The Impact of Social Network Websites on Social Movement Involvement

Elizabeth A. G. Schwarz University of California, Riverside

Word count: 9918

1 August 2011

Direct correspondences to: Elizabeth A. G. Schwarz, University of California, Riverside, Sociology Department, 900 University Ave., Riverside, CA 92521; eschw001@ucr.edu.

  2  

Abstract The Middle East revolutions in early 2011 brought attention to the involvement of online social

networks in social movement activity. Using data from a survey of attendees fielded at the U.S.

Social Forum (USSF), a national meeting of social movement participants, this research

examines individuals who learned of the social movement event through social network websites

(SNSs), such as Facebook or Twitter. Specifically, the study focuses on attendees’ offline protest

activities and organizational memberships, while controlling for individual factors and other

ways of hearing about the forum. Results show that learning of the USSF through SNSs

significantly impacts attendees’ organizational memberships and the number of offline protests

attended. Findings suggest activists should consider using SNSs to supplement more traditional

social networks and information channels.

Keywords: social movement, Internet, protest, social network website, network

  3  

Introduction

The Middle East revolutions in early 2011 set off widespread speculation about the role

of the Internet, and particularly online social network tools such as Facebook and Twitter, in

facilitating social movement activity (Mejias 2011). On February 5, 2011 a New York Times

article headline announced, “Facebook and YouTube Fuel the Egyptian Protests” (Preston 2011).

A February 1, 2011 CNN.com article headline proclaimed, “Google, Twitter, help give voice to

Egyptians” (Gross 2011). However, not everyone holds such enthusiastic views of online social

networks (SNSs) and instead downplay the role of online social networks in the revolutions

(Mejias 2011). Demonstrating a more moderate view, recent writings on the Middle East

revolutions place the accomplishments of the revolutions squarely on the shoulders of the people

of the Middle East while arguing that SNSs are important as well (Tufekci 2011; Zhuo,

Wellman, and Yu 2011). Tufekci (2010) emphasizes that developing an understanding of the role

online social network tools play in protests requires a focus on the operation of networks and

examinations of how to sustain the participatory, non-hierarchical environment often created by

online social networks.

Many questions remain regarding how networking occurs online and what types of

activists, movements, and organization are poised to best make use of such networking. The

importance of social networks is well established in social movement literature, revealing the

impact of personal and organizational connections on engaging in political and civic activities

(e.g., Snow, Zurcher, and Ekland-Olson 1980, McAdam 1986, McAdam and Paulsen 1993, Kitts

2000, Passy and Giugni 2001, Bennett et. al. 2008). A large number of researchers have also

focused on the influence of social networks formed on the Internet and social movement activity

(e.g., Diani 2000, Wellman 2002, della Porta and Mosca 2005, Fisher and Boekkoi 2010). In

addition, many researchers call attention to the emergence of social movements that are built on

  4  

non-hierarchical, diversely networked bases that are often highlighted as ideal movements to

make use of the Internet (e.g.; Castells 1996, Ronfeldt and Arquilla 2001, Castells 2004, Juris

2004, Bennett et. al. 2008). However, there is a dearth of research that specifically focuses on the

impact of SNSs on social movements.

This study uses survey data from the United States Social Forum (USSF) to answer Polat

(2005) and Kavada’s (2005) call for research that examines specific facets of the Internet by

examining whether SNSs impact organizational membership and offline protest activity. I

thereby extend the research on the Internet, social movements, and networking to include the

impact of SNSs. The findings suggest that SNSs have a significantly positive impact on the two

outcomes examined, even when controlling for individual factors. As activists and social

movements continue to increase their use of SNSs to involve participants in social movement

activity, the knowledge of these individuals’ characteristics will be vital to activists, social

movement organizations, and processes like the social forums. The following sections will

review the impact of networks on social movement activity in three realms: offline, on the

Internet, and finally on SNSs.

The Journey From Offline to Online Networks

Offline Social Networks and Collective Action

Traditional network theory examines the impact of networks created through

interpersonal and organizational ties. Larger social pattern emerge through social networks and

interactions between individuals (Granovetter 1973). Networks play integral roles in behavior

change, such as smoking cessation and are also important to emotion dispersion, such as

happiness (Fowler and Christakis 2008).

In essence, social movements can be thought of as networks. Diani (1992) describes

social movements as “networks of informal relationships between a multiplicity of individuals

  5  

and organizations, who share a distinctive collective identity, and mobilize resources on

conflictual issues.” Because of this strong connection to networks, social movement literature

draws significantly from traditional network theory (e.g. Granovetter 1973; Granovetter 1983;

Miller McPherson, Popielarz, and Drobnic 1992). The social movement literature confirms

networks do matter for social movements, demonstrating the influence of personal and

organizational networks on engaging in collective action (Snow, Zurcher, and Ekland-Olson

1980, McAdam 1986, McAdam and Paulsen 1993, Kitts 2000, Passy and Giugni 2001).

Networks are central to recruitment, maintaining support, and discouraging leaving groups

(Miller McPherson et.al. 1992). Interpersonal ties or informal networks are seen as primary

motivators for individuals to join movements. People are much more likely to participate in

movement activity if they have a connection to someone already involved in the movement

(Snow et. al. 1980). Furthermore, people’s interests in certain topics increase when they engage

with individuals who have interests similar to their own (Kitts 2000). Research looking at ties

across movements demonstrates how, in certain cases, those ties can lead to common viewpoints,

shared identities, and collective action (Carroll and Ratner 1996).

The distinction between tie strength is one important area of examination. Strong ties, or

ties between close friends or family, have been thought to offer stronger social incentives to

participate in social movement activity and consequently are more effective recruitment channels

than weak ties, or ties between friends of friends (McAdam 1986). However, more recent

research finds it may not be tie strength that matters as much as common interests and shared

identities between individuals (Lim 2009). Regardless, weak ties are still important as they can

act as bridges between groups and offer access to information and resources that family and

immediate friends may not provide (Granovetter 1983).

  6  

In addition to interpersonal networks, ties generated through organizational networks are

also central to social movement activity. Being part of multiple movements and organizations

can help information, resources, and expertise flows more freely between movements and

organizations. Affiliation with organizations is one of the strongest predictors of participation in

social movement activities (McAdam 1986, McAdam and Paulsen 1993). Research shows

organizational ties are often more important to participants than individual ties when they decide

to engage in social movement activity (McAdam and Paulsen 1993). In support of this argument,

research finds social movement organizations play a significant role in mobilizing and

supporting participation in large-scale protests (Fisher, Stanley, Berman, and Neff 2005).

The Introduction of the Internet

In addition to personal and organizational networks outlined above, scholars increasingly

argue that connections made over the Internet also plays key roles in shaping political and

cultural life (Kahn and Kellner 2004). Connections made over the Internet are considered another

form of social network (Wellman 2001). Castells (1996) asserts that CMC (computer-mediated

communication) and other mediated social networks have transformed society into a networked

society where information exchange is instantaneous and global. The Internet society is less

constrained by geographic location than previous societies (Hugill 1999). In part from the

introduction of the Internet, the nature of social relationships has shifted toward networked

individualism (Wellman 2002). With this shift, individuals have multiple and shifting work

partners and partial involvement with shifting set of workgroups that are not based on location,

but rather based on the network ties of the individual. Many relationships initiated through

connections made online transition to offline meetings and, in many cases, research reveals

Internet users have richer social relationships (Hampton and Wellman 2001) More importantly,

  7  

research suggests most Internet users make use of the Internet to extend their offline participation

in various activities (Wellman, Haase, Witte, and Hampton 2001).

Social movements gravitated to the Internet and a growing body of literature examines

the impact of social networks found online on social movements (e. g. Diani 2001; della Porta

and Mosca 2005; Bennett et. al. 2008; Van Laer 2010). Anduiza, Cantijoch, Gallego (2009) and

Garrett (2006) identify various mechanisms linking the Internet to political activity that influence

activists’ social networks and the role of social networks in social movement activity. These

mechanisms fall into three general areas: resources for participation, information, and collective

identity and community.

The first mechanism involves resources for participation (Garrett 2006; Adnuiza et. al.

2009). Online activist activities can require fewer resources and can be easier entries to engaging

in social movement activity than offline activism, which lowers participation thresholds for

individuals to get involved in collective behavior (Garrett 2006; Anduiza et. al. 2009; Van Laer

and Van Aelst 2010). And most often, online collective action is often related to offline

collective action (Brunsting and Postmes 2002a, Brunsting and Postmes 2002b, Kahn and

Kellner 2004, Reid and Chen 2007, Wojcieszak 2009). For example, research finds offline and

online protests strongly relate and tend to reinforce each other (della Porta and Mosca 2005).

In addition, the structure and functionality of the Internet offers social movements the

increased speed and range of communication that technology, such as printing, the postal system,

the telephone, and fax did in the past (della Porta and Mosca 2005). Use of the Internet can

reduce the cost of communication while reaching wider audiences and increasing networks

(Garrett 2006; Bennett et. al. 2008). It may also increase the accuracy of messaging and

interaction between organizations and activists (Diani 2000). Those using the Internet to

  8  

communicate and organize gain valuable experiences in processing and analyzing information

that can be applied to offline movement settings and events (Anduiza et. al. 2009).

The second mechanism involves information (Anduiza et. al. 2009). User generated

content can be created and disseminated by individual activists to wide numbers of people

(Kavada 2009; Bennett 2011). Using the Internet as a resource for information and forum for

discussion leads to increased civic engagement (Shah, Cho, Eveland, and Kwak 2005). Social

movement participants can use the Internet to spread their own uncensored messages and impact

the mass media (della Porta and Mosca 2005). The hyperlinked communication networks found

on the Internet allow individuals to find multiple points of entry into varieties of political action

and offer independence from the mass media and other conventional institution organizations

(Bennett 2003, Castells 2004, Bennett et al. 2008).

Scholars consider network ties found on the Internet weak ties (Donath and boyd 2004,

Haythornthwaite 2005). Weak ties can help social movements share information and facilitate

communication for collective organization and action (Kavanaugh, Reese, Carroll, and Rosson

2005). Internet users may also have greater opportunity to be asked to participate in social

movement activity (Van Laer 2010). For example, Fisher and Boekkooi (2010) find the Internet

plays a major role in mobilizing participants for global days of action. However, scholars caution

that much of the time people must actively seek out the information for themselves and with the

rise of user-generated content there is also the chance of sharing misinformation (Anduiza et. al.

2009).

The third mechanism involves collective identities (Anduiza et. al. 2009) and community

(Garrett 2006). Once information is online or an online environment has been created to facilitate

communication and discussion, the Internet can also help to foster collective identifies by

providing a space where otherwise isolated, distant individuals and networks can come together

  9  

and work toward forms of collective action (Diani 2001; della Porta and Mosca 2005; Langman

2005; Garrett 2006). Furthermore, tools like email and online forums create virtual public

spheres that allow people to reflect on movement events and discuss their thoughts with others

from across the globe (della Porta and Mosca 2005; Langman 2005). Online communities

reinforce existing networks and although the ties between online users may be weak, they can

result in collective action (Hampton 2003).

As individuals have increased abilities to create information, communicate, and organize

through the use of the Internet, relationships individuals have with organizations may also

change. Social movement participants may aspire to have increasingly flexible relationships with

organizations that may have had more of a central role in organizing and communicating in the

past. More recently, research has shown that Internet users belong to increased numbers of

organizations (Bennett et. al. 2008, Van Laer 2010, Walgrave, Bennett, Van Lear, Breunig

forthcoming). Various Internet activity, such as forwarding emails, can help bridge disparate

networks individuals in different organizations (Walgrave et. al. forthcoming). As such, the

Internet may be transforming social movement structures into configurations that encourage

looser networks of individuals (della Porta and Mosca 2005; Langman 2005). In turn, this may

change the role that organizations play in social movement mobilization (Bennett et. al. 2008).

Individuals with multiple organizational ties may play a larger role in organizing social

movement activity (Bennett et. al. 2008). Recent research finds that the Internet allows activists

to provide network links between movements which can help spread information and bring

otherwise disparate networks together (Walgrave et. al. forthcoming).

Online Social Networks

While connections made between individuals online are considered to be another form of

social network (Wellman 2001), much of the previous social movement research examining

  10  

online networks maintain that activity on the Internet, such as participation in online

communities, gathering information online, or sending email, constitutes the existence of an

online social network or digital network. However, not all tools or activities performed on the

Internet are alike, which suggests research should also examine specific aspects of the Internet

individually (Kavada 2005).

Advancing Internet technologies brought about SNSs, such as Facebook, MySpace, and

Twitter. Differing from traditional websites, SNSs are ‘‘web-based services that allow

individuals to (1) construct a public or semipublic profile within a bounded system, (2) articulate

a list of other users with whom they share a connection, and (3) view and traverse their list of

connections and those made by others within the system’’ (boyd and Ellison 2007). boyd (2010)

identifies profiles, Friends lists, public commenting tools, and stream-based updates as important

features that are unique to SNSs. She argues the properties of the sites influences the flow of

information, how individuals interact with the information, and users interactions with others.

Unlike other Internet ties, ties found on SNSs can be a mix of both strong and weak ties (Donath

2007; Ellison et. al. 2007; Ellison et. al. 2011).

Although the first SNS launched in 1997, social movement research specifically focusing

on SNSs, is currently not as robust as research focused on other aspects of the Internet. Donath

(2007) asks the question, “Will SNS-based social “supernets” transform society?” I apply this

question to social movement activists. Specifically, I will examine the impact of SNSs on

activists’ levels of social movement involvement by focusing on two established facets of social

movement involvement: organizational membership as a measure of interpersonal network ties

(Bennett et. al. 2008; Walgrave et. al. forthcoming) and offline protest activity as a measure of

movement activism (McAdam 1986; della Porta and Mosca 2005; Van Laer 2010).

Impact on Organizational Membership

  11  

As in offline social networks and connections made through the Internet, increased

number of ties created using SNSs may increase individuals’ access to information and

opportunities (Donath and boyd 2004; Ellison, Steinfield, and Lampe 2007). In addition, much

like offline public spaces where face-to-face interactions occur, or virtual public spheres found

on the Internet, SNSs can be viewed as “networked publics” that promote sociability (boyd and

Ellison 2007). The architecture of the site makes it possible for users to identify others with

whom they have similarities. Researchers propose that Facebook users may be able to convert

latent ties, or ties which aren’t active, into weak ties using SNSs. Activating the weak ties found

on SNSs can lead to increased information, resources, and help bridge networks (Ellison et. al.

2007). However, it is not the norm to initiate contact with strangers on SNSs, which means it

may be seen as less acceptable to do so by the online community (Ellison et. al. 2011).

Recent research demonstrates blogging and SNSs have positive relationships with

participation in civic organizations (Valenzuela, Park, and Kee 2009). Van Laer (2010) posits

activists can more easily find others who care about similar causes using SNSs and can watch the

support of groups on SNSs like Facebook grow, which can be seen as an indication of a group’s

efficacy and encourage others to join the group. Furthermore, individuals can create their own

groups on Facebook for or against certain causes and invite other members of their own social

networks to join (Van Laer Van Aelst 2010). Therefore, I expect:

H1: Finding out about the USSF through online social networks impacts the number of

attendees’ organizational memberships.

Impact on Offline Protest Activity

The properties of the SNSs are ideal for encouraging interpersonal interaction,

broadening social ties, and providing valuable information about how to become civically and

politically involved (Valenzuela et. al. 2009). Most often, SNSs are used to support existing

  12  

offline social relations and activities (boyd and Ellison 2007). Features of SNSs, such as the

inclusion of publicly visible profile information combined with the ability to send messages to

others may be used to trigger offline interactions (Ellison et. al. 2011). Research examining

SNSs shows support for increased civic engagement by young online social network users

(Pasek, More, and Romer 2009). Examining the role SNSs played in the 2008 Presidential

election, results show a positive relationship between online social network use and civic

participation (Zhang, Seltzer, and Bichard 2010).

However, a study of young users of the SNS Facebook and political behavior reveals

mixed findings. While there is a positive relationship between the use of Facebook for political

purposes and general political participation, there is a negative relationship between increased

Facebook use and general political participation. While the researchers acknowledge this result is

difficult to explain, they suggest users may be using Facebook to supplement political activity in

other venues (Vitak, Zube, Smock, Carr, Ellison, and Lampe 2010).

Looking at a specific example, Kavada (2009) shows how the global web movement,

Avaas, uses Facebook, MySpace, and YouTube to engage social networks. She identifies the

SNSs potential for interaction, user generated content, social networks, and content sharing as

central to their successful use of the medium. Most recently, an exploratory study of activists’

perspectives on SNSs and social movement reveals that activists believe SNSs are an important

part of activism. Findings support the assertions that online relationships can lead to offline

relationships. In addition, researchers found regardless of activists’ levels of online activism,

they all had the same levels of offline activism (Harlow and Harp 2011). Based on these

findings, I expect:

H2: Finding out about the USSF through online social networks impacts attendees’

number of offline protests.

  13  

The Case: 2010 USSF

To explore these hypotheses this study uses data from a survey fielded at the 2010

USSF1. In Detroit, Michigan in June 2010 approximately 20,000 activists, representing various

organizations and social movements, gathered together in the largest meeting of progressive

global social justice (GSJ) movement activists in the U.S. The 2010 USSF provides a unique

environment in which to study the impact of online social networks for two reasons.

First, both the GSJ movement and social forum processes are cited as examples of

loosely networked, non-hierarchical structures that promote inclusiveness and diversity of

individuals and causes (Bennett et. al. 2008; Juris, Caruso, and Mosca 2008; Reese

Breckenridge-Jackson, Elias, Everson and Love 2011). The fact that the GSJ movement uses a

non-hierarchical organization process and a foundation built on networks makes it an ideal

movement for citizens to make use of the Internet to organize and mobilize (Castells 2004;

Rodfeldt and Arquilla 2001)2.

In addition to the networked foundation of the forum, the USSF organizing committees

used a variety of recruitment methods to draw attendees to the forum, relying on face-to-face

social and organizational networks to get the word out as well as other mediated information

channels, such as radio and newspaper. Along with these more traditional information channels,

the USSF also used SNSs, such as Facebook and Twitter, to recruit attendees to the forum. As of

July 18, 2011 the USSF had 2,824 followers and was listed 195 times on Twitter (Twitter 2011).

                                                                                                               1  The use of data collected at social forums has been used in other research. For example, see della Porta and Mosca 2005, Kavada 2005, and Kavada 2010.  2  As mentioned in past studies focused on the Internet and the GSJ movement, the hypotheses presented in this research may not hold true for all types of movements. The GSJ movement covers broad bases of protest issues and draws individuals with broad interests. The hypotheses presented in this paper may not be as applicable to those movements that are based on more hierarchical organizational structures with very narrow focuses. However, more recent research suggests that the Internet may be as relevant for more traditional movements as well (Walgrave et. al. 2011).  

  14  

16926 users liked the USSF fan page on Facebook (Facebook 2011). The wide variety of

recruitment methods used allows for an examination of the SNS information channel while

controlling for face-to-face and mediated channels.

Methods

Data

A team of researchers collected data from 569 adult participants through a written survey

at the 2010 United States Social Forum from June 22-26, 2010 in Detroit, Michigan. Historically,

surveys have been shown to be effective tools for examining social movement activity (Bennett,

Breunig, and Givens 2008; Fisher et al. 2005; Fisher and Boekkooi 2010). The 50-question

survey gathered information about respondents’ demographic and socio-economic

characteristics, political views, affiliations with organizations and social movements, and

political activities.

The sampling frame included participants at the USSF. Researchers acknowledge the

difficulty of sampling at such events (Kavada 2005, Bennett et. al. 2008). A full list of

participants was unavailable at the start of the USSF and the length of the survey required

respondents to spend at least 30 minutes completing it. Because of these factors a convenience

sampling method was used and as many surveys as could be collected were, at a variety of event

venues including registration, the lobby area, workshops, evening plenaries, organizations’

tables, and cultural performances. This method is consistent with other survey research projects

fielded at previous social forums (Kavada 2005). To help verify the representativeness of our

sample, a comparison was made with another academic survey fielded at the USSF, which

revealed comparable demographic results.

Despite best efforts to obtain a representative sample, it is likely that certain sampling

biases resulted. Participants with fewer responsibilities and more free time may have been

  15  

oversampled. The attendees who could not read, were not literate in Spanish or English, or those

who were uncomfortable completing written surveys may have been under sampled.

Variables and Measurement

The first dependent variable I analyze is organizational membership, which reflects the

number of organizations of which individuals were members (Bennett et. al. 2008). Responding

to a question inquiring about the types of organizations respondents were members of,

respondents indicated which types of organizations they were affiliated with by checking

responses that included: “Labor union/organizations; Non-governmental organizations;

Government agencies; Cultural groups; Professional associations; Political parties; Media

organizations; Social or recreational groups; Religious institutions/movements; Social

movements/political organizations; or Other.” In order to create a single variable, I first summed

the number of organizational types for each individual. Then, I dichotomized the variable using

the median of the summed value, which equaled two, as the point at which the variable was split

into 0 (equal to or less than two types of organizations) or 1 (greater than 2 types of

organizations).

The second dependent variable is protests, which measures the degree of movement

activism. Responding to an open-ended question, respondents self-reported the number of public

protests or demonstrations they participated in during the last 12 months. Protests is a continuous

variable.

In each model the same key independent variables and control variables were used. The

variable information channels was created to capture how participants found out about the 2010

USSF. Responding to a question inquiring as to how participants found out about the 2010

USSF, respondents were offered the following responses: “Radio or television; Newspapers

(print or online); Alternative online media; Advertisement, flyers, and/or posters; Family

  16  

member and/or partner; Friends and/or acquaintances; People at your school or work; Fellow

members of an organization or association; or Online social networks (e.g. Facebook, Twitter).”

I separated the variable information channels into three variables, online social networks,

mediated, and face-to-face, similar to the categories created by Fisher and Boekkooi (2010) and

Van Laer (2010).

Online social networks is the key independent variable. Responding to a question

inquiring as to how participants found out about the 2010 USSF, respondents who heard about

the USSF through SNSs indicated so by checking the response “Online social networks (e.g.

Facebook, Twitter).” These participants may also have selected other responses available for that

question. The variable online social network was dichotomous for which 1 indicated “Online

social networks” was selected and 0 indicated that “Online social networks” was not selected.

To establish the additional impact of SNSs, mediated and face-to-face variables were

used to control for the influence of other information channels (Van Laer 2010). First, the

variable mediated was created. Responding to a question inquiring as to how participants found

out about the 2010 USSF, respondents who heard about the USSF through mediated channels

indicated so by checking any of the following responses: “Radio or television, Newspapers (print

or online), Alternative online media, Advertisement, flyers, and/or posters.” Mediated was a

dichotomous variable for which 1 indicated one of the mediated responses was checked and 0

indicated no mediated responses were checked.

The dichotomous variable face-to-face was also created using responses from the

previously referenced question. Face-to-face was coded 1 if the respondents checked any of the

following responses: “Family member and/or partner, Friends and/or acquaintances, People at

your school or work, or Fellow members of an organization or association.” Otherwise, face-to-

face was coded 0. Because respondents could select more than one entry for this question, it was

  17  

possible for one observation to have multiple affirmative values for the face-to-face, mediated

and online social network variables.

In order to isolate the impact of various information channels on organizational

membership and offline protest activity, it is important to control for a number of individual

factors that have been shown to predict them. First, age has been shown to influence Internet use

and activism (Van Laer 2010, Best and Krueger 2005, Schussman and Soule 2005). Therefore, to

ensure age did not influence the outcomes, age was used as a control variable. Responding to an

open-ended question inquiring as to the year the respondent was born, respondents self-reported

the year in which they were born. Year was then converted to the age of the respondent for the

purpose of analysis using SAS. Age is a continuous variable.

Next, to address additional issues surrounding the digital divide3 and demographic

influences of protest activity, gender, race, and personal income were also used as control

variables. Responding to a question inquiring as to their gender, respondents selected “Female,

Male, or Other.” People who don’t identify with one particular gender category or don’t adhere

to gender categorization selected “Other.” Gender was a categorical variable. Responding to a

question inquiring as to their race, respondents selected their race. Options included: “Black,

Middle Eastern, South Asian, East Asian, Island Pacific, Indigenous, Latino/Hispanic, White,

Multiracial and Other.” Because of limited numbers of observations, South Asian, East Asian,

and Island Pacific were collapsed into the response Asian. Race was a categorical variable.

Responding to a question inquiring about their approximate annual personal income, respondents

selected the category in which their approximate annual income fell. Responses included “None-

                                                                                                               3  There is concern that the digital divide impacts who has access to the Internet and the ability to use it. For example, this occurs between individuals who are more politically active and those who are less active based on socioeconomic status, age, and prior political participation, which may reinforce current political activity in society. High socioeconomic status individuals are more likely to receive mobilization messages online and offline. For further discussion of the digital divide, see e. g. Castells 1996, Hugill 1999, Castells 2004, Best and Krueger 2005, Martin and Robinson 2007, Hargittai 2008, Goldfarb and Prince 2008, Van Laer 2010.  

  18  

$14,999; $15,000-$20,999; $21,000-$39,999; $40,000-$51,999; $52,000-$63,999; $64,000-

$100,000; or Above $100,000.” Because of limited numbers of observations, the last two

response options were collapsed into the response $64,000 or above. Personal income was a

categorical variable. In my model, I used female, None-$14,999, and white as the reference

group for the gender, race, and personal income variables, respectively. Using these control

variables will help to isolate the impact of SNSs.

Table one contains descriptive statistics for the variables. Twenty-four percent of the

sample found out about the social forum using SNSs. Eighty-eight percent of the sample learned

about the forum through face-to-face communication whereas 41% of the sample learned of the

forum through mediated channels. The highest percentage of participants has income levels

lower than $14,999. Skewness was used to examine how close to normal the data are for the

continuous variables. The skewness for protests is 5.34. This indicates the distribution for protest

is not normal. The skewness for age is .97, which indicates it has a normal distribution. The

remaining variables are not continuous. An alpha level of .05 was used in the analyses.

Table 1 about here

Statistical Estimation

In order to test the first hypothesis that learning of the USSF through SNSs impacts

organizational membership, I use logistical regressions. The logistic regression equation for the

log odds of Y is:

Log Odds(Y=1) = β 0 + β 1X1 + β 2X2 + β 3X3 …+ β KXK

Logistic regression is an appropriate test because this research investigates if the discrete

dependent variable, higher than median organization membership, can be predicted by finding

out about the USSF through SNSs with gender, income, age and race as control variables. SAS

  19  

9.2 was used to run the regression and descriptive statistics and to calculate the probability that

each coefficient is actually one.

In order to test hypothesis two, exploring the association of finding out about the USSF

through SNSs and offline protest activity, I use Poisson regressions. Poisson is part of the

generalized linear model family. It is a statistical technique used when dealing with a Poisson

random variable. These random variables are usually counts of events. Typically, in the Poisson

process successful outcomes are rare. Poisson distributions are inherently skewed and the

analysis models counts of event occurrences. As the dependent variable for this model, protests,

is skewed, Poisson is appropriate to use for this analysis. SAS 9.2 was used to run the regressions

and descriptive statistics.

The probability mass function of the Poisson distribution is:

P(i) = e – λ λi/i!

This indicates: “the probability of observing some value or count (i) is equal to the

exponentiated value of the negative value of lambda multiplied by lambda to the ith power

divided by i factorial where i is a given value, e is the exponential constant (approximately

2.718), λ is an average rate of occurrence, and P is the Poisson probability of a specific count of

i“ (Kposowa 2011).

Estimation of the Poisson model was accomplished through a link function. In this case, a

log-linear function was used and is specified as follows:

Log µi = β0 + β 1Xi1 + β 2Xi2 + … + β kXik

The data did not meet the main assumption of Poisson regression, equidispersion, as the

variance of the dependent variable should equal its mean and the variance (155.25) of protests

exceeded the mean (7.01). This indicates the dependent variable was over dispersed. One

potential reason for over dispersion is that events are not completely independent. Using Poisson

  20  

regression with over dispersed data can lead to coefficient estimates that are inefficient and

standard errors that are biased downward. One way to correct for the over dispersion issue is to

use a negative binomial regression. This is the corrective measure that was taken in this analysis.

Negative binomial regressions maintain the Poisson structure and allow for analyses when

variances and means are not the same by introducing scale parameters and error terms. The

model for the negative binomial regression is:

Log µi = β 0 + β 1Xi1 + β 2Xi2 + … + β kXik + ei

Results

Before performing the main analyses, to ensure multicollinearity was not a factor in the

analysis, variance inflation factors (VIF) were examined. For this analysis multicollinearity did

not appear to be a factor. No value exceeded 2, with values ranging from 1.04 (Middle Eastern)

to 1.39 (Age).

The results concerning the impact of SNSs on organizational membership, reported in

Table Two, support hypothesis one. Overall, finding out about the USSF through SNSs

significantly impacts the log odds of a person having above median organizational membership.

Model 1 provides a baseline model, containing only control variables. Age is the only significant

variable in model 1. For every one-year increase in age, attendees are 2.5% more likely to be

affiliated with greater numbers of organizations4. The -2 Log Likelihood is 359.12.

Model 2 adds online social networks. In model 2, online social networks, the key variable

of interest, is significant at the 5% level (chi-sq = 6.22, p-value = 0.013). Individuals who found

out about the USSF through online social networks are 108% more likely to be affiliated with

greater numbers of organizations. The -2 Log Likelihood for this model is 352.94. To further

establish the impact of the variable online social network on organizational membership, a log

                                                                                                               4  To interpret the results of each model, the unreported odds ratios were subtracted by one and multiplied by 100.  

  21  

likelihood test comparing this model to model 1 indicates the addition of online social networks

explains a significant amount of variation of organizational membership (chi-sq = 6.18, df = 1, p-

value = 0.013).

I also run models containing other control channels to determine if this finding is unique

for online social networks. In model 3, I include face-to-face in addition to the control variables.

The variable face-to-face is not significant. In model 4, I include mediated in addition to the

control variables. The variable mediated is not significant. In both models, log likelihood tests

were performed and confirmed the addition of these variables did not significantly impact model

fit.

Finally, I run model 5, a saturated model, which includes online social networks, face-to-

face, and mediated as well as the individual-level control variables. I include all three

information channels to control for other ways individuals found out about the forum. In the full

model the variables online social networks (chi-sq = 4.55, p-value = 0.03) and age (chi-sq = 7.23,

p-value = 0.01) are significant at the 5% level. Attendees who found out about the USSF through

online social networks were 91% more likely to have above median organizational membership.

In addition, with every one-year increase in age the probability of having had above median

organizational membership increases by 2.7%.

Table two about here

Next, results reported in Table Three support hypothesis two, examining the impact of

SNSs on protest activity. Learning of the USSF through SNSs significantly impacts the number

of offline protests. Model 1 is a baseline model, containing only individual-level control

variables. Age, female, refused for income, and other for race are the significant variables in

model 1. For every one-year increase in age, the expected number of protests attended increased

  22  

by 2%.5. The expected number of offline protests attended by females was 26% lower than for

males. The expected number of offline protests attended by individuals who refused to answer

the income question was 84% lower than for those in the lowest income group. In addition, the

expected number of offline protests was 143% higher for the other race group than for whites.

Model 2 adds online social networks. In model 2, online social networks, the key variable

of interest, is significant at the 5% level (chi-sq = 12.30, p-value = 0.0005). In addition, the same

control variables are significant as found in model 1. Examining attendees who found out about

the USSF through online social networks, the expected number of offline protests attended is

64% higher than those who did not. To further establish the impact of the variable online social

network on protest activity, a log likelihood test comparing this model to Model 1 indicates the

addition of online social networks explains a significant about of variation of the number of

expected protests (chi-sq = 12.54 df = 1, p-value = 0.0004).

I also run models containing other control channels to determine if this finding is again

unique for online social networks. In Model 3, I include face-to-face in addition to the control

variables. The variable face-to-face is not significant. In Model 4, I include mediated in addition

to the control variables. The variable mediated is not significant. However, the control variables

that were significant in models 1 and 2 were also significant in models 3 and 4. In both models,

log likelihood tests were performed and confirmed the addition of these variables did not

significantly impact model fit.

Finally, I run Model 5, a saturated model, which includes online social networks, face-to-

face, and mediated as well as the individual-level control variables. I include all three channels to

control for other ways individuals found out about the forum. In the full model the variable

                                                                                                               5  These interpretations of these models were made using the IDR value, which was found by taking the exponential of the parameter estimate, subtracting one from that number and then multiplying it by 100 to turn the number into a percent.  

  23  

online social networks (chi-sq = 11.45, p-value = 0.0007) is significant at the 5% level. For

attendees who learned of the USSF through online social networks, the expected number of

offline protests attended is 64% higher than those who did not. Age, female, refused for income,

and other for race are the significant variables in model 5. For every one-year increase in age, the

expected number of protests attended increased by 2%. The expected number of offline protests

attended by females was 25% lower than for males. The expected number of offline protests

attended by individuals who refused to answer the income question was 85% lower than those in

the lowest income group. In addition, the expected number of offline protests was 158% higher

for the other race group than whites. To further establish the impact of the variable online social

network on protest activity, a log likelihood test comparing this model to the base model

indicates the addition of online social networks explains a significant about of variation of the

number of expected protests (chi-sq = 15.63 df=3).

Table 3 about here

Discussion and Conclusion

The goal of this research was to use results from a survey fielded at the 2010 USSF to

examine whether finding out about the forum through SNSs related to attendees’ organizational

memberships and offline protest activities. Generally, findings support past research that show

that use of the Internet increases offline social movement involvement (della Porta and Mosca

2005; Fisher and Boekkooi 2010; Van Laer 2010).

Hypothesis one proposed learning of the USSF through SNSs impacts organizational

membership. Findings support results from prior research that maintain that Internet users belong

to multiple organizations or have increased levels of organizational membership (Bennett et. al.

2008; Van Laer 2010; Walgrave et. al. forthcoming). More importantly, this assertion can now

  24  

be expanded to include not only the use of the Internet but also specifically the use of online

social networks.

However, besides knowing that participants are members of the organizations, the type of

relationship or how strongly participants are embedded in the organizations cannot be discerned

from these findings and offer the opportunity for future research. Users of SNSs may have more

flexible relationships with organizations, which means they may have the opportunity to be

involved with increased numbers of organizations. They have the ability to learn about more

events and get together with others who support similar causes offline. The fact that using SNSs

as an information channel shows increased organizational membership could mean SNS users

have access to increased numbers of personal contacts. This supports the idea that use of the

Internet, “enables the organization of networks operating beyond the reach of formal

organizations” (Bennett et. al. 2008; 273). This could also mean that individual activists will

become more involved with coordinating social movement events while traditional organizations

play less of a central role (Walgrave et. al. forthcoming).

Hypothesis two examined the relationship between using SNSs as an information channel

and a specific degree of movement activity, number of protests attended in a year. Results

support past findings indicating Internet users are more likely to have protested in the past (della

Porta and Mosca 2005; Van Laer 2010). Results also support the assertion that the Internet

supplements other forms of offline interaction (Polat 2005). One benefit of the Internet is the

facilitation of communication and interaction across different networks. This increases the

chance that participants might be asked to take part in social movement activity (Van Laer 2010).

Results show that men are more likely to protest than women. Overall, this research allows a

better understanding of use of SNSs as an information channel. In addition, results from both

  25  

models help support the notion of the strength of weak ties (Granovetter 1983; Donath and boyd

2004; Kavanaugh, Reese, Carroll, and Rosson 2005).

More broadly, the implications of this research support the notion that SNSs matter in

facilitating social change. As depicted by the results of this study and in the discussions

surrounding the role of SNSs in the Middle East revolutions (Zhuo, et al. 2011), there are myriad

implicit and explicit effects of SNSs that influence the organization and mobilization of social

movement activity. While not taking the place of more traditional forms of communication, the

role of SNSs needs to be considered when examining social movement communication and

organization.

Practically, for members of social movements, activists should add SNSs to the repertoire

of more traditional recruitment, organizational, and communication outlets they have available to

them, as they strive to pursuit their movement goals. Activists can use SNSs to spread

information, organize, and mobilize individuals to facilitate social change. Although SNSs may

not always exist in their current forms and the technologies may evolve, the technologies are

worth pursuing as communication continues to change. Activists can learn from other groups

who are successfully using SNSs to support their activities. However, activists should be aware

that often times content of SNSs is created by other online users outside of the organizing group

of individuals and becomes part of the organizations online public profile (Kavada 2009).

This research does have its limitations. Fielding surveys at events such as the USSF is

challenging. Therefore, the USSF sample results in limitations to the study as attendees at the

USSF may not be the same as typical activists. Activity at the USSF, an event specifically

developed to be a non-hierarchical, participatory environment and created under the ideology of

the GSJ movement, may not be transferrable to other social movement events. In addition, the

  26  

respondents were largely U.S. based. It would be interesting to see if similar results would be

found in other parts of the world.

Future research could explore the nuances of the relationships between SNSs users and

organizations such as their positions in organizations to better understand how embedded SNSs

users are in organizations. Future studies could also examine the particular online tools and

technologies that people use, such as Twitter and Facebook, and their influence on online and

offline social movement activities. The relationships SNSs users have online should also be

explored. Moving away from survey work, future research could also use more qualitative

methods, such as interviews or ethnography, to obtain a better understanding as to how social

movement activities make use of SNSs and which mechanisms lead individuals to use online

social networks. Research could also explore if certain kinds of online activism using particular

SNSs spurs specific offline activity. Overall, these findings help reveal the importance of the

Internet, and specifically SNSs, in social movement activity but continued research is needed to

further explore the ways that SNSs influence social movements and how movements can best

make use of the new technologies.

  27  

References Anduiza, E., Cantijoch, M., and A. Gallego. 2009. “Political Participation and the Internet.”

Information, Communication & Society 12(6): 860-878. Arquilla, John and David Ronfeldt. Networks and Netwars: The Future of Terror, Crime, and

Militancy. Santa Monica, CA: RAND Corporation, 2001. Retrieved May 13, 2011 (http://www.rand.org/pubs/monograph_reports/MR1382).

Bedoyan, I., Van Aelst, P. and S. Walgrave. 2004. “Limits and Possibilities of Transnational

Mobilization: the Case of EU Summit Protesters in Brussels, 2001.” Mobilization 9(1): 39-54.

Bennett, W. 2003. “Communicating Global Activism.” Information, Communication & Society

6(2): 143-168. Bennett, W., Breunig, C., and T. Givens. 2008. “Communication and Political Mobilization:

Digital Media and the Organization of Anti-Iraq War Demonstrations in the U.S.” Political Communication 25(3): 269-289.

Bennett, W. and A. Segerberg. 2011. “Digital Media and the Personalization of Collective

Action.” Information, Communication & Society. 1-30. Best, S. and B. Krueger. 2005. “Analyzing the Representativeness of Internet Political

Participation.” Political Behavior 27(2): 183-216. boyd, d. m. and N. Ellison. 2007. “Social Network Sites: Definition, History, and Scholarship.”

Journal of Computer-Mediated Communication 13: 210-230. boyd, d. 2010. “Social network sites as networked publics: Affordances, dynamics, and

implications.” Networked Self: Identity, Community, and Culture on Social Network Sites (e.d. Zizi Papacharissi) 39-58.

Brunsting, S. and T. Postmes. 2002a. “Collective Action in the Age of the Internet: Mass

Communication and Online Mobilization.” Social Science Computer Review 20(3): 290-301.

Brunsting, S. and T. Postmes. 2002b. “Participation in the Digital Age: Predicting Offline and

Online Collective Action.” Small Group Research 33(5): 525-554. Carroll, W. K. and R.S. Ratner. 1996. “Master Framing and Cross-Movement Networking in

Contemporary Social Movements.” Sociological Quarterly 37(4): 601-625. Castells, M. 1996. The Information Age. Vol. I: The Rise of the Network Society.

Oxford/Cambridge, MA: Blackwell.

  28  

Castells, M. 2004 The Information Age. Economy, Society and Culture: The Power of Identity. Malden, MA: Blackwell.

Christakis, N. and James H. Fowler. 2008. “The Collective Dynamics of Smoking in a Large

Social Network.” The New England Journal of Medicine 358: 2249-2258. della Porta, D and L. Mosca. 2005. “Global-net for Global Movements? A Network of Networks

for a Movement of Movements.” Journal of Public Policy 25(1): 165-190. Diani, M. 2000. “Social Movement Networks Virtual and Real.” Information, Communication &

Society 3(3): 386-401. Donath, J., 2007. “Signals in Social Supernets.” Journal in Computer-Mediated Communication

13(1): Donath, J., & boyd, D. 2004. “Public Displays of Connection. BT Technology Journal, 22(4), 71. Earl, J. 2006. “Pursuing Social Change Online: The Use of Four Protest Tactics on the Internet.”

Social Science Computer Review 24(3): 362-377. Ellison, N.B., Steinfield, C, and C. Lampe. 2007. “The Benefits of Facebook ‘Friends:’ Social

Capital and College Students’ Use of Online Social Network Sites.” Journal of Computer-Mediated Communication 12(4).

Ellison, N. B., Steinfield, C., and C. Lampe. 2011. Connection Strategies: Social Capital

Implications of Facebook Communication Practices New Media and Society 1-20 Fisher, D. R. and M. Boekkoi. 2010. “Mobilizing Friends and Strangers: Understanding the role

of the Internet in the Step It Up day of action.” Information, Communication & Society 13(2): 193-208.

Fisher, D. R., Stanley, K., Berman, D. and G. Neff. 2005. “How do organizations matter?

Mobilization and support for participants at five global protests.” Social Problems 52(1): 102-121.

Fowler, J. and Nicholas Christakis. 2008. “Dynamic spread of happiness in a large social

network: longitudinal analysis over 20 years in the Framingham Heart Study.” BMJ 337: 1-9.

Garrett, R. K. 2006. “Protest in an Information Society: a review of literature on social

movements and new ICTs.” Information, Communication & Society 9(2): 202–224. Goldfarb, A. and Jeff Prince. 2008. “Internet adoption and usage patterns are different:

Implications for the digital divide.” Information Economics and Policy 20: 2-15. Granovetter, M. S. 1973. The strength of weak ties. American Journal of Sociology 78(6), 1360–

1380.

  29  

Granovetter, M. S. 1983. “The Strength of Weak Ties: A Network Theory Revisited.” Sociological Theory 1: 203-233.

Gross, Doug. 2011. “Google, Twitter help give voice to Egyptians.” CNN.com. February 1,

2011. Retrieved April 15, 2011. (http://www.cnn.com/2011/TECH/web/02/01/google.egypt/index.html)

Hampton, K. and Barry Wellman. 2001. “Long distance community in network society: contact

and support beyond Netville.” American Behavioral Scientist 45(3): 476-495. Hampton, K. 2003. “Grieving for a Lost Network: Collective Action in a Wired Suburb.” The

Information Society 19: 417-428. Hampton, K., Lee, C., and E.J. Her. (in press) “How New Media Affords Network Diversity:

Direct and Mediated Access to Social Capital Through Participation in Local Social Settings.” New Media & Society

Hargittai, E. 2008. “The Digital Reproduction of Inequality.” In Social Stratification. Edited by

David Grusky. Boulder, CO: Westview Press. 936-944. Harlow, S., and Dustin Harp. 2011. “Collective Action on the Web” Information,

Communication & Society Haythornthwaite, C. 2005. “Social networks and Internet connectivity effects.” Information,

Communication, & Society 8(2), 125–147. Hugill, P. J. 1999. Global Communications Since 1844: Geopolitics and Technology. Baltimore

and London, John Hopkins University Press. Juris, J. S. 2004. ‘Networked Social Movements: Global Movements for Global Justice’, in M.

Castells (ed.) The Network Society: A Cross-Cultural Perspective. London: Edward Elgar, 341-362.

Juris, J. S., Caruso, G., and L. Mosca. 2008. “Freeing Software and Opening Space: Social

Forums and the Cultural Politics of Technology.” Societies Without Borders 3: 96-117. Kahn, Richard and Douglas Kellner 2004. “New Media and Internet Activism: from the ‘Battle

of Seattle’ to Blogging.” New Media & Society 6(1): 87-95. Kavada, Anastasia. 2005. “Exploring the Role of the Internet in the ‘Movement for Alternative

Globalization’: The Case of the Paris 2003 European Social Forum.” Westminster Papers in Communication and Culture 2(1): 72-95.

Kavada, Anastasia. 2009. “Engagement, bonding and identity across multiple platforms: Avaaz

on Facebook, YouTube and MySpace” Paper presented at ECPR General Conference, Potsdam, September 2009.

  30  

Kavada, Anastasia. 2010. “Email List and Participatory Democracy in the European Social Forum.” Media, Culture & Society 32(3): 355-372.

Kavanaugh, A. L., Reese, D. D., Carroll, J. M., and M. Rosson. 2005. “Weak Ties in Networked

Communities.” The Information Society 21: 119-131. Kitts, James. 2000. “Mobilizing in Black Boxes: Social Networks and Participation in Social

Movement Organizations.” Mobilization 5(2): 241-257. Kposowa, Augustine. 2011 Analysis of Count Outcomes: Poisson Regression Lecture 8.

February 23, 2011. Langman, L. 2005. “From Virtual Public Spheres to Global Justice: A Critical Theory of

Internetworked Social Movements.” Sociological Theory 3(1): 42-74. Lim, Chaeyoon. 2008. “Social Networks and Political Participation: How Do Networks Matter?”

Social Forces 87(2): 961- Martin, S., and J. Robinson. 2007. “The Income Digital Divide: Trends and Predictions for

Levels of Internet Use.” Social Problems 54(1): 1-22. McAdam, Doug. 1986. “Recruitment of High Risk Activism: the Case of Freedom Summer.”

American Journal of Sociology 92(1): 64-90. McAdam, Doug and Ronnelle Paulsen. 1993. “Specifying the Relationship between Social Ties

and Activism.” American Journal of Sociology 99(3): 640-667. Mejias, Ulises. 2011. “The Twitter Revolution Must Die.” International Journal of Learning and

Media 2(4): 3-5. Miller McPherson, Popielarz, P, and S. Drobnic. 1992. “Social networks and organizational

dynamics.” American Sociological Review 57(2): 153-170. Pasek, J., More, E., and Romer, D. 2009. “Realizing the Social Internet? Online Social

Networking Meets Offline Civic Engagement.” Journal of Information Technology and Politics Vol. 6. No. 3/4, p. 197-215.

Passy, Florence, and Marco Giugni. 2001. “Social Networks and Individual Perceptions:

Explaining Differential Participation in Social Movements.” Sociological Forum Vol. 16(1): 123-154.

Polat, Rabia. 2005. “The Internet and Political Participation: Exploring the Explanatory Links.”

European Journal of Communication 20(4): 435-459. Preston, Jennifer. 2011. “Facebook and YouTube Fuel the Egyptian Protests.” New York Times.

February 5, 2011. Retrieved April 15, 2011: (http://www.nytimes.com/2011/02/06/world/middleeast/06face.html?_r=2&scp=4&sq=egypt+facebook&st=nyt).

  31  

Reese, Ellen, Ian Breckenridge-Jackson, Edwin Elias, David W. Everson, and James Love. 2011.

"The Global Justice Movement and the Social Forum Process." In Routledge International Handbook of World-Systems Analysis, edited by Salvatore Balbones and Christopher Chase-Dunn. Abingdon, UK: Routledge.

Schussman, Alan and Sarah A Soule. 2005. "Process and Protest: Accounting for

Individual Protest Participation." Social Forces 84:1083-1108. Shah, D. V., Cho, J., Evelands, W. Jr. and N. Kwak. 2005. “Information and Expression in a

Digital Age.” Communication Research 32(5): 531-565. Snow, David A., Louis A. Zurcher Jr. and Sheldon Ekland-Olson. 1980. “Social Networks and

Social Movements: A Microstructural Approach to Differential Recruitment.” American Sociological Review 45(5): 787-801.

Tufekci, Zeynep. 2011. “Can “Leaderless Revolutions” Stay Leaderless: Preferential

Attachment, Iron Laws and Networks” Technosociology Retrieved May 13, 2011. (http://technosociology.org/?p=366)

US Social Forum – Another World Is Possible. 2011. http://www.facebook.com/ussocialforum.

Accessed July 18, 2011. USSF. 2011. http://twitter.com/#!/ussf. Accessed July 18, 2011. Valenzuela, S., Park, N. and K. F. Kee. 2009. “Is There Social Capital in a Social Network Site?:

Facebook Use and College Students' Life Satisfaction, Trust, and Participation.” Journal of Computer-Mediated Communication 14: 875-901.

Van Laer, J. 2010. “Activists online and offline: The Internet as an Information Channel for

Protest Demonstrations.” Mobilization 15(3): 347-366. Van Laer, J. and P. Van Aelst. “Internet and Social Movement Repertoires: Opportunities and

Limitations.” Information, Communication, and Society 1-26. Vitak, J., Zube, P., Smock, A., Carr, C., Ellison, N., Lampe, C. 2010. “It’s Complicated:

Facebook Users’ Political Participation in the 2008 Election.” CyberPsychology, Behavior, and Social Networking 0(0): 1-8.

Walgrave, S., Bennett, W.L., Van Laer, J. and Breunig, C. forthcoming. “Multiple engagements

and network bridging in contentious politics: digital media use of protest participants.” Mobilization.

Wellman, B. 2001. “Computer networks as social networks.” Science 293: 2031–2034. Wellman, Barry. 2002. “Little Boxes, Glocalization, and Networked Individualism” Revised

Papers from the Second Kyoto Workshop on Digital Cities II, Computational and

  32  

Sociological Approaches. http://homes.chass.utoronto.ca/~wellman/publications/littleboxes/littlebox.PDF

Wellman, B., Haase, A. Q., Witte, J. and K. Hampton 2001. “Does the Internet Increase,

Decrease, or Supplement Social Capital?: Social Networks, Participation, and Community Commitment.” American Behavioral Scientist 45(3): 436-455.

Zhang, W. Johnson, T. J. Seltzer, T., S. Bichard. 2010.“The Revolution Will be Networked: The

Influence of Social Networking Sites on Political Attitudes and Behavior.” Social Science Computer Review 28: 75-92

Zhuo, Xiaolin, Wellman, Barry and Justine Yu. 2011. “Egypt: The First Internet Revolt?” Peace

Magazine http://homes.chass.utoronto.ca/~wellman/publications/egypt/PMag-1107-Egypt-offprint.pdf Accessed July 1, 2011.

  33  

Table 1. Descriptive Statistics for Organizational Membership, Protests, and Online Social Networks

Variables N Std Dev

Mean Skewness Min Max

Protests 500 12.46 7.01 5.34 0 115 Organizational Membership

High 160 0.46 0.30 0.85 0 1 Low 290 0.50 0.51 -0.06 0 1 Information channel Online social networks 134 0.43 0.24 1.23 0 1 Mediated 229 0.49 0.41 0.38 0 1 Face-to-face 564 0.32 0.88 -2.36 0 1 Age 509 16.57 36.47 0.97 18 93 Gender Female 299 0.50 0.56 -0.24 0 1 Male 218 0.49 0.41 0.37 0 1 Other 17 0.18 0.03 5.35 0 1 Personal income None - $14,999 166 0.50 0.43 0.28 0 1 $15,000 - $20,999 46 0.32 0.12 2.36 0 1 $21,000 - $39,999 78 0.40 0.20 1.49 0 1 $40,000 - $51,999 31 0.27 0.08 3.10 0 1 $52,000 - $63,999 16 0.20 0.04 4.62 0 1 $64,000 or Above 20 0.23 0.05 3.92 0 1 Refused 7 0.14 0.02 7.03 0 1 Race White 281 0.50 0.55 -0.21 0 1 Latino/Hispanic 73 0.35 0.14 2.04 0 1 Black 54 0.31 0.11 2.56 0 1 Multiracial 47 0.29 0.09 2.82 0 1 Asian 26 0.22 0.05 4.09 0 1 Middle Eastern 4 0.09 0.01 11.17 0 1 Indigenous 4 0.09 0.01 11.17 0 1 Other 16 0.17 0.03 5.38 0 1

  34  

Table 2. Logistic Regression Analysis Results of the Effects of Mobilization Through Online Social Networks on Organizational Membership.

Model 1 Model 2 Model 3 Model 4 Model 5 Information channel Face-to-face -.736 -.572 (.412) (.426) Mediated .293 .096 (.267) (.282) Online social networks .730 * .647 * (.293) (.303) Age .024 * .028 ** .024 * .025 * .027 ** (.010) (.010) (.010) (.010) (.010) Gender Male ------- ------- ------- ------- ------- Female .372 .393 .389 .392 .410 (.276) (.279) (.277) (.277) (.280) Other .536 .476 .575 .592 .534 (.634) (.647) (.635) (.637) (.647) Personal income None - $14,999 ------- ------- ------- ------- ------- $15,000 - $20,999 -.581 -.615 -.557 -.580 -.593 (.421) (.430) (.424) (.422) (.432) $21,000 - $39,999 -.412 -.380 -.421 -.409 -.386 (.350) (.353) (.351) (.350) (.354) $40,000 - $51,999 -.933 -.886 -.958 -.979 -.917 (.549) (.550) (.554) (.551) (.555) $52,000 - $63,999 -.635 -.583 -.615 -.706 -.594 (.678) (.679) (.685) (.685) (.689) $64,000 or Above -.426 -.332 -.462 -.394 -.356 (.652) (.663) (.665) (.655) (.678) Refused -1.679 -1.563 -1.752 -1.689 -1.633 (1.160) (1.167) (1.180) (1.166) (1.182)

  35  

Race White ------- ------- ------- ------- ------- Latino/Hispanic .192 .243 .159 .205 .214 (.407) (.412) (.409) (.408) (.415) Black -.158 -.106 -.194 -.087 -.113 (.487) (.492) (.494) (.490) (.501) Multiracial -.124 -.156 -.162 -.060 -.163 (.461) (.465) (.467) (.466) (.474) Asian -.288 -.179 -.433 -.249 -.293 (.704) (.713) (.712) (.706) (.72) Middle Eastern -13.457 -13.198 -13.405 -13.382 -13.163 (770.400) (772.300) (769.900) (777.500) (774.000) Indigenous 1.066 1.006 1.096 0.893 0.977 (1.476) (1.526) (1.476) (1.486) (1.527) Other .224 .212 .290 .194 .257 (.816) (.849) (.817) (.822) (.847) Intercept -1.594 *** 1.966 *** -0.907 -1.766 *** -1.444 *

(.407) (.443) (.556) (.439) (.637) R-squared 0.065 0.092 0.079 0.070 0.101 Sample Size 305 305 305 305 305 Notes: Numbers in parentheses are standard errors. *p<.05; **p<.01; ***p<.001 (two-tailed test).

  36  

Table 3. Negative Binomial Regression Results for Protests and Online Social Networks

Model 1 Model 2 Model 3 Model 4 Model 5 Mobilization channel Face-to-face .289 .392 (.235) (.233) Mediated .202 .096 (.134) (.135) Online social networks .496 *** .496 *** (.142) (.147) Age .019 *** .020 *** .020 *** .018 *** .022 *** (.005) (.005) (.005) (.005) (.005) Gender Male ---- ---- ---- ---- ---- Female -.300 * -.272 * -.315 * -.290 * -.291 * (.133) (.131) (.133) (.132) (.131) Other .360 .414 .321 .408 .380 (.331) (.323) (.331) (.330) (.323) Personal income None - $14,999 ---- ---- ---- ---- ---- $15,000 - $20,999 -.118 -.129 -.120 -.108 -.131 (.203) (.198) (.204) (.203) (.198) $21,000 - $39,999 -.019 .017 -.031 -.022 -.001 (.169) (.167) (.169) (.169) (.167) $40,000 - $51,999 .104 .155 .085 .080 .120 (.229) (.225) (.229) (.228) (.226) $52,000 - $63,999 -.142 -.030 -.192 -.190 -.120 (.321) (.316) (.324) (.321) (.320) $64,000 or Above -.353 -.181 -.374 -.315 -.197 (.328) (.325) (.328) (.328) (.324) Refused -1.978 ** -1.815 * -2.063 *** -1.961 *** -1.920 ** (.739) (.731) (.741) (.739) (.732)

  37  

Race White ---- ---- ---- ---- ---- Latino/Hispanic .237 .243 .258 .258 .281 (.206) (.202) (.206) (.205) (.202) Black -.289 -.333 -.245 -.211 -.240 (.246) (.241) (.249) (.250) (.249) Multiracial .357 .406 .417 .426 .528 * (.219) (.216) (.225) (.223) (.228) Asian .169 .333 .217 .233 .428 (.356) (.352) (.358) (.357) (.354) Middle Eastern -1.308 -1.109 -1.302 -1.233 -1.064 (.814) (.806) (.813) (.814) (.805) Indigenous -.077 -.149 -.057 -.196 -.178 (.790) (.788) (.788) (.791) (.788) Other .877 * .992 ** .843 * .872 * .946 ** (.373) (.367) (.374) (.371) (.366) Intercept 1.205 *** .926 *** .900 *** 1.091 *** .461

(.194) (.204) (.315) (.207) (.332)

Sample Size 295 295 295 295 295 Log Likelihood Chi-sq ---- 12.54 *** 1.45 2.29 15.63 **