A reflection on discussing both sides of the social capital argument presented in the articles you read this week. What is digital inequality? ?What risks and needs do you negot

Whose Space? Differences Among Users and Non-Users of Social Network Sites

Eszter Hargittai

Communication Studies and Sociology

Northwestern University

Are there systematic differences between people who use social network sites and those

who stay away, despite a familiarity with them? Based on data from a survey adminis-

tered to a diverse group of young adults, this article looks at the predictors of SNS

usage, with particular focus on Facebook, MySpace, Xanga, and Friendster. Findings

suggest that use of such sites is not randomly distributed across a group of highly wired

users. A person’s gender, race and ethnicity, and parental educational background are

all associated with use, but in most cases only when the aggregate concept of social net-

work sites is disaggregated by service. Additionally, people with more experience and

autonomy of use are more likely to be users of such sites. Unequal participation based

on user background suggests that differential adoption of such services may be contrib-

uting to digital inequality.

doi:10.1111/j.1083-6101.2007.00396.x

Introduction

Social network sites (SNSs) have become some of the most popular online destina- tions in recent years (comScore, 2007a, 2007b). Not surprisingly, this level of user

attraction has been accompanied by much coverage in the popular press, including speculations about the potential gains and harms stemming from the use of SNS

services (Hempel, 2005; Magid, 2006; Stafford, 2006). Academic researchers have started studying the use of SNSs, with questions ranging from their role in identity

construction and expression (boyd & Heer, 2006) to the building and maintenance of social capital (e.g., Ellison, Steinfeld, & Lampe, 2007) and concerns about privacy (e.g., Gross & Acquisti, 2005; Hodge, 2006). While these areas of inquiry are all

important and worthy of exploration, a significant antecedent question has been largely ignored: Are there systematic differences between who is and who is not a SNS

user, and are people equally likely to join the various types of services that exist? This article sets out to address this question.

A significant challenge for studies trying to answer questions about who is and is not using SNSs is that the samples on which they are based (e.g., Ellison et al., 2007)

Journal of Computer-Mediated Communication

276 Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association

typically include such a small number of non-users that there is little variance present to explain differentiated basic adoption of the services. On the rare occasions when

data have been available on non-users in addition to users, the focus of the studies has been elsewhere. For example, Pasek, More, and Romer (2007) have disaggregated

data by site and variance on the usage of SNSs, but they look at the predictive power of SNS usage on civic engagement, employing SNSs as an independent variable, rather than exploring what explains their use in the first place. This article fills

a gap in the literature by: (1) explaining differences in SNS adoption and (2) dis- aggregating SNS usage by specific service to see whether it is possible to predict use of

one service over another based on the background characteristics of the user, infor- mation about the social context of use, and experiences with the medium.

Disaggregating usage by site also makes an important methodological contribu- tion to the study of SNSs. As the results show, disaggregating which specific site one

is researching is important, because people do not randomly select into their uses, and aggregate analyses of SNS use may make it difficult to identify important trends. This suggests that researchers should tread lightly when generalizing from studies

about the use of one SNS to the use of another such service. While these sites do share commonalities, they also have distinct features—whether at the level of site design or

the particular communities who comprise their user base—that may attract different populations and may encourage different types of activities. Thus, an examination of

SNSs both in the aggregate and with respect to specific sites is important in order to gain a better understanding of how use of such sites is spreading across various

population segments and the social implications of their usage.

Differentiating Types of Internet Uses

The New Yorker’s now-classic cartoon proclaimed in 1993 that ‘‘[o]n the Internet,

nobody knows you’re a dog’’ (Steiner, 1993), suggesting that identity was so hidden online that opportunities would be widely open to all, regardless of background

characteristics that may have traditionally disadvantaged some people compared to others. The idea that people would be on an equal footing online assumes that

offline characteristics are not mirrored in people’s online pursuits. However, sub- sequent research has found this not to be the case, for example, with respect to

gender identity (Herring, 1993). Researchers have observed that despite initial impressions and arguments about how users shed their offline identities in online interactions (Turkle, 1995), offline identities very much carry over to online behavior

(boyd, 2001; Smith & Kollock, 1999). This suggests that the Internet is not necessarily leveling the playing field in the way that the above-mentioned cartoon would have us

believe, given that people bring constraints and opportunities from their offline lives with them to their online interactions and activities.

Indeed, studies looking at how different people use the Internet in their everyday lives have found systematic differences across types of users. For example, even after

women caught up with men (in the United States) concerning basic connectivity

Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association 277

statistics, their uses continued to differ. Men have been shown to spend more time online and claim higher-level skills than women (Bimber, 2000; Hargittai & Shafer,

2006; Jackson, Ervin, Gardner, & Schmitt, 2001; Ono & Zavodny, 2003), consistent with earlier literature on women and technology use more generally (Frissen, 1995; Hall &

Cooper, 1991; Herring, 1994; Livingstone, 1992). Factors such as socioeconomic status have also been shown to predict types of Internet uses (Howard, Rainie, & Jones, 2001; Livingstone & Helsper, 2007; Madden & Rainie, 2003). For example, so-called ‘‘capital-

enhancing’’ activities (DiMaggio & Hargittai, 2002), such as looking for financial, polit- ical, or government information online, are associated with socioeconomic status

(Howard et al., 2001). Moreover, the circumstances under which people use the medium—such as their autonomy (Hassani, 2006) and experience of use (Howard

et al., 2001)—are also related to the purposes to which they put the medium. Research has shown that more locations where one has Internet access and more time spent online

are associated with more diverse types of uses (Hargittai & Hinnant, 2005). Research on refined understandings of the digital divide has found that even once

people go online, differences exist among their online pursuits (DiMaggio, Hargittai,

Celeste, & Shafer, 2004; Hargittai, 2002, 2007; Livingstone & Helsper, 2007; Mossberger, Tolbert, & Stansbury, 2003; van Dijk, 2005). Given that various back-

ground characteristics of people, the context of their Internet uses, and their level of experience have all been shown to influence types of Web uses in general, it is worth

considering whether they may also relate to social network site usage in particular. That is, given earlier work on differentiated Internet use among people from differ-

ent backgrounds, there is no reason to assume equal adoption of SNSs across popu- lation segments. Work that focuses solely on users of social network sites excludes, by

definition, people who are not SNS users. Insofar as these people are systematically different from those who embrace these services, it is problematic not to know anything about them, since researchers thereby risk unintentionally excluding entire

groups of people from discussion about SNSs.

The Challenges of Studying SNS Adoption

An important reason for the scarcity of work that predicts SNS usage is the lack of appropriate data necessary to address such questions. Despite Internet user studies

starting to focus on particular online behaviors, rather than considering all online actions to be uniform (Howard & Jones, 2004; Wellman & Haythornthwaite, 2002), categorizations of online activities have remained relatively broad, making it difficult

to understand who does what online, why, and how this influences the rest of people’s lives. Additionally, because the popularity of SNSs is relatively recent, initial

data collection efforts about Web uses did not focus on them. It is more customary to ask about the topics people encounter on websites (e.g., Internet use for the purposes

of gathering information about news or health matters) than to inquire in detail about the particular sites and communities in which people may be participating.

Moreover, because individuals’ goals and activities on SNSs are extremely varied,

278 Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association

investigating their uses through traditional survey instruments poses several new and distinct challenges. Perhaps due to such methodological challenges, most related

work has focused on more exploratory questions regarding SNS usage, typically relying on qualitative methods (e.g., boyd, 2008; Dwyer, 2007).

Another challenge in studying social network site usage stems from the fact that large-scale questionnaires (e.g., the Current Population Survey and the General Social Survey) have mainly focused on adult populations, with relatively few young

people represented in their samples. Yet, young people are known to be some of the most likely to participate on some SNSs (e.g., Facebook’s initial focus on college

students and then high school students left out older people by design), suggesting that concentrating on adolescents and young adults is especially important if

researchers are to gain a better understanding of how such sites are being incorpo- rated into people’s lives. Moreover, because young adults are much more wired than

their older counterparts (Fox, 2004; Madden, 2006), it can be beneficial to focus studies on this population, especially if the goal is to understand refined measures of use once basic access and connectivity are controlled for.

One study has addressed questions similar to those raised here, although it focused on a somewhat different age group (12–17 year olds) and different aspects

of SNS use. The Pew Internet and American Life Project administered a survey on the social network site usage of teens in late 2006 (Lenhart & Madden, 2007). Although

the survey did not ask about social network site usage by service (except to inquire on which service users updated their profiles the most often), the study offers helpful

insight into differences in various young people’s adoption of such sites. Namely, the data suggest different uptake by age and gender within the group of 12–17 year olds

in the sample and also some differences by race and ethnicity. However, the study does not present more detailed analyses and also lacks the data that would allow comparison of SNS adoption by service.

College students in the U.S. constitute an ideal population in which to study differences in particular types of digital media uses, given their high connectivity

levels. Often, the lack of data on young people’s experiences with information and communication technologies makes it difficult to know whether assumptions about

their active online participation are warranted. It would be incorrect to assume that simply using the medium can be equated with equal use of all sites in similar ways.

A systematic study of everyday digital media practices is essential to understanding how communication and information technologies are affecting the lives of different types of young adults. The next section introduces the unique data set used in this

study to address these questions, followed by findings from bivariate and logistic regression analyses explaining differential social network site adoption.

Methods

The analyses presented here are based on data representing a diverse group of mainly

18- and 19-year-old college students. The study was conducted in February and

Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association 279

March of 2007 at the University of Illinois, Chicago, which is a U.S. urban public research university.1 U.S. News and World Report (2006) ranked this campus among

the top 10 national universities as regards campus ethnic diversity, suggesting that this school offers an ideal location for studies of how different kinds of people use

online sites and services. The project had the support of the First-Year Writing Program at the university,

ensuring that a representative sample of the school’s undergraduate student body

would participate. The writing course offered through this program is the only course on campus that is required of all students; thus, enrollment in it does not

pose any selection bias. Out of the 87 sections offered as part of this course, 85 took part in the study, constituting a 98% participation rate on the part of course sections.

Overall, there was a final response rate of 82% based on all of the students enrolled in the course. In order to control for time in the program, this article focuses on

students in the first-year class. The survey was administered on paper instead of online. Relying on an online

questionnaire when studying Internet uses could create a bias toward people who spend

more time online, given that they may be more inclined to fill out the questionnaire and also, perhaps, more inclined toward higher rates of participation on the sites of

research interest. The average survey completion time was approximately 30 minutes. The survey included detailed questions about respondents’ Internet uses (e.g., experi-

ence, types of sites visited, and online activities) and their demographic background. Basic demographic information was measured using standard modes of opera-

tionalization. Students were asked their year of birth, and this information was used to calculate their age, which is included in the models as a continuous variable. Male

is the base gender category (male = 0, female = 1). Information about race and ethnicity was collected using the U.S. Census Bureau (2000) questionnaire format, and dummy variables are used in the statistical model, with White as the omitted

category. Consistent with work by others, parental education was used as a measure of socioeconomic status (e.g., Carlson, Uppal, & Prosser, 2000; Lamborn, Mounts,

Steinberg, & Dornbusch, 1991; Stice, Cameron, Hayward, Taylor, & Killen, 1999). Since asking about household income has limited utility with such an age group

(both because students do not know their parents’ income and because those who live in dorms may not know how to interpret ‘‘household’’), and since educational

level is constant in this group (every respondent is in the first year of college), parental schooling is a helpful measure. This information is included in the model as dummy variables, with some college education (but no college degree) as the base.

Both the question about living at home with parents and the question about having access to the Internet at a friend’s or family member’s house is included as

a dummy variable, where 1 signals yes to that question, and 0 stands for no. Finally, figures for both hours spent online per week and number of years a respondent has

been an Internet user are logged in the analyses, given that an additional hour or year, respectively, likely has diminishing returns as the values increase. The analyses first

consider only the core background characteristics of the user (age, gender, race and

280 Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association

ethnicity, parental education). Then, a second model includes information about context and experience with use supplementing the core demographic variables.

The 1,060 first-year students included in these analyses represent a diverse group of people.2 Fifty-six percent of the respondents are female, 44% are male. Almost all

are 18 or 19 years old, with a mean age of 18.4 and a median of 18. Fewer than half are White and non-Hispanic. Slightly less than 8% claim African or African- American descent, almost 30% are of Asian or Asian American ancestry, and just

under one-fifth are of Hispanic origin. These students come from varied family backgrounds. Over a quarter of respondents have parents whose highest level of

education is high school, with an additional 20% whose parents do not have a college degree. While it may seem that sampling from a college population assumes a highly

educated group, 25% of first-years at this university drop out of college by their second year (Ardinger et al., 2004) and fewer than half (43.6%) will graduate within

six years of enrollment (University of Illinois-Chicago, 2004). Unlike many U.S. colleges, over half of the students at this university commute from home and live with their parents (53.1%).

Baseline access and use statistics (Table 1) for the sample suggest that the Inter- net is not a novel concept in most of these students’ lives. On average, participants

have access to the Internet at over six locations and have been users for over six years. When asked how often they go online, the vast majority report doing so several times

a day. They estimate spending 15.5 hours visiting Web sites weekly (excluding email, chat, and VoIP). While there is certainly some amount of variation in access and use,

there are no basic barriers standing in the way of these young adults accessing the Internet. Limits may be put on their uses due to other factors (e.g., the need to share

resources at home, limited hours of access due to employment), but they all have basic access. This suggests that traditional concerns about the so-called digital divide do not apply to these students as regards basic availability of the Internet. Thus

looking at such a wired group of users allows us to hold basic access to digital media constant and focus on differences in details of use instead.

Findings

Differences in Social Network Site Usage

Who uses SNSs, and are different students equally likely to use the various services

available in this realm? The survey included questions about six SNSs: Bebo,

Table 1 Basic IT access and use statistics for sample participants

Mean Standard deviation

Number of Internet access locations 6.2 (2.1)

Number of Internet use years 6.4 (2.0)

Number of hours on the Web weekly* 15.5 (10.0)

Note: *This measure only concerns Web use and excludes time spent on email, chat, or VoIP.

Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association 281

Facebook, Friendster, MySpace, Orkut, and Xanga. For each, respondents were first asked to report whether they had ever heard of the site. Next, they were asked to

indicate their experiences with it, using the following options: ‘‘no, have never used it,’’ ‘‘tried it once, but have not used it since,’’ ‘‘yes, have tried it in the past, but do not

use it nowadays,’’ ‘‘yes, currently use it sometimes,’’ and ‘‘yes, currently use it often.’’ Overall, 88% of respondents are SNS users, and 74% report using at least one SNS

often. Only one student claims not to have heard of any of the six SNSs included on the

survey, so non-use is not a result of not being familiar with these services. Rather, despite knowing about such sites, over 12% of the sample does not use any of them.

Table 2 shows the proportion of SNS users by specific site. Facebook is the most popular service among these students, with almost four in five using it, and over half

of the overall sample doing so frequently. MySpace is used by more than half of the sample, although just over one-third uses it often. The other four sites (Xanga,

Friendster, Orkut, and Bebo, in that order of popularity) are significantly less wide- spread in this group, with each used by less than 10% of the sample.

Table 3 reports the demographic breakdown of SNS users, first in the aggregate

(second column) and then by site (columns 3–6). Orkut and Bebo are excluded from the table due to their extremely low levels of use in this group.

The differences among the user populations of these services are not particularly pronounced on most variables. Some trends, nonetheless, are notable. First, the

percentage of Asian/Asian American users fluctuates considerably, depending on the service. In particular, Asian/Asian American students in the sample are least

represented on MySpace, whereas Xanga and Friendster are especially popular with this group. Second, students of Hispanic origin make up a considerably larger

segment of MySpace users than their representation in the sample as a whole. Third, there is a relationship between parental education and use of some SNSs. In parti- cular, students who have at least one parent with a graduate degree are more rep-

resented on Facebook, Xanga, and Friendster than they are in the aggregate sample, while students whose parents have less than a high school education are dispropor-

tionately users of MySpace.

Table 2 Familiarity and experience with social network sites among participants

(percentages)

Uses it* Has heard

of it

Has never

used it

Tried it once,

but no more

Used to use it,

no longer

Facebook 78.8 (62.8) 99.4 14.2 3.6 3.4

MySpace 54.6 (38.4) 99.5 20.8 9.4 15.2

Xanga 6.2 (1.9) 76.4 61.7 11.8 20.3

Friendster 3.3 (1.0) 43.3 84.7 5.6 6.4

Orkut 1.6 (.6) 5.8 97.1 .5 .8

Bebo .6 (0) 9.6 95.4 2.8 1.2

Notes: *These figures summarize the percentage of students who currently use the site ‘‘some-

times’’ and ‘‘often.’’ Figures for those reporting use of the site often are in parentheses.

282 Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association

This rather simple look at the data shows that social network site usage in the

aggregate attracts a diverse set of students across services, but that certain groups are more represented on some sites than others. The important methodological take-

away point here—in addition to the substantive ones about specific groups of users—is that when studying users of one SNS, researchers should exercise caution

in generalizing the findings to users of another social network site. Another way to look at the data is to consider the levels of SNS popularity by type

of user attribute. Table 4 presents SNS usage statistics broken down by gender, race and ethnicity, and parental educational background. This breakdown is presented for SNS use in the aggregate and then separately for Facebook, MySpace, Xanga, and

Friendster. (Due to Bebo’s and Orkut’s low rate of use in this sample, no disaggre- gated figures are presented for those two sites.)

Table 4 shows significant differences according to type of user. When it comes to aggregate SNS usage, women are more likely to use such services than are men, but

once disaggregated by type of site, depending on the service, the differences all but disappear. That is, while female students in the sample are much more likely to use

MySpace, there is little difference between young women and young men in the group when it comes to Facebook, Xanga, or Friendster use.

Regarding race and ethnicity, the most pronounced findings concern students of Hispanic and Asian origin. Hispanic students are significantly less likely to use Face- book (60% compared to 75% or more for other groups), whereas they are much

Table 3 Descriptive statistics for the sample demographics (percentages)

Full

sample

SNS

users

Facebook

users

MySpace

users

Xanga

users

Friendster

users

Women 55.8 56.9 56.3 60.4 56.9 60.0

Age

18 64.8 65.3 66.1 65.9 61.5 68.6

19 32.2 31.6 31.5 30.4 36.9 28.6

20–29 3.0 3.1 2.4 3.6 1.5 2.8

Race and Ethnicity

White, non-Hispanic 42.7 43.2 44.9 44.0 20.6 3.0

Hispanic 18.8 18.4 14.5 25.2 9.5 3.0

African American, non-Hispanic 7.7 7.4 7.9 8.2 3.2 0

Asian American, non-Hispanic 29.6 29.9 31.6 21.3 65.1 93.9

Native American, non-Hispanic 1.2 1.1 1.1 1.3 1.6 0

Parent’s Highest Level of Education

Less than high school 7.4 7.4 6.0 10.0 1.5 0

High school 19.0 18.3 17.6 20.1 16.9 8.6

Some college 20.1 19.5 18.8 20.9 20.0 11.4

College 34.4 35.5 37.4 34.9 33.9 57.1

Graduate degree 19.1 19.2 20.1 14.1 27.7 22.9

Lives with parents 53.1 51.4 48.2 54.5 49.2 58.8

Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association 283

more likely than others to use MySpace (73% among Hispanic students compared to 58% or less among all others). In contrast, like White students, Asian and Asian

American students are much more likely to use Facebook than others, but they are significantly less likely to use MySpace. Additionally, this group of students is espe-

cially active on Xanga and Friendster compared to others. There are also significant differences according to parents’ level of education. The

most pronounced finding is that students whose parents have less than a high school degree are significantly less likely to be on Facebook and are significantly more likely

to be MySpace users. In contrast, those who have at least one parent with a college education are significantly more likely to be Facebook users, while those who have at

least one parent with a graduate degree are considerably less likely to spend time on MySpace. Xanga also seems to appeal more to those whose parents have higher levels of education. However, since there is a relationship between parental education and

a student’s race and ethnicity, it is best to look at these associations using more advanced statistical techniques that allow other factors to be controlled while the

relationship between the various background variables and SNS usage is examined. The next section does this by considering what predicts SNS use on the whole and

with regard to specific sites when controlling for other factors in the model.

Explaining Differences in SNS Use

In this section, using logistic regression analyses, I look at the relationship of seve- ral factors and SNS usage concurrently.3 I first consider the relationship of the

Table 4 Percentage of different groups of people who use any SNS and specific social net-

work sites 1

Any SNS Facebook MySpace Xanga Friendster

Gender

Male 85* 78 49*** 6 3

Female 89* 80 59*** 6 4

Race & ethnicity

White, Non-Hispanic 89 83** 57 3*** 0***

Hispanic 86 60*** 73*** 3* 1*

African American, NH 84 80 58 0 0*

Asian American, NH 88 84** 39*** 13*** 10***

Native American, NH 83 75 58 8 0

Parental education

Less than high school 88 64*** 73*** 1* 0*

High school 83* 73* 57 6 2

Some college 85 74* 57 6 2

College 90* 86*** 55 6 6

Graduate degree 88 83 41*** 9* 4

Notes: 1 Use is defined as ‘‘use sometimes’’ or ‘‘use often.’’ *p , .1, **p , .01, ***p , .001

284 Journal of Computer-Mediated Communication 13 (2008) 276–297 ª 2008 International Communication Association

background variables to any social network site usage (Table 6), followed by an examination of the likelihood of using four specific sites: Facebook, MySpace,

Xanga, and Friendster (Table 7). This approach is helpful, in that it isolates the relationship of various background characteristics and SNS usage while controlling

for other factors.

Explaining Any SNS Use

The findings presented in Table 5 suggest that numerous factors influence whether a student uses social network sites, while the results in Table 6 suggest that the

predictors are not uniform across different services. The figures presented in both tables are ‘‘odds ratios,’’ meaning that any number greater than 1 constitutes a higher

propensity to engage in SNS usage, whereas a number less than 1 suggests that the type of characteristic lowers the likelihood of social network site usage. First, I

consider the findings for overall SNS usage, followed by an examination of specific site uses separately.

The first column in Table 5 shows how core background characteristics are

associated with any SNS use. The only variable in the core demographic model that is related to SNS use at a statistically significant level is gender. Women are more

likely to use SNS than their male counterparts are. This finding is consistent with literature on women’s larger propensity to engage in person-to-person communi-

cation online as compared to men (e.g., Pew Internet and American Life Project, 2000). In this aggregate model with demographic background characteristics only,

other factors, such as a student’s race and ethnicity and parental schooling level, do not show a statistically significant relationship with use of SNS.

Once more variables are added to the model, additional correlates of use emerge (see the second column on Table 5). Gender remains an important predictor of SNS use, while the other demographic variables continue to show no statistically signif-

icant relationship with aggregate SNS usage. However, in addition to gender, both context of use and experience with the medium are related to the adoption of such

services. In p

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