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Soc. Sci. 2016, 5(4), 58; doi:10.3390/socsci5040058

Support for Protests in Latin America: Classifications and the Role of Online Networking
School of Journalism, College of Communication Arts and Sciences, Michigan State University, East Lansing, MI 48824, USA
Department of Journalism & Electronic Media, College of Media & Communication, Texas Tech University, Lubbock, TX 79409, USA
School of Journalism, Moody College of Communication, University of Texas at Austin, Austin, TX 78712, USA
Lyndon B. Johnson School of Public Affairs, University of Texas at Austin, Austin, TX 78712, USA
Author to whom correspondence should be addressed.
Academic Editor: Martin J. Bull
Received: 30 June 2016 / Accepted: 18 September 2016 / Published: 28 September 2016


In recent years, Latin Americans marched the streets in a wave of protests that swept almost every country in the region. Yet few studies have assessed how Latin Americans support various forms of protest, and how new technologies affect attitudes toward protest tactics. Using data from the Latin American Public Opinion Project (N = 37,102), cluster analyses grouped citizens into four distinct groups depending on their support for protests. Most Latin Americans support moderate forms of protest, rejecting more radical tactics. Online networking is associated with support for both moderate and radical protests. But those who support only moderate protests use online networking sites more than Latin Americans as a whole, while those who support radical protests use online networking sites significantly less. Our findings suggest that only peaceful and legal demonstrations have been normalized in the region, and online networking foments support for moderate protest tactics.
social movements; online networking; support for protests; Latin America; political communication

1. Introduction

Since 2011, the Western Hemisphere has witnessed a surge of protests across major cities in the continent. From student movements in Chile to Occupy Wall Street in the United States, thousands of Americans have marched the streets in political movements that demanded solutions to an array of grievances. Leaders of social movement organizations in the region attribute the scope of the protests to the mobilizing power of online networking websites, such as Twitter and Facebook [1]. But how do Latin Americans support the “right to protest”? What is the actual relationship between online networking and support for protests in the Americas? And how does it differ across countries?
Scholars approach exploring the phenomenon of social movements and protests in two different analytical levels: micro and macro. While the micro level includes questions about what motivates people to protest and what tactics they decide to use, the macro level questions why protests emerge and how they can change society [2]. This study focuses on the attitudinal micro level of analysis, referring to what perceptions citizens in Latin America have regarding the “right to protest” and the perceived legitimacy of certain protest tactics. Ultimately, we are interested in assessing what—if any—forms of collective action have been “normalized” by the public and are understood as legitimate practices of political action in democracies in the region. Thus, the general research questions driving this study are: how do Latin Americans approve or disapprove the “right to protest”? Does this support vary regarding different types of protest? How does online networking relate to attitudes toward different protest tactics?
In the last decade, scholars have consistently found a positive relationship between frequency of online networking and protest participation [3,4]. We aim to further the theoretical understanding of this relationship in the context of Latin American democracies, which have experienced multiple waves of protest in the last few years. Across the continent, journalists and activists have credited social media as the organizing force behind recent mobilizations [1], but very few empirical studies have actually assessed if online networking for politics leads to changes in protest attitudes. Our study contributes to this emerging body of literature by analyzing protest attitudes and testing models previously established in other contexts. We seek to shed light on the situation in the region, but also elucidate and further develop such models. By analyzing a multitude of countries from different backgrounds, we obtained a more thorough picture of how online networking can affect approval of people’s right to protest, also taking into account the different types of protest tactics across the region.

2. Protests and Public Opinion in Latin America

Following the fall of military regimes in the 1980s, most countries in Latin America have had decades of relatively stable democracies, despite more than 22 popular protests demanding the resignation of a democratically elected president [5]. However, few studies have assessed how citizens in the region perceive protest participation as a form of democratic engagement [6,7].
Here, it is useful to make a distinction between protests and social movements. If social movements are marked by continuity and organization [8,9], protests represent one element of social movements’ inventory of actions: part of what Tilly [10] calls their “repertoire of contention.” This analysis focuses on the public’s perception regarding the “right to protest,” a measure of acceptance of protest as a legitimate form of political participation.
In Latin America, an incipient body of research has found that higher socioeconomic variables, such as education and social class, lead to less tolerance for protests in the region [7]. In contrast, Moseley and Moreno [6] found that socioeconomic variables were not as relevant as political interest and civic engagement when predicting protest behavior in Argentina and Bolivia. Others contend that, rather than individual variables, it is the perceived strength of political institutions that influences support for protest participation [11]. Such divergent findings urge for a comprehensive approach to understanding protest attitudes in the region. Using 2012 data from 23 Latin American countries, we draw from two of the main theories of social movements—collective behaviorism and strategic resource mobilization—to understand what micro-level variables influence the way Latin Americans support different types of protests in the region.

3. Two Approaches to Understand Support for Protests

Building upon Marx’s and Tocqueville’s grievances-based theories, the collective behaviorism school emerged in the early twentieth century and emphasized the role of structural strains on promoting a disruptive psychological state that ultimately leads to social unrest. According to collective behaviorists, social movements can be explained based on the individual psychology of their participants, including grievances, trust in the government and external efficacy (for an overview of collective behaviorism, see [12]). For collective behaviorism, it is the level of psychological disruption of the individual that explains protest participation. Social movements are, therefore, a manifestation of strenuous conditions and relative deprivation, where participants seek to promote a new order of life through interactions and new symbolic mediations [13]. While recent sociologists have rejected psychologically based theories, some of the literature in Latin America still emphasizes the role of grievances and economic strain in the emergence of social movements [6]. In a study comparing protest participation in Argentina and Bolivia, Moseley and Moreno found some elements of collective behaviorism—which can also be called “disaffected radicalism”—to be predictors of protest participation, most notably younger age and dissatisfaction with government [6].
In contrast to collective behaviorism, resource mobilization theory explains social movements as a rational strategic resource for political participation. Protestors are well organized and make the decision to join a social movement after cost-benefit analyses [14]. In this theory, grievances are not as important to the emergence of social movements, and what guarantees their success is the way they can mobilize material resources such as money, labor and facilities. Strategically oriented theories, such as resource mobilization, defend that protests have been “normalized” as another form of political participation in democratic governments, especially when it comes to modular—or standardized—types of protests, such as petitions or legal street demonstrations [15,16].
For resource mobilization theorists, the late twentieth century could be described as the era of a “movement society,” where social unrest and protest politics have been generally accepted [16]. However, despite the mounting evidence that certain types of protest have been institutionalized by modern social movement organizations, little empirical evidence regarding an increasing acceptance of protest tactics is found in the literature. In a study about protest attitudes in Europe, Crozat [17] found that the public still disapproves of certain forms of moderate protests, such as boycotting and sit-ins. In fact, attitudes about different protest tactics have not changed drastically from 1974 to 1990, even when these tactics became widely used by organizations. In addition, groups whose goals and tactics threaten the status quo are more likely to get negative media attention, which in turn could sway the public opinion away from the social movement [18,19,20].
Our study aims to fill this gap and assess how Latin American societies support or reject the “right to protest” using different tactics and the factors influencing these attitudes. For resource mobilization theorists, variables affecting protest support and participation are similar to those that lead to other forms of institutionalized political engagement: higher socioeconomic status, political interest and internal efficacy [6,21]. More recently, scholars have also found that online networking use is a strong predictor of political knowledge and participation, and the effects of the Internet on political engagement have increased over time [22]. In line with strategic resource mobilization theory, our study extends online networking’s impact on political participation to protest attitudes.

Online Networking and Protests

In recent years, political communication scholars have worked to assess the role of online networking on political movements such as the Arab Spring and Occupy Wall Street [4,23]. Information Communication Technologies (ICTs), most notably social media, have affected the way movement organizations communicate, allowing them to reach larger audiences and augment financial support [23,24,25]. In a comprehensive review of the literature on social movements and ICTs, Garret [26] found that new technologies allow challengers to reduce required resources and shape the language of the movement.
Karatzogianni divides the history of digital activism—defined as political participation organized in digital networks—into four stages [27]. The first one (1994–2001) encompasses the early years of the World Wide Web and was characterized by an optimist view of technology’s role in politics. The second one starts with 9/11 and was marked by dissenting voices starting to be able to challenge official narratives of the War on Terror. The third phase (2007–2010) was characterized by the beginning of the blending between online and offline activism, and Barack Obama’s use of social media during his campaign. The fourth phase (2010–2014) is marked by a further incorporation of digital activism in mainstream politics as exemplified by the Wikileaks, the Arab Spring and the Occupy movements. The author argues that digital activism post-2014 is likely to become less important as it is normalized into mainstream “politics as usual.” Further, Morozov argues that the focus on the positive “emancipatory” aspects of online political participation often ignores the downsides of the Web: how authoritarian regimes can take advantage of Internet freedom to spy, manipulate and maintain oppressive systems [28].
Empirical evidence suggests that online networking has had a mobilizing impact on civic engagement, including protest behaviors such as public demonstrations, petitions and boycotts [3,4,29,30]. This impact is even stronger when online networking sites are used as spaces for political discussion. In an investigation on youth protest behavior, Valenzuela and colleagues found that Facebook use was associated with protest activity, especially when the website was used for news [31]. But research on the relationship between online networking and support for protests has been limited, with studies in the Caucasus region finding evidence of the positive impact of social media in protest attitudes in Azerbaijan [32]. In Latin America, Harlow and Harp found that online activism translated into offline actions in Colombia and Nicaragua, where social networking sites were used to mobilize supporters [25]. In Guatemala, Facebook was used to post motivational comments and organize anti-violence mass protests, essentially mobilizing an online movement that went offline [24]. Because online networking is associated with higher levels of socioeconomic status, political interest and participation, its link to support of protests is more closely related to variables from resource mobilization theory than collective behaviorism.
When it comes to protest behavior, the literature suggests that online networking fosters the communication between people with similar interests, allowing for the collective identity construction necessary for social mobilization [23]. Bennett and Segerberg identify online group identity construction through information hubs as connective action [23]. Similarly, other scholars hypothesized that online networking sites function as a place where people get feedback, share similar interests, and reinforce group norms [31,33,34].
Wolfsfeld, Segev and Shaefer point out the importance of understanding the role of social media within the context of the political environment in which they operate [35]. In other words, one cannot extrapolate findings from one region to another without taking into consideration how social media relates to other political variables in each specific scenario. In addition, the authors found that, during the Arab Spring, social media adoption actually increased after the main protest activities in the streets. Similarly, Karatzogianni contends that emphasizing social media as the cause or main factor for the Arab Spring protests misses important elements from each country’s context, reducing them to a homogeneous group equally influenced by ICTs [27].

4. Research Questions and Hypotheses

This article focuses on the role of digital media for political communication in Latin America. We follow a quantitative, survey-based approach and seek to provide a starting point to study social media and support for protests. This study aims to understand how people differ when it comes to support for moderate and radical protest tactics, and how those differences relate to people’s online networking use. Hence, this study poses the following research question:
How do citizens classify depending on how they support moderate and radical protest tactics?
Upon evidence suggesting that online networking is positively associated with civic engagement and political participation [3,4,29,30] and in tandem with the resource mobilization theory, we hypothesize:
Online networking has a positive relationship with support for the right to protest using (a) legal demonstrations; (b) groups organizing to solve problems; (c) blocking roads to protest; (d) invading property to protest; and (e) violently overthrowing an elected government.
Finally, after classifying groups according to their support for moderate and/or radical protest tactics, we assess the different groups’ use of online networking. As such, we ask:
How do different attitudinal groups vary regarding online networking use?

5. Methods

This study relies on data obtained from the 2012 Americas-Barometer public opinion survey, carried out in 25 countries1 in the region by the Latin American Public Opinion Project, LAPOP [36]. According to LAPOP, “each survey is implemented based on a national probability design. In some cases, oversamples are collected to allow precise analysis of opinion within sub-national regions. Survey participants are voting-age adults interviewed face-to-face in their households, except in Canada and the United States where the interviews are web-based” [36]. Data was collected between January and May 2012, and details about sample size and sampling errors can be found in the Appendix (Table A1).
Since we are studying support for protests in Latin American countries, Canada and the United States were excluded from the analysis. The final dataset contained 37,102 cases.

5.1. Dependent Variables

Support for protests is the dependent variable in this study. Five questions were used to assess support for different protest tactics, all asking “How much do you approve of…” on a 10-point Likert-type scale where 1 = Strongly disapprove and 10 = Strongly approve. The questions were:
“…people participating in legal demonstrations” (M = 6.90; SD = 2.91)
“…people participating in an organization or group to try to solve community problems” (M = 7.83; SD = 2.51)
“…people participating in the blocking of roads to protest” (M = 3.89; SD = 2.95)
“…people invading private property or land in order to protest” (M = 2.53; SD = 2.26)
“…people participating in a group working to violently overthrow an elected government” (M = 2.47; SD = 2.22)
Then, a factor analysis was performed to verify how those items relate to each other, and two factors emerged from the data: support for moderate and support for radical protests. Table 1 shows the rotated component matrix for the factor analysis.
Support for moderate protests included two items asking respondents how much they approved of “people participating in legal demonstrations” and “people participating in an organization or group to try to solve community problems” (Eigenvalue = 1.622, 2 items; α = 0.71; range = 1 to 10; M = 7.4; SD = 2.4).
Similarly, support for radical protests was measured by three items asking respondents how much they approved of “people participating in the blocking of roads to protest,” “people invading private property or land in order to protest,” and “people participating in a group working to violently overthrow an elected government” (Eigenvalue = 1.622, 3 items; α = 0.69; range = 1 to 10; M = 2.9; SD = 1.9).
The two variables are similar to the concept of “level of deviance” in protest literature [20]. A group’s “level of deviance” is determined by its protest goals and tactics. While these two dimensions are correlated, Boyle and colleagues argue for their conceptual separation when analyzing a group’s level of “radicalism” [37]. The authors found that it is the protest tactics, rather than its goals, that determine the valence of its news coverage. As such, this study focuses on support associated with specific protest tactics, which can be moderate or radical. Moderate protest tactics include peaceful and non-disruptive demonstrations, while radical tactics encompass violence and civil disobedience [18,37]. The models in this paper use both the indexes and their separate components as dependent variables in the analysis. This decision was made for two reasons. First, we used the indexes to provide a more parsimonious model for mapping and clustering purposes. Then, in the regression models, we opted to use the five dependent variables separately to provide more nuance to the analysis.

5.2. Independent Variables

Online networking. To measure people’s online networking use for political information, respondents were asked whether they read or shared any political information on online networking sites, such as Twitter, Facebook or Orkut,2 in the last 12 months. Of all respondents, about 11% used online networking sites for political information in 2012.
Political Satisfaction. We created two variables to account for political satisfaction—satisfaction with government and satisfaction with services. For satisfaction with government, we added two items asking respondents how they rated the job performance of “the president of your country” and “the members/senators and representatives of Congress/Parliament of your country.” Answers were measured on a 5-point Likert-type scale where 1 = Very bad and 5 = Very good (2 items; α = 0.65; range = 1 to 5; M = 3.1; SD = 0.79). For satisfaction with services, we created an index by adding three items asking respondents how satisfied they were with “the condition of the streets, roads, and highways;” “the quality of public schools,” and “the quality of public medical and health services.” Answers were measured on a 4-point Likert-type scale where 1 = Very dissatisfied and 4 = Very satisfied (3 items; α = 0.65; range = 1 to 4; M = 2.5; SD = 0.58).
Trust. Trust here was conceived as institutional trust, which emphasizes trust as an individual’s response to the performance of institutions [38]. Trust in institutions, both explicitly and implicitly political, is linked to political involvement. Furthermore, scholars have used aggregated scales like the one we use here to measure institutional trust in relation to political action [38] and corruption in a variety of countries [39], including a study of Mexico that also uses LAPOP data [40]. In order to measure trust in institutions, we added 13 items asking respondents to what extent they trusted different institutions in their countries: the justice system, Supreme Electoral Tribunal, Armed Forces,3 National Legislature, national police, Catholic Church, Evangelical/Protestant Church, political parties, President/Prime Minister, Supreme Court, local or municipal government, mass media, and elections. Answers were measured on a 7-point Likert-type scale where 1 = Not at all, and 7 = A lot (13 items; α = 0.91; range = 1 to 7; M = 3.9; SD = 1.2).
Efficacy. We accounted for both external and internal efficacies. For external efficacy, we used one item asking respondents if they believed those running the country are interested in “what people like the respondent think” on a 7-point Likert-type scale where 1 = strongly disagree, and 7 = strongly agree (range = 1 to 7; M = 3.31; SD = 1.89). Similarly, we measured internal efficacy using an item that asked if the respondent “feels like he/she understands the most important political issues of their country” using the same 7-point Likert-type scale (range = 1 to 7, M = 3.84; SD = 1.80).
Political interest measures people’s general level of curiosity about politics. Respondents answered the question: “Generally speaking, how interested are you in politics?” on a 4-point Likert-type scale where 1 = None and 4= A lot (range= 1 to 4; M = 2.1; SD = 0.95).
Political knowledge. Respondents were asked two questions measuring their knowledge about politics: “Who is the President of the United States?” and “How many years is the President’s term of office in your country?” Answers were measured as 1 = right answer and 0 = wrong answer, added and averaged to create an index of political knowledge (two items; range: 0−1, KR-20 = 0.48; M = 0.89; SD = 0.23).
Strength of partisanship was initially measured on a 10-point scale where 1 = left and 10 = right. Respondents were asked to think of their own political leanings and place themselves on this scale (range = 1 to 10; M = 5.52; SD = 2.63). Then, results were folded to assess the strength of partisanship of the respondent, following the recommendation of Gil de Zúñiga and Valenzuela [41]. The final scale measures partisanship on a 5-point scale where 1 = strong and 5 = weak (range = 1 to 5; M = 3.14; SD = 1.55).
Demographics. This study accounts for four key demographic variables: people’s age (M = 37.84; SD = 14.2), gender (male = 49.6%), as well as respondent’s level of formal education, measured as years of school (M = 8.98; SD = 3.78). Income was understood as the monthly income in the respondents’ household, measured in 17 categories based on the currency and distribution of the country (range = from 1 to 16; M = 8.13; SD = 3.87; Median = 8.0).

5.3. Statistical Analyses

RQ1 asks: How do citizens classify depending on how they support moderate and radical protest tactics? To answer this question, a two-step cluster analysis was performed using both support for moderate protests and support for radical protests as the variable criteria to classify cases. Once the clusters were created, a series of goodness-of-fit chi-squares was run to show each country’s highest positive residual by cluster. The classified proportions for each country were compared to Latin American respondents as a whole. This inspection of residuals provides a way of assessing how different each country is from the average response in the region, and how these differences appear for each protest tactic.
To test the hypotheses posed by this study, zero-order Pearson’s correlations were performed to ascertain the ways in which all variables of interest related to each other. Furthermore, two linear regressions were estimated for each of our five dependent variables—support for legal demonstrations, groups organizing to solve problems, blocking streets to protest, invading private property to protest and overthrowing elected governments. These analyses allowed us to test the relationship between online networking and the dependent variables, while controlling for the effects of a set of key influential variables previously identified by the literature, such as political satisfaction, trust, efficacy, political interest, political knowledge, strength of partisanship and demographics. The models also included a block controlling for each country’s fixed effects (see Appendix Table A2).
RQ2 asks whether online networking varies between attitudinal groups, and therefore, affects support for protests differently. To answer this question, chi-square tests were calculated by correlating the created clusters with people’s online networking use.
Finally, to give a better impression of how the data are distributed geographically, we mapped the different attitudinal groups and the relationship between online networking and support for protests by country using ArcGIS4. Residuals are mapped on a gradient of light to dark in four different colors, depending on the attitudinal group. An additional map displays statistically significant relationships between online networking and protest attitudes, differentiating between moderate and radical protest tactics.
Given the high number of cases in the dataset (more than 37,000) all statistical analyses (correlations, hierarchical regressions and chi-squares) were performed with 5000 bootstrapped bias-corrected resamples [42].

6. Results

Our first research question asks how citizens classify depending on how they support protests. Cluster analysis revealed respondents cluster into four groups based on support for moderate and radical protests. The first cluster accounts for 18.2% of respondents. They support radical protests more than the average (range = 3 to 10; M = 4.61; SD = 1.25) but not more moderate forms (range = 1 to 7; M = 5.15; SD = 1.36).
A second cluster (23.3%) supports all types of protests more than the average–radical (range = 3 to 10; M = 5.07; SD = 1.74) and moderate (range = 6.5 to 10; M = 9.14; SD = 0.96). On the opposite end, the third and largest cluster (29.8%) expressed little support for either type of protest, radical (range = 1 to 3; M = 1.66; SD = 0.69) or moderate (range = 1 to 7.5; M = 5.34; SD = 1.75).
Lastly, the fourth cluster (28.7%) mirrors the first—these respondents only support moderate protests (range = 8 to 10; M = 9.44; SD = 0.74), but not more radical tactics like blocking the streets or invading property (range = 1 to 3; M = 1.53; SD = 0.65). Table 2 shows the proportions of those in each country who fell into each of the four clusters. With the exception of Chile, Paraguay, Trinidad and Tobago, and Guatemala, all other countries’ largest groups fall either into the “moderate protest” cluster, or the “no protest at all” cluster (see Table 2).
Goodness-of-fit chi-squares were run for each country to show each country’s highest positive residual by cluster. Proportions for each country by cluster were compared to the Latin American respondents as a whole based on protest attitudes. Inspection of the residuals from these chi-squares reveals that all countries differed significantly from Latin America as a whole, except Brazil. The largest positive residuals from each country are reported in Figure 1. The examination of residuals suggests that most countries have significantly larger groups than the average of people not supportive of protests at all or supporting moderate protest only. The residuals suggest that a smaller set of countries differ from Latin America as a whole in their increased support for all forms of protests. Additionally, results suggest that residents of Bolivia, Honduras, and Guatemala support radical protests more so than Latin Americans broadly. The geographic locations of the countries are shown in Figure 1.
Hypothesis 1 assessed the impact of online networking on support for moderate and radical protests. Results in Table 3 show correlations between education, income, internal efficacy, external efficacy, strength of partisanship, political interest, political knowledge, satisfaction with services, satisfaction with government, trust in government, online networking and attitudes toward moderate and radical protests. Online networking was positively correlated with legal demonstrations (r = 0.079, p < 0.001), people organizing in groups to solve problems (r = 0.069, p < 0.001), blocking streets (r = 0.044, p < 0.001), and seizing private property to protest (r = 0.013, p < 0.05). The relationship between online networking and protests aiming to overthrow an elected government is not statistically significant. Taken together, these numbers indicate that support for less radical protests is more strongly correlated with online networking use.
Correlations also suggest that variables associated with strategic resource mobilization theory—higher levels of income, education, political knowledge, and interest—have a higher impact on support for moderate protests such as legal demonstrations and group organizing. Variables associated with collective behaviorism—dissatisfaction with government and services, lower age, income and education—are associated with support for radical protests—blocking streets, seizing property and overthrowing government. One exception occurs when it comes to internal efficacy: the belief that they understand the most important political issues of their country is associated with support for all types of protest tactics. Furthermore, online networking emerged as a variable strongly correlated with other strategic resource mobilization theory variables, such as political interest (r = 0.170, p < 0.001), higher income (r = 0.185, p < 0.001), higher education (r = 0.271, p < 0.001) and internal efficacy (r = 0.128, p < 0.001).
In order to address the set of hypotheses, five linear regression models were estimated, one for each protest tactic. After ensuring the absence of collinearity by examining variance inflation factor scores (<1.48), the models were estimated with all variables entered simultaneously. Table 4 summarizes linear regression models of support for each protest activity for all countries in the data. For readability purposes, the fixed effects for country were omitted from Table 4 and can be found separately in the Appendix (Table A2).
As hypothesized, online networking was a significant predictor of a more positive attitude in regard to legal protests (β = 0.038, p < 0.001); therefore, hypothesis 1a was supported. Males (β = −0.027, p < 0.01), older people (β = 0.028, p < 0.01), those with higher income (β = 0.048, p < 0.001) and higher education (β = 0.077, p < 0.001) were found to be significant predictors of higher support for legal protests. Political interest (β = 0.041, p < 0.001), trust (β = 0.073, p < 0.001), weaker partisanship (β = −0.046, p < 0.001), dissatisfaction with government (β = −0.069, p < 0.001), and dissatisfaction with services (β = −0.023, p < 0.05) were also predictors of positive attitudes regarding legal demonstrations. While external efficacy was a predictor of less support for legal protests (β = −0.050, p < 0.001), internal efficacy was a strong and positive predictor of support for legal protests (β = 0.081, p< 0.001). The full model explained 12.6% of the variance observed.
H1b asks about the relationship between online networking and support for “people organizing in groups to solve problems.” Results reveal that online networking was significantly correlated with support for this tactic (β = 0.34, p < 0.01). As such, the same patterns emerged for support for legal and group organization, further confirming the results of the factor analysis for support for moderate protest tactics. The second column of Table 4 reveals that those who are male (β= −0.019, p < 0.05), higher income (β = 0.048, p < 0.01), higher education (β = 0.038, p < 0.01), higher political interest (β = 0.030, p < 0.01), higher levels of trust (β = 0.073, p < 0.01), less strength of partisanship (β = −0.066, p < 0.01), less satisfaction with the government (β = −0.052, p < 0.01) and services (β = −0.049, p < 0.01), higher internal efficacy (β = 0.062, p < 0.01), but lower external efficacy (β = −0.069, p < 0.01) are more likely to support groups organizing to solve problems as a form of collective action. The model explains 15% of the variance observed. Hypotheses 1b was supported.
Table 4 also shows the linear regression model of support for more radical protest activities. Online networking had a positive and significant relationship with support for blocking streets to protest (β = 0.034, p < 0.01) (Hypotheses 1c was supported). Those who are younger (β = −0.095, p < 0.01), poorer (β = −0.090, p < 0.01), have less political knowledge (β = −0.043, p < 0.01), less partisanship (β = −0.020, p < 0.05), more political interest (β = 0.038, p < 0.01), trust (β = 0.053, p < 0.01), are dissatisfied with both government (β = −0.072, p < 0.01) and services (β = −0.035, p < 0.01), and have higher internal efficacy (β = 0.056, p < 0.01) are more likely to support protestors blocking the streets. The model explains 6.3% of the variance.
As for the second type of radical protest tactic (H1d—Invading property), online networking had a significant relationship (β = 0.030, p < 0.01) (Hypotheses 1d was supported).Those who are younger (β = −0.093, p < 0.001), poorer (β = −0.085, p < 0.001), have less education (β = −0.055, p < 0.001), political knowledge (β = −0.048, p < 0.001), more political interest (β = 0.021, p < 0.05), trust (β = 0.076, p < 0.001), are dissatisfied with services (β = −0.028, p < 0.01) and have higher external efficacy (β = 0.066, p < 0.01) are more likely to support demonstrators invading private property to protest.
Finally, the last column of Table 4 depicts the model for support for protests aiming to overthrow an elected government. In this case, online networking was not significantly associated with support for protests aiming to overthrow elected governments (H1e not supported). Perhaps not surprisingly to scholars in Latin America, the results from the other independent variables are very similar to the models on blocking the streets or invading private property. Those who support this tactic were younger (β = −0.083, p < 0.001), poorer (β = −0.081, p < 0.001), have less education (β = −0.064, p < 0.001), political knowledge (β = −0.060, p < 0.001), trust (β = 0.032, p < 0.001), are dissatisfied with services (β = −0.026, p < 0.01) and government (β = −0.064, p < 0.001), have higher external efficacy (β = 0.064, p < 0.01) and internal efficacy (β = 0.027, p < 0.01).
In addition, we ran the regression models by country in order to verify the relationship between online networking and protest attitudes individually, rather than treating Latin America as a homogeneous entity. After splitting the cases, the relationship between online networking and support for moderate protests remained significant or marginally significant for Guatemala (β = 0.13, p < 0.01), Chile (β = 0.09, p < 0.05), Panama (β = 0.07, p < 0.08), Dominican Republic (β = 0.08, p < 0.06), and Venezuela (β = 0.09, p < 0.06). For radical protests, online networking led to more supportive attitudes in Trinidad and Tobago (β = 0.10, p < 0.09), Chile (β = 0.09, p < 0.05), Belize (β = 0.08, p < 0.06), El Salvador (β = 0.14, p < 0.01), Ecuador (β = 0.08, p < 0.08), and Venezuela (β = 0.09, p < 0.06). Peru (β = −0.08, p < 0.06) was the only country in the sample where online networking led to negative attitudes towards protesting. Results of these country-specific models can be seen in Figure 2.
To answer RQ2 about how the clusters may vary regarding online networking use, chi-square tests were run to examine the extent to which each cluster engaged in the use of online networking sites. As a base level, an analysis of the whole sample reveals that 11.1% of the respondents reported using online networking. Results indicate that there are substantive differences in the ways online networking is used by clusters that represent the four typologies of support for protests. Those who support radical protests only report low levels of online networking (8.3%), and use online networking sites significantly less than Latin Americans as a whole (χ2(1) = 50.31, p < 0.000). Of Latin Americans who do not express support for any type of protests, only 8.8% use online networking, which is significantly less than expected (χ2(1) = 54.88, p < 0.000). However, those who support all protests use online networking significantly more than expected, with 14.7% reporting some use (χ2(1) = 107.47, p < 0.000). Likewise, those who support only moderate protests use online networking more than expected (χ2(1) = 40.13, p < 0.000)—13.1% of those in the fourth cluster report using online networking sites. See Table 5 for full results.

7. Discussion

In the decades since the military relinquished control of the government, Latin American countries maintained relatively stable democracies, with several decades of democratically elected governments being challenged by popular protests. However, studies that have attempted to understand how Latin Americans view protests are rare. As such, this paper makes at least two theoretical and methodological contributions. First, we classified citizens into four distinct groups depending on their level of support for moderate or radical protests: radicals, moderates, all protests and no protests. Despite the increase in protests in the region after military regimes, the numbers presented here suggest Latin Americans tend to only support the right to peacefully protest while rejecting protests that threaten the public order in any way. Then, we assessed the impact of online networking on support for both types of protests. We find that online networking predicts support for both moderate and radical protests. However, Latin Americans who support only moderate protests use online networking more than Latin Americans as a whole, while those who support only radical protests use online networking sites significantly less.
Surprisingly, we find that countries with a tradition of street protests, such as Argentina, Haiti and Bolivia, showed less support for any type of protests than average. One can only speculate as to why. Perhaps these findings come as a result of power shifts in those countries, where historically anti-government forces that led protests in the past are now in power. The findings may indicate a fear that any movement that threatens political order could lead to another coup d’état. We urge future studies to qualitatively investigate the political culture in specific countries in order to understand this phenomenon.
When it comes to online networking, those who support moderate demonstrations spend more time online than supporters of more radical forms, as well as those who do not support protests at all. Evidence suggests that the use of online networking sites, such as Twitter and Facebook, is related to support for the right to protest using both moderate and radical tactics, with the exception of protests aiming to remove an elected government from power.
Our country-by-country analysis also revealed that the relationship between online networking and support for protests is not homogeneous across the region. Figure 1 and Figure 2 illustrate significant differences within Latin America and can be helpful for future research focusing on sub-regional levels of analysis, or those wishing to present their research to a public unfamiliar with the region. In countries like Chile and Venezuela, online networking use is positively associated with positive attitudes towards moderate and radical protests. In other countries like Colombia and Brazil, this relationship is not significant at all. While it is not within the scope of this paper to account for such differences, we believe that the uniqueness of Chile and Venezuela comes from a sharp increase in the number of protests after 2010 in both countries. Our findings echo those of Wolfsfeld and colleagues who recommend scholars take political context into consideration when assessing the relationship between social media and protests [35]. Future research should analyze the impact of online networking on a country-by-country basis while also accounting for the role of different protest tactics and attitudinal subgroups presented in this study.
Our findings support the notion that peaceful, legal demonstrations have been “normalized” in the region: people tend to overwhelmingly support moderate protests, and online networking is related to this support, which speaks to the predictions of Karatzogianni on the normalization of digital activism [27]. In tandem with resource mobilization theorists, our findings suggest that moderate protest tactics have not only been “normalized” and accepted as a legitimate form of participation, but also that online networking is a form of resource that can be mobilized to facilitate acceptance of protest behavior. Echoing Norris et al. [19], we do not find evidence that ascending support for protests have negative consequences for democratic stability in Latin America. If demonstrations are understood as a form of legitimate political expression, then their acceptance in the region indicates the health of democracy. This finding is particularly important because it distinguishes protest attitudes in the region from protests explicitly aimed to regime change, such as the Arab Spring movement. More than ever, the issues raised by Tarrow [16] are relevant here: what does the institutionalization of protests mean for the future of social movements and repertoires of contention? Will an increase in contentious acts obscure other routine forms of political participation, such as electoral campaigning, strikes and petitions?
It is important to note that the variables tested only accounted for 6% to 12% of the variance observed, a strong indicator of the shortcomings of the variables from the literature developed in Europe and the United States to explain what leads people to support protests in the region. It is also significant to highlight that the effect sizes of online networking on protest attitudes are small. This is in tandem with the recent argument made by theorists that the role of social media for protests has been highly overrated [27,28,35]. Rather than suggesting causality, our results reveal a small and significant relationship between using social media and supporting protest behavior, especially when it comes to moderate tactics. Another limitation may come from the measure used by LAPOP to tap into online networking use, which specifically asks about users reading and sharing political information on social media. It is possible that users get incidentally exposed to political information while using platforms for other activities (e.g., keeping up with family and friends). We urge future scholars using primary data analyses to include nuanced social media activities in their measures.
It is also important to note that obstructing traffic and trespassing are not normally identified as “radical” protest tactics in the United States or Europe. Nevertheless, Latin Americans’ views on such activities were more closely correlated with violently overthrowing an elected government than with legal demonstrations. This is especially interesting since the recent protests in the region involved some level of radical tactics (e.g., Black Blocs in Brazil), a behavior strongly disapproved of by the respondents in our sample.
The models presented in this study are exploratory rather than definitive. In light of the current wave of protests in the region, findings point to the need for further research regarding what makes people protest in the Americas, especially when it comes to radical tactics. While our results support the idea that online networking foments support for demonstrations, how this support translates to actual behavior is yet to be analyzed.


The authors thank the Digital Media Research Program and the Annette Strauss Institute at The University of Texas at Austin for their support during this project.

Author Contributions

Rachel R. Mourão conceived and designed the study; Rachel R. Mourão and Shannon C. McGregor wrote the overview of the literature; Rachel R. Mourão, Magdalena Saldaña and Shannon C. McGregor performed the data analysis; Magdalena Saldaña wrote the methods and results sections; Adrian D. Zeh conceived and designed the maps.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Sample sizes and sampling errors in the 2012 Americas Barometer.
Table A1. Sample sizes and sampling errors in the 2012 Americas Barometer.
CountrySample SizeSampling Error
Mexico/Central America
El Salvador1497±2.5%
Costa Rica1498±2.5%
Andean/Southern Cone
Dominican Republic1512±2.5%
Trinidad & Tobago1506±2.5%
Notes: Confidence intervals based on unweighted sample sizes. For cross-national analysis purposes, LAPOP weights each sample to 1500; These sampling errors are based on SRS and not adjusted for stratification and clustering; For information on the impact of the complex sample design on confidence intervals, see section VII of this document.
Table A2. Fixed effects for country of respondent—Linear Regression Models for Protest Attitudes.
Table A2. Fixed effects for country of respondent—Linear Regression Models for Protest Attitudes.
CountryModerate AttitudeRadical Attitude
Legal DemonstrationsGroups to Solve ProblemsBlocking StreetsInvading PropertyOverthrowing Government
Mexico (ref)
Guatemala−0.007−0.027 *0.073 **0.109 ***0.062 **
El Salvador−0.048 ***0.021−0.018−0.021−0.016
Honduras−0.109 ***−0.135 **0.0200.049 ***0.018
Nicaragua0.079 ***0.105 **0.019−0.050 ***−0.020
Costa Rica0.061 ***0.042 **0.050 **−0.011−0.036 **
Panama−0.069 ***0.028 *0.037 **0.053 ***−0.041 **
Colombia0.024 *0.029 **0.084 **0.054 ***−0.008
Ecuador−0.045 ***−0.039 **0.025 *0.0120.013
Bolivia−0.082 ***−0.100 **0.097 **0.0150.067 **
Peru0.002−0.034 **0.0040.0150.007
Paraguay0.063 ***0.079 **0.096 **−0.021 *−0.037 **
Chile0.033 **−0.0090.140 **0.165 ***0.037 **
Uruguay0.081 **0.087 **0.061 **0.006−0.092 **
Brazil0.038 *0.0130.070 **0.044 **0.025
Venezuela0.0110.020 *0.020−0.006−0.057 **
Argentina0.026 *0.029 **0.012−0.020 *−0.057**
DR0.054 ***0.108 **0.091 **−0.032 **−0.016
Haiti−0.067 ***−0.119 **0.0050.035 **0.003
Jamaica0.043 **0.075 **0.007−0.029 **−0.038 **
Trinidad0.057 ***0.049 **0.105 **−0.008−0.040 **
Belize0.056 ***0.051 **0.040 **0.0200.015
Suriname−0.0030.040 **0.014−0.0040.007
Notes: * p < 0.05; ** p < 0.01; *** p < 0.001; P-values were based on bootstrap results on 5000 samples; Cell entries are final-entry OLS standardized Beta (β) coefficients.


  1. Camilla Costa. “Brasileiros ‘Descobrem’ Mobilização em Redes Sociais Durante Protestos.” BBC Brasil. 11 July 2013. Available online: (accessed on 01 July 2016).
  2. Bert Klandermans, and Suzanne Staggenborg. Methods of Social Movement Research. Minneapolis: University of Minnesota Press, 2002, vol. 16. [Google Scholar]
  3. Timothy Macafee, and J. J. De Simone. “Killing the bill online? Pathways to young people’s protest engagement via social media.” Cyberpsychology, Behavior, and Social Networking 15 (2012): 579–84. [Google Scholar] [CrossRef] [PubMed]
  4. Zeynep Tufekci, and Christopher Wilson. “Social media and the decision to participate in political protest: Observations from Tahrir Square.” Journal of Communication 62 (2012): 363–79. [Google Scholar] [CrossRef]
  5. David Mares, and Rafael Martínez. Debating Civil-Military Relations in Latin America. Eastbourne: Sussex Academic Press, 2014. [Google Scholar]
  6. Mason Moseley, and Daniel Moreno. “The normalization of protest in Latin America.” AmericasBarometer Insights 42 (2010): 1–7. [Google Scholar]
  7. Mitchell A. Seligson, and Margarita Corral. “When Education Doesn’t Matter, Are Elites Still More Tolerant than Masses? A Natural Experiment in Bolivia.” Available online: (accessed on 29 July 2016).
  8. Charles Tilly. Contentious Performances. Cambridge: Cambridge University Press, 2008. [Google Scholar]
  9. David A. Snow, Sarah A. Soule, and Hanspeter Kriesi. The Blackwell Companion to Social Movements. Hoboken: John Wiley & Sons, 2008. [Google Scholar]
  10. Charles Tilly. “Contentious repertoires in Great Britain, 1758–1834.” Social Science History 17 (1993): 253–80. [Google Scholar] [CrossRef]
  11. Fabiana Machado, Carlos Scartascini, and Mariano Tommasi. “Political institutions and street protests in Latin America.” Journal of Conflict Resolution 55 (2011): 340–65. [Google Scholar] [CrossRef]
  12. William A. Gamson. “The social psychology of collective action.” In Frontiers in Social Movement Theory. Edited by Aldon D. Morris. New Haven: Yale University Press, 1992, pp. 53–76. [Google Scholar]
  13. Herbert Blumer. “Collective Behavior.” In Principles of Sociology. Edited by Robert Park. New York: Barnes and Noble, 1938, pp. 221–32. [Google Scholar]
  14. John D. McCarthy, and Mayer N. Zald. “Resource mobilization and social movements: A partial theory.” American Journal of Sociology 82 (1977): 1212–41. [Google Scholar] [CrossRef]
  15. Ronald Inglehart. Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies. Princeton: Princeton University Press, 1997, vol. 19. [Google Scholar]
  16. Sidney G. Tarrow. Power in Movement: Social Movements, Collective Action and Politics. Cambridge: Cambridge University Press, 1994. [Google Scholar]
  17. Matthew Crozat. “Are the times a-changin’? Assessing the acceptance of protest in western democracies.” In The Social Movement Society: Contentious Politics for the New Century. Edited by David S. Meyer and Sidney G. Tarrow. Lanham: Rowman & Littlefield, 1998, pp. 59–82. [Google Scholar]
  18. Michael P. Boyle, and Cory L. Armstrong. “Measuring level of deviance: Considering the distinct influence of goals and tactics on news treatment of abortion protests.” Atlantic Journal of Communication 17 (2009): 166–83. [Google Scholar] [CrossRef]
  19. Todd Gitlin. The Whole World Is Watching: Mass Media in the Making & Unmaking of the New Left. Berkeley: University of California Press, 1980. [Google Scholar]
  20. Douglas M. McLeod, and James K. Hertog. “Social control, social change and the mass media’s role in the regulation of protest groups.” In Mass Media, Social Control, and Social Change: A Macrosocial Perspective. Edited by David Demers and Kasisomayajula Viswanath. Ames: Iowa State University Press, 1999, pp. 305–30. [Google Scholar]
  21. Pippa Norris, Stefaan Walgrave, and Peter Van Aelst. “Who demonstrates? Antistate rebels, conventional participants, or everyone? ” Comparative politics 37 (2005): 189–205. [Google Scholar] [CrossRef]
  22. Shelley Boulianne. “Does Internet use affect engagement? A meta-analysis of research.” Political communication 26 (2009): 193–211. [Google Scholar] [CrossRef]
  23. W. Lance Bennett, and Alexandra Segerberg. The Logic of Connective Action: Digital Media and the Personalization of Contentious Politics. New York: Cambridge University Press, 2013. [Google Scholar]
  24. Summer Harlow. “Social media and social movements: Facebook and an online Guatemalan justice movement that moved offline.” New Media & Society 14 (2012): 225–43. [Google Scholar] [CrossRef]
  25. Summer Harlow, and Dustin Harp. “Collective action on the Web: A cross-cultural study of social networking sites and online and offline activism in the United States and Latin America.” Information, Communication & Society 15 (2012): 196–216. [Google Scholar] [CrossRef]
  26. R. Kelly Garrett. “Protest in an information society: A review of literature on social movements and new ICTs.” Information, Communication & Society 9 (2006): 202–24. [Google Scholar] [CrossRef]
  27. Athina Karatzogianni. Firebrand Waves of Digital Activism 1994–2014: The Rise and Spread of Hacktivism and Cyberconflict. Basingstoke: Palgrave Macmillan, 2015. [Google Scholar]
  28. Evgeny Morozov. Net Delusion: The Dark Side of Internet Freedom. New York: PublicAffairs, 2012. [Google Scholar]
  29. Christopher Wilson, and Alexandra Dunn. “Digital media in the Egyptian revolution: Descriptive analysis from the Tahrir data set.” International Journal of Communication 5 (2011): 1248–72. [Google Scholar]
  30. Weiwu Zhang, Thomas J. Johnson, Trent Seltzer, and Shannon L. Bichard. “The revolution will be networked: The influence of social networking sites on political attitudes and behavior.” Social Science Computer Review 28 (2009): 75–92. [Google Scholar] [CrossRef]
  31. Sebastián Valenzuela, Arturo Arriagada, and Andrés Scherman. “The social media basis of youth protest behavior: The case of Chile.” Journal of Communication 62 (2012): 299–314. [Google Scholar] [CrossRef]
  32. Katy E. Pearce, and Sarah Kendzior. “Networked authoritarianism and social media in Azerbaijan.” Journal of Communication 62 (2012): 283–98. [Google Scholar] [CrossRef]
  33. Tetsuro Kobayashi, Ken’ichi Ikeda, and Kakuko Miyata. “Social capital online: Collective use of the Internet and reciprocity as lubricants of democracy.” Information, Community & Society 9 (2006): 582–611. [Google Scholar] [CrossRef]
  34. Zizi Papacharissi. A Networked Self: Identity, Community, and Culture on Social Network Sites. New York: Routledge, 2010. [Google Scholar]
  35. Gadi Wolfsfeld, Elad Segev, and Tamir Sheafer. “Social media and the Arab spring politics comes first.” The International Journal of Press/Politics 18 (2013): 115–37. [Google Scholar] [CrossRef]
  36. Americas Barometer. “Sample Design.” Available online: (accessed on 9 September 2016).
  37. Michael P. Boyle, Douglas M. McLeod, and Cory L. Armstrong. “Adherence to the protest paradigm: The influence of protest goals and tactics on news coverage in US and international newspapers.” The International Journal of Press/Politics 17 (2012): 127–44. [Google Scholar] [CrossRef]
  38. William Mishler, and Richard Rose. “What are the origins of political trust? Testing institutional and cultural theories in post-communist societies.” Comparative Political Studies 34 (2001): 30–62. [Google Scholar] [CrossRef]
  39. Eric C. C. Chang, and Yun-han Chu. “Corruption and trust: Exceptionalism in Asian democracies? ” Journal of Politics 68 (2006): 259–71. [Google Scholar] [CrossRef]
  40. Stephen D. Morris, and Joseph L. Klesner. “Corruption and trust: Theoretical considerations and evidence from Mexico.” Comparative Political Studies 43 (2010): 1258–85. [Google Scholar] [CrossRef]
  41. Homero Gil de Zúñiga, and Sebastián Valenzuela. “The mediating path to a stronger citizenship: Online and offline networks, weak ties, and civic engagement.” Communication Research 38 (2011): 397–421. [Google Scholar] [CrossRef]
  42. Ralph Henderson. “The bootstrap: A technique for data-driven statistics. Using computer-intensive analyses to explore experimental data.” Clinica Chimica Acta 359 (2005): 1–26. [Google Scholar] [CrossRef] [PubMed]
  • 1Including all of North, Central and South America, and the Caribbean.
  • 2Orkut was a social media site that was prevalent in Latin America and India between 2004–2014.
  • 3Not in Costa Rica or Haiti.
  • 4The ArcGIS platform is a professional Geographic Information Systems software package used to map, display and analyze spatial information. In addition to applications in business, planning, architecture and the physical sciences, ArcGIS has been increasingly used in the social sciences for data that has spatial characteristics.
Figure 1. Different attitudinal groups for support for protests.
Figure 1. Different attitudinal groups for support for protests.
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Figure 2. Type of protests supported and online networking.
Figure 2. Type of protests supported and online networking.
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Table 1. Factor analysis for support for different protest tactics.
Table 1. Factor analysis for support for different protest tactics.
Legal demonstrations0.8720.071
Organizing to solve problems0.870−0.072
Blocking streets to protest0.2890.743
Invading property to protest−0.0530.855
Overthrowing elected government−0.1580.790
Initial Eigenvalue1.6221.924
Percent explained variance32.4438.47
Cumulative percent32.44%70.91%
Table 2. Countries by Cluster.
Table 2. Countries by Cluster.
Radicals (1)All Protests (2)No Protests (3)Moderates (4)
Trin. & Tob.10.6%39.0%17.0%33.5%
El Salvador14.3%19.4%41.9%24.4%
Costa Rica12.1%24.4%24.6%38.9%
Dom. Rep.12.0%33.5%18.2%36.3%
Notes: Shaded cells indicate countries in each cluster; χ2(66) = 3688.8, p < 0.000.
Table 3. Zero-order Pearson correlations.
Table 3. Zero-order Pearson correlations.
20.554 ***1
30.215 ***0.128 ***1
4.−0.041 ***−0.117 ***0.349 ***1
50.018 **−0.079 ***0.480 ***0.529 ***1
60.079 ***0.069 ***0.044 ***0.0020.013 *1
7−0.040 ***−0.018 ***−0.001−0.010.001−0.049 **1
80.108 ***0.096 ***−0.028 ***−0.075 ***−0.094 ***0.185 ***−0.097 ***1
90.0050−0.089 ***−0.081 ***−0.074 ***−0.157 ***−0.007−0.061 ***1
100.098 ***0.064 ***−0.005−0.058 ***−0.054 ***0.271 ***−0.031 ***0.376 ***−0.309 ***1
110.042 ***0.043 ***−0.038 ***−0.058 ***−0.050 ***0.068 ***−0.060 ***0.127 ***−0..018 **0.184 ***1
120.086 ***0.068 ***0.046 ***0.016 **0.034 ***0.170 ***−0.100 ***0.101 **0.0030.135 ***0.073 ***1
130.076***0.073 ***0.023 ***0.021 ***0.076 ***−0.010.017 ***−0.035 ***0.064 ***−0.065 ***−0.0020.176 ***1
14−0.044 ***−0.073 ***−0.032 ***0.008−0.001−0.0010.0080.053 ***−0.047 ***0.103 ***−0.01−0.087 ***−0.026 ***1
15−0.010.002−0.040 ***−0.026 ***0.026 ***0.0030.009−0.006−0.005−0.043 ***−0.0110.131 ***0.502 ***−0.033 ***1
16−0.04−0.039 ***−0.044 ***−0.01−0.002−0.032 ***−0.014 *−0.059 ***0.026 ***−0.068 ***−0.0010.032 ***0.281 ***−0.041 ***0.290 ***1
17−0.015 **−0.023 ***0.019 ***0.064 ***0.084 ***0.007−0.011 *−0.010.017 **−0.015 **−0.017 **0.115 ***0.422 ***−0.043 ***0.354 ***0.177 ***1
180.109 ***0.086 ***0.064 ***0.030 ***0.039 ***0.128***−0.115 ***0.132 **0.025 ***0.168 ***0.062 ***0.271 ***0.225 ***−0.028 ***0.113 ***0.040 ***0.297 ***1
Notes: * p < 0.05; ** p < 0.01; *** p< 0.001; 1. Legal demonstrations; 2. Groups organizing to solve problems; 3. Blocking streets; 4. Overthrowing government; 5. Seizing property; 6. Online networking; 7. Gender; 8. Income; 9. Age; 10. Education; 11. Political Knowledge; 12. Political interest; 13. Trust; 14. Strength of partisanship; 15. Satisfaction with government; 16. Satisfaction with services; 17. External efficacy; 18. Internal efficacy.
Table 4. Linear Regression Models for Protest Attitudes.
Table 4. Linear Regression Models for Protest Attitudes.
Moderate AttitudeRadical Attitude
Legal DemonstrationGroups to Solve ProblemsBlocking StreetsInvading PropertyOverthrowing Government
Online Networking0.038 ***0.034 **0.034 **0.030 **0.017
Gender−0.027 **−0.019 *0.001−0.009−0.020 *
Income0.048 ***0.048 **−0.090 **−0.085 ***−0.081 **
Age0.028 **−0.004−0.095 **−0.093 ***−0.083 **
Education0.077 ***0.038 **−0.008−0.055 ***−0.064 **
Political Knowledge0.0010.015−0.043 **−0.048 ***−0.060 **
Political Interest0.041 ***0.030 **0.038 **0.021 *0.014
Trust0.073 ***0.073 **0.053 **0.076 ***0.032 *
Strength of Partisanship−0.046 ***−0.066 **−0.020 *0.0120.018
Satisfaction with Government−0.069 ***−0.052 **−0.072 **−0.017−0.064 **
Satisfaction with Services−0.023 *−0.049 **−0.035 **−0.028 **−0.026 *
External Efficacy−0.050 ***−0.069 **0.0190.066 ***0.064 **
Internal Efficacy0.081 ***0.062 **0.056 **0.0160.027 *
Total R20.1260.1500.0630.0830.065
Notes: * p < 0.05; ** p < 0.01; *** p< 0.001; p-values were based on bootstrap results on 5000 samples; Cell entries are final-entry OLS standardized Beta (β) coefficients.
Table 5. Online networking by protest attitude cluster.
Table 5. Online networking by protest attitude cluster.
Online Networking (Yes)
Radicals (1)8.3%
All Protests (2)14.7%
No Protests (3)8.8%
Moderates (4)13.1%
Notes: χ2(3) = 246.6, p < 0.000; Cramer’s V = 0.084—figures based on 5000 bootstrap samples.
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