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; [email protected].
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
2 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
3 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
4 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).
5 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,
6 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
7 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
8 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
9 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
10 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
11 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.
12 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).
13 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
14 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
15 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-toface was coded 0. Because respondents could select more than one entry for this question, it was
16 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. 17 $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
18 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
19 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. 20 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, pvalue = 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-toface, 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
21 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-toface, 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. 22 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
23 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
24 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
25 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.
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32 Table 1. Descriptive Statistics for Organizational Membership, Protests, and Online
Social Networks
Variables
Protests
Organizational
Membership
500
Std
Dev
12.46
High
Low
Information channel
Online social networks
Mediated
Face-to-face
Age
Gender
Female
Male
Other
Personal income
None - $14,999
$15,000 - $20,999
$21,000 - $39,999
$40,000 - $51,999
$52,000 - $63,999
$64,000 or Above
Refused
Race
160
290
0.46
0.50
0.30
0.51
0.85
-0.06
0
0
1
1
134
229
564
509
0.43
0.49
0.32
16.57
0.24
0.41
0.88
36.47
1.23
0.38
-2.36
0.97
0
0
0
18
1
1
1
93
299
218
17
0.50
0.49
0.18
0.56
0.41
0.03
-0.24
0.37
5.35
0
0
0
1
1
1
166
46
78
31
16
20
7
0.50
0.32
0.40
0.27
0.20
0.23
0.14
0.43
0.12
0.20
0.08
0.04
0.05
0.02
0.28
2.36
1.49
3.10
4.62
3.92
7.03
0
0
0
0
0
0
0
1
1
1
1
1
1
1
281
73
54
47
26
4
4
16
0.50
0.35
0.31
0.29
0.22
0.09
0.09
0.17
0.55
0.14
0.11
0.09
0.05
0.01
0.01
0.03
-0.21
2.04
2.56
2.82
4.09
11.17
11.17
5.38
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
White
Latino/Hispanic
Black
Multiracial
Asian
Middle Eastern
Indigenous
Other
N
Mean
Skewness
Min
Max
7.01
5.34
0
115
33 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
Gender
Male
Female
Other
Personal income
None - $14,999
$15,000 - $20,999
$21,000 - $39,999
$40,000 - $51,999
$52,000 - $63,999
$64,000 or Above
Refused
.024*
(.010)
.028**
(.010)
.024*
(.010)
.025*
(.010)
.027**
(.010)
-------
-------
-------
-------
-------
.372
(.276)
.536
(.634)
.393
(.279)
.476
(.647)
.389
(.277)
.575
(.635)
.392
(.277)
.592
(.637)
.410
(.280)
.534
(.647)
-------
-------
-------
-------
-------
-.581
(.421)
-.412
(.350)
-.933
(.549)
-.635
(.678)
-.426
(.652)
-1.679
(1.160)
-.615
(.430)
-.380
(.353)
-.886
(.550)
-.583
(.679)
-.332
(.663)
-1.563
(1.167)
-.557
(.424)
-.421
(.351)
-.958
(.554)
-.615
(.685)
-.462
(.665)
-1.752
(1.180)
-.580
(.422)
-.409
(.350)
-.979
(.551)
-.706
(.685)
-.394
(.655)
-1.689
(1.166)
-.593
(.432)
-.386
(.354)
-.917
(.555)
-.594
(.689)
-.356
(.678)
-1.633
(1.182)
34 Race
White
Latino/Hispanic
Black
Multiracial
Asian
Middle Eastern
Indigenous
Other
Intercept
-------
-------
-------
-------
-------
.192
(.407)
-.158
(.487)
-.124
(.461)
-.288
(.704)
-13.457
(770.400)
1.066
(1.476)
.224
(.816)
.243
(.412)
-.106
(.492)
-.156
(.465)
-.179
(.713)
-13.198
(772.300)
1.006
(1.526)
.212
(.849)
.159
(.409)
-.194
(.494)
-.162
(.467)
-.433
(.712)
-13.405
(769.900)
1.096
(1.476)
.290
(.817)
.205
(.408)
-.087
(.490)
-.060
(.466)
-.249
(.706)
-13.382
(777.500)
0.893
(1.486)
.194
(.822)
.214
(.415)
-.113
(.501)
-.163
(.474)
-.293
(.72)
-13.163
(774.000)
0.977
(1.527)
.257
(.847)
-1.594***
(.407)
1.966***
(.443)
-0.907
(.556)
-1.766***
(.439)
-1.444*
(.637)
R-squared
Sample Size
0.065
0.092
0.079
0.070
0.101
305
305
305
305
305
Notes: Numbers in parentheses are standard errors. *p<.05; **p<.01; ***p<.001 (two-tailed
test).
35 Table 3. Negative Binomial Regression Results for Protests and Online Social Networks
Model 1
Model 2
Mobilization channel
Face-to-face
Model 3
.289
(.235)
Mediated
.202
(.134)
Online social networks
Age
Gender
Male
Female
Other
Personal income
None - $14,999
$15,000 - $20,999
$21,000 - $39,999
$40,000 - $51,999
$52,000 - $63,999
$64,000 or Above
Refused
Model 4
.496***
(.142)
Model 5
.392
(.233)
.096
(.135)
.496***
(.147)
.019***
(.005)
.020***
(.005)
.020***
(.005)
.018***
(.005)
.022***
(.005)
----
----
----
----
----
-.300*
(.133)
.360
(.331)
-.272*
(.131)
.414
(.323)
-.315*
(.133)
.321
(.331)
-.290*
(.132)
.408
(.330)
-.291*
(.131)
.380
(.323)
----
----
----
----
----
-.120
(.204)
-.031
(.169)
.085
(.229)
-.192
(.324)
-.374
(.328)
-2.063***
(.741)
-.108
(.203)
-.022
(.169)
.080
(.228)
-.190
(.321)
-.315
(.328)
-1.961***
(.739)
-.131
(.198)
-.001
(.167)
.120
(.226)
-.120
(.320)
-.197
(.324)
-1.920**
(.732)
-.118
(.203)
-.019
(.169)
.104
(.229)
-.142
(.321)
-.353
(.328)
-1.978**
(.739)
-.129
(.198)
.017
(.167)
.155
(.225)
-.030
(.316)
-.181
(.325)
-1.815*
(.731)
36 Race
White
Latino/Hispanic
Black
Multiracial
Asian
Middle Eastern
Indigenous
Other
Intercept
Sample Size
Log Likelihood Chi-sq
----
----
----
----
----
.237
(.206)
-.289
(.246)
.357
(.219)
.169
(.356)
-1.308
(.814)
-.077
(.790)
.877*
(.373)
.243
(.202)
-.333
(.241)
.406
(.216)
.333
(.352)
-1.109
(.806)
-.149
(.788)
.992**
(.367)
.258
(.206)
-.245
(.249)
.417
(.225)
.217
(.358)
-1.302
(.813)
-.057
(.788)
.843*
(.374)
.258
(.205)
-.211
(.250)
.426
(.223)
.233
(.357)
-1.233
(.814)
-.196
(.791)
.872*
(.371)
.281
(.202)
-.240
(.249)
.528*
(.228)
.428
(.354)
-1.064
(.805)
-.178
(.788)
.946**
(.366)
1.205***
(.194)
.926***
(.204)
.900***
(.315)
1.091***
(.207)
.461
(.332)
295
----
295
12.54***
295
1.45
295
2.29
295
15.63**
37