—40 group and the male group are employed as bases in
6
We cannot simultaneously examine age and gender with one integrated model which incorporates all
four covariates because of multicollinearity problem. Specifically, for example, the sum of 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
and 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 is exactly the same with the sum of 𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 and 𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 at every t.
14
each model. Furthermore, previous studies (e.g., Jung and Lee 2010) suggest that SNS platforms
are utilized relatively less during weekends and holidays as users prefer outdoor activities. To
control for the potential effect of weekends and holidays, we include a dummy variable, 𝐻𝑡 ,
which is equal to 1 when day t is either a weekend or a holiday.
A negative binomial regression was adopted as a main analytical strategy to test the
homophily principle in the context of online SNS. Trusov et al. (2010) assume that the number of
log-ins on SNS platforms of individual i on day t, denoted by 𝑌𝑖,𝑡 follows the Poisson distribution
with mean parameter 𝜆𝑖,𝑡 . This type of Poisson distribution has a strong assumption of “equidispersion,” which means that both the mean and variance of 𝑌𝑖,𝑡 are equal to the mean parameter
𝜆𝑖,𝑡 . This assumption is often identified as an inherent weakness of the Poisson regression
(Greene 2011). It is not uncommon to find empirical cases where the variance of 𝑌𝑖,𝑡 exceeds the
mean of 𝑌𝑖,𝑡 , which reflects over-dispersion in the data (Hausman et al. 1984). The Poisson
regression’s failure to consider this possibility increases the magnitude of the effect on reported
standard errors and t statistics (Cameron and Trivedi 2013).
Hausman et al. (1984) propose a standard mechanism to deal with this over-dispersion
problem. Their method relaxes the mean parameter to follow a gamma distribution with
parameter (𝛾𝑖,𝑡 , 𝛿𝑖 ) and also specifies 𝛾𝑖,𝑡 = 𝑒 𝑋𝑖,𝑡𝛽 , where 𝑋𝑖,𝑡 is a vector of covariates for
individual i at time t and 𝛽 is a vector of corresponding regression parameters to be estimated
(Equation 1). Then, 𝑌𝑖,𝑡 follows the negative binomial distribution with parameter (𝛾𝑖,𝑡 , 𝛿𝑖 ), and
the variance to mean ratio is greater than 1, accordingly ( 𝑉(𝑌𝑖,𝑡 )/𝐸(𝑌𝑖,𝑡 ) = (1 + 𝛿𝑖 )/𝛿𝑖 > 1). To
test the possibility of over-dispersion in our data, we adopt Cameron and Trivedi's (1990) simple
2
regression-based procedure. Specifically, the empirical model regresses 𝑧𝑖 = {(𝑦𝑖 − 𝜆̂𝑖 ) − 𝑦𝑖 } /
15
(𝜆̂𝑖 √2), where 𝜆̂𝑖 is a fitted value from the Poisson regression, on either a constant term or 𝜆̂𝑖
without the constant term. Following this procedure, the null hypothesis (e.g., the presence of
equi-dispersion) is rejected as both t statistics are significantly different from zero at the 99%
level of confidence7. Therefore, it is more robust to model 𝑌𝑖,𝑡 based on a negative binomial
regression (Equations 1, 2A, and 2B) than the Poisson regression:
𝛿𝑖 𝛾𝑖,𝑡
𝑃𝑟(𝑌𝑖,𝑡 = 𝑦𝑖,𝑡 |𝛾𝑖,𝑡 , 𝛿𝑖 ) =
(
) (1 + 𝛿𝑖 )−𝑦𝑖,𝑡
𝛤(𝛾𝑖,𝑡 )𝛤(𝑦𝑖,𝑡 + 1) 1 + 𝛿𝑖
𝛤(𝛾𝑖,𝑡 + 𝑦𝑖,𝑡 )
(1)
𝛾𝑖,𝑡 = 𝑒𝑥𝑝( 𝛽0 + 𝛽1 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽2 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛽11 𝐴𝑔𝑒1𝑖 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽12 𝐴𝑔𝑒1𝑖 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛽21 𝐴𝑔𝑒2𝑖 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽22 𝐴𝑔𝑒2𝑖 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛽31 𝐴𝑔𝑒3𝑖 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽32 𝐴𝑔𝑒3𝑖 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
(2A)
𝛽3 𝐻𝑡 )
𝛾𝑖,𝑡 = 𝑒𝑥𝑝( 𝛽 ′ 0 + 𝛽 ′1 𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 + 𝛽 ′ 2 𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 +
𝛽 ′11 𝐹𝑒𝑚𝑎𝑙𝑒𝑖 ∗ 𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 + 𝛽 ′12 𝐹𝑒𝑚𝑎𝑙𝑒𝑖 ∗ 𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 +
𝛽′3 𝐻𝑡 )
(2B)
where 𝐴𝑔𝑒1𝑖 , 𝐴𝑔𝑒2𝑖 , and 𝐴𝑔𝑒3𝑖 are dummy variables, and each takes the value of 1 if
the age of individual i is in under—18, 19—29, or 30—39. Likewise, 𝐹𝑒𝑚𝑎𝑙𝑒𝑖 is coded
1 if individual i is female and 0 if male.
7
t-statistics of the parameter estimate in the first regression for the first and second model are 136.53 and
119.57. t-statistics of the parameter estimate in the second regression for the first and second model are
145.58 and 119.27.
16
Estimation
The individual level panel data we collected allow us to model the behavioral heterogeneities
across individuals (Greene 2011). Both the fixed effect and the random effect models control for
all time-invariant effects specific to each individual. However, the fixed effect model can only
identify the unobserved individual effects to be correlated with the included regressors. In
contrast, the random effect model produces inconsistent estimators when a correlation exists
between the regressors and the random effect. The Hausman (1978) specification test reveals that
we cannot reject the null hypothesis. This rejection indicates that the estimates based on the two
modelling approaches should not differ systematically8. Consequently, we adopted the fixed
effect model, which is less restrictive. The fixed effect model enables us to account for
unobserved individual specific heterogeneity, which could result in a bias against the number of
log-ins. The fixed effect negative binomial model proposed by Hausman et al. (1984) relates the
joint probability of the counts for each individual to the sum of the counts for the individual
∑𝑡 𝑦𝑖,𝑡 . Parameters are estimated to maximize the following log-likelihood function, where 𝑤𝑖
indicates individual weights (Equation 3). This modeling specification addresses the issues of
both the over-dispersion in data distribution and the potential serial correlation that may stem
from an individual specific heterogeneity.
𝑙𝑛𝐿 = ∑ 𝑤𝑖 [𝑙𝑛𝛤 (∑ 𝛾𝑖,𝑡 ) + 𝑙𝑛𝛤 (∑ 𝑦𝑖,𝑡 + 1) − 𝑙𝑛𝛤 (∑ 𝛾𝑖,𝑡 + ∑ 𝑦𝑖,𝑡 )
𝑖
𝑡
𝑡
𝑡
𝑡
(3)
+ ∑{𝑙𝑛𝛤(𝛾𝑖,𝑡 + 𝑦𝑖,𝑡 ) − 𝑙𝑛𝛤(𝛾𝑖,𝑡 ) − 𝑙𝑛𝛤(𝑦𝑖,𝑡 + 1)}]
𝑡
8
Prob>chi2 = 0.0000 for both models.
17
RESULTS
Homophily in Mobile Social Network Platforms
Figure 1 provides a holistic representation with respect to the extent of age homophily across
four age clusters (the darker the color, the stronger the age homophily). Online age homophily is
most prevalent in the teenager group (under—18) and is least pervasive in the senior group (over
40). The diagonal line shows that the propensity of age homophily diminishes gradually as the
age of the group increases.
Figure 1: Extent of online age homophily across four age groups
Table 2A presents the statistical analyses, showing that age homophily exists across all four
age clusters, but in different directions and magnitudes. Thus, Hypothesis 1 is partially
supported. Teenagers (the under—18 group) increase their SNS usage frequency when new
members of a similar age enter their social networks (p<0.001). These adolescent users,
18
however, tend to reduce their usage when new members from different age groups become
connected with them (p<0.001). In contrast to teenagers, users in their 20s (the 19—29 group)
welcome new members regardless of their age group (p<0.001). However, users in this group
also exhibit age homophily: they are more positively responsive to new members from their own
age group than to those from other age clusters.
The remaining two age groups do not show strong age homophily. Users in their 30s (the
30—39 group) do not increase their SNS usage frequency upon the arrival of new members of a
similar age (p>0.1). Interestingly, they react positively to new members of dissimilar ages
(p<0.001). Finally, the most senior group in our sample (the over 40 group) exhibits a similar
pattern in that they increase their usage frequency when new members with age dissimilarity
enter their social networks. In contrast, they reduce the frequency when new members of a
similar age join the network. These findings collectively suggest that age homophily is pervasive
among teenagers and users in their 20s. However, users in their 30s and 40s wish to socialize
with people in other age categories, particularly with younger users.
Table 2A. Estimation Results for Full Sample
Age group
Variable
Parameter
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
𝛽1 + 𝛽11
under—18
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝛽2 + 𝛽12
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
𝛽1 + 𝛽21
19—29
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝛽2 + 𝛽22
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
𝛽1 + 𝛽31
30—39
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝛽2 + 𝛽32
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
𝛽1
over—40
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝛽2
𝐻𝑡
𝛽3
Observations
Number of users
LL


Coefficient
0.0013414***
-0.0001214***
0.0006973***
0.0000608***
0.0000447
0.0003149***
-0.0003986***
0.0002374***
-0.1776356***
Standard Errors
0.0001903
0.0000182
0.000031
5.63e-06
0.0000274
0.0000245
0.0000642
0 .0000311
0.0185301
944,224
9,484
-1,957,090.6
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
19
Table 2B suggests that gender homophily is prevalent only in the male group. In contrast, the
female group exhibits gender heterophily. That is, female users reduce their frequency when
members of a female group enter their social networks, but they increase their SNS usage
frequency when members of a male group enter their social network. On the other hand, male
users increase their usage frequency when other male users enter their social networks, but
reduce their usage frequency when female users enter their social networks. These findings
resonate with earlier findings that focus on gender homophily in offline social networks (e.g.,
Ibarra 1992, 1997; Brass 1985) and they lend credence to the notion that men tend to build more
sex homophilous networks than women.
Maccoby (1998) found similar asymmetric patterns in gender homophily, noting that boy
groups tend to be more cohesive than girl groups. The observed difference between females and
males with respect to homophilous propensities might be caused by the distinct ways that resolve
intransitivity (Eder and Hallinan 1978; Tuma and Hallinan 1979). Furthermore, both gender
types choose men as a network path through which to acquire information and knowledge in
domains that extend beyond their usual reach (Aldrich et al. 1989; Bernard et al 1988).
Table 2B. Estimation Results for Full Sample
Gender group
Female
Male


Variable
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝐻𝑡
Observations
Number of users
LL
Parameter
𝛽′1 + 𝛽′11
𝛽′2 + 𝛽′12
𝛽′1
𝛽′2
𝛽3
Coefficient
-0.0001178***
0.0004172***
0.0003577***
-0.000214***
-0.1742745***
SE (Standard Errors)
0.00003
0.0000597
0.0000607
0.0000309
0.0190271
944,224
9,484
-1,960,280
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
20
Multi-Homing and Online Homophily
Tables 3A and 3B suggest that individual users who adopt both SNS platforms (“multihoming”) differ from those who consume only one SNS platform (“single-homing”) with respect
to their age and gender homophily tendencies. Table 3A shows that teenagers loyal to a single
SNS channel increase their usage frequency when new members with age similarity enter the
social network. However, these adolescent users decrease their usage frequency when new
members are from different age groups. An exactly opposite pattern was observed for single SNS
users in their 40s. These user groups decrease, rather than increase, their usage frequency when
new members of the same age group join their social networks. Furthermore, single SNS users in
their 20s and 30s exhibit age homophily as they react more positively to new members with a
similar age than to new members with a dissimilar age. Users of multiple SNS exhibit different
age homophily patterns across diverse age categories. Multiple SNS users in their 10s and 40s
did not show age homophily tendency as their usage frequency was not affected by the addition
of new members regardless of age groups.
Table 3B shows that female users who consume only one SNS platform reduce their usage
frequency when new members of same gender (i.e. female) enter their social network. However,
they increase their usage frequency when new members of different gender (i.e. male) enter their
social network. Male users loyal to a single SNS are found to be the exact opposite. They
increase their usage frequency when new members of same gender (i.e. male) enter their social
network, but reduce their frequency when new members of different gender (i.e. female) enter
their social network. Additionally, users of multiple SNS do not react negatively to new
members regardless of their gender. This result is congruent with the prior argument that users
exhibit multi-homing behavior to enhance the network benefits. Specifically, female users who
21
Table 3A. Estimation Results for Users of single and multiple SNS platforms
Single-Homing Users
Standard
Age group Variable Parameter Coefficient
Errors
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽11
0.00173***
0.0002092
under—18
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽12 -0.0001187*** 0.0000199
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽21 0.0007379*** 0.0000346
19—29
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽22
0.000043***
6.49e-06
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽31 0.0003967*** 0.0000324
30—39
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽32 0.0001579*** 0.0000279
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
-0.0003934***
0.00007
𝛽1
over—40
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
0.0002963*** 0.0000339
𝛽2
-0.1982788*** 0.0208879
𝐻𝑡
𝛽3
Observations
789,716
Number of users
8,171
LL
-1.613e+06


Multi-Homing Users
Standard
Coefficient
Errors
0.0001943
0.0004586
-8.96e-06
0.0000461
0.0007154*** 0.0000727
0.0001108***
0.0000115
0.000074
0.0000594
0.0003547*** 0.0000559
-0.0000155
0.0001702
0.0000775
0.0000804
-0.0779072**
0.039059
154,508
1,313
-340630.68
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
Table 3B. Estimation Results for Users of single and multiple SNS platforms
Single-Homing Users
Gender
Standard
Variable
Parameter Coefficient
group
Errors
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′1 + 𝛽′11 -0.0000434*** 0.0000352
Female
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′2 + 𝛽′12 0.0004333*** 0.0000701
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
0.0004712*** 0.0000713
𝛽′1
Male
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
-0.0002299*** 0.0000361
𝛽′2
-0.1877742*** 0.0214597
𝐻𝑡
𝛽3
Observations
789,716
Number of users
8,171
LL
-1,614,826.9


Multi-Homing Users
Standard
Coefficient
Errors
0.0002063*** 0.0000608
-0.0000426
0.0001189
-0.000152
0.0001203
0.0001816*** 0.0000625
-0.1162095*** 0.0401782
154,508
1,313
-341,388.98
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
adopt multiple SNS increase their usage frequency when new members of the same gender enter
their social network, but they do not react to the addition of new members of different gender.
Male multi-homing users are not affected by the addition of new male members, but they
increase their usage frequency when new female members enter their social network. These
results provide partial support for Hypothesis 2.
22
Platform Openness and Online Homophily
Finally, Table 4 reports the moderating effect of SNS type on age and gender homophily. It
should be noted that users who adopt both SNS platforms are excluded from this analysis
because they may contaminate the results. CS users react positively to the addition of new
members regardless of age. However, teenagers using CS exhibit strong age homophily. Their
usage frequency increases more substantially when new members from their own age group
enter their social networks than when new members from different age clusters join the network.
Similar patterns are observed for CS users who are in their 20s and 40s. Among OS users,
teenagers consistently exhibit strong age homophily (p<0.001). Users in their 30s are also
influenced by age homophily, although not to such a significant degree as teenagers. However,
these age homophilous patterns were not detected for OS users in their 20s and 40s.
Additionally, both female and male groups exhibit weak gender homophily regardless of the
platform openness (See Appendix A). Overall, the findings suggest that OS users behave
differently from CS users in terms of their reaction to the addition of new members to their
networks, especially with respect to the age homophily. This finding lends support for
Hypothesis 3.
Robustness Checks
Causality between Homing-Preference and Homophily Behaviors
We find that multi-homing mobile SNS users exhibit a homophily tendency different from
single-homing users in terms of its directions and magnitudes. However, the observed flow of
causality between homing-preference and homophily behaviors should be clarified to address the
23
Table 4. Estimation Results for open and closed SNS
Age group
Variable
Parameter
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽11
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽12
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽21
19—29
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽22
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽31
30—39
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽32
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
𝛽1
over—40
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝛽2
𝐻𝑡
𝛽3
Observations
Number of users
LL
under—18



OS
CS
Standard
Errors
0.0048713*** 0.0009885
0.0010906
0.0009036
0.00141
0.000935
0.0023796*** 0.0008662
0.0027855*** 0.0009599
0.0014827
0.0009063
0.0015203
0.0009817
0.0017578*
0.0009061
-0.127454*
0.0644556
217,506
2,465
-495,398.86
Standard
Errors
0.0040064*** 0.0003685
0.0005516**
0.0002686
0.0014422*** 0.0003149
0.0008978*** 0.0002659
0.0014369*** 0.0002693
0.000841***
0.0002809
0.0011718***
0.0002865
0.0008234*** 0.0002718
-0.0581756
0.0702908
572,210
5,706
-1,116,931.5
Coefficient
Coefficient
Note: Users with only a single SNS platform (N=8,171) are included in the analysis. Users with
multiple SNS platforms (N=1,313) are excluded as they may confound the results.
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
potential endogeneity issue. To validate the direction of causality, we conducted robustness
checks by scrutinizing users who have switched their homing preference from either single to
multiple or vice versa. Because our data doesn’t cover the entire span of each user’s SNS usage,
we adopted a rigorous process to identify a user as switching his or her homing preference if all
three of the following conditions are met9: (1) he or she initially used a single platform, but
ended up using multiple platforms10; (2) he or she maintained a single-homing status, at least, for
one week from the first day the user subscribed to the service; and (3) a user who switched from
single-homing to multi-homing maintained the new status for at least one week11. As a result of
9
We define a user switching his or her homing preference from multi-homing to single-homing in the
same way.
10
A user’s homing status at time t is single-homing if he/she uses only one SNS at time t, or multihoming if he/she uses both SNSs at time t.
11
As we will discuss, we replicate the result with a different cutoff value (i.e. at least two weeks) which is
used to classify an individual as changing his or her homing-preference.
24
these conditions, 262 users were identified as switching their homing preferences from singlehoming to multi-homing, and 256 users were classified as switching from multi-homing to
single-homing.
Next, we defined a dummy variable 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 indicating whether or not user i switches
his or her homing preference since time t. If individual i switches his or her homing preference
since time 𝑡 ∗ , 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 will take the value of 1 for all 𝑡 ≥ 𝑡 ∗ . We included this variable in
our model (Equation 2) because switching the homing preference could account for substantial
variations in the usage of each SNS. Then, we incorporated the interaction of 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 for
𝑁𝑆𝐴𝑖,𝑡−1 and 𝑁𝐷𝐴𝑖,𝑡−1 into our model to investigate our main question: Does the switching of
homing preference cause changes in users’ homophily propensities? In addition to these
analyses, we studied whether the effect of switching the homing preference on homophily
tendency varies across diverse age groups. The following model (Equation 4) is established by
incorporating the interactions of 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 × 𝑁𝑆𝐴𝑖,𝑡−1 and 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 × 𝑁𝐷𝐴𝑖,𝑡−1 for three
different age groups: (1) under—18, (2) 19—29, and (3) 30—39. It should be noted that the
over—40 group is used as a base group.
Table 5A presents the result of robustness check for individuals who have switched their
homing-preference during the study period. We find that a switch from single-homing to multihoming (i.e. adopting one more SNS) causes the change in age homophily propensity across all
four age groups, but in different degrees and directions. Users in their 20s (the 19—29 group)
exhibit a strong age heterophily when they are “single-homing”. They increase their usage
frequency when new members with age dissimilarity enter their social networks, but reduce their
usage frequency when new members with same age group enter their social network. However,
after they adopt multiple SNSs (i.e. multi-homing), their responses to users of the same and
25
𝛾𝑖,𝑡 = 𝑒𝑥𝑝( 𝛽0 + 𝛽1 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽2 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛽11 𝐴𝑔𝑒1𝑖 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽12 𝐴𝑔𝑒1𝑖 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛽21 𝐴𝑔𝑒2𝑖 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽22 𝐴𝑔𝑒2𝑖 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛽31 𝐴𝑔𝑒3𝑖 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛽32 𝐴𝑔𝑒3𝑖 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛼0 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 +
𝛼1 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛼2 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
(4)
𝛼11 𝐴𝑔𝑒1𝑖 ∗ 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛼12 𝐴𝑔𝑒1𝑖 ∗ 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛼21 𝐴𝑔𝑒2𝑖 ∗ 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛼22 𝐴𝑔𝑒2𝑖 ∗ 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛼31 𝐴𝑔𝑒3𝑖 ∗ 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 + 𝛼32 𝐴𝑔𝑒3𝑖 ∗ 𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡 ∗ 𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 +
𝛽3 𝐻𝑡 )
different age groups appear to be equally positive. Teenagers (the under—18 group) show a
similar tendency that switching to multi-homing reduces the degree of age heterophily. Contrary
to this, age heterophily tendencies exhibited by the 30—39 group are strengthened after they
adopted multiple SNSs. Moreover, the over—40 group shows their age heterophily tendencies as
they begin to adopt multiple SNSs. Taken together, asymmetric patterns are observed with
respect to homing preference and age homophily. A switch to multi-homing appears to reduce
the degree of age heterophily for the young groups (under—18 and 19—29 groups), but it
increases the degree of age heterophily for the senior group (30—39 & over—40 groups).
Table 5B reveals that a switch from multi-homing to single-homing results in a significant
change in age homophily tendency across all four age groups. The age homophily tendencies
exhibited by the under—18 group are strengthened substantially after they subscribed to only
one SNS. Moreover, the 19—29 group exhibits significant age homophily tendencies as they
switched to a single SNS. However, the over—40 group, which did not show any age homophily
or heterophily tendencies before the switching to single-homing, exhibits strong age heterophily
after they began using only one SNS. These results collectively suggest that a user’s homing
26
Table 5A. Robustness Check Result (From Single-Homing to Multi-Homing)
Age group
under—18
19—29
30—39
over—40
Variable
Parameter
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽11 (+𝛼1 + 𝛼11 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽12 (+𝛼2 + 𝛼12 )
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽21 (+𝛼1 + 𝛼21 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽22 (+𝛼2 + 𝛼22 )
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽31 (+𝛼1 + 𝛼31 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽32 (+𝛼2 + 𝛼32 )
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
𝛽1 (+𝛼1 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝛽2 (+𝛼2 )
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



𝛼0
𝛽3
From Single-Homing
To Multi-Homing
Coefficient
Coefficient
-0.0022512
0.0018366*
(0.0014816)
(0.0009549)
0.0003718***
0.0001041
(0.0001326)
(0.000087)
-0.0007035***
0.0003518**
(0.0002653)
(0.0001667)
0.0003902***
0.0003783***
(0.000059)
(0.0000348)
-0.0005779**
-0.0007331***
(0.0002518)
(0.000146)
0.0008626***
0.0038816***
(0.0002078)
(0.0008411)
-0.0001483
-0.0022445***
(0.0005666)
(0.0003695)
0.00022
0.0012999***
(0.0002632)
(0.0001766)
-0.2997325* (0.1738768)
-0.458293***(0.1135709)
25,484
262
-61,446.491
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
preference has a significant impact on his or her age homophily tendency. We obtained similar
results with respect to gender12 (See Appendix B). It is worth noting that those results are robust
to using different cutoff values for identifying a user as switching his or her homing preference
(See Appendix C).
12
We cannot observe the effect of changing homing preferences on gender homophily tendency when we
focused on the users who change their homing preferences from multi-homing to single-homing (See
Appendix B).
27
Table 5B. Results of Robustness Check (From Multi-Homing to Single-Homing)
Age group
under—18
19—29
30—39
over—40
Variable
Parameter
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽11 (+𝛼1 + 𝛼11 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽12 (+𝛼2 + 𝛼12 )
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽21 (+𝛼1 + 𝛼21 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽22 (+𝛼2 + 𝛼22 )
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1 𝛽1 + 𝛽31 (+𝛼1 + 𝛼31 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1 𝛽2 + 𝛽32 (+𝛼2 + 𝛼32 )
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
𝛽1 (+𝛼1 )
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝛽2 (+𝛼2 )
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



𝛼0
𝛽3
From Multi-Homing
To Single-Homing
Coefficient
Coefficient
0.0008129
0.014298***
(0.0015373)
(0.0023619)
-0.0003758***
-0.0014064***
(0.0001382)
(0.0002087)
0.0002579
0.0023875***
(0.0002175)
(0.0003323)
-0.0000131
-0.0000798*
(0.0000372)
(0.0000455)
0.0003642**
0.0006138**
(0.0001794)
(0.0003001)
-0.0103398***
-0.0001717
(0.0009878)
(0.0002751)
0.000622
-0.0062647***
(0.0005088)
(0.0007934)
-0.0001017
0.0029301***
(0.0002393)
(0.0003818)
-1.348388*** (0.2519029)
0.1481901 (0.1227929)
24,267
256
-49,413.443
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
Calibration with different cutoff values
As noted earlier, we identified a user as multi-homing if he or she utilizes another SNS
channel at least once a week. Because this condition could seem somewhat arbitrary or stringent,
we replicate our results using more relaxed condition that a user is considered as multi-homing if
he or she utilizes another SNS channel at least once per two weeks. We find that our results are
robust in response to the variations in defining and operationalizing the notion of multi-homing.
Similar to the earlier finding based on the conservative definition, single-homing SNS users
exhibit stronger age homophily than multi-homing users when the multi-homing condition is
relaxed. However, the difference with respect to the magnitude of age homophily changes
28
slightly as the condition is relaxed. For example, teenagers loyal to a single SNS channel exhibit
strong age homophily in both conditions. However, teenagers using multiple SNSs did not show
age homophily tendency when the conservative operationalization scheme was used (Table 3A).
However, when the relaxed scheme is adopted, adolescent users exhibit weak age homophily
(Table 6). Similar tendencies emerged with respect to gender homophily (See Appendix D).
Table 6. Estimation Results for Users of single and multiple SNS platforms
Single-Homing Users
Multi-Homing Users
Age group Variable Parameter Coefficient Standard Errors Coefficient Standard Errors
𝑁𝑆𝐴𝑖,𝑡−1 𝛽1 + 𝛽11 0.0026424***
0.0002582
0.0006278**
0.0002856
under—18
𝑁𝐷𝐴𝑖,𝑡−1 𝛽2 + 𝛽12 -0.0001568***
0.0000245
-0.00003
0.0000275
𝑁𝑆𝐴𝑖,𝑡−1 𝛽1 + 𝛽21 0.0009437***
0.0000456
0.0006458***
0.0000451
19—29
𝑁𝐷𝐴𝑖,𝑡−1 𝛽2 + 𝛽22
0.000108***
8.99e-06
0.0000494***
7.28e-06
𝑁𝑆𝐴𝑖,𝑡−1 𝛽1 + 𝛽31 0.0005758***
0.0000505
0.0000637*
0.0000376
30—39
𝑁𝐷𝐴𝑖,𝑡−1 𝛽2 + 𝛽32 0.0001592***
0.0000392
0.0002984***
0.0000351
𝑁𝑆𝐴𝑖,𝑡−1
-0.0000871
0.000088
-0.000593***
0.0001004
𝛽1
over—40
𝑁𝐷𝐴𝑖,𝑡−1
0.0002704***
0.0000415
0.0002868***
0.0000488
𝛽2
-0.2165384***
0.0247237
-0.1088911***
0.027992
𝐻𝑡
𝛽3
Observations
546,019
398,205
Number of users
5,807
3,677
LL
-108,9116.7
-866,874.94
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
Our results for open and closed SNS platforms are also found robust with respect to all
dimensions (i.e. age and gender). However, the results for gender homophily change slightly
when the relaxed scheme is adopted. Specifically, Table 7 shows that users of closed mobile
SNS exhibit stronger age homophily than users of open mobile SNS even when the relaxed
condition is enforced. Finally, the results for users of closed and open type SNS platforms with
respect to gender homophily are also robust, but the significance levels diminish slightly when
the relaxed condition is used (See Appendix D).
29
Table 7. Estimation Results for open and closed SNS
OS
Age group Variable Parameter Coefficient Standard Errors
𝑁𝑆𝐴𝑖,𝑡−1 𝛽1 + 𝛽11
-0.0015039
0.0014019
under—18
𝑁𝐷𝐴𝑖,𝑡−1 𝛽2 + 𝛽12
-0.0017983
0.0013163
𝑁𝑆𝐴𝑖,𝑡−1 𝛽1 + 𝛽21 -0.0028458**
0.0013719
19—29
𝑁𝐷𝐴𝑖,𝑡−1 𝛽2 + 𝛽22
0.0000678
0.0012483
𝑁𝑆𝐴𝑖,𝑡−1 𝛽1 + 𝛽31
0.0003493
0.0014799
30—39
𝑁𝐷𝐴𝑖,𝑡−1 𝛽2 + 𝛽32
-0.002114
0.0013236
𝑁𝑆𝐴𝑖,𝑡−1
-0.0013792
0.0014461
𝛽1
over—40
𝑁𝐷𝐴𝑖,𝑡−1
-.00017255
0.0013223
𝛽2
-0.3520112***
0.0931668
𝐻𝑡
𝛽3
Observations
96,425
Number of users
1,240
LL
-22,2741.17



CS
Coefficient Standard Errors
0.0052127***
0.0004231
0.0004334
0.0002982
0.0016872***
0.0003577
0.0008972***
0.0002958
0.0013219***
0.0002984
0.00096***
0.0003124
0.0011428***
0.0003187
0.0008727***
0.0003014
-0.0659224
0.0781915
449,594
4,567
-865,980.67
Note: Users with only a single SNS platform (N=5,807) are included in the analysis. Users with
multiple SNS platforms (N=3,677) are excluded.
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
IMPLICATIONS
This study has several implications for research and practice. For research, one of this study’s
key findings suggests that homophily is a complex and dynamic organizing principle that
facilitates and inhibits social relationships in online social network platforms. It is noteworthy
that asymmetric or unbalanced patterns emerged regarding the social interaction driven by age
and gender homophily in online social environments. For example, as opposed to adolescent
users who show a strong age homophily, senior users over 40 exhibit strong heterophily as they
prefer interacting with users in different age groups. Similarly, men tend to form strong
homophilous ties with other men on online SNSs, but women prefer socializing with men.
Structural irregularities that are seemingly prevalent in online social establishments prompt a
need for a final level of granularity when investigating the nature and pattern of homophily in
general and online homophily in particular. The asymmetric social relationships observed across
30
diverse sub-categories of a particular socio-demographic attribute (e.g., age or gender) deserve
additional attention from the research community.
Another important scholarly implication is that the effect of homophily on behavioral
isomorphism and social dynamics can be either beneficial or detrimental. Existing studies on
homophily and social influence (e.g., Aral et al. 2009, Trusov et al. 2010) have focused mainly
on the positive effect of homophily on social diffusion, peer-based cascading, and viral
spreading, but its negative aspect has been kept in silence. While homophily certainty can
reinforce the social bond, it can also weaken the healthy circulation of social influence and
damage the social integration. The findings of our study suggest that researchers should maintain
a more balanced view regarding the social and economic effects of homophily in online
environments.
This study also examined the association between platform characteristics and individuals’
homophily propensities. In addition, close attention was paid to the extent to which a user’s
multi-homing environment influences his or her homophily tendency. The findings indicate that
environmental factors (e.g., platform trait and multi-homing) characterizing online social
establishments can influence users’ norms and values towards social interactions and
interpersonal relationships. Network benefits accruing from multi-homing, as well as strong ties
driven by platform characteristics, both play important roles in determining one’s perception of
and attribution towards online homophily. Consequently, researchers should not view
individuals’ propensities to online homophily as a stationary state, but rather as a moving target
that can dynamically change in response to environmental inducements.
Traditionally, studies on homophily have typically conceptualized and measured
homophilous relationships based on the “status” of connections between nodes rather than the
31
“intensity” of connections. It is not uncommon for users who form friend relationships through
online SNSs to rarely interact and eventually unfriend each other. Therefore, research that uses
friend’s lists as a means to determine homophilous ties could overestimate the degree of
homophily, resulting in inaccuracies when interpreting the findings (Trusov et al. 2010). To
redress this oversight, we utilized the longitudinal log-in activities of SNS app users to gauge the
magnitude of users’ homophily tendencies. Furthermore, this study adopted a fixed effect model
of negative binomial regression, which addresses the issues of both the over-dispersion in data
distribution and the potential problem of serial correlation.
This study also has several practical implications. Although further inquiry is certainly called
for, users’ homophilous tendencies, particularly among adolescent age cohorts, do explain the
current outflow of membership experienced by Facebook and other major open SNS providers.
These SNS companies should understand the potential for precipitous decline caused by a
massive user migration to alternative SNS’s where members can take the full pleasure of
interacting with similar others. It seems that one format does not fit all when it comes to the
pattern and preference of social intercourse in online SNS spaces. To curtail this exodus, open
SNS platforms should provide users with closed socialization mechanisms in parallel with their
generally open online environment so that they can enjoy the best of both worlds. FB’s recent
acquisition of Whatsapp, a mobile messaging service company with only 50 employees, for $19
billion appears to be an effective portfolio strategy. This allows users in different age groups to
communicate and socialize through their choice of best mechanisms without costing Facebook
any revenue streams. Facebook CEO Mark Zuckerberg says that he will continue this portfolio
strategy in his interview with Businessweek in January 2014: “We just think that there are all
32
these different ways that people want to share, and that compressing them all into a single blue
app is not the right format of the future.”13
Alternatively, smaller SNS providers should focus on a specific age group rather than the
entire age spectrum, implementing a niche strategy to target the most profitable age segment. For
example, LinkedIn’s niche strategy has been successful as reflected in its share price having
more than tripled since its IPO in May 2011. Unlike Facebook, which promotes its subscribers in
all age groups to post their personal updates, LinkedIn has been targeting specific age groups,
particularly those who are looking to build careers and search for employment. Furthermore,
SNS companies should enhance their profitability by implementing more engaging advertising
campaigns that take advantage of users’ homophilous tendencies. Facebook has recently
launched a new advertising format similar to Twitter’s promoted tweets called “sponsored
stories,” which convert friends news feed activities into a paid ad by integrating Facebook’s
special functions, such as Likes and Check-Ins. If privacy concerns are adequately addressed,
this advertising scheme can be highly successful because it takes advantage of individuals’
homophilous traits related to consumption behaviors.
CONCLUSION
This study sought to explore the extent of homophily and the pattern of social structures in online
SNS platforms using actual SNS consumption data. The findings suggest that SNS users across
diverse socio-demographic groups exhibit homophily and heterophily, reacting differently to
13
http://www.businessweek.com/articles/2014-02-19/facebook-acquires-whatsapp-for-19-billion
33
new members depending on a new entrant’s age and gender. Interestingly, SNS users in their 40s
exhibit age heterophily, rather than homophily; they reduce their usage frequency when new
members of the same age cohort enter their social networks. Similarly, contrary to our
expectation, female users exhibit heterophily, preferring social interactions with male users.
However, whether an SNS is open or closed significantly moderates the homophily or
heterophily propensities of individual users across diverse cohorts. Whether users adopt a single
SNS or multiple SNS platforms can also predict their homophily propensities.
One of the key empirical regularities observed from the data suggests that social segregation
driven by age and gender differences appears to be pervasive and variegated on the major SNSs.
This stratification can prevent mutual understanding among different cohorts, resulting in
increased prejudice across different generations and genders. Consequently, social integrity can
become damaged and values and norms divergent. SNS providers and developers should respond
to this stratification and its potential for hostility by designing new services that reduce
generation gaps and encourage users from all life stages and gender groups to socialize and
understand their differences.
34
REFERENCE
Aldrich, H., Reese, P.R., and Dubini, P. 1989. "Women on the Verge of a Breakthrough:
Networking among Entrepreneurs in the United States and Italy," Entrepreneurship &
Regional Development (1:4), pp. 339-356.
Bernard, H. R., Killworth, P. D., Evans, M. J., McCarty, C., and Shelley, G. A. 1988. "Studying
Social Relations Cross-Culturally," Ethnology (27:2), pp. 155-179.
Blau, P.M. 1977. Inequality and Heterogeneity: A Primitive Theory of Social Structure. Free
Press New York.
Brass, D.J. 1985. "Men's and Women's Networks: A Study of Interaction Patterns and Influence
in an Organization," Academy of Management journal (28:2), pp. 327-343.
Cameron, A.C., and Trivedi, P.K. 1990. "Regression-Based Tests for Overdispersion in the
Poisson Model," Journal of Econometrics (46:3), pp. 347-364.
Cameron, A.C., and Trivedi, P.K. 2013. Regression Analysis of Count Data. Cambridge
university press.
DiMaggio, P., and Garip, F. 2012. "Network Effects and Social Inequality," Annual Review of
Sociology (38), pp. 93-118.
Doganoglu, T., and Wright, J. 2006. "Multihoming and Compatibility," International Journal of
Industrial Organization (24:1), pp. 45-67.
Eder, D., and Hallinan, M.T. 1978. "Sex Differences in Children's Friendships," American
Sociological Review (43:2), pp. 237-250.
Feld, S.L. 1982. "Social Structural Determinants of Similarity among Associates," American
Sociological Review (47:6), pp. 797-801.
Feld, S.L. 1984. "The Structured Use of Personal Associates," Social Forces (62:3), pp. 640-652.
Fiore, A.T., and Donath, J.S. 2005. "Homophily in Online Dating: When Do You Like Someone
Like Yourself?," CHI'05 Extended Abstracts on Human Factors in Computing Systems:
ACM, pp. 1371-1374.
Fischer, C.S. 1982. To Dwell among Friends: Personal Networks in Town and City. University
of Chicago Press.
Fischer, C.S., Stueve, C., Jones, L.M., Jackson, R.M., Gerson, K., and Baldassare, M. 1977.
Networks and Places: Social Relations in the Urban Setting. Free Press New York.
Friedkin, N.E. 2006. A Structural Theory of Social Influence. Cambridge University Press.
Gilbert, E., and Karahalios, K. 2009. "Predicting Tie Strength with Social Media," Proceedings
of the SIGCHI Conference on Human Factors in Computing Systems: ACM, pp. 211-220.
Greene, W.H. 2011. Econometric Analysis. Prentice Hall.
Hausman, J.A. 1978. "Specification Tests in Econometrics," Econometrica: Journal of the
Econometric Society (46:6), pp. 1251-1271.
Hausman, J.A., Hall, B.H., and Griliches, Z. 1984. "Econometric Models for Count Data with an
Application to the Patents-R&D Relationship," Econometrica (52:4), pp. 909-938.
Huckfeldt, R.R., and Sprague, J. 1995. "Citizens, Politics, and Social Communication:
Information and Influence in an Election Campaign,"NewYork: Cambridge University Press.
Ibarra, H. 1992. "Homophily and Differential Returns: Sex Differences in Network Structure and
Access in an Advertising Firm," Administrative Science Quarterly (37:3), pp.442-447.
Ibarra, H. 1997. "Paving an Alternative Route: Gender Differences in Managerial Networks,"
Social psychology quarterly (60:1), pp. 91-102.
35
Jung, G., and Lee, B. 2010. "Analysis on Social Network Adoption According to the Change of
Network Topology: The Impact of Open Api to Adoption of Facebook," Proceedings of the
12th International Conference on Electronic Commerce: Roadmap for the Future of
Electronic Business: ACM, pp. 23-32.
Kalleberg, A., David K., Peter M., and Joe S. 1996. Organizations in America: Analyzing Their
Structures and Human Resource Practices. Sage
Kalmijn, M., and Vermunt, J.K. 2007. "Homogeneity of Social Networks by Age and Marital
Status: A Multilevel Analysis of Ego-Centered Networks," Social Networks (29:1), pp. 2543.
Katz, M.L., and Shapiro, C. 1994. "Systems Competition and Network Effects," Journal of
economic perspectives (8:2), pp. 93-93.
Kohli, M. 1988. "Ageing as a Challenge for Sociological Theory," Ageing and Society (8:4), pp.
367-394.
Kossinets, G., and Watts, D.J. 2006. Empirical analysis of an evolving social network. Science,
311(5757), 88–90.
Kossinets, G., and Watts, D.J. 2009. "Origins of Homophily in an Evolving Social Network,"
American Journal of Sociology (115:2), pp. 405-450.
Lazarsfeld, P.F., and Merton, R.K. 1954. "Friendship as a Social Process: A Substantive and
Methodological Analysis," Freedom and control in modern society (18:1), pp. 18-66.
Maccoby, E.E. 1998. The Two Sexes: Growing up Apart, Coming Together. Harvard University
Press.
Marsden, P.V. 1988. "Homogeneity in Confiding Relations," Social networks (10:1), pp. 57-76.
McPherson, J.M., and Smith-Lovin, L. 1987. "Homophily in Voluntary Organizations: Status
Distance and the Composition of Face-to-Face Groups," American sociological review
(53:3), pp. 370-379.
McPherson, M., Smith-Lovin, L., and Cook, J.M. 2001. "Birds of a Feather: Homophily,"
Annual Review of Sociology (27), pp. 415-444.
Mital, M., and Sarkar, S. 2011. "Multihoming Behavior of Users in Social Networking Web
Sites: A Theoretical Model," Information Technology & People (24:4), pp. 378-392.
Rochet, J.C., and Tirole, J. 2003. "Platform Competition in Two‐Sided Markets," Journal of the
European Economic Association (1:4), pp. 990-1029.
Shrum, W., Cheek Jr, N.H., and MacD, S. 1988. "Friendship in School: Gender and Racial
Homophily," Sociology of Education (61:4), pp. 227-239.
Singla, P., and Richardson, M. 2008. "Yes, There Is a Correlation:-from Social Networks to
Personal Behavior on the Web," Proceedings of the 17th international conference on World
Wide Web: ACM, pp. 655-664.
Smith, S., Maas, I., and van Tubergen, F. 2014. "Ethnic Ingroup Friendships in Schools: Testing
the by-Product Hypothesis in England, Germany, the Netherlands and Sweden," Social
Networks (39), pp. 33-45.
Thelwall, M. 2009. “Homophily in Myspace,” Journal of the American Society for Information
Science and Technology, (60:2), pp. 219-231
Torche, F. 2010. “Educational Assortative Mating and Economic Inequality: A Comparative
Analysis of Three Latin American Countries.” Demography (47:2), pp. 481–502.
Trusov, M., Bodapati, A.V., and Bucklin, R.E. 2010. "Determining Influential Users in Internet
Social Networks," Journal of Marketing Research (47:4), pp. 643-658.
36
Tuma, N.B., and Hallinan, M.T. 1979. "The Effects of Sex, Race, and Achievement on
Schoolchildren's Friendships," Social Forces (57:4), pp. 1265-1285.
Uhlenberg, P., and de Jong Gierveld, J. 2004. "Age-Segregation in Later Life: An Examination
of Personal Networks," Ageing and Society (24:1), pp. 5-28.
Verbrugge, L.M. 1977. "The Structure of Adult Friendship Choices," Social Forces (56:2), pp.
576-597.
37
Appendix A
Table A1. Estimation Results for open and closed SNS
Gender
group
Female
Male



Variable
Parameter
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′1 + 𝛽′11
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′2 + 𝛽′12
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝛽′1
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝛽′2
𝐻𝑡
𝛽3
Observations
Number of users
LL
OS
CS
Standard
Errors
0.0031349*** 0.0008697
0.00251***
0.0008694
0.002938***
0.0008683
0.00251***
0.0008694
-0.0668241
0.0628666
217,506
2,465
-496,093.03
Standard
Errors
0.0012306*** 0.0002649
0.0010498*** 0.0002667
0.0010801*** 0.0002676
0.0010498*** 0.0002667
-0.0240027
0.0696479
572,210
5,706
-1,118,368.3
Coefficient
Coefficient
A complete set of time dummy variables are included to account for fixed time effects.
Interaction term for 𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 (𝛽′12 ) is dropped in estimation process because of multicollinearity.
***p<0.01, **p<0.05, *p<0.1.
38
Appendix B
Table B1. Robustness Check Result (From Single-Homing to Multi-Homing)
Age group
Variable
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Female
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Male
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



From Single-Homing
To Multi-Homing
Coefficient
Standard Error
Coefficient
Standard Error
-0.0001783
0.000264
0.0012956**
0.0005948
0.0014379***
0.0005304
-0.0005517
0.0004873
0.00049
0.000541
0.0019639
0.0012119
0.0002206
0.0002799
-0.0017689*
0.0009551
0.9014551*** (0.2232266)
-0.488058*** (0.1152497)
26,957
276
-65,048.194
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
Table B2. Robustness Check Result (From Multi-Homing to Single-Homing)
Age group
Variable
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Female
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Male
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



From Multi-Homing
To Single-Homing
Coefficient
Standard Error
Coefficient
Standard Error
-0.0008734
0.000651
-0.0003153
0.0002955
1.25e-06
0.00054
-0.0000451
0.0005783
-0.0010433
0.0013184
-0.0004852
0.0005677
-0.0004075
0.0010607
-0.0004539
0.0002858
0.4086392* (0.2345436)
-0.0617455 (0.1251295)
25,637
270
-52,563.43
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
39
Appendix C
We replicated our result using more strict cutoff value to identify a user as switching his or her
homing preference: (1) He or she initially used a single platform, but ended up consuming
multiple platforms; (2) He or she maintained a single-homing status, at least, for two weeks from
the first day the user subscribed to the service; (3) A user who switched from single-homing to
multi-homing maintained the new status for at least two weeks.
Table C1. Robustness Check Result (From Single-Homing to Multi-Homing)
Age group
Variable
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
under—18
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
19—29
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
30—39
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
over—40
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



From Single-Homing
To Multi-Homing
Coefficient
Standard Error
Coefficient
Standard Error
-0.0036762**
0.001562
-0.0002808
0.0010902
0.0004093***
0.0001388
0.0000875
0.0000976
-0.0014552***
0.000281
-0.000869***
0.0001923
0.0004598***
0.0000641
0.0004489***
0.0000397
-0.0004725*
0.0002533
-0.0002431
0.0001614
0.0007093***
0.000212
0.0019064**
0.00092430
-0.0006285
0.0006192
-0.0012157***
0.0004121
0.0002402
0.0002826
0.0006207***
0.0001998
0.0139997 (0.1850528)
-0.5692066*** (0.1217267)
20,841
216
-51,301.453
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
Table C2. Robustness Check Result (From Multi-Homing to Single-Homing)
Age group
Variable
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
under—18
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
19—29
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
30—39
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑁𝑆𝐴𝑔𝑒𝑖,𝑡−1
over—40
𝑁𝐷𝐴𝑔𝑒𝑖,𝑡−1
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



From Multi-Homing
To Single-Homing
Coefficient
Standard Error
Coefficient
Standard Error
0.0065076***
0.0020747
0.0044642
0.0027527
-0.0010783***
0.0001932
-0.0012486***
0.0002298
-0.0003572
0.0002838
-0.0016118***
0.0004397
-0.0000471
0.0000447
-0.0000528
0.0000541
0.0007159***
0.0002598
0.0039706***
0.000375
-0.0092541***
0.0011475
-0.0032727***
0.0003529
0.0022168***
0.0006687
-0.000225
0.0009645
-0.0010559***
0.0003172
-0.000771
0.0004847
0.9042376*** (0.312335)
-0.1317212 (0.1528269)
15,211
156
-30,771.017
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
40
Table C3. Robustness Check Result (From Single-Homing to Multi-Homing)
Age group
Variable
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Female
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Male
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



From Single-Homing
To Multi-Homing
Coefficient
Standard Error
Coefficient
Standard Error
-0.0001034
0.0002977
0.0007373
0.0006984
0.0013029**
0.0006065
-0.0000137
0.0005718
0.0003482
0.0006174
0.0011888
0.0014042
0.0002335
0.0003187
-0.0010831
0.0011112
0.8028635*** (0.2605821)
-0.6109768*** (0.1246535)
20,841
216
-51,544.584
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
Table C4. Robustness Check Result (From Multi-Homing to Single-Homing)
Age group
Variable
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Female
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
Male
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔𝑖,𝑡
𝐻𝑡
Observations
Number of users
LL



From Multi-Homing
To Single-Homing
Coefficient
Standard Error
Coefficient
Standard Error
-0.0010419
0.0008878
-0.0002251
0.0003806
0.0002083
0.0007265
-0.0002676
0.0007456
-0.0016351
0.0017551
-0.0008183
0.0007293
0.0005106
0.0014228
0.0000347
0.0003679
0.4506537 (0.3224106)
-0.135268 (0.1643776)
15,211
156
-31,002.587
A complete set of time dummy variables are included to account for fixed time effects.
Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.
41
Appendix D
Table D1. Estimation Results for Users of single and multiple SNS platforms
Single-Homing Users
Gender
Standard
Variable
Parameter Coefficient
group
Errors
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′1 + 𝛽′11
0.0000175
0.0000501
Female
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′2 + 𝛽′12 0.0005157*** 0.0001001
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
0.0008438*** 0.0001016
𝛽′1
Male
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
-0.0003844*** 0.0000511
𝛽′2
-0.1984739*** 0.0257148
𝐻𝑡
𝛽3
Observations
546,019
Number of users
5,807
LL
-1,091,013.1


Multi-Homing Users
Standard
Coefficient
Errors
0.0000424
0.0000417
0.0000512
0.0000825
-0.0002519*** 0.0000833
0.0001499*** 0.0000424
-0.1399022*** 0.0286464
398,205
3,677
-867,949.92
A complete set of time dummy variables are included to account for fixed time effects.
***p<0.01, **p<0.05, *p<0.1.
Table D3. Estimation Results for open and closed SNS
Gender
group
Variable
Parameter
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′1 + 𝛽′11
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 𝛽′2 + 𝛽′12
𝑁𝑆𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝛽′1
Male
𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1
𝛽′2
𝐻𝑡
𝛽3
Observations
Number of users
LL
Female



OS
CS
Standard
Errors
0.0008374
0.0012532
-0.0000408
0.0012503
0.0007496
0.0012508
-0.0000408
0.0012503
-0.2279856**
0.0900515
96,425
1,240
-223,293.63
Standard
Errors
0.0011841***
0.0002945
0.0010099*** 0.0002968
0.0010207*** 0.0002975
0.0010099*** 0.0002968
-0.0452016
0.0776164
449,594
4,567
-867,431.77
Coefficient
Coefficient
A complete set of time dummy variables are included to account for fixed time effects.
Interaction term for 𝑁𝐷𝐺𝑒𝑛𝑑𝑒𝑟𝑖,𝑡−1 (𝛽′12 ) is dropped in estimation process because of multicollinearity.
***p<0.01, **p<0.05, *p<0.1.
42