DEPARTMENT OF POLITICAL SCIENCE

Master’s Thesis: 30 credits

Programme: Master’s Programme in Political Science

Date: 2021-05-25

Supervisors: Valeriya Mechkova and Sebastian Hellmeier

Words: 14 901

THE CONTRADICTORY ROLE OF THE

INTERNET IN AUTOCRACIES

Exploring differences between reactive and proactive online repression

Ella Turta

Abstract

Ever since the early days of the Internet the “freedom of the Internet” has been a subject for

debate. Is it characterized by an anti-authoritarian ideology that fosters public dissent and

challenges the authoritarian rule? Or are autocrats using modern technology for their own

illiberal purposes? Recent research argues that viewing modern communication technology as

inherently liberative or repressive undermines the fact that the Internet functions in a

constantly evolving political context. Its intersections with different societal and political

phenomena should not be considered universal but rather dependent on the autocrat’s de facto

control over the Internet. By building on previous research this thesis elaborates the reasons

behind the variation in how the Internet intervenes with mass mobilization in autocracies. The

main ambition of this thesis is to move beyond general notions of online repression. By

distinguishing between reactive and proactive repression strategies this thesis enhances the

understanding about the impact of online repression on mass mobilization in autocracies. By

exploring how gradual change in Internet penetration rates affects levels of mass mobilization

using cross-country time series analysis the results of this thesis indicate that increased

Internet penetration rates do not have a significant positive effect on mass mobilization. Most

importantly, the results indicate that autocrats implement a range of online repression

strategies to combat the potential liberative power of the Internet and that proactive and

reactive online repression strategies do not seem to be equally effective tools in constraining

mass mobilization.

Key words: Online repression, mass mobilization, asymmetrical control, internet policy,

digital authoritarianism

Contents Introduction ................................................................................................................................ 1

Disposition of the thesis ......................................................................................................... 3

Previous research ....................................................................................................................... 3

Changing patterns of collective action in the digital era ........................................................ 3

The authoritarian response to the expansion of the Internet .................................................. 5

Combating the challenges with repressive strategies ............................................................. 6

Does the Internet facilitate collective action? ........................................................................ 7

The research gap and the aim of this thesis ............................................................................ 8

Theoretical framework and hypotheses ..................................................................................... 9

Collective action in the digital era.......................................................................................... 9

The asymmetrical control over the Internet ......................................................................... 10

Online repression.................................................................................................................. 12

Proactive online repression .................................................................................................. 13

Reactive online repression ................................................................................................... 14

The importance of distinguishing between repression strategies ......................................... 15

Data and methods ..................................................................................................................... 17

Dependent variable ............................................................................................................... 17

Independent variable ............................................................................................................ 18

Moderating variables ............................................................................................................ 19

Control variables .................................................................................................................. 20

Results ...................................................................................................................................... 21

Global trends of Internet penetration and mass mobilization .............................................. 21

The link between the Internet and mass mobilization .......................................................... 22

Different forms of online repression .................................................................................... 26

Robustness checks ................................................................................................................ 31

Discussion ................................................................................................................................ 32

Conclusions .............................................................................................................................. 33

Bibliography ............................................................................................................................ 36

Appendix .................................................................................................................................. 42

1

Introduction

In connection to when Internet had gained prominence as the primary tool used by billions to

interact in the early 2010s, the US Secretary of State Hillary Clinton stated, “The freedom to

connect is like the freedom of assembly, only in cyberspace. /…/ Once you're on the Internet,

you don't need to be a tycoon or a rock star to have a huge impact on society”. These

optimistic views about the effect of the digital revolution in altering state-citizen relations in

autocracies gained momentum in the aftermath of the popular uprisings during the so-called

Arab Spring. The extensive use of the Internet as a medium of mobilizing protest movements

focused the public attention towards the egalitarian elements of the Internet (Gohdes,

2014:2). The unfiltered Internet access was expected to challenge authoritarian control over

the public discourse, offer a space for free expression, and provide means to facilitate

collective action (Lynch, 2011; Tufekci & Wilson, 2012 & Howard & Hussain, 2013).

Especially in the early days of the Internet (Fisher, Margolis, & Resnick, 1996), but also in

the aftermath of the Arab Spring, a common discourse among decision- and policy makers

alongside academics was that these antiauthoritarian features of the Internet would empower

the people to challenge the authoritarian rule (Chang & Lin, 2020).

In today’s light, the "freedom to connect" is in many cases limited, as repressive governments

are not neglecting the potential egalitarian power of the Internet (see Deibert & Rohozinski,

2010; Dainotti, et al., 2014; Frantz, Kendall-Taylor, & Wright, 2020). Reports about large-

scale Internet disruptions aiming to curtain the mobilizing force of the Internet were frequent

during the Arab Spring as well as today. Currently, a vast majority of people in Myanmar is

being cut from the Internet during the world's longest recorded Internet shutdown that has

attempted to stem protests for over 19 months and still counting (Access Now, 2021). In 2020

only, organizations mapping down occasions of this form of authoritarian online repression

documented over 150 Internet shutdowns (Access Now, 2021). The latest “Freedom of the

Net” report by Freedom House marks 2020 the 10th consecutive year when the Internet has

become less free (Shahbaz & Funk, 2020). A growing body of literature opposing the

"liberation technology hypothesis" (Diamond, 2010) highlights that autocrats are both

expanding the Internet in a way that benefits them (Weidmann & Rød, 2019) and intensifying

their repressive arsenal to cope with the potential threats increased Internet penetration might

pose (Hellmeier, 2016; Frantz et al., 2020 & (Placeholder1)Gohdes, 2015). In line with

2

growing Internet penetration rates1, the Internet is frequently argued to have turned into a

platform for new digital authoritarianism (Shahbaz, 2018) or networked authoritarianism

(Pearce & Kendzior, 2012). In many cases, the technology that was supposed to empower

people is strategically used to control them (Frantz et al., 2020).

Even though the linkages between Internet penetration, online repression, and their impact on

mass mobilization are an increasingly popular field of research, I acknowledge the following

empirical gaps in the literature. Firstly, few systematic attempts to examine the link between

Internet penetration and mass mobilization have been done with comprehensive and up to

date data.2 Secondly, studies examining the link between Internet penetration and collective

action seldom elaborate on the mechanisms that might alter the relationship (e.g., Ruijgrok,

2017 & Weidman & Rød, 2019). Lastly, as the concept of online repression is still evolving,

scholars have discussed the difficulties in measuring different components of it (see

Hellmeier, 2016; Howard et al.; 2011 & Nash 2013). Empirically, online repression is often

studied as a complication of different methods (e.g., Frantz et al., 2020) although the

strategies autocrats devise to control and repress the Internet diverge widely (Gohdes, 2020).

As short-term actions such as Internet shutdowns are still widely used to rein in anti-

government mobilization, long-term control through strategies like censorship and

monitoring is increasingly popular in countries with relatively connected populations, like

China, Russia, and Saudi Arabia (Warf, 2010). In fact, Internet filtering is becoming a

“global norm” (Deibert et al., 2010:5).

This thesis argues that emphasizing a comprehensive distinction between reactive and

proactive forms of online repression is of importance in research aiming to understand the

mechanisms behind the relationship between the Internet and collective action. Against this

background, this thesis aims to further explore the link between Internet penetration and mass

mobilization by 1) accounting for the potential moderating effect of online repression and 2)

applying updated data. Through the theoretical lens of collective action, authoritarian

asymmetrical power over the Internet, and online repression, the research questions this thesis

aims to find answers to are the following. How does Internet penetration affect mass

mobilization in autocracies and: How does authoritarian governments' use of online

1 A total of 59.5% of the global population is using the Internet on an everyday basis compared to just over 30% at the end of

2010 (Statista, 2021) 2 Weidmann and Røds study ‘The Internet and Political Protest in Autocracies’ (2019) covers the 2003-2012 period.

Ruijgrok (2017) covers 1993-2010.

3

repression strategies impact the relationship between Internet penetration and mass

mobilization?

By exploring how gradual change in Internet penetration rates affects levels of mass

mobilization using cross-country time series analysis, the results of this thesis indicate that

increased Internet penetration rates do not lead to a substantial increase in levels of mass

mobilization in autocracies. However, the analysis shows that autocratic states implement a

range of online repression strategies to combat the potential liberative power of the Internet.

Most importantly, the results indicate that proactive and reactive strategies are not equally

effective tools in constraining mass mobilization.

Disposition of the thesis

This thesis is constructed as follows. The following chapter presents a literature review over

the existing literature on the Internet’s role in autocratic countries, online repression, and

mass mobilization in the digital era. This section also lays out the ground for the theoretical

framework for this study. The section concludes with a discussion about the limitations of the

previous research yielding into the aim of this study. Next, the theoretical framework and the

hypotheses for this thesis are presented followed by a presentation of the data and methods.

The section presenting the results concludes with a discussion and is followed by concluding

remarks on the analysis about the Internet’s role in mobilizing political protest in

authoritarian contexts.

Previous research

This chapter provides an overview of the previous research within the field and concludes

with a discussion of the empirical gap yielding into the aim of this thesis.

Changing patterns of collective action in the digital era

Since the Internet has developed to be an increasingly important part of people’s everyday

lives and interpersonal communication, research aiming to explore societal and political

interactions in autocracies stresses the importance of not undermining the role of the Internet

(see, Bennett & Segerberg, 2012; Hellmeier, 2016; Diamond, 2010; Earl & Kimport, 2011;

Tucker et al., 2017 & Chang & Lin 2020). Consequently, a growing body of literature has

focused on examining the “liberation technology hypothesis” (Diamond, 2010), i.e., the role

of the Internet in fostering freedom of expression (Nash, 2013), empowering the opposition

4

(Lynch, 2011 & Ruijgrok 2017), and changing citizen-state relations (Shirky, 2011 & Abbot,

2012).

The use of communication technology as a medium in organizing protests during the Arab

Spring led to an increased amount of literature focusing on studying the role of the Internet in

facilitating collective action in authoritarian states (Lynch, 2011; Howard & Hussain, 2013;

Tufekci & Wilson, 2012 & Ruijgrok, 2017). Lynch (2011) posits that one of the most

important outcomes of the uprisings during the Arab Spring was the general emergence of a

new public sphere capable of connecting people and challenging the state's monopoly over

the information flows (Lynch 2011:307). Likewise, as stated by Abbot (2012), the Arab

Spring provided evidence that the Internet can challenge authoritarian rule and control over

the public opinion. Abbot (2012) argues that the reason behind this is that modern

communication technologies allow the everyday user to become both the producer and the

consumer of information (Abbott, 2012:347). Shirky (2011) refers to this change in the

balance of power as the “conservative dilemma” that is a result of the state losing its

monopoly on controlling the public discourse. The Internet opens new venues such as

international newspapers and blogs for finding otherwise restricted information supporting

the citizens' efforts to monitor the state (Abbott, 2012:350). This “shared awareness” that

increased Internet use enhances, is argued to challenge the state-citizen relationships by

altering the power of traditional authoritarian propaganda methods (Shirky, 2011, Abbott,

2011).

Referring to the expansion of social space that the Internet has allowed, previous research

identifies at least three reasons explaining why the increased use of the Internet and social

media has made wider political participation possible. Firstly, as argued by Earl & Kimport

(2011), Bimber, Flanagin, & Stohl (2012), and Hallam (2016), it can provide a solution to the

collective action problem by reducing costs of mobilizing large masses and decreasing the

risk of sanctions related to conventional forms of collective action. Secondly, as illustrated by

Nash (2013), the Internet’s antiauthoritarian ideology has an important function in fostering

political engagement, and democratic attitudes and grievances (Nash, 2013:445). And lastly,

the Internet provides a platform to formulate public grievances and has increased individuals’

opportunities to express themselves (Nash, 2013 & Ruijgrok, 2017). On these premises, the

Internet can offer a platform to organize large and small-scale collective action, protest

networks, and all forms of social mobilization (Hellmeier, 2016; Abbot, 2012; Gohdes 2015;

Howard, et al., 2011; Tufekci & Wilson, 2012 & Golkar, 2011).

5

The authoritarian response to the expansion of the Internet

Parallel to the emergence of literature recognizing the Internet’s liberative potential, another

strand of the literature has focused on analyzing the authoritarian response to the possible

coercions this new public sphere might give rise to (e.g., Kalathil & Boas, 2003; Morozov,

2012; King, Pan, & Roberts, 2013; Beyer & Earl, 2018 & Weidmann & Rød, 2019). If the

widespread use of the Internet fosters public dissent challenging authoritarian stability, why

would authoritarian leaders introduce the Internet in the first place? One argument for that, as

stated by Li (2019) is that expanding the Internet is, in today’s global and connected world,

essential both politically but first and foremost economically. Corrales & Westhoff (2006)

suggest that rich and market-oriented autocratic states have stronger incentives to increase the

speed of Internet adaption (Corrales & Westhoff, 2006). Similarly, Rød & Weidmann (2015)

argue that one of the key reasons explaining why autocrats introduce and expand the Internet

is because restricting Internet access is extremely costly for governments. Tkacheva et al.

(2013) posit this as one of the key reasons why North Korea stands out as virtually the only

example of an authoritarian state that has completely banned the widespread public use of the

Internet (Tkacheva et al., 2013:188).

Thus, as argued by Weidmann and Rød (2019), to balance between the benefits and the risks,

the autocratic response is, rather than limiting national Internet access, to strategically

introduce it in a way that suits their purposes. In line with Milner (2006) and Corrales &

Westhoff (2006), they argue that regimes that are concerned about the public opinion and

maintaining full control over the public sphere are more likely to introduce the Internet in the

first place. Weidmann and Rød (2019) base their theory on the asymmetrical control of the

Internet, supported by the fact that it is within the government's power to control when and

how the Internet is introduced, but also to regulate and limit access to specific online content.

They argue that the asymmetrical control enables increased control over the new public

sphere, providing the authoritarian leader's incentives to introduce and expand the Internet.

Consequently, previous research exploring why autocrats expand the Internet illustrates how

the liberating function of the Internet might backfire since connected population provides

incentives for autocratic leaders to implement new forms of repressive control to monitor and

control the public opinion (Deibert & Rohozinski, 2010; Howard et al., 2011; Morozov, 2011

& Tucker et al., 2017).

6

Combating the challenges with repressive strategies

So, while autocrats identify the benefits of the Internet, they are not neglecting its potential

downsides. Even though autocrats might tolerate some protest activities reinforced by

increased Internet use, Deibert & Rohozinski (2010) suggest that both offline and online mass

mobilization function as a catalyst for different forms of online repression. I will highlight

some of the studies examining different forms of online repression autocrats implement to

disadvantage political mobilization.

Firstly, as argued by Rød and Weidmann (2015), the authoritarian response is often to restrict

the use of the Internet in particular areas where public dissent or political tensions have or are

expected to emerge. Partial or large-scale Internet and social media shutdowns were widely

used during the Arab Spring (Hussain & Howard, 2013; Howard et al., 2011; Dainotti, et al.,

2014 & Hassanpour, 2014), the Syrian civil war (Gohdes, 2015), and during the Iranian 2009

uprisings (Golkar, 2011). More recently, reports have highlighted the use of Internet

blackouts in constraining antigovernmental protests in Sudan (Moore, 2020) and silencing

critical voices in Myanmar (Access Now, 2021). Rydzak (2018) argues that the common

denominator when applying shutdowns is that it is a strategy that the authoritarian

governments rely on especially in times of crisis or political unrest (Rydzak, 2018). Deibert

(2008) refers to Internet shutdowns as an extreme form of government censorship but

whether the method is effective in constraining mass mobilization is questioned. Rydzak et

al. (2020) argue that cutting connection is likely to backfire and instead give fuel to the

oppositional movements (Rydzak, Karanja, & Opiyou, 2020). Likewise, Rød and Weidmann

(2015) posit that partial or regional shutdowns can be ineffective in case the dissent lacks a

clear geographical context. Furthermore, previous research argues that this reactive approach

can also be costly (Weidmann, 2016) which is why governments are increasingly relying on

preventing anti-regime collective action by more advantaged forms of online repression

(Frantz, et al., 2020 & Deibert & Rohozinski, 2010).

The other branch of online repression studies focuses on analyzing methods autocrats

implement to obtain the economic benefits of the Internet while securing full political control.

Collective action is likewise often identified as the subject of repression when autocrats

implement repressive strategies such as online monitoring, censorship, and filtering (Deibert

et al., 2010; Hellmeier, 2016; Frantz et al., 2020 & Chang & Lin 2020). Previous research

highlights that while some forms of anti-regime online speech might be tolerated, online

content that calls for different forms of collective action is likely to be blocked or filtered in

7

autocracies (see Tkacheva et al. 2013; King, Pan & Roberts, 2013; Deibert & Rohozinski,

2010 & Chang & Lin 2020). For example, China relies on selective filtering of the Internet by

banning international social media platforms such as Facebook while introducing highly

controlled home-grown alternatives (Tkacheva et al. 2013:189). China is often highlighted as

an example of a country with an extensive capacity to set and control the public agenda and

monitor its citizens by filtering and controlling online communications (King, Pan, &

Roberts, 2013; Zittrain & Edelman, 2003; Yang, 2009 & Ong, 2021). However, online

repression in forms of filtering and censorship is widely used by most authoritarian regimes

(Rød & Weidmann, 2015:341). For example, in Russia, Internet content is seldom completely

blocked as the control strategies are sophistically directed to control when and how people

get information (Deibert & Rohozinski, 2010:16). Stukal et al. (2020) emphasize how the

Russian government is also using artificial intelligence technologies, such as social media

bots, to spread pro-government propaganda and demobilize opposition supporters (Stukal,

Sanovich, Bonneau, & Tucker, 2020). Similar “electronic armies” are applied to spread pro-

governmental propaganda in countries like Venezuela, Kenya, and Syria (Deibert, 2015).

These examples of online repression strategies exemplify how authoritarian governments use

modern technology to exercise online agenda control that, in the same way as traditional

propaganda mechanisms, amplify their efforts to control what sort of information is

accessible.

Does the Internet facilitate collective action?

Whether or not the state's capacity to control the Internet and autocrats' actual repressive

methods manage to constrain mass mobilization and obtain existing structures of political

power is contested (Davenport & Inman, 2012). Chang and Lian (2020) study the relationship

between Internet censorship and civil society engagement and posit that Internet censorship

leads to a decline in civil society engagement. Consistent with Chang and Lian’s results,

Frantz, et al. (2020) find evidence indicating that online repression significantly reduces the

risk of protest, particularly in dictatorships. Deibert (2015) and Ong (2021) argue that

strategies such as cyberspace regulations, monitoring, censoring, and espionage provoke self-

censorship, resulting in declined political activism. On the contrary, Ruijgrok (2017) finds

evidence indicating that increased Internet penetration increases the likelihood of protests

(Ruijgrok, 2017). Similarly, with examples from China, Yang (2009), argues that regardless

of the governments' extensive censure, Internet users adopt more creative acts of subversion

to bypass the government’s attempts to repress the freedom of the Internet. As presented

8

earlier, Weidmann and Rød (2019) find evidence implying that the Internet serves to reduce

the emergence of protest in the long run. In line with Hussain & Howard (2013), Golkar

(2011), and Tufekci & Wilson (2012), they argue that while the Internet does not

significantly strengthen the opposition, it can serve as an important platform once a protest

emerges.

The research gap and the aim of this thesis

Given the breadth of literature arguing for dissenting views on how the Internet affects the

possibilities for collective action, it is important to point out that, empirically, these topics are

often addressed using a variety of different methods and data. Empirically, the intersection

between modern communication technologies and collective action is often studied by event

or county-based case studies utilizing data mapping down the use of different repression

strategies during specific events (e.g., Gohdes, 2015; Golkar, 2011 & Hassanpour, 2017). As

case studies can provide important information about the societal and political context and

dynamics under which different repressive responses emerge, their findings cannot be

generalized. Yet, quantitative evidence on this subject is limited, and relatively few

systematic attempts to examine the link between Internet penetration and mass mobilization

have been done applying large global samples (e.g., Weidmann & Rød, 2019 & Ruijgrok,

2017). Only some have examined the link with current and comprehensive data. Weidmann

and Røds study “The Internet and Political Protest in Autocracies” (2019) is a significant

contribution on both theory and method-development regarding the influence of Internet

penetration on mass mobilization in autocratic regimes. However, their data only extends to

the year 2012, leaving out the vast digital development both when it comes to rapidly

increased levels of Internet users but also the precipitous expansion of different social media

platforms.3 This empirical gap motivates to examine whether the negative link between

increased Internet penetration and mass mobilization they identify holds when applying

updated data.

Most importantly, this thesis argues that there is a clear lack of empirical focus on connecting

the dots between how online repression strategies interfere with the relationship between the

Internet and mass mobilization. Neither of the previously mentioned studies that study the

relationship between the Internet and mass mobilization with large-N cross-national studies

3 For example, the amount of daily Facebook user has increased from just surpassing 1 billion in 2012 to over 3,7 billion

active users today. Also, the global digital divide has decreased since the overall percentage of the global population using

Internet frequently has increased significantly from just above 2 billion people in 2012 to nearly 4 billion in 2019 (Statista,

2021).

9

(Weidmann & Rød, 2019 & Ruijgrok, 2017) account for the possibility that the relationship

might vary depending on the government’s de facto control of the Internet. The dynamics

under which the Internet functions in autocracies are in varying degrees repressive

(Keremoğlu & Weidmann, 2020), which is why I argue the importance of adding online

repression into the equation. Furthermore, as emphasized before, the repressive strategies

autocrats implement to face the potential challenges of the Internet differ (Deibert et al.,

2010). Not only in how they operate but also regarding their effectiveness to weaken the

opposition. Therefore, I argue that it is important to make a clear distinction between reactive

and proactive online repression strategies in order to examine the underlying dynamics of

online repression. Compartmentalizing reactive and proactive repression strategies lacks

empirical attention but, I argue, is beneficial because it allows developing a more

sophisticated approach to the overall study of online repression and its implications on the

citizen-state power hierarchies in autocracies. Against this background, the main ambition of

this study is to move beyond general notions of online repression and towards concretizing

how different forms of online repression strategies are utilized in autocracies.

Consequently, the research questions this study aims to find answers to are first: How does

Internet penetration affect mass mobilization in autocracies, and secondly: how does

authoritarian governments' use of online repression strategies impact the relationship

between Internet penetration and mass mobilization?

Building on previous research this thesis elaborates the potential variation in how the Internet

intervenes with mass mobilization by using updated data and accounting for the moderating

effect of online repression. Furthermore, by conceptualizing and systematically comparing

different repression strategies and how they intervene with the impact of the Internet on mass

mobilization, I argue that this thesis contributes to the research field studying the

intersections between modern technology and political participation in repressive settings.

Theoretical framework and hypotheses

This section outlines the theoretical framework implied in this study and defines the

hypotheses that will be tested.

Collective action in the digital era

Securing control over the public discourse to prevent the rise of popular opposition

movements and uprisings is traditionally identified as one of the most important features of

10

authoritarian survival (Rød & Weidmann, 2015). This thesis aligns with the assumption that

collective action is of importance for the emergence and consolidation of democracy and that

large-scale protest movements can pose a significant threat to authoritarian rule (Frantz et al.,

2020 &Vladisavljević, 2016). Furthermore, this thesis theorizes, in line with the previously

presented research, that the Internet and increased use of modern technology can empower

the political opposition and serve to organize, coordinate, and facilitate protest movements

(Abbot, 2012; Gohdes, 2015; Howard, et al., 2011; Tufekci & Wilson, 2012; Tucker et al.,

2017, & Golkar, 2011). As traditional media is often strongly controlled and used as a tool

for government propaganda autocracies, the increased use of the Internet as a medium to

produce, share and communicate alternative information can alter the citizen-state dynamics

(Diamond, 2010). As exemplified by Shirky (2011), introducing the Internet can result in a

change in the balance of power as the state's monopoly on controlling the public agenda

might be challenged because of the increased access to alternative information that the

Internet often enables. For these reasons, it is theoretically motivated to assume that the

increased use of the Internet has affected the traditional citizen-state relationships and the

premises for political participation in autocracies. However, as the abovementioned potential

liberative function of the Internet can significantly challenge the ground for authoritarian

stability and survival, this thesis aligns with the assumption that the possible threats that the

Internet might pose, are not neglected by the authoritarian leaders. The theoretical framework

supporting this assumption is outlined in the following sections.

The asymmetrical control over the Internet

As outlined in the section of previous research, the underlying conditions under which the

Internet interacts in authoritarian countries are repressive and like other public spaces, the

Internet is highly controlled by authoritarian governments (Frantz et al. 2020 & Deibert,

2015). Although cyberspace is to a large extent owned and governed by private companies,

autocrats have significant power in shaping cyberspace by different forms of regulations and

restrictions (Deibert & Rohozinski, 2010). Hence, this thesis aligns with the assumption that

autocratic states have an asymmetrical control over the Internet, i.e., the Internet is

introduced in a way that suits the government’s interests and ensures, as well as in some

cases, increases their control over their citizens (Weidmann & Rød, 2019). The baseline of

asymmetrical control is that the ways authoritarian governments adapt and control the

Internet depend on the political context and what is perceived as strategically beneficial for

the government’s interests. Since isolating a country from the Internet has a devastating effect

11

on the economy, autocrats that aim to take part in the economic benefits of the Internet while

also controlling the possible democratizing influences of the Internet are faced with what is

often referred to as the “dictator's dilemma” (Morozov, 2012). This concept refers to the

authoritarian government's efforts to balance between the costs and the benefits of the

Internet (Hellmeier, 2016) and supports the hypothesis that autocrats are, rather than limiting

Internet access, strategically introducing it in a way that benefits their purposes. The

“dictator's dilemma” explains why autocrats might tolerate momentary protest activities

reinforced by the increased Internet use as a way of enhancing long-term regime stability.

Expanding the Internet delivers economic development, which is often linked to regime

stability (Kalathil & Boas, 2003:8) and provides the government with information needed to

deal with public grievances (Lorentzen, 2013:129). As argued by Weidmann and Rød (2015),

autocrats that are worried about public opinion are more likely to introduce the Internet since

the asymmetrical power over the Internet provides the government with information about

public grievances and potential dissident movements.

Therefore, introducing and expanding the Internet is ultimately expected to play in the hands

of the autocrats since it can enhance regime stability and provides incentives to apply greater

control strategies to identify and deal with oppositional movements (Lorentzen, 2013; Deibert

& Rohozinski, 2010 & Weidmann & Rød, 2019). Consequently, as autocrats balance between

the costs and the benefits of the Internet, they might tolerate some levels of political

mobilization that might also serve them with important information about the opposition’s

grievances. Against this background, I hypothesize that the asymmetrical control that

autocratic governments have over the Internet suppresses the potential long-term

democratizing impact of the Internet. In other words, increased levels of Internet penetration

are not expected to substantially strengthen the political mobilization of the opposition.

Instead, autocrats weigh the costs and benefits and strategically introduce the Internet in a

way that, in the long run, does not alter but rather strengthens their power. Therefore, I expect

that:

H1: As the level of Internet penetration increases, mass mobilization does not increase

significantly.

However, advocates for the liberative technology hypothesis claim the opposite (e.g.,

Ruijgrok, 2017). As discussed in the section of previous research, viewing the Internet solely

either as a liberative technology or as the authoritarian regime's extended arm of repression is

12

simplistic. As outlined by Deibert and Rohozinski (2010), the Internet is a constantly

evolving “multilevel ecosystem of physical infrastructure, software, regulations, and ideas”

and its true impact is dependent on the political context within a country (Deibert &

Rohozinski, 2010:45). Hence, the Internet functions in a constantly evolving political context,

and its potential impact on mass mobilization should not be considered universal but rather

dependent on the state's de facto efforts to control the Internet. Against this background, I

argue that to measure the Internet's true impact on mass mobilization in autocracies, it is

important to analyze the underlying mechanisms that might affect its potential impact. In the

next section, I will present the theoretical arguments supporting this assumption.

Online repression

According to traditional repression studies, autocratic leaders react to new challenges and

adopt different repressive strategies to ensure their power over the public agenda and to

maintain regime stability (Davenport & Inman, 2012). Just like any other change in the

political dynamics, introducing the Internet can provide incentives to implement repressive

strategies designed to meet those new challenges (Deibert & Rohozinski, 2010). As outlined

by Davenport (2007) political repression involves: “the actual or threatened use of physical

sanctions against an individual or organization, /.../ for the purpose of imposing a cost on the

target as well as deterring specific activities and/or beliefs perceived to be challenging to

government personnel, practices or institutions (Davenport, 2007:2 & Goldstein 1978).

Online repression involves the same components and should be analyzed as a complementary

part of autocrats’ overall repression strategies (Frantz et al. 2020 & Gohdes, 2015). Online

repression can substitute for different forms of violent repression when used to censor or

block online information or information channels to constrain mass mobilization (Keremoğlu

& Weidmann, 2020). Furthermore, online repression can be used to reinforce conventional

repression tactics such as restricting the freedom of the press when used to control and censor

online communication platforms and channels (Keremoğlu & Weidmann, 2020).

In line with Frantz et al. (2020) among others, I argue that repressive methods that autocrats

implement to monitor, filter, and even manipulate public opinion are effective tools to repress

critics and to strengthen authoritarian power. Therefore, this thesis theorizes that autocrats

combat the potential liberative power of the Internet by developing their repression arsenal

with strategies designed to meet the digital challenges (Shirky, 2011; Morozov, 2012; Gohdes

2015, & Frantz et al., 2020). As all autocracies use online repression to some degree (Frantz

& Kendall-Taylor, 2014), this thesis theorizes that the Internet's ”repressive versus

13

emancipative” role in autocracies can best be understood by studying how the Internet is

controlled and used to advance the autocrats interests. Therefore, I argue that the use of

online repression alters the focal relationship between the Internet and mass mobilization

which is assumed to be stronger in countries with high levels of online repression. In other

words, the potential repressive impact of increased Internet penetration levels on mass

mobilization is expected to be reinforced by the extensive use of online repression. I expect

that:

H2: Internet penetration leads to a higher decrease in mass mobilization in countries with

high levels of online repression compared to countries with low levels of online repression.

However, research on state repression argues that the de facto impact repression has on

collective action depends on the nature of repression (Davenport & Inman, 2012 &

Davenport, 2007). The same observed diversity in effect applies to the research studying the

impact of online repression. In some cases, repression is argued to mitigate anti-regime

mobilization (Frantz, et.al., 2020) whereas, in others, it is expected to backfire and promote

the goals of the opposition (Howard & Hussein, 2011). I argue that one of the reasons behind

the opposing views regarding the impact and efficiency of online repression is the lack of

comprehensive efforts to disaggregate online repression into its component parts. I thus stress

the importance of recognizing the specific mechanisms behind different strategies to gain

insights on how and why certain strategies function as repressive. Therefore, to advance the

understanding of the dynamics of online repression, this study distinguishes between

proactive and reactive online repression strategies.

Proactive online repression

Proactive online repression strategies are theorized to consist of preventive tactics with the

aim to mitigate risks for public dissent by, in different ways, controlling and influencing

public opinion. In practice, proactive repression applies tactics such as Internet filtering,

online censorship, and social media monitoring. In line with traditional forms of surveillance,

censorship, and propaganda (Beyer & Earl, 2018) these approaches are used to control public

opinion, limit access to critical information and therefore mitigate the risks for public dissent.

The use of proactive strategies is theorized to strengthen the autocrat’s asymmetrical control

since they allow the government to gain information about public grievances and monitor the

opposition's beliefs, demands, and activities (Gohdes, 2020 & Feldstein, 2021). In that sense,

proactive strategies are used to mitigate the information dilemma and respond to public

14

grievances that might, if neglected, lead to public unrest or anti-regime protests (Chen & Xu,

2015 & Gunitsky, 2015). Extensive Internet and social media monitoring can enhance

perceptions of increased costs related to expressing critical opinions or participating in anti-

regime activities online and thus also breed self-censorship (Ong, 2021 & Deibert, 2015). In

addition, authoritarian leaders can also use the Internet and social media platforms to enforce

state propaganda and discredit political opponents (Tufekci, 2017; Gunitsky, 2015 & Ong,

2021) and thus secure political stability. Consequently, as proactive repression strategies can

be considered as an important part of the authoritarian toolkit to stay in power, their function

presupposes a connected population.

Reactive online repression

Distinct proactive strategies, methods such as disrupting Internet access, are more frequently

used reactively, i.e., to constrain critical voices once public dissent or unrest has emerged

(Deibert & Rohozinski, 2010). As previous research demonstrates (see Howard & Hussein,

2011; Gohdes, 2015; Golkar, 2011 & Rydzak, et al., 2020), partial or complete Internet

shutdowns are often applied as a reaction to public dissent or evident public debate

disadvantaging the autocratic leaders or regime stability. This extreme form of government

censorship (Deibert, 2008) is an increasingly popular tactic to silence critical voices in the

aftermath of events such as elections (Rydzak, et al., 2020 & Aday, Farrell, & Lynch, 2010).

As proactive repression strategies are more often targeting specific groups, (King, Pan, &

Roberts, 2013), reactive measures such as Internet shutdowns do into a larger extent affect

the whole population (Dainotti, et al., 2014). Consequently, they are often costly4, ineffective

in constraining political opposition, and at risk for backfiring as they are often followed by

increased protest participation5 (Howard & Hussein, 2011 & Rydzak et al., 2020). As

discussed above, since a connected population is a premise for effective utilization of

proactive methods such as monitoring the public opinion, it is reasonable to assume that

extensive use of reactive methods can diminish the proficiency of the government's overall

online repression.

Table 1 below presents an illustrative classification of different online repression strategies.

4 For example: the Internet shutdown during Arab Spring in Egypt cost the government approximately $90 million dollar or

4 percent of its annual GDP (Howard & Hussain, 2013:75) 5 For example, when the Mubarak regime shut down the Internet as a response to the public uprisings during the Arab

Spring, but the tactic backfired resulting in increased protest participation (Howard & Hussein, 2011).

15

Table 1. Classification of different online repression strategies

Proactive online repression Reactive online repression

Targeted or passive surveillance Complete or limited Internet shutdowns

Blocking, censoring, or filtering online content Partial or complete mobile or social media

shutdowns or disruptions

Targeted or passive social media and Internet

monitoring

Restrictions or disruptions of communication

networks

Blocked or censored ICT infrastructure (VPN

technology e.g., China’s great firewall or Russia’s

RuNet)

Intentional Internet slowdowns

State agenda setting infrastructure (propaganda,

disinformation, disarming government critics

employing algorithms and bots, etc.)

Implementation of cyber laws and regulations

(restricting the freedom of speech and user rights

online)

The importance of distinguishing between repression strategies

For the aforementioned reasons, I argue that there are significant theoretical advantages in

pursuing the differences between reactive and proactive repression tactics. Although they

might often overlap and complement each other, their actual repressive efficiency in forms of

constraining collective action is argued to vary (see, Frantz et al., 2020 & Rydzak, et al.,

2020). Thus, I hypothesize that autocratic governments use different forms of digital

strategies to both prevent and react to collective action. With a reference to studies on state

coercion and domestic dissent (Davenport, 2014) I argue that the distinction between

proactive and reactive repression strategies can theoretically be equated with targeted and

untargeted repression (Gohdes, 2014:92). Proactive repression in the form of filtering,

censoring, and monitoring online communication is theorized to target specific individuals or

groups that are perceived as threats to the status quo. Based on the theoretical framework, it is

assumed that proactive methods are effective tools to repress collective action mainly for two

reasons. Firstly, since proactive tools can be used to monitor online dissent and censor or

filter content calling for collective action, and secondly because extensive Internet monitoring

can spread fear amongst the opposition thus breeding self-censorship.

On the contrary, reactive online repression is theorized to target the population at large

affecting even the more indiscriminate public. Reactive online repression strategies in the

16

form of Internet and social media shutdowns are hypothesized to be ineffective in

constraining collective action for two reasons. Mainly since this extreme use of force risks

backfiring as it targets even the otherwise agnostic population providing them with means to

turn against the government (Gohdes, 2014), but also because Internet and social media

shutdowns undermine the efficiency of proactive strategies.

To examine the difference between how proactive and reactive online repression are used and

how they operate, the moderating effect of both reactive and proactive online repression is

examined. The use of proactive strategies is assumed to reinforce the potential negative effect

Internet has on mass mobilization. Thus, I expect that:

H3: The impact of Internet penetration on mass mobilization is stronger in countries that

apply extensive proactive online repression strategies.

Against the theoretical background, the use of reactive online repression is not expected to

have a significant impact on the focal relationship between mass mobilization and Internet

penetration. Thus, I assume that:

H4:The impact of Internet penetration on mass mobilization is not altered by the extensive

use of reactive online repression strategies.

The theoretical assumptions are illustrated in the figure below.

Figure 1. Illustration of the model

17

Data and methods

The purpose of this study is to explore how gradual change in Internet penetration rates

affects levels of mass mobilization. To do so, the study capitalizes on country-level variation

in both the dependent and independent variables, which motivates the use of cross-country

time series analysis. To measure the impact of Internet penetration on mass mobilization,

linear regression analysis with country fixed effects will be conducted. Country fixed effects

are used to account for country-specific confounders and slow changing variables such as

culture or geography. Fixed effects modeling is often preferred since it effectively reduces

bias (Mummolo & Peterson, 2018) and controls for both measured and unmeasured time-

invariant predictors (Allison, 2009:27). As fixed effects does not account for time-variant

variables, the potentially confounding time-variant variables are included as controls. I

discuss below the choice of control variables. The time trend resulting from the coefficients

for all years not being jointly equal to zero is accounted for by adding year dummies in the

full models (Allison, 2009). Heteroscedasticity, or the fact that observations within a country

are not independent of each other, is accounted for by using cluster-robust standard errors by

country in all the models. Different alternative model specifications are conducted to test the

robustness of the results. The results from analyses using random effects, negative binomial

regression, and fixed effects when outliers are excluded can be found in the Appendix.

Dependent variable

The object of study in this thesis is mass mobilization measured as events of political protest

in autocracies. In line with Weidmann & Rød (2019), I argue that it is important to exclude

pro-regime mobilization when focusing on the democratizing force of mobilization, which is

why mass mobilization characterized as pro-regime is excluded from the analysis. Therefore,

this thesis focuses on events that are 1) political in the sense that they address the government

or public affairs of the country in question, 2) involve at least 25 people, and 3) take place in

the public sphere (Weidmann & Rød, 2019:52). This conceptualization of mass mobilization

captures a broad sphere of events considering both organized and unorganized activities.

The data used to measure mass mobilization is provided by the Mass Mobilization in

Autocracies Database (MMAD) (Keremoğlu, Hellmeier & Weidmann, 2020). MMAD

provides a comprehensive dataset measuring mass mobilization in autocracies based on event

reports. Different from some of the most predominant datasets used to measure mass

18

mobilization6, MMAD draws information from several sources, coding events reported by

international news agencies BBC, AP, and AFP. The articles from these sources are first

filtered by using a machine-learned classifier separating the relevant articles. The relevant

articles are then processed by human coders (Weidmann & Rød, 2019:57). This combination

of both automatic and human coding increases the reliability of the data since it addresses the

challenges regarding coding biases connected with both fully machine and human coding

(Weidmann & Rød, 2019:57). Furthermore, differently from other available datasets with

more limited scopes7, MMAD provides information on events from 2003 to 2018. The

dataset covers 72 autocratic countries between 2003-2018 with some of the countries only

covered for limited periods. The variable measures event frequencies and the distribution of

observations is rather skewed, with many observations having the value zero, while the

highest value is 443. As pointy distribution can create issues in regression analysis, the

dependent variable is log transformed to reduce the skewness8 (Mehmetoglu & Jakobsen,

2017 & Benoit, 2011).

Independent variable

The main independent variable of this analysis is Internet penetration that is measured as the

annual percentage of the population using the Internet. Internet penetration is measured using

yearly data from the World Bank drawing information from International

Telecommunications Union (World Bank, World Telecommunication/ICT Indicators

Database, 2020). The reliability of the World Bank Data is generally perceived as high and as

the information about Internet diffusion consists of raw data from telecom suppliers, the risk

of data being biased is limited. The variable measures individuals' actual use of the Internet

and Internet users are defined as individuals who have used the Internet (from any location)

during the last three months (World Bank, 2020). The Internet can be used via different

devices such as computers, mobile phones, or digital TV. Internet penetration is introduced as

a continuous variable with a scale running from 0 to 100 (World Bank, 2020). Figure A8 in

the Appendix illustrates the percentage of Internet users per country.

6 Cross-National Time-Series Data Archive only draws information from the New York Times.

7 Global Data on Events, Location and Tone database has a narrower scope measuring events from 1979 to 2012. 8 Logarithmic transformation is commonly used to transform a highly skewed variable into one that is more approximately

normal (Benoit, 2011). The histograms illustrating the dependent variable before and after log transformation can be found

in the appendix (A4 & A5).

19

Moderating variables

In line with the theoretical conceptualization of online repression, authoritarian governments'

efforts to repress the Internet is measured by drawing information from different variables

assessing attributes of online repression measured by the Digital Society Project (Mechkova,

Pemstein, Seim, Wilson, 2019). The data collected by the Digital Society Project (DSP) is a

component of the Varieties of Democracy project (Coppedge et al., 2020) and provides a

relatively unresearched set of data on the political environment of the Internet (Mechkova et

al., 2019). All variables included in the DSP dataset are coded by country experts with deep

knowledge of the country and its political culture (Mechkova et al., 2019). There are

relatively few reliable time-series datasets covering online repression (Hellmeier, 2016), and

another possible dataset to measure the level of online repression could have been Freedom

House, however, it was not selected due to its limited scope.9

This study applies the DSP dataset to create three different indices measuring online

repression. Firstly, an index measuring the overall levels of online repression is created and

used as the main moderating variable. The broad concept of online repression this study

aligns with includes different forms of autocratic interference with the free flow of

information and expression online. The broad concept of online repression includes complete

or partial restrictions or bans on access to specific platforms or certain content and the use of

Internet monitoring and filtering. A similar measurement of online repression has been used

by Frantz et al., (2020) and others such as Chang (2020) have analyzed online repression

using the DSP data.

Furthermore, as this study distinguishes between reactive and proactive online repression,

another two additive indices are created and introduced as moderating variables. First, to

measure reactive online repression, I have created an additive index consisting of the

following variables: government social media shut down in practice and government Internet

shut down in practice (Mechkova et al., 2019). Secondly, to measure proactive online

repression I have created an additive index of the following variables: government social

media monitoring, government social media censorship, and government Internet filtering in

practice (Mechkova et al., 2019). These indices are created by combining the variables into a

single scale using Cronbach’s α, to produce a standardized linear combination of the items

similar to the primary component which will secure scale reliability. For all indices, the scale

9 Freedom House Data lacks observations from several countries of interest for this analysis.

20

reliability coefficient is over 0.9 indicating that the scale is reliable (Ursachi, Horodnic, &

Zait, 2015). The indices are on an interval scale running from 0 to 10, with higher values

indicating higher levels of different forms of online repression. The item-test correlations for

each variable in the three online repression indices can be found in Figure 3 in the Appendix.

Control variables

To isolate the effect of Internet penetration on mass mobilization, a set of control variables

that are assumed to have an independent effect on mass mobilization and Internet penetration

are included. The control variables used in previous research on mass mobilization and state

repression account for example for the state of the economy and education (Ruijgrok, 2017,

Bak, Sriyai, & Meserve, 2018). Economically powerful countries are expected to be more

integrated in the world economy and therefore also more dependent on high Internet diffusion

(Li, 2019). Economic strength is generally also associated with lower rates of protest

movements. Therefore, economic indicators such as GDP per capita and GDP growth are

controlled for. Economic development level is measured using the natural logarithm of GDP

per capita from The Maddison Project Database (Bolt et al., 2018). As high Internet coverage

is often crucial for economic development, a rise in GDP per capita is associated with higher

levels of technology adoption (Andrés, Cuberes, Diouf, & Serebrisky, 2007). Secondly, a

measurement of GDP growth is included as a decrease in economic growth is expected to be

associated with higher levels of social unrest and protest movements (Chang, 2020). In

addition, indicators measuring social population structure such as population size (World

Bank Development Indicators, 2019) and the average year of education of citizens over 15

years old (Clio Infra, 2020), are added as controls. All control variables originate from the V-

Dem v10 dataset (Coppedge et al., 2020).

The data selected for this analysis has been carefully chosen since it is considered to fit well

for the purpose of this analysis. As discussed in this section, the chosen datasets provide

comprehensive measurements of the components included in this analysis and are considered

to provide the information needed. After merging these three datasets into one cross-sectional

time-series dataset aggregated by country and year, the number of observations sums into a

total of 869, consisting of observations from 72 autocratic regimes during the period 2003 -

201810. The summary statistics and the correlation matrix for all variables are presented in

Tables A1 and A2 attached in the Appendix. The data is unbalanced since the main

10 Since all independent variables are lagged by one year the observations from 2003 are omitted.

21

dependent variable has more observations from some countries and time periods than others.

Importantly, the sample size is acceptable for the purposes of this analysis. The analysis

would, however, gain statistical power and reliability by having a larger sample size.

Methodological limitations and the shortcomings of the data are discussed in the concluding

remarks.

Results

In this section, I present the results of the analysis and their interpretation. The results will be

presented in three steps starting with descriptive statistics highlighting trends in the

development of the main dependent and independent variables. Thereafter, the main

hypothesis about the relationship between Internet penetration and mass mobilization and the

moderating effect of online repression is assessed. Lastly, results examining different

repression strategies are presented and followed by a discussion of the results and hypotheses.

Global trends of Internet penetration and mass mobilization

As mentioned earlier, the growth in the number of frequent Internet users is a global trend

including autocracies. However, there are substantial differences between Internet penetration

rates between regions and countries. Many of the countries included in the analysis still have

exceptionally low Internet diffusion rates measured as the percentage of the population using

the Internet. The percentage of the population using the Internet is especially low in Sub-

Saharan Africa where the mean of Internet users has not increased at the same pace as in

other regions and less than 20% of the population is using the Internet frequently (World

Bank, 2020). As can be seen in Figure 2 panel A below, the long-term trend in all regions is

mainly increasing but varies between units11. The overall mean of Internet diffusion among

autocracies included in the sample has increased from around 5% to just above 40% between

2003 and 2018 (source: V-Dem Dataset v10; World Bank, 2020 & MMAD v3.0).

11 As the figure 2 indicates a time trend in the data on the main independent variable, year dummies are added in the full

model.

22

Figure 2. Internet usage growth and mobilization trends between 2003-2018

Figure 3 panel a-b. Source: V-Dem Dataset v10; World Bank, 2020 & MMAD v3

Panel B illustrates global trends of mass mobilization presented by region. Political

phenomena such as the color revolutions in Eastern Europe (thick black line) in the early

2000s and the uprisings during the Arab Spring beginning in 2010 (dotted black line) are

evident in the figure. Worth highlighting is also the low levels of mass mobilization in the

Sub-Saharan region (thick red line) in comparison to other regions.

The link between the Internet and mass mobilization

The analysis starts with assessing the focal hypothesis (1) about the relationship between

Internet penetration and mass mobilization. The scatterplots in the figure below present the

relationship between the two variables of interest at four points in time: 2003 (3.A) 2007

(3.B), 2011 (3.C), and 2018 (3.D). The figure reveals a relatively weak relationship that has

changed over time. The relationship has gone from slightly negative at the beginning of the

period (2003) to slightly positive at the end of the measurement period (2018). At the outset

of the measurement period, only a handful of countries were placed on the upper levels

regarding Internet diffusion whilst the differences in Internet penetration levels have

increased towards the end of the measurement period (2018). The increased accessibility and

use of social media tools are reflected in the slightly more positive relationship towards the

end of the measurement period. For example, Facebook was only accessible to a very limited

group of students in the early 2000’s but the number of users increased to around 845 million

in 2011 and over 2 billion users in 2018 (Statista, 2021).

23

Figure 3. The relationship between Internet penetration and mass mobilization

Figure 3. Panel a-d, with focal the relationship, best-fit lines for Internet use (x-axes) and events of mass

mobilization (y-axes) in A) 2003, B) 2007, C) 2011 and D) 2018 (from upper left).12 Source: V-Dem Dataset

v10; World Bank, 2020 & MMAD v3.0.

Some of the countries such as Russia experience rather high levels of mass mobilization in

each period. The frequency of mass mobilization events varies between years in most of the

countries and a majority of the countries in the sample have had zero or less than 50 mass

mobilization events during the observed period. The global mean for mass mobilization for

the whole period 2003-2018 is about 22 events of mass mobilization per year and the

variation between units is large13. As the scatterplots show, the distribution of observations

on the dependent variable mass mobilization is rather skewed, with many observations

having the value zero, while the highest value is 443. As discussed in the methods section,

12 Labels for some of the datapoints are omitted for visual reasons, however all included in the model. 13 Standard deviation for mass mobilization between 2003 and 2018 is 48.58577, indicating that the observations are highly

spread out.

24

the skewness is taken into account by using the log transformed dependent variable in the

regression models. 14 The results from regression analysis with fixed effects models are

presented in Table 2. As can be seen in model 1, the effect of Internet penetration on mass

mobilization is weak and positive, however not statistically significant. This effect is slightly

reduced when the control variables are added to the model (model 3) and the effect remains

insignificant in all models. When controlling for GDP growth in models 3 and 5, it is, against

my expectation, negatively correlated with mobilization while GDP per capita has a weak

positive effect. In line with the expectations, education has a positive and statistically

significant effect on mobilization. The coefficient for mobilization is also more negative in

electoral autocracies compared to closed autocracies.

Table 2. Regression results - effect of Internet penetration on mass mobilization with

interaction effects

Model 1

(Binary model

FE)

Model 2

(Rival

independent

model FE)

Model 3

(Full model FE)

Model 4

(Interaction

term online

repression)

Model 5

(Full model

with

interaction)

Internet penetration 0.00350

(0.00353)

0.00470

(0.00358)

0.00241

(0.00730)

0.0120

(0.00703)

0.00958

(0.0112)

Online repression -0.165

(0.107)

-0.158

(0.125)

-0.196

(0.120)

-0.164

(0.123)

Regime type -0.00320

(0.238)

-0.0518

(0.227)

Education 1.275**

(0.425)

1.282**

(0.431)

GDP per capita 0.0214

(0.295)

0.0626

(0.301)

GDP growth -0.328

(0.646)

-0.372

(0.637)

Population 0.580

(1.186)

0.678

(1.121)

Online repression #

Internet

-0.00142

(0.00146)

-0.00170

(0.00217)

Country fixed effects Yes Yes Yes Yes Yes

Year fixed effexts No No Yes No Yes

Observations 789 789 578 869 578

Adjusted R2 0.002 0.008 0.082 0.018 0.089

Comment: p<0.5=* p<0.01=** p<0.001=***. Standard errors in parenthesis. Country Fixed

Effects included in all models & Time effect controlled for in models 3 and 5. All independent variables are

lagged by one year. Repression scale 1-10, 10=high repression. Source: V-Dem v10, Digital Society Project

v2, World Bank, 2020 & MMAD v3.0

14 The histograms illustrating the dependent variable before and after log transformation can be found in figures A4 & A5 in

the Appendix.

25

Model 2 illustrates the negative effect online repression has on mass mobilization. Models 4

and 5 include interaction between Internet penetration and online repression. The estimation

in model 5 suggests that for each unit increase in online repression, the effect of Internet

penetration on mass mobilization decreases by 0.00170. However, this change is very low

and not statistically significant (as can be seen in models 4 and 5). Since the interaction term

is not statistically significant, there is no evidence to claim that the difference in effect

between different volumes of online repression is significant. However, these results could be

due to the low number of observations and the fact that interaction models are relatively

demanding in terms of statistical power needed.

To interrogate these findings further, I explore descriptively the observed pattern. I calculate

and graph the predicted values on the dependent variable mass mobilization in Figure 4

below. There are evident signs suggesting that the effect of Internet penetration decreases

when online repression increases. To illustrate the differences, figure 4 below shows the

predicted values for mass mobilization by Internet penetration at different levels of online

repression. The figure emphasizes estimates for two scenarios 1) when levels of online

repression are low and 2) when levels of online repression are high.

Figure 4. Illustration of the moderating effect of online repression

Source: V-Dem v10, Digital Society Project v2, World Bank, 2020 & MMAD v3.0

26

The difference in the effect of the Internet when comparing countries with high and low

online repression is evident. The steeper slope for autocracies with low levels of online

repression illustrates that in these countries, mass mobilization is expected to increase when

levels of Internet penetration increase. The slope remains almost unaffected in countries with

high online repression, indicating a weaker effect of increased Internet penetration among the

population. However, the confidence intervals are overlapping15, which explains the

statistically insignificant results in Table 2.

Although the effect size is marginal and insignificant, a weak difference in the effect size

when comparing countries with different levels of online repression can be observed. This

provides an indication of the weaker effect of Internet penetration on mass mobilization in

countries where the Internet is highly controlled. In countries with low repression, the levels

of mass mobilization differ more depending on the Internet levels and are expected to

increase in line with increased Internet penetration. Thus, the results provide some support

for hypothesis 2 stating that the focal relationship depends on the levels of online repression

used to control the Internet. However, as the results are statistically insignificant, the

hypothesis about the moderating effect of online repression cannot be fully accepted.

Although, it can be observed that the original effect of Internet penetration was weakly

positive, while it turned out to be negative in countries with high repression. Hence, there is

some indication that there might be a moderating effect, but due to the limitations of the

applied data, the null hypothesis cannot be rejected.16

Different forms of online repression

Moving further, I explore whether different forms of online repression affect the focal

relationship differently. As can be observed in Figure 5 below, the use of different forms of

online repression diverges substantially between countries.17 The use of reactive and

proactive repression strategies is in many cases intertwined and on average, most autocracies

rely on both proactive and reactive strategies. However, the use of proactive methods such as

filtering, and monitoring of online content is often more extensive than the use of reactive

strategies in terms of actual Internet or social media shutdowns.

15 To interpret the conditionality of the focal relationship within different subgroups, we need to explore the confidence

intervals (Aneshensel, 2002:225). As can be seen in the figure, the confidence intervals overlap indicating an unconditional

relationship. The confidence intervals are also rather wide, which might be due to the relatively small sample size. 16 The relatively small sample size can result in a larger margin of error which will be discussed later. 17 Mean for reactive and proactive strategies during the whole period between 2003-2018. Source: V-Dem v10, Digital

Society Project v2, World Bank, 2020 & MMAD v3.0

27

Figure 5. Levels of proactive and reactive repression by country

This aligns with the

theoretical assumptions

hypothesizing that

reactive strategies are

not very effective in

controlling the Internet

and can additionally

undermine the

authoritarian efforts to

control public opinion by

monitoring and filtering

online content. Thus, the

theoretical assumptions

could explain why many

autocratic countries seem

to rely more extensively

on proactive repression

strategies. It can be

assumed that they

provide more efficient

tools to control and

manipulate the public

opinion and therefore

also mitigate the

potential risks increased

Internet penetration

might pose. The extensive use of proactive strategies is predominant in countries such as the

United Arab Emirates, Tunisia, China, Thailand, and Singapore, which are also examples of

countries associated with higher Internet penetration rates in relation to the whole sample.

Only in a handful of countries such as Gabon, Togo, Chad, and Haiti, the levels of reactive

online repression overrun the levels of proactive repression. North Korea stands out having

highly extensive combined online repression strategies. The repressive strategies are reflected

28

in the fact that the widespread use of the Internet is almost completely banned in North

Korea.18

Comparing the means of reactive and proactive online repression in countries with low and

high Internet diffusion rates illustrate potential differences in how the national level of

Internet penetration influences the utilization of different online repression strategies. Even

though the differences between groups are not significant for all groups, the mean of

proactive online repression in countries with a highly connected population differs notably

from the mean in countries with lower levels of Internet penetration (mean in countries with

low penetration=4.4 compared to mean in countries with high penetration=6.1). On the

contrary, reactive online repression is more widely used in countries with lower levels of

Internet penetration (mean in countries with low penetration=4.3 compared to mean in

countries with high penetration=4). These differences in how the overall Internet penetration

rate might affect the implementation of different repression strategies are illustrated in the

figure below.

Figure 6. The use of different repression strategies varying by the percentage of the

population using the Internet

Comment: Mean of Internet penetration (x) and online repression (y). The lines illustrate the predicted values of

both reactive and proactive repression by Internet penetration. Source: V-Dem v10, Digital Society Project v2,

World Bank, 2020 & MMAD v3.0

18 Table over percentage of Internet users per country can be found in Figure A9 in the Appendix.

29

Figure 6 illustrates that the use of reactive repression strategies such as Internet and social

media shutdowns is predicted to decrease in line with increased Internet penetration rates. On

the contrary, the use of proactive methods is predicted to increase in line with growing

Internet rates. Levels of both reactive and proactive repression are similar in countries with a

low percentage of Internet users but the gap between the two strategies expands as Internet

penetration increases. These variations are in line with the theoretical assumptions and

motivate to further explore the different strategies and whether they moderate the effect

between Internet penetration and mass mobilization. Results from the regression analysis

examining the differences between these two strategies are presented in Table 3 below.

Table 3. Regression results - effect of Internet penetration on mass mobilization with

interaction effects

Model 6

(Rival

independent

model)

Model 7

(Rival

independent

model)

Model 8

(Full model)

Model 9

(Interactions)

Model 10

(Interactions)

Internet penetration 0.00492

(0.00382)

0.00428

(0.00347)

0.00318

(0.00680)

0.0134

(0.00874)

0.00483

(0.0125)

Proactive online

repression (medium)

-0.795

(0.408)

-0.880**

(0.295)

-0.870*

(0.430)

Proactive online

repression (high)

-0.654

(0.495)

-0.391

(0.367)

-0.201

(0.504)

Reactive online

repression

(medium)

0.285

(0.617)

0.915

(0.609)

0.510

(0.568)

Reactive online

repression (high)

-0.0935

(0.735)

0.378

(0.648)

0.0117

(0.626)

Interactions (low repression as the reference category)

Proactive repression #

Internet (medium)

-0.00214

(0.00753)

Proactive repression #

Internet (high)

-0.0174*

(0.00759)

Reactive repression #

Internet (medium)

-0.00505

(0.0104)

Reactive repression #

Internet (high)

-0.00145

(0.0122)

Controls:

Regime, education,

GDP capita, GDP

growth & population

No No Yes Yes Yes

Year Fixed Effect No No Yes Yes Yes

Country Fixed Effect Yes Yes Yes Yes Yes

Observations 789 789 578 578 578

Adjusted R2 0.022 0.006 0.118 0.114 0.085

Comment: p<0.5=* p<0.01=** p<0.001=***. Standard errors in parenthesis. Country Fixed

Effects included in all models & Time effect controlled in model 8, 9 & 10. Repression scales 0-3 low, 3-5

medium, 5-10 high repression. All independent variables lagged by one year. Source: V-Dem v10, Digital

Society Project v2, World Bank, 2020 & MMAD v3.0

30

Models 6 and 7 indicate that the use of proactive strategies is more negatively connected with

mobilization than the use of reactive strategies. Models 9 and 10 examine the moderating

effect of these strategies when they are introduced as dummy variables with low repression

used as the reference category19. As observed in model 9, the effect of proactive repression is

negative and significant in countries with high levels of repression when compared to

countries with low levels of repression. The effect of reactive strategies is not significant but

as illustrated in model 10, weaker when compared to proactive strategies. The results indicate

some differences when comparing the effect of proactive and reactive online repression on

the relationship between Internet penetration and mass mobilization. The results from models

9 and 10 are illustrated in the figures below.

Figure 7. Variations in the focal relationship depending on different online repression

strategies

Note: Panel A=proactive online repression & Panel B=reactive online repression. Source: V-Dem v10, Digital

Society Project v2, World Bank, 2020 & MMAD v3.0

The thick red line in Figure 7 panel A illustrates the weak slightly negative effect of proactive

strategies indicating that the effect of Internet penetration on mass mobilization turns

negative in countries where the government employs extensive proactive repression

strategies. As the blue line in the same figure illustrates a positive slope, the opposite is

expected to be observed in countries with minimal Internet control. Panel B on the right

illustrates that the relationship between Internet use and mobilization does not turn negative

19 Repression scales 0-3 low, 3-5 medium, 5-10 high repression.

31

although countries implement reactive repression strategies. Thus, the respective effect is not

evidently different in countries with high and low levels of reactive strategies, again

illustrating the potential shortcomings of reactive strategies in constraining mass

mobilization.

The results indicate that the focal relationship differs depending on the use of proactive

repression strategies. Extensive use of proactive strategies is shown to alter the relationship

whereas the use of reactive strategies does not have a significant impact on the relationship

between Internet penetration and mobilization. These indicative findings are in line with the

theoretical assumptions. Hypotheses 3 and 4 suggest that the use of proactive methods would

have a more repressive effect on mobilization while the use of reactive strategies was

hypothesized to be inefficient in constraining mass mobilization. The results provide some

support for both hypotheses 3 and 4, however weak. Furthermore, as demonstrated previously

there are clear trends in how the overall Internet penetration rates might influence what type

of repression strategies autocrats choose to apply to control the Internet (as seen in Figure 6).

Robustness checks

To test the robustness of the results I conduct different alternative model specifications. As

discussed previously, the observations on the dependent variable are not normally distributed

and as a way of dealing with this, the dependent variable is log transformed as often used

treatment to deal with skewness and improve the model’s ability to fit the data (Benoit,

2011). However, as the data measuring mass mobilization contains many “zero” observations

and because the variable measuring mass mobilization is unlikely to have a normal

distribution, log transforming the dependent variable could be considered not ideal (King,

1989). Instead, considering the dependent variable as discrete rather than continuous could be

more appropriate. Therefore, to control the robustness of the model, a model using negative

binomial models for count data was tested. Similarly, the results indicate a weak positive

relationship between Internet penetration and mass mobilization, however statistically

significant. The differences, when compared to linear regression, can be seen in tables A12

and A13 in the Appendix.

Furthermore, a model with random effects was tested and provided similar results as can be

seen in tables A10 and A11 in the Appendix. The model with random effects was rejected

since random effects models can introduce bias (Mehmetoglu & Jakobsen, 2017).

Furthermore, as North Korea stands out as a country with low values both on the main

32

dependent and independent variable but with the highest values of online repression, it could

be considered as an outlier. The results were not affected when North Korea was excluded as

can be seen in Tables A14 and A15 in the Appendix.

Discussion

The results indicate, in line with previous research (see, Weidmann & Rød, 2019), that

increased Internet penetration rates do not seem to have a significant positive effect on the

political mobilization of the opposition. The explanatory power of Internet penetration as the

main independent variable is, however, extremely low. The adjusted R-square increases

marginally as the interaction term for online repression and control variables are introduced

in the models (from 0.002 to 0.082) and the explanatory power of the full models remains

low. It is not surprising that the results indicate that mass mobilization is more likely to be

driven by more structural factors within society. Even though the Internet and the expanded

political and societal space it offers can help to overcome the collective action problem

(Bennett & Segerberg, 2012) the results highlight the weak function of the Internet in

facilitating mass mobilization in autocracies. Thus, the low explanatory power provides

means to question the “liberation technology hypothesis”. Consequently, this analysis

provides some support for hypothesis 1 stating that increased levels of Internet penetration do

not lead to significantly increased levels of mass mobilization.

The results emphasize the importance of acknowledging that the same infrastructure that can

strengthen the political opposition can also be applied for authoritarian purposes. When

exploring the effect of online repression on the focal relationship, two distinct trends can be

observed as Internet penetration rates increase. The results presented in Table 2 indicate that,

as expected, the slightly positive effect of increased Internet penetration rate disappears in

autocracies with high levels of online repression. Lastly, when distinguishing the difference

in effect that the proactive versus reactive online repression was expected to have, the results

indicate that the extensive use of proactive strategies alters the focal relationship turning it

negative whilst the focal relationship was not shown to differ dependent on the use of

reactive strategies. Therefore, some indications supporting hypotheses 3 and 4 can be found.

Most importantly, when differentiating the effect of proactive repression and reactive

repression in table 3, the interaction term for proactive repression is significant. As the

interaction term for overall repression tested in Table 2 is insignificant, the results highlight

the importance of analyzing different repression strategies separately in order to make

33

meaningful assumptions about how online repression functions to constrain mass

mobilization in autocracies. This is discussed more in detail in the concluding remarks.

It is important to highlight some of the main limitations of this study. First and foremost, the

statistical power of the models is limited due to the relatively small sample size. Even though

the MMAD-data used to measure mass mobilization, covers a variety of mass mobilization

events across 72 countries, spanning 16 years, the sample is relatively small. Many of the

countries lack observations from several years resulting in an unbalanced dataset. The

limitations of the data also result in large standard errors and statistically unstable results. The

results remain constant in different models, but future studies could address to a greater

extent the non-linearity of the dependent variable, for example by applying a count model

approach that could provide more accurate results.

Conclusions

Given the breadth of literature arguing for dissenting views on how the Internet affects the

possibilities for collective action in autocracies, the aim of this thesis is to highlight the

importance of examining the underlying mechanisms that might affect the relationship. In

line with previous research (see, Frantz, et al., 2020; Chang & Lin, 2020 & Deibert, 2015) the

findings from this thesis suggest that the “freedom to connect” does not necessarily translate

to freedom of assembly in cyberspace. Just like freedom of assembly, the freedom to connect

and the overall freedom of the Internet is highly controlled and repressed in autocracies

(Morozov, 2012; Kalathil & Boas, 2003 & Frantz et al., 2020). As a result of the

asymmetrical control that autocrats have over the Internet, they can craft it in a way that

mitigates the risks of fostering an empowered civil society (Weidmann & Rød, 2019). By

emphasizing the importance of distinguishing online repression as a central part of autocratic

control in the digital era, this thesis contributes to a broader understanding of the

authoritarian asymmetrical control over the Internet. As the underlying dynamics under

which the Internet functions in autocracies are assertively repressive (Davenport, 2007), I

argue that research interested in understanding its intersections with different political or

societal phenomena needs to consider that its impact might be mediated by factors such as the

use and intensity of online repression. I argue that the lack of empirical focus regarding the

moderating impact of online repression can to some extent explain the conflicting views

about the Internet's liberative versus repressive impact on political mobilization in

autocracies.

34

Most importantly, the main ambition of this study was to move beyond general notions of

online repression to exemplify the variations in how reactive and proactive repression

function as a part of the authoritarian asymmetrical control over the Internet. I argue that the

lack of empirical attention given towards conceptualizing different forms of online

repression, can also produce misleading results as the outcome of their implementation seems

to vary. The results of this analysis indicate an unconditional relationship between Internet

penetration and mass mobilization when online repression is treated as a combined set of

strategies. When treated separately, the extensive use of proactive repression strategies is

shown to alter the relationship distinct from the use of reactive strategies. Thus, in line with

theoretical expectations, the results suggest that proactive online repression in form of

Internet censorship, filtering, and monitoring is an effective strategy to constrain anti-regime

movements in autocracies. Backed up by previous case studies emphasizing the inefficiency

of reactive methods (e.g., Rydzak, et al., 2020), I argue that the use of Internet or social

media shutdowns is not strengthening the authoritarian control over the Internet. Cutting out

the population from the Internet does not only stem the flow of information from the citizens

but also from the autocrats themselves consequently diminishing the authoritarian efforts to

control the public opinion. Since Internet shutdowns impact the overall efficiency of the

authoritarian online control, I argue that reactive strategies should not be understood as a

deliberate part of the authoritarian’s toolkit of online repression but rather as an undesirable

emergency measure. Due to the disparity in effect, hypotheses about the moderating effect of

online repression might be deceptive without distinguishing the distinct impact of proactive

and reactive strategies.

It is important to note that, given the lack of empirical attention on compartmentalization of

reactive and proactive strategies, the definitions applied in this analysis should be considered

as illustrative and not exhaustive. As the concept of online repression is still relatively new

(Howard, et al., 2011 & Deibert et al., 2010), developing the concepts of reactive and

proactive repression should be a subject for future research. A possible starting point in

developing the concepts could be to systemically compare how different online repression

strategies align with conventional repression techniques drawing a distinction between hard

and soft repression. I identify two additional strategies that future research can have in

consideration when operationalizing online repression. Firstly, as exemplified by Stukal et al.

(2020), pro-government propaganda and disinformation campaigns can be a vital part of

autocrats' digital agenda control strategy. In addition, policies and regulations criminalizing

35

critical online speech are increasingly used as methods to silence opponents (Deibert, 2015).

Since regulations criminalizing online speech can breed self-censorship they can function as

effective tools in constraining collective action (Ong, 2021 & Deibert, 2015). Additionally,

hostile legal sanctions can be effective in discharging opposition since opponents might

pursue an increased cost related to expressing their views or participating in anti-regime

activities online (Ong, 2021). Although, the true influence of cyber laws lies in how they are

implemented to justify and legitimize the use of online repression (Ong, 2021). Hence, these

strategies should be considered as important features of the authoritarian asymmetrical

control over the Internet. Future research with a similar focus could also develop

measurements of mass mobilization covering both digital and offline mobilization. As argued

by Bennett & Segerberg (2012) and Beyer and Earl (2018), actions taken online can facilitate

offline mobilization but can likewise have an important independent impact in action making.

Lastly, I argue that both future research and data collection would benefit from further

developing the concepts of reactive and proactive repression. Well-defined concepts are a

presumption for measuring the use of different online strategies in a comprehensive manner

and could stimulate more fine-grained data collection on online repression. In this case, the

theoretically expected fluctuations in levels of online repression were not present in the

applied data.20 The levels of online repression were rather stable even though it is reasonable

to argue that against the theoretical background, the levels should vary more depending on

variations in the political landscape. For instance, as argued by previous research, Internet

shutdowns are often reinforced by changes in dynamics between the ruling party and the

opposition and the levels can therefore be assumed to vary and increase for example in times

of elections. On the contrary, it can be reasonable to assume that levels of proactive strategies

increase in line with growing Internet penetration rates since they are an important part in

maintaining the autocrat's asymmetrical control over the Internet and thus, securing regime

stability. The difficulties of measuring online repression have been highlighted by previous

research (e.g., Hellmeier, 2016) and I argue that this thesis has also emphasized the

importance of systematic operationalization of online repression to enhance validity during

data collection.

20 Figures A7 & 8 in the Appendix illustrate the time trend in proactive and reactive online repression

36

Bibliography

Abbot, J. (2012). Democracy@Internet.Org Revisited: Analysing the Socio-Political Impact

of the Internet And New Social Media In East Asia. Third World Quarterly, Vol. 33,

No. 2, 333-357. DOI:10.1080/01436597.2012.666015.

Access Now. (2021, 13 March). Keep It On – Report On The 2020 Data. Retrieved 2020-04-

24 from: https://www.accessnow.org/cms/assets/uploads/2021/03/KeepItOn-report-

on-the-2020-data_Mar-2021_3.pdf

Aday, S., Farrell, H., & Lynch, M. (2010). Blogs and Bullets: New media in contentious

politics. United States Institute of Peace, Peaceworks 65.

Allison, P. D. (2009). Fixed Effects Regression Models. Los Angeles: SAGE.

Andrés, L., Cuberes, D., Diouf, M., & Serebrisky, T. (2007). Diffusion of the Internet: A

Cross-Country Analysis. Telecommunications Policy. 34.323-340. DOI:

10.1016/j.telpol.2010.01.003.

Aneshensel, C. S. (2002). Theory-based data analysis for the social sciences. California:

Thousand Oaks, Pine Forge Press.

Bak, D., Sriyai, S., & Meserve, S. A. (2018). The Internet And State Repression: A Cross-

National Analysis Of The Limits Of Digital Constraint. Journal Of Human Rights,

17:5, 642-659. DOI: 10.1080/14754835.2018.1456914.

Bennett, L. W., & Segerberg, A. (2012). The Logic of Connective Action. Information,

Communication & Society, 15:5, 739-768. DOI: 10.1080/1369118X.2012.670661

Benoit, K. (2011). Linear Regression Models with Logarithmic Transformations.

Methodology Institute, London School of Economics.

Beyer, J., & Earl, J. (2018). Backfire Online. In L.A. Lester & R. Kurtz, The Paradox of

Repression and Nonviolent Movements. Syracuse University Press.

Bimber, B., Flanagin, A., & Stohl, C. (2012). Collective action in organizations: Interaction

and engagement in an era of technological change. Collective Action in

Organizations: Interaction and Engagement in an Era of Technological Change. , 1-

224. DOI:10.1017/CBO9780511978777.

Chang, C, & Lin, T. (2020). Autocracy login: internet censorship and civil society in the

digital age. Democratization, 27:5, 874-895, DOI: 10.1080/13510347.2020.1747051.

Chen, J., & Xu, Y. (2015). Information Manipulation and Reform in Authoritarian Regimes.

Political Science. Political Science Research and Methods. 5, 1-16. DOI:

10.1017/psrm.2015.21.

Corrales, J., & Westhoff, F. (2006). Information Technology Adoption and Political Regimes.

International Studies Quarterly. 50, 911 - 933 10.1111/j.1468-2478.2006.00431.x.

37

Dainotti, A., Squarcella, C., Aben, E., Claffy, K., Chiesa, M., Russo, M., & Pescapé, A.

(2014). Analysis of Country-wide Internet Outages Caused by Censorship.

IEEE/ACM Transactions on Networking, 22(6), 1964-1977.

Davenport, C. (2007). State Repression and Political Order. Annual Review of Political

Science 10 (1), 1‐23.

Davenport, C., & Inman, M. (2012). The State of State Repression Research Since the 1990s,

Terrorism and Political Violence. Terrorism and Political Violence , 619-634. DOI:

10.1080/09546553.2012.700619.

Deibert, R. &. (2010). Liberation Vs. Control: The Future Of Cyberspace. Journal of

Democracy, 21(4), 43-57. DOI: 10.1353/jod.2010.0010

Deibert, R. (2015). Authoritarianism Goes Global: Cyberspace Under Siege. Journal of

Democracy 26, no. 3, 64–78. DOI: 10.1353/jod.2015.0051.

Deibert, R., & Rohozinski, R. (2010). Access Denied: The Practice And Policy Of Global

Internet Filtering. Cambridge, Mass.: MIT Press.

Deibert, R., & Rohozinski, R. (2010). Beyond denial: Introducing next-generation

information access controls. In J. P. Ronald Deibert, Access Controlled: The Shaping

of Power, Rights, and Rule in Cyberspace. Cambridge, MA: MIT Press.

Diamond, L. (2010). Liberation Technology. Journal of Democracy. 21, 69-83. DOI:

10.1353/jod.0.0190.

Earl, J., & Kimport, K. (2011). Digitally Enabled Social Change: Activism in the Internet

Age. Cambridge, Massachusetts; London, England: The MIT Press.

Feldstein, S. (2021). The Rise of Digital Repression: How Technology Is Reshaping Power,

Politics, and Resistance. Oxford University Press.

Fisher, B., Margolis, M., & Resnick, D. (1996). Surveying the Internet: Democratic Theory

and Civic Life in Cyberspace. Southeastern Political Review 2 Southeastern Political

Review 24 (3), 399-429.

Frantz, E., & Kendall-Taylor, A. (2014). A dictator’s toolkit: Understanding how co-optation

affects repression in autocracies. Journal of Peace Research. 51(3), 332-346.

DOI:10.1177/0022343313519808.

Frantz, E., Kendall-Taylor, A., & Wright , J. (2020). Digital Repression In Autocracies.

Users Working Paper, Series 2020:27 The Varieties of Democracy Institute.

Coppedge, M., Gerring J., Knutsen, C.H., Lindberg S.I., Teorell, J., Altman, D., ….. Ziblatt,

D. (2020). ”V-Dem [Country–Year/Country–Date] Dataset V10”. Varieties Of

Democracy (V-Dem) Project. Retrieved 2021-02-15 from:

https://doi.org/10.23696/vdemds20.

Gohdes, A.R. (2014). Repression in the Digital Age: Communication Technology and the

Politics of State Violence. Dissertation, Mannheim. Retrieved 2021-05-20 from

https://madoc.bib.uni-mannheim.de/37902/.

38

Gohdes, A. (2015). Pulling the plug: Network disruptions and violence in civil conflict.

Communication, Technology, and Political Conflict, 352-367.

Gohdes, A. (2020). Repression Technology: Internet Accessibility and State Violence.

American Journal of Political Science. 64.

Golkar, S. (2011). Liberation or suppression technologies? The internet, the Green

Movement, and the regime in Iran. International Journal of Emerging Technologies

and Society, 9(1), 50–70.

Gunitsky, S. (2015). Corrupting the Cyber-Commons: Social Media as a Tool of Autocratic

Stability. Perspectives on Politics 13.1, 42-54, DOI.:10.1017/S1537592714003120.

Hallam, R. (2016). How the internet can overcome the collective action problem: conditional

commitment designs on Pledgebank, Kickstarter, and The Point/Groupon websites.

Information, Communication & Society, 19:3, 362-379, DOI:

10.1080/1369118X.2015.1109696.

Hassanpour, N. (2014). Media Disruption Exacerbates Revolutionary Unrest: Evidence from

Mubarak’s Quasi-Experimen. Political Communication, 31:1, 1-24, DOI:

10.1080/10584609.2012.737439.

Hellmeier, S. (2016). The Dictator's Digital Toolkit: Explaining Variation In Internet

Filtering In Authoritarian Regimes. Politics & Policy, 44(6), 1158-1191.

Howard, & Hussein. (2011). When Do States Disconnect Their Digital Networks? Regime

Responses To The Political Uses Of Social Media. The Communication Review, 14:3,

216-232. DOI: 10.1080/10714421.2011.597254.

Howard, P. N., & Hussain, M. M. (2013). Democracy’s fourth wave? Digital media and the

Arab Spring. Oxford: Oxford Univ. Press.

Howard, P. N., Sheetal, D., Agarwal, S. D., & Hussain, M. (2011). When Do States

Disconnect Their Digital Networks? Regime Responses To The Political Uses Of

Social Media. Communication Review, 14:3, 216-232, DOI:

10.1080/10714421.2011.597254.

Kalathil, S., & Boas, T. (2003). Open Networks, Closed Regimes: The Impact of the Internet

on Authoritarian Rule. Carnegie Endowment for International Peace.

Keremoğlu, E., & Weidmann, N. (2020). How Dictators Control the Internet: A Review

Essay. Comparative Political Studies Vol. 53(10-11), 1690–1703.

Keremoğlu, E., Hellmeier, S., & Weidmann, N.B. (December 18, 2020). Coding Instructions

for the Mass Mobilization in Autocracies Database, version 3.0.

Keremoğlu, E., Hellmeier, S., & Weidmann. N.B. (2020). Mass Mobilization in Autocracies

Database, version 3.0. Retrieved 2020-02-09 from: https://mmadatabase.org/get/

King, G. (1989). Event Count Models for International Relations: Generalizations and

Applications. International Studies Quarterly, 33, 123–147.

39

King, G., Pan, J., & Roberts, M. (2013). How censorship in China allows government

criticism but silences collective expression. American Political Science Review,

107(2), 326–343.

Li, Y. (2019). Influence of the Internet on the Economic Growth of the Belt and Road

Region. Global Journal of Emerging Market Economies. 2019;11(3):, 248-259.

doi:10.1177/0974910119887054.

Lorentzen, P. L. (2013). Regularizing Rioting: Permitting Public Protest in an Authoritarian

Regime. Quarterly Journal of Political Science: Vol. 8: No. 2,, 127-158.

Lynch, M. (2011). After Egypt: The Limits and Promise of Online Challenges to The

Authoritarian Arab State. Perpectives On Politics 9(2), 301-310.

Mechkova, V., Pemstein, D., Seim, B., Wilson. B. (2020). Digital Society Project Dataset v3.

Mehmetoglu, M., & Jakobsen, T. G. (2017). Applied Statistics Using Stata. SAGE

Publications .

Moore, J. (2020, May 12). Anatomy of an Internet Shutdown. Rest of World Retrieved 2021-

05-16 from: https://restofworld.org/2020/sudan-revolution-internet-shutdown/

Morozov, E. (2012). The Net Delucion: The Dark Side of Internet Freedom. Public Affairs.

Mummolo, J., & Peterson, E. (2018). Improving the Interpretation of Fixed Effects. Political

Science Research and Methods.

Nash, V. (2013). Analyzing Freedom of Expression Online: Theoretical, Empirical, and

Normative Contributions. The Oxford Handbook of Internet Studies, DOI:

10.1093/oxfordhb/9780199589074.013.0021.

Ong, E. (2021). Online Repression and Self-Censorship: Evidence from Southeast Asia. .

Government and Opposition, 56(1), 141-162. doi:10.1017/gov.2019.18.

Pearce, K. E., & Kendzior, S. (2012). Networked Authoritarianism and Social Media in

Azerbaijan,. Journal of Communication, Volume 62, Issue 2, 283–298.

Rød, E. G., & Weidmann , N. B. (2015). Empowering activists or autocrats? The Internet in

authoritarian regimes. Journal of Peace Research. 52(3), 338-351.

doi:10.1177/0022343314555782.

Rød, E. G., & Weidmann, N. B. (2015). Empowering activists or autocrats? The Internet in

authoritarian regimes. Journal of Peace Research Vol. 52(3) , 338–351.

Ruijgrok, K. (2017). From the Web to The Streets: Internet and Protests Under Authoritarian

Regimes. Democratization, 24:3, 498-520.

Rydzak, J. A. (2018). A total eclipse of the net: The dynamics of network shutdowns and

collective action responses. Available from ProQuest Dissertations & Theses Global;

Social Science Premium Collection. (2097206798).

Rydzak, J., Karanja, M., & Opiyou, N. (2020). Dissent Does Not Die in Darkness:Network

Shutdowns and Collective Action in African Countries. International Journal of

Communication 14(2020), 4264–4287.

40

Shahbaz, A. (2018). The Rise of Digital Authoritarianism. Freedom House. Retrieved 2021-

02-01 from: Https://Freedomhouse.Org/Report/Freedom-Net/2018/Rise-Digital-

Authoritarianism

Shahbaz, A., & Funk, A. (2020). Freedom on the Net 2020. Freedom House, Retrieved 2021-

03-04 from: https://freedomhouse.org/report/freedom-net/2020/pandemics-digital-

shadow

Shirky, C. (2011). The political power of social media: Technology, the public sphere, and

political change. Foreign Affairs, 90(1). Foreign Affairs, 90(1), 28-41.

Statista. (2021, May 21). Facebook: number of monthly active users worldwide 2008-2021.

Retrieved from: https://www.statista.com/statistics/264810/number-of-monthly-

active-facebook-users-worldwide/

Stukal, D., Sanovich, S., Bonneau, R., & Tucker, J. A. (2020). Bots For Autocrats. How Pro-

Government Bots Fight Opposition in Russia.

Tkacheva, O. S. Schwartz, L.H., Libicki, M.C., Taylor, J.E., Martini J. & Baxter C. (2013).

Internet Freedom and Political Space. RAND Corporation.

Tucker, J. A., Theocharis, Y., Roberts, M. E., & Barberá, P. (2017). From liberation to

turmoil: Social media and democracy. Journal of Democracy, 28(4), 46-59.

doi:http://dx.doi.org.ezproxy.ub.gu.se/10.1353/jod.2017.0064.

Tufekci, Z. (2017). Twitter and Tear Gas: The Power and Fragility of Networked Protest.

New Haven: Yale University Press.

Tufekci, Z., & Wilson, C. (2012). Social Media and the Decision to Participate in Political

Protest: Observations From Tahrir Square. Journal of Communication 62(2), 363–

379.

Ursachi, G., Horodnic, I., & Zait, A. (2015). How reliable are measurement scales? External

factors with indirect influence on reliability estimators. Procedia Economics and

Finance 20, 679 – 686.

Vladisavljević, N. (2016). Competitive authoritarianism and popular protest: Evidence from

Serbia under Milošević. International Political Science Review, 37(1), 36-50.

Warf, B. (2010). Geographies of global Internet censorship. Springer Science Business Media

B.V, DOI 10.1007/s10708-010-9393-3.

Weidmann, N., & Rød. G. (2019). The Internet and Political Protest in. Oxford University

Press, 2019.

Weidmann, N.B. & Rød. E., The Internet and Political Protest in Autocracies. Chapter 4.

Oxford University Press, 2019

World Bank, (2020), Individuals using the Internet (% of population), International

Telecommunication Union ( ITU ) World Telecommunication/ICT Indicators

Database. Retrieved 2021-02-17 from:

https://data.worldbank.org/indicator/IT.NET.USER.ZS

41

Yang, G. (2009). The Power of the Internet in China: Citizen Activism Online. Columbia

University Press.

Zittrain, J., & Edelman, B. (2003). Internet filtering in China. Internet Computing, IEEE. 7.

70 - 77. 10.1109/MIC.2003.1189191.

42

Appendix

A1. Descriptive Statistics

Variable Obs Mean Std. Dev. Min Max

Mass mobilization 869 21.498 48.586 0 443

Mass mobilization (log

transformed)

869 1.831 1.555 0 6.096

Internet penetration 869 22.025 24.211 0 100

Online repression 869 4.227 1.99 0 10

Reactive online repression 869 4.062 2.101 0 10

Proactive online repression 869 4.567 1.981 0 10

Regime type 869 .776 .593 0 3

Education 742 6.616 2.599 1.428 11.748

GDP per capita 719 8.585 1.23 6.127 11.466

GDP growth 719 .051 .104 -.59 1.309

Population 862 16.58 1.296 14.01 21.055

Year 869 2010.724 4.715 2003 2018

A2. Intercorrelation matrix

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Mass mobilization 1.000

Internet penetration 0.115*** 1.000

Online repression -0.017 0.021 1.000

Reactive online rep. -0.010 -0.142*** 0.938*** 1.000

Proactive online rep. -0.021 0.134*** 0.971*** 0.828*** 1.000

Education 0.124*** 0.525*** 0.181*** 0.022 0.277*** 1.000

GDP_per capita 0.087** 0.725*** 0.152*** 0.005 0.242*** 0.654*** 1.000

Regime type 0.018 -0.158*** -

0.452***

-

0.352***

-

0.488***

-0.078** -0.254*** 1.000

GDP_growth 0.007 -0.147*** -0.012 -0.006 -0.015 0.112*** 0.054 0.000 1.000

Population 0.384*** 0.022 0.226*** 0.217*** 0.216*** -0.036 -0.133*** -0.098*** 0.019 1.000

Year 0.033 0.493*** 0.025 0.008 0.035 0.016 0.147*** 0.049 -0.195*** 0.094*** 1.000

*** p<0.01, ** p<0.05, * p<0.1

43

A3. Item-test correlations for each item in the online repression indices

A4 & 5. Histogram of the dependent variable before and after log transformation

Index 1. Online repression

Item Obs Sign Item-test

correlation

Item-rest

correlation

Average interim

covariance

Alpha

Goverment filtering in practice 869 + 0.9200 0.8708 1.23687 0.9230

Goverment shutdown 869 + 0.8961 0.8392 1.30366 0.9291

Goverment shutdown in practice

869 + 0.9428 0.8703 1.20515 0.9233

Government social media

monitoring

869 + 0.8320 0.7483 1.391226 0.9443

Government social media shut

down in practice

869 + 0.9313 0.8877 1.212361 0.9198

Test scale 1.269655 0.9418

Index 2. Proactive Online repression

Goverment filtering in practice 869 + 0.9399 0.8591 1.181122 0.8312

Government social media monitoring

869 + 0.9268 0.8220 1.20247 0.8659

Government social media

censorship in practice

869 + 0.8923 0.7770 1.4812 0.9021

Test scale 1.269655 0.9418

Index 3. Proactive Online repression

Government social media shutdown in practice Goverment Internet shutdown

Test scale 1.465207 0.9450

44

A6. Distribution of the main independent variable and moderating variables

A7 & 8. Time trend in proactive and reactive online repression

45

A9: Internet penetration by country

46

A10. Main table 2 with random effects

Model 1 Model 2 Model 3 Model 4 Model 5

Internet penetration 0.00434 0.00516 0.00574 0.0103 0.00595

(0.00344) (0.00344) (0.00639) (0.00711) (0.00979)

Online repression -0.120 -0.211*** -0.150 -0.219**

(0.0713) (0.0568) (0.0858) (0.0692)

Education 0.190** 0.190**

(0.0634) (0.0640)

GDP per capita -0.141 -0.131

(0.149) (0.148)

GDP growth -0.278 -0.279

(0.640) (0.619)

Population 0.756*** 0.769***

(0.103) (0.105)

Online repression #

Internet

-0.00100 -0.000274

(0.00149) (0.00191)

Intercept 1.847*** 2.320*** -9.926*** 2.433*** -10.19***

(0.172) (0.334) (1.955) (0.380) (1.932)

Observations 789 789 619 869 619

A11. Table 3 with random effects

Model 6 Model 7 Model 8 Model 9 Model 10

Internet penetration 0.00563

(0.00370)

0.00519

(0.00330)

0.00954

(0.00661)

0.0157

(0.00876)

0.00334

(0.00940)

Proactive online

repression (medium)

-0.687*

(0.350)

-1.129***

(0.265)

-0.955**

(0.366)

Proactive online

repression (high)

-0.561

(0.404)

-1.073***

(0.307)

-0.693

(0.366)

Reactive online repression

(medium)

0.456

(0.373)

0.879**

(0.306)

0.281

(0.350)

Reactive online repression

(high)

-0.0147

(0.463)

0.487

(0.347)

-0.202

(0.363)

Interactions (low repression as the reference category)

Proactive repression #

Internet (medium)

-0.000299

(0.00834)

Proactive repression #

Internet (high)

-0.0110

(0.00843)

Reactive repression #

Internet (medium)

0.00212

(0.00886)

Reactive repression #

Internet (high)

0.00428

(0.00952)

Controls:

Regime, education, GDP

capita, GDP growth &

population

No No Yes Yes Yes

Year Random Effect No No Yes Yes Yes

Country Random Effect Yes Yes Yes Yes Yes

Intercept 2.280*** 1.682*** -8.601*** -9.432*** -8.456***

(0.294) (0.302) (1.789) (1.795) (2.067)

Observations 789 789 578 578 578

47

A12. Main table 2 with count model (negative binomial regression)

Model 1 Model 2 Model 3 Model 4

Internet penetration 0.00658*** 0.00755*** 0.00511** 0.0108*

(0.00171) (0.00174) (0.00171) (0.00469)

Online repression -0.167*** -0.150***

(0.0295) (0.0346)

Proactive online

repression

-0.168***

(0.0290)

Reactive online repression -0.143***

(0.0292)

Online repression #

Internet

-0.000982

(0.00102)

Intercept 0.377** 0.425** 0.273* 0.306

(0.140) (0.144) (0.139) (0.159)

ln_r -0.467** -0.470** -0.464** -0.474**

(0.154) (0.154) (0.154) (0.154)

ln_s 1.046*** 1.030*** 1.073*** 1.031***

(0.227) (0.226) (0.227) (0.227)

Observations 869 869 869 869

A13. Table 3 with different repression strategies using count model (negative binomial

regression)

Model 6 Model 7 Model 9 Model 10

Internet penetration 0.0131** 0.0201* 0.0104* 0.00847

(0.00427) (0.00784) (0.00441) (0.00614)

Proactive online

repression

-0.234*** -0.214***

(0.0372) (0.0416)

Reactive online repression -0.178*** -0.187***

(0.0369) (0.0421)

Regime type 0.275 0.268 0.353* 0.356*

(0.146) (0.147) (0.145) (0.145)

Education 0.119*** 0.121*** 0.0871* 0.0871*

(0.0357) (0.0359) (0.0363) (0.0362)

GDP per capita -0.163 -0.171 -0.164 -0.161

(0.0942) (0.0944) (0.0957) (0.0959)

GDP growth 0.389 0.318 0.480 0.494

(0.602) (0.605) (0.603) (0.604)

Population 0.510*** 0.516*** 0.482*** 0.481***

(0.0582) (0.0585) (0.0601) (0.0601)

(0.224) (0.224) (0.226) (0.226)

Interactions

Internet # proactive -0.00142

(0.00134)

Internet # reactive 0.000483

(0.00109)

Intercept -7.462*** -7.588*** -7.084*** -7.050***

(1.176) (1.177) (1.210) (1.214)

48

ln_r -0.0267 -0.0238 -0.133 -0.126

(0.193) (0.193) (0.189) (0.190)

ln_s 1.888*** 1.888*** 1.710*** 1.725***

(0.274) (0.274) (0.271) (0.274)

Observations 578 578 578 578

A12. Table 2 Regression results with fixed effects North Korea excluded

Model 1 Model 2 Model 3 Model 4 Model 5

Internet penetration 0.00350

(0.00353)

0.00470

(0.00358)

0.00263

(0.00776)

0.0120

(0.00704)

0.0102

(0.0116)

Online repression -0.165

(0.107)

-0.155

(0.126)

-0.196

(0.120)

-0.162

(0.124)

Regime type 0.00284

(0.240)

-0.0468

(0.229)

Education 1.330** 1.341**

(0.451) (0.460)

GDP per capita 0.0440 0.0865

(0.303) (0.310)

GDP growth -0.316 -0.361

(0.649) (0.640)

Population 0.612 0.725

(1.256) (1.180)

Online repression #

Internet penetration

-0.00142

(0.00146)

-0.00178

(0.00219)

Observations 775 775 565 854 565

Adjusted R2 0.002 0.008 0.081 0.018 0.087

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

49

A13. Table 3 Regression results fixed effects North Korea excluded

Model 6 Model 7 Model 8 Model 9 Model 10

Internet penetration 0.00492

(0.00382)

0.00428

(0.00347)

0.00352

(0.00717)

0.0141

(0.00933)

0.00572

(0.0131)

Proactive online

repression (medium)

-0.795

(0.408)

-0.874**

(0.299)

-0.859

(0.434)

Proactive online

repression (high)

-0.654

(0.495)

-0.378

(0.376)

-0.183

(0.517)

Reactive online repression

(medium)

0.285

(0.617)

0.911

(0.612)

0.518

(0.571)

Reactive online repression

(high)

-0.0935

(0.735)

0.366

(0.651)

0.0149

(0.630)

Interactions (low repression as the reference category)

Proactive repression #

Internet (medium)

-0.00260

(0.00763)

Proactive repression #

Internet (high)

-0.0179*

(0.00774)

Reactive repression # Internet

(medium)

-0.00556

(0.0104)

Reactive repression # Internet

(high)

-0.00191

(0.0124)

Controls:

Regime, education, GDP

capita, GDP growth &

population

No No Yes Yes Yes

Observations 775 775 565 565 565

Adjusted R2 0.022 0.006 0.117 0.113 0.084

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001