Media Disruption Exacerbates Revolutionary Unrest:

Evidence from Mubarak’s Natural Experiment

Navid Hassanpour!

navid.hassanpour@yale.edu

August 07, 2011

Abstract

Conventional wisdom suggests that lapses in media connectivity - for example, disruption

of Internet and cell phone access - have a negative e!ect on political mobilization. I argue

that on the contrary, sudden interruption of mass communication accelerates revolutionary

mobilization and proliferates decentralized contention. Using a dynamic threshold model

for participation in network collective action I demonstrate that full connectivity in a social

network can hinder revolutionary action. I exploit a decision by Mubarak’s regime to disrupt

the Internet and mobile communication during the 2011 Egyptian uprising to provide an

empirical proof for the hypothesis. A di!erence-in-di!erence inference strategy reveals the

impact of media disruption on the dispersion of the protests. The evidence is corroborated

using historical, anecdotal, and statistical accounts.

Keywords: Revolution, Social Networks, Learning, Media Disruption, Political Violence, Cas-

cade, Egyptian Uprising 2011, Mobilization

!The author would like to thank Craig Calhoun, Alexandre Debs, Stefan Eich, Stephen Farrell, Stathis Kalyvas,Ellen Lust, Sergio Pecanha, Nicholas Sambanis, Jason Stearns, Sekhar Tatikonda, Elisabeth Wood, and participantsin APSA meetings of 2010 and 2011, EITM workshop 2011, and Berlin Summer School in Social Sciences 2011 fortheir helpful comments on this project.

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Following three days of unrest and to counter the growing urban protests across Egypt, in

early hours of January 28th Mubarak’s regime shut down the Internet and cell phone networks

across the country. The surprising events of the next day suggest the incumbent’s tactics were

misguided. The protests in Cairo which were contained in Tahrir square and surroundings up to

that day, proliferated across the city and flared in every corner of Cairo. By 6 P.M. on January

28th, the police forces were overwhelmed, and the military was called in to replace the police. In

the following days a practically neutral military played a major role in the political developments

of the country resulting in the ouster of Mubarak on February 11th. The expansion of the protests

on the 28th questions common wisdom on the role of social media in civil unrest. The disruption

of the media across Egypt at early morning hours of January 28th, proliferated the unrest and

exacerbated the decentralized nature of revolutionary contention. In the course of this study I

examine the role of media at the time of revolutionary unrest and argue that disrupting social and

mobile media, contrary to Mubarak’s intent, fostered more contention of a decentralized nature.

Disrupting media is a common characteristic of many revolutionary situations. Sometimes it is a

byproduct of the paralyzing unrest; often it is the result of a governmental crackdown. In both

cases, I will argue that the disruption acts as a catalyst of the revolutionary process and hastens

the disintegration of the status quo. The recent Egyptian uprising provided a unique opportunity

to put such a hypothesis to test.

The disruption of media prior to major revolutionary upheavals is not limited to the case of

the Egyptian Revolution of the 2011, but the evidence is not as clear-cut. On November 6, 1978,

in solidarity with the other factions of the Iranian society and in opposition to the Shah’s newly

appointed military government, Iranian journalists and newspaper sta!ers announced an indefinite

strike plunging the country into an information blackout for two months till the reopening of the

press on January 05, 1979. The largest demonstration of the Iranian revolution of 1979 took place

during this news industry hiatus on December 10 and 11, 1978 (Historical New York Times n.d.).

The information vacuum was filled with audio-cassettes, pamphlets and other decentralized means

of face-to-face communication. Not surprisingly, during Tehran’s post-election protests of 2009,

the authorities repeatedly disrupted mobile communications and Internet-based social media, but

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never imposed a universal blackout.

Similarly on the 25th of February 1917, in the midst of an urban revolt, Petrograd newspapers

ceased publication just a few days before the Duma’s dissolution. They resumed circulation on

the first of March, after which the Duma acted quickly to limit and control the actions of the press

(Historical New York Times n.d.). The proliferation of protests happened on the 26th immediately

after the disruption of normal communications across the city on the 25th. The violent response

to the unrest on the 26th culminated in an army rebellion and the take over of the Duma on the

27th (Hasegawa 1981, Wade 2005). Both of the above examples hint at the disruption of media

as a revolutionary catalyst but do not present a fully convincing evidence. Instead the abundance

of microlevel information on the Egyptian case provides a reliable test for the hypothesis.

In the following I argue that the disruption of media can fuel revolutionary unrest. Using

a network stylization I detail situations in which higher connectivity can stall collective action.

After proposing a social network formalization, I employ statistical evidence as well as micro-level

data on the extent of contention during instances of revolutionary unrest to back the hypothesis.

In particular, the Egyptian media disruption of January 28, 2011 is examined in greater detail to

provide an account of underlying processes that increase the dispersion of protests.

1 Dissent and the Media

The debate on the role of the media in social unrest was revitalized during the Arab Spring and

in the ensuing debates. In the past two decades, the rise of the Internet and decentralized online

communication coincided with numerous instances of mass uprisings across the world. Many

analysts have taken the social media to be an indispensable part of the mobilization process in

mass protests in places as diverse as Ukraine, Iran, Moldova, Thailand, and Egypt. Various

arguments have been put forward: some suggest that the new technology makes coordination

easier, while others highlight the role the media play in broadcasting scenes of confrontation to

the outside world, thereby encouraging the aggrieved population. Monitoring communication in

the context of the social media also seems to be more di"cult.

The proponents of such arguments overlook several facts. Social media can act against grass

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roots mobilization. They discourage face-to-face communication and mass presence in the streets.

Similar to more traditional and highly visible media, they create greater awareness of risks involved

in protests, which in turn can discourage people from taking part in demonstrations. In the

following I will argue that lack of credible information at times benefits cascades of contention.1

Kern and Hainmueller (2009) cite similar processes as an explanation for why watching West

German television broadcasts might have discouraged East Germans from applying for visas to

travel to the West: once they saw it on television, they were less likely to embark on a personal

exploration to see the unknown. Similarly knowing about the situation on Facebook and Twitter

and having access to news propagation sources may make personal moves and physical presence

unnecessary.

The lack of the intermediary sources of communication between the state and the people

fosters local news production and propagation on the individual level, deprives the state of a

normalizing apparatus, and sets the stage for cascades of collective action. I would like to argue

that the paralyzing mass demonstrations and widespread antagonistic uprisings in question would

not have happened if the media had continued channeling their supervised, censored, and perhaps

realistic narration of the events.

In the absence of the mass media, information is communicated locally. Without state in-

tervention, crowds shape an idea of risk that is independent from the government, testing their

perceptions by staging demonstrations. The government’s response to the acts of public defiance

signals either weakness or strength on the side of the state (Chwe 2001, Kuran 1991). If the

demonstrators’ speculations about the weakness of the incumbent regime turn to be correct as it

did in Tehran in December 1978 or Leipzig in 1989-90 (Lohmann 1994), the cascade of events can

grow to unanticipated dimensions.2

1In fact a number of mass uprisings in the Eastern Block were initiated by rumors. See section (1.1).2On the di!erence between revolutions and riots: in this study revolution is defined as a mass violent act targeted

at the governing body, intended to topple the incumbent regime. Mass uprisings under the title revolutions showdistinctive common traits: they are large in scale (thousands involved if not millions); their major aim is todismantle the political status quo and the ruling apparatus; the new rulers (in the case of successful execution)would be the prior underclass; the ruling elite would be confiscated from their political and economic power; andfinally successful revolutions bring vast and far reaching changes in legal and judicial practice. Defined as such,revolutions are rare events. They are di!erent from riots in several aspects. First, they a!ect lives of a sizablepopulation inside the domestic polity, second the stakes are higher. Participants face higher risks, their ultimategoal being a stand o! against the ancien regime. Both of these characteristics are in contrast with the defining

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1.1 Revolutions and Misinformation

The Weberian definition of the state (Weber 1958) anticipates such destabilizing moments. The

state is defined as the monopoly over physical force and bureaucracy. In addition to the military

and police, the press act as a proxy for the state’s control bureaucracy, hence any interruption of

this industry would alter the functionality of the state. Weber’s definition is also in line with the

transformation of the press after the French Revolution. While decentralized pamphleteering was

a common practice among the revolutionaries, the state they created moved to standardize and

regulate the process.

According to Schumpeter (1950) these notions extend to democracies and dictatorships alike.

In a well functioning democracy the media are used to shape electoral opinion. Likewise in the

Gramsci’s depiction of totalitarianism (Gramsci 1971), the media impose an aura of normalcy and

oppressive calm under the cultural hegemony of the state. What is left out from Schumpeter and

Gramsci’s accounts are the brief moments where the outreach of the media does not exist or is

interrupted. When the normalizing force of the media collapses, production of opinion outside

the reach of the incumbent regime can force the polity to change course under the pressure of an

opposing public sphere.

In a society on the verge of political unrest, the state’s control over news media prevents

widespread dissatisfaction from turning into a united opposition. Media outlets are highly visible

and not hard to control. The population that relies on the media for estimating the political atmo-

sphere is provided with a view that is supervised by the ruling power. The elite use their influence

to pacify the population or discourage sedition. Nevertheless, the widely acknowledged view of

the role of the media in revolutions points at the opposite direction. Popkin (1995) among others,

notes the positive impacts of the free media on bringing about revolutions. According to him

print media disseminate knowledge and awareness. The constituents are informed about political

possibilities and grievances; therefore they are more inclined to engage in a resurrection against

oppression and incompetence. This argument is misguided on two grounds, first it overlooks the

fact that most seditious communication is invisible to the ruling elite. If they were aware of it,

factors of riots (Wilkinson 2009) as smaller gatherings, which although can be violent, are not of the magnitude ofrevolutionary movements and are not directed toward the demise of the principal political hegemony.

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they would disrupt it. The centralized media, including semi-autonomous dailies, are too exposed

to foster revolutionary violence. Second, those who engage in radical acts of dissidence usually are

not primed by potential free discussions in the press. The free media excites the intelligentsia and

is more likely to result in a political transition of more usual types. What incites mass revolts is

“misinformation” properly defined.3

To confirm such hypotheses and to add to the descriptive and speculative statements on the

positive or negative role of the media on mass protests, one needs to find situations in which

social media coverage changes sharply, then gauge the impact of such a change on the level of

unrest. In fact authoritarian regimes repeatedly provide such a scenario: in the course of street

protests, mobile communications are often disrupted and internet access is restricted. However

ideally the disruption should be ubiquitous and universal and the level of confounding factors kept

to the minimum for the conclusions to be meaningful. In the following I provide a stylization

using a dynamic social network model and present a recent example of media disruption in Cairo

as evidence.4

1.2 Dynamic Models of Network Collective Action and Media Influ-

ence

Media disruption drastically changes the way information is transmitted in society. In response to

such disruptions new links for imitation and deliberation are set up and new spheres of influence

are created. In the following I propose a stylization of such dynamics. First I outline a dynamic and

structural version of a well known threshold model of collective action proposed by Granovetter

(1978) and Schelling (1978) and later expanded by Kuran (1989), Lohmann (1994), Gould (1993),

and Siegel (2009) among others. According to the threshold model, individuals have di!erent

3Consider the case of the Czech Velvet Revolution. According to the recent accounts, the movement was ignitedby false rumors of the brutal death of a 19-year-old university student, see http://nyti.ms/2tioTq. At thetimes of civil unrest, exaggeration tactics are known to be highly e!ective. In a similar manner the fall of theBerlin Wall started with a false and ambiguous statement at a news conference, see http://wapo.st/4wXDkC. Avaguely communicated decision on the Eastern German television prompted protesters to demand free passage tothe Western side of Berlin.

4The contingencies involved in a revolutionary event ask for very detailed accounts (Sewell 2005). Later in theempirics section I use journalistic reports to cope with such nuances in event research.

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risk-taking habits represented with a personal participation threshold. If the percentage of one’s

network neighbors engaging in action exceeds one’s threshold, she switches from inaction to action.

Granovetter’s model can be improved upon by adding two components, first a model of social

structure and second a model of threshold dynamics.

Why structure? Taking the overall level of participation to be fully visible to all society

members is an unrealistic assumption. An individual’s perceived levels of participation can be

quite myopic. In fact a major disruption of media and mobile communications does exactly that:

it reduces a globally connected network relying on a backbone of information propagating nodes to

a multitude of smaller local networks barely connected to each other. These local micronetworks

are strongly influenced by patterns of interpersonal links and spatial confines. Because of the

disproportionate size of the core of mobilization, structural patterns also represent the relations

between protest leaders and the rest of the population. In the context of mass demonstrations often

one needs to imitate, observe, and update beliefs based on the neighbors’ acts in a local network.

Such modes of behavior based on limited information can result in a fast-paced contagion of

political participation.

Why dynamics? In addition to action, personal thresholds are also in flux. It is plausible

to think that (1) while interacting with others one would be influenced by his network neighbors’

beliefs (2) when participating becomes prevalent, one’s threshold could decrease; or as inaction

becomes the norm, thresholds also increase. Kuran (1989) and Lohmann (1994) propose dynamics,

while Gould (1993) and Siegel (2009) combine particular dynamic models with structural models.

Hence the distinctive characteristics of mass collective action are products of two factors, first

the structure of the social network and the characteristics of network relations underlying political

action, and second dynamics of inter-personal learning, imitation, and influence. The following

section contains a model for formalizing both components.

2 The Model

In this section I propose a formalization of the Granovetter threshold model for participation in

collective action in networks, which takes both the network structure and belief updating into

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account.5 In order to make verifiable predictions, I outline a graph theoretical model for threshold

updating using DeGroot learning (see (Jackson 2008)). I demonstrate that full connectivity in

a social network sometimes can hinder collective action. Later I will show that with some as-

sumptions on the structure of the social network, repeated threshold updating takes the network

to an equilibrium on the network graph; hence, the updating procedure acts as an equilibrium

selection mechanism based on network parameters and initial participation thresholds. When

these assumptions do not hold, cycles of participation and disengagement can occur. Unlike the

Granovetter/Kuran model, this formalization predicts non-monotone participation levels and het-

erogeneous outcomes at the final equilibrium, where some individuals act and some do not. Hence,

it provides a more realistic model of mobilization dynamics, which can explain the ebb and flow in

large-scale political demonstrations. Later I examine network transformation as a result of media

disruption and hypothesize an increase in protest dispersion resulting from such a transformation.

The empirics in the next section confirm the plausibility of the model.

2.1 Formalization

Each individual decides to either join a collective act of dissent or to stay put based on a personal

threshold and the percentage of his acquaintances who have already joined in. If the percentage

is above that threshold, he would join in, otherwise he would not act. The model is based on two

parameters, the personal thresholds of each agent, and the social network structure which dictates

the details of interpersonal influence. There are radicals with very small thresholds whose acts

start the process.

The network is represented by a graph G(I, E), where I is the set of all nodes in the network

i = 1, . . . , n, and E is the set of all edges connecting these nodes. Each node represents an agent,

and each link is a social connection. Edges can be directed, i.e. some can not see others acting,

while they can be seen by others.6

Each of the agents is deciding between taking (A) or not taking (N) action–this is a binary

choice between N and A. The decision is made based on the proportion of the network neighbors

5Those not interested in technical details may skim through Notes 1 to 4 and skip to section (2.4).6There is always a self-loop, because everybody is aware of what he himself does, or what threshold one has.

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who are acting, according to the following rules. Take pi(t) to be the proportion of i’s neighbors

acting at time t = 1, . . . , T (self included), and #i(t) to be the i’s threshold at time t. At each

time t, i acts if pi(t) " #i(t), and does not act otherwise. This defines a game in which each agent,

based on her threshold, has to choose between A and N .

2.2 Threshold Game’s Equilibria in Heterogeneous Networks

An equilibrium in this network game is defined similar to conventional games. Each agent should

not have an incentive to deviate. There can be more than one network equilibrium. The case

of networks with agents with equal thresholds (a homogeneous network) was studied by Morris

(2000), (see (Jackson 2008) for a short summary).

Consider the following homogeneous example. Note that I have not included self-loops in these

figures, but it is implicit in the model.

N

N N

N N

N

AA A A

A

A#!#! 1/3 $ ! < 1/2 1/2 $ ! < 2/3

Figure 1: Equilibria for a network game, all agents have a common threshold !

All players share a common threshold ! . Contingent upon ! , the game in figure (1) can have

multiple equilibria. Note that for any value of 0 < ! < 1, all nodes acting (A, A, A), and none

of them acting (N, N, N) are two equilibria of the game. There exist two other asymmetrical

ones as well. For example when 1/2 $ ! < 2/3, there is another equilibrium (N,A,A), the third

configuration from left in figure (1). The central player is in equilibrium because ! < 2/3 (A), the

peripheral ones are as well, because for the first player ! " 1/2 (N), and for the third ! < 1 (A).

Homogeneity assumption is limiting. For instance, it is clear that the relation between media,

the state, and citizens demands a threshold model with at least three classes of thresholds. Before

modeling a tripartite case, consider the following heterogeneous example. The central actor has a

negligible but larger than zero threshold ("). In other words she is a radical, while the two other

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players have identical thresholds ! , i.e. the threshold triplet is (!, ", !). In this case the equilibria

of the game are di!erent from the previous case.

N

N N

N N

N

AA A

A

AA#!#! ! > 1/2 ! > 1/2

Figure 2: Equilibria for a network game, agents have thresholds (!, ", !)

Here when ! < 1/2, there are only two equilibria (N, N, N) and (A, A, A). When ! > 1/2,

there are four, (N, N, N), (A, A, A), (N, A, A), and (N, A, N), see figure (2).

Chwe (1999) also studied games with a similar threshold strategy. In addition to Chwe’s

analysis, Gould (1993) and Siegel (2009) consider dynamic versions of similar network games.

In (Hassanpour 2010a,b), the DeGroot learning scheme (Jackson 2008) is superimposed on the

threshold model. Again the learning dynamics acts as an equilibrium selection mechanism. The

network game is played repeatedly, and the thresholds are updated at each iteration. In this model

the asymptotic values of the thresholds and the agents’ final choices between A (action) and N

(non-action) are of interest.

In the following subsection I outline a dynamic model to explain transitions between two

distinct equilibria.

2.3 A Dynamic Model

One expects that at each action period, agents revise their action thresholds based on the history

of previous actions. If the majority of their neighbors in the network are eagerly active in collective

action and have low participation thresholds, the agents update their thresholds accordingly and

will be more prone to participation in the next round. In the case of inaction, the same mechanism

is at work. Lohmann (1994) takes threshold updates to be Bayesian. I instead adopt an averaging

mechanism based on the DeGroot model. Political actors need to approximate the situation and

quickly extrapolate about future. It is plausible to assume that they simply adopt a weighted

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average of their own thresholds with their neighbors’. The weights are proportional to the level of

interpersonal influences. The averaging weights between two agents is not necessarily symmetric.

i can take j’s recommendation very seriously, while the opposite might not be true. An individual

may closely watch the acts of an opinion leader, while the leader does not care as much about a

follower’s beliefs. The weights individual i assigns to person j are taken to be #ijs. I normalize

the #s so that!

j #ij = 1. Note that one’s neighbors’ thresholds are not always fully known, and

are hard to exchange in the course of fast paced mobilization. Hence, actors have to infer the

real value of thresholds from the acts of each of their neighbors. For example they can take i s

threshold to be the the proportion of the times she has failed to act. Or if keeping a detailed

history is implausible, one can make a coarse approximation and take i’s threshold to be 1 if i did

not act in the previous round, and 0 if she did.

In the following, I examine two updating mechanisms. One is based on averaging neighbors’

thresholds, and the other infers a neighbor’s threshold from his act in the previous round. I show

that these two dynamics result in quite di!erent asymptotic outcomes.

2.3.1 First Model, Full Threshold Knowledge

First type of dynamics is to replace one’s threshold with a weighted average of one’s own and

neighbors’ thresholds; and act at each time t according to the threshold and the level of activism

in one’s neighborhood. Because of the continuous nature of political action, I take this process to

be repeated multiple times. Ideally the objective is to use the model to find asymptotic acts and

thresholds of each individual in the network.

Dynamics of Thresholds: At time t, i updates his threshold to be a linear combination of

his neighbors’ thresholds and his own. Define Matrix $n!n = [#ij] equal to the weight that i gives

to neighbor j’s threshold. Take #n!1(t) to be the vector of thresholds for individuals 1 to n at

time t.

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#(t) = $#(t % 1)

#i(t) ="

j:ij"G

#ij#j(t % 1)

Dynamics of Action: take An!1(t) as the vector of individuals’ action. 1 implies action, and

0 non-action. At each time t, individuals either join in collective action or refrain. The perceived

level of participation for individual i, pi(t), can be a product of her personal network, or a number

known to everybody and the same for all, pi(t) = p(t), #i. The decision to act or not act is made

based on the comparison of pi(t) and #i(t). At time t, person i acts if pi(t% 1) " #i(t) and would

not act if pi(t % 1) < #i(t). For the purpose of analysis in this paper I assume that pi(t) is the

percentage of i’s neighbors acting at time t. Note that there could be various ways of modeling

pi(t). For example we could assume a universally accepted p(t) or perceived participation levels,

pi(t), that are di!erent from real pi(t)s.

We are interested in the dynamics of both # and A, specifically their asymptotic behavior when

t & '

Note that in this model, the action vector A is a derivative of the threshold vector #. Define

D ( Dij=1

deg(i ) I) + 1if ij ) E ,

Adding +1 for the self-loop. D is fixed for all t,

A(t) = sgn(p(t % 1) % #(t))

= sgn(DA(t % 1) % #(t)).

sgn(t) is the sign function, sgn(t)=0 if t < 0, sgn(t)=1 if t " 0. Therefore these two equations

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together characterize the Markov dynamics of this system,

#(t) = $#(t % 1) (1)

A(t) = sgn(DA(t % 1) % #(t)). (2)

In the following examples we take $ = D, hence

#(t) = D#(t % 1) (3)

A(t) = sgn(DA(t % 1) % #(t)) (4)

Initial conditions: #i(0) is given. Ai(0) = 1 if #i(0) = 0, otherwise Ai(0) = 0.

Example: Consider the following two networks in figure (3). In this case, there is one radical

agent with initial threshold 0 and two normal individuals with identical thresholds 0 < ! < 1.

At each time unit, players update their thresholds and decide to act or not. The relations are

symmetric. Which means #ij = 1/(degree of i + 1). The initial thresholds and the configurations

are as shown below.

(!, 2/3!)

(0, 2/3!) (!, 2/3!)

(!, 0.57!)

(0, 0.57!) (!, 0.57!)

Figure 3: (initial threshold, steady state thresholds), full connectivity is not always helpful

Note that for the fully connected graph, threshold updating gives (2/3!, 2/3!, 2/3!) for time

t = 1 onwards. For action set, if ! < 1/2 the final action profile will be (A, A, A) for t = 1 onwards;

otherwise it is (N, N, N) for t = 1 onwards.

For the other network, the dynamics are not trivial. Applying the dynamic model in equation

(1) gives the following progression in table (1),

The asymptotic thresholds for both networks are reflected in figure (3). Note that the fully

connected graph has a larger final threshold compared to the other one, i.e. full connectedness is

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Thresholds Actions!(1) = (2!/3, !/2, !/2) A(1) = (A, N, N)

!(2) = (5!/9, 7!/12, 7!/12) A(2) = (A, A, A) if ! < 1/2; o.w. = (N, A, A)!(3) = (31!/54, 41!/72, 41!/72) A(3) = (A, A, A) if ! < 6/7; o.w. = (A, N, N)

......

!(') = (0.57!, 0.57!, 0.57!) A(') = (A, A, A) if ! < 0.875; o.w. = (N, N, N)

Table 1: Dynamics of the star network in figure (3)

not always helpful.

Note 1: Connectivity does not always help collective action. It is usually assumed that

full connectivity among participants in collective action is beneficial to the act of mobilization.

Now consider cases where there are few radicals who aim at recruiting ordinary individuals. Fur-

ther connections among ordinary individuals can foster inaction. The above example in figure (3)

shows that establishing separation among ordinary individuals can e!ectively help mobilization.

This is inline with similar observations in (Gould 1993) and (Siegel 2009) and provides an intuitive

explanation for why disrupting media and mobile communications can assist mobilization instead

of impeding it. Removing regular communication channels provides radicals with more e!ective

venues for organization and encourages citizens to frequently engage in face-to-face communica-

tions. This all weakens the incumbent’s control and provides more opportunities for grass roots

mobilization.

Asymptotic Thresholds: Consider the thresholds at time t, #(t) = $#(t % 1) = $t#(0),

with the condition that *i, #i(0) = 0. If the network’s graph G is aperiodic and irreducible,7

the steady state thresholds will be the same for all the individuals in the network and is equal

to v where v is the normalized8 unit left eigenvector of the matrix $ (the left eigenvector with

eigenvalue equal to 1) and the final threshold for each individual is (Jackson 2008)

#i(') = vT .#(0).

7A graph is aperiodic if the greatest common divisor of all of its cycles is 1. This is true of all graphs discussedin this paper, because of the self-cycle (each individual counts her own threshold in her averaging). A graph isirreducible if there is a path from each node to any other node. Again it is true of all of the graphs in this studyunless it is stated otherwise.

8Such that the final vector is stochastic i.e. its elements add to one.

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Further derivations on #i(') in specific network configurations can be found in the appendix,

section (6.1).

Note that while the threshold perceptions are changing indefinitely (although converging), the

actions might reach the steady state and remain the same. Here we have the private preferences

changing while the actions remain the same.

2.3.2 Dynamics, Second Model, Only Action Knowledge

Dynamics: Consider a case where there is not enough information about personal thresholds of

one’s neighbors. It is usually the case that the agent has to infer the neighbors’ thresholds based

on their actions. Take the coarse estimation of a neighbor j’s threshold at time t to be 1 if j does

not act at time t % 1, and 0 if he does act.

To update the threshold one takes an average of his own threshold and an estimation of his

neighbors’ thresholds based on their prior acts. The dynamics of this new updating mechanism is

#i(t) = Dii.#i(t % 1) +"

j #=i

Dij.(1 % Aj(t % 1)) (5)

A(t) = sgn(DA(t % 1) % #(t)) (6)

The initial conditions are the same as the previous case in section (2.3.1).

The section (6.2) in the appendix contains the details of the asymptotic thresholds and actions

for star and fully connected networks. There are a number of noteworthy points in section (6.2).

First, the dynamics based on inference from actions results in oscillations, even in conventional

topologies such as star networks. The periphery and the central agent switch between action and

inaction. In the case of threshold based updating, such oscillations do not happen.

Note 2: Incomplete information brings about oscillation. Dynamics are not always

monotone. Protests and mass political acts ebb and flow through time.9 An exact or approximate

knowledge of thresholds is usually unavailable. Instead, the actions of one’s neighbors can give

clues on the their proclivity for participating in mobilization. One expects that inference based on

9Kuran (1989)’s model predicts a monotonically increasing or decreasing level of participation.

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only previous actions (not the thresholds themselves) slow down convergence toward a final steady

state. In other words, oscillations are more likely when updating is based on speculations instead

of accurate knowledge. For example Ermako! (2008)10 observes series of vacillations among French

parliamentarians in the process of voting to establish the Vichy government in July 1940. He takes

these oscillations to be a product of insu"cient communication among the members of the French

parliament. The above stylized examples qualitatively confirm Ermako!’s speculations.

Finally an observation on the size of critical mass (CM) in the 3-star and the triangle networks

in figure (3). Note that for any 1/2 < ! < 0.875, the CM for the 3-star network is 1, i.e. one

radical is enough to incite the whole network to act, while the CM for the fully connected network

is larger than 1 because the asymptotic result of the dynamic is non-action for all.11

Note 3: Size of critical mass is contingent upon network structure. The impor-

tance of a body of radical actors who unconditionally engage in mobilization is well known

(Marwell and Oliver 1993). A network model can predict the smallest size of such a group needed

for engaging the whole population. Sudden changes in network structure, e.g. through media

disruption, can change critical mass needed for universal participation. While before disruption

radicals were not able to incite a rebellion, their influence grows in a highly connected news

propagation/mobilization network on the local level.

2.3.3 Finding Steady State Thresholds and Actions

In the above equations (1), (2), (5), and (6) analytical expressions for the steady state vectors A

and # sometimes can be found by putting A(t) = A(t % 1) and #(t) = #(t % 1). For example one

can find the steady state # from equation (1) and substitute it in (2). Then one could simply try

all of the 2n possibilities for A and find the ones that satisfy the identity A(t) = A(t%1). Because

the size of the possible As is well bounded, finding the equilibria is feasible. Same techniques can

be applied to the equations (5), and (6). In the case of dynamics in (5) and (6) such trials are

particularly helpful because both equations are non-linear and finding closed-form solutions is not

10Ruling Oneself Out Ch.911In the appendix (6.2) I show that in the fully connected network, the size of critical mass (CM) is at least half

of the actors. In any network it is possible to find the minimum size of critical mass needed for inciting globalaction.

16

easy.

Also note that any steady state is an equilibrium of the network game (excluding the cases

leading to oscillation). If that is not the case, in the next period, all players choose actions that are

in equilibrium, hence it is impossible to have a non-equilibrium outcome as the steady state. As

it was mentioned before, the dynamics give us an equilibrium selection mechanism. At the same

time, one can assume a particular action equilibrium A, and find #s that result in that equilibrium.

Hence one could design a network to achieve a desired action equilibrium.12

Note 4: A dynamic model proposes an equilibrium selection mechanism. It is

plausible to assume that there are multiple equilibria for the network game defined based on the

threshold mechanism. Network dynamics act as an equilibrium selection mechanism.

Along the same lines as the above mentioned notes, there are four ways through which a

cataclysmic event such as disrupting connections across a society can change the levels of mo-

bilization: through changing the patterns of news production and learning among individuals,

through changing the amount of information one can acquire about the acts and intentions of

one’s social network neighbors, through changing the size of critical mass on the local level, and

finally through changing the asymptotics by altering the dynamics.

2.4 State, Media, and Citizens, A Network Threshold Game

In section (1), I argued that media have a pacifying role in the societies facing mass political

turmoil and their sudden disruption can aggravate the situation. Before continuing with histor-

ical anecdotes and statistical evidence, I o!er an explanation using the threshold network game

described in the previous subsection.

Consider a network model with three classes of actors, modeled in the context of a tree network.

The sole central player is the “state,” the nodes connected to the state represent the media visible

to the state and under its influence. Each of the media nodes provides a number of individuals

with news and information on the revolutionary situation. See the topology in figure (4). Links

here represent functional and behavioral influence. For example media are influenced by the state

12Hence we can perform a structural mechanism design. Knowing the final outcome, we can find a networkconstruct that induces the desired equilibrium.

17

policy, hence the links from the state to the media nodes. Individuals’ level of risk taking is

influenced by the content of the media, hence the links from the media to the individuals. Note

that influence is not synonymous with control. A change in strategy on a Facebook page due

to information security concerns amounts to a link between the incumbent and the social media,

although the state does not dictate the page’s content. Similarly the existence of the page provides

citizens connected to it with a forum in lieu of face-to-face communication. Social media nodes that

serve as news propagation forums often regulate news aggregation and oversee news distribution

among the online population.

More traditional venues such as printed press serve the same purpose but are not as a!ected by

their audience as the novel social media. For the purpose of this study I examine two separate cases

in turn. One is the traditional media such as the daily print press, where there is no established

mechanism for channeling the opinion of the audience in real time. The second category contains

new social media. Ideally they represent an aggregation of their audience’s attitude toward risk

besides serving as an information source for the users. The distinction between two media classes is

not complete. There are cases in between, e.g. independent newspapers or a government sponsored

blogosphere.

Figure 4: The state, centralized media and citizens, represented as a tree graph

18

Figure 5: The state and citizens in the absence of media, multiple local networks-each highlyconnected

Case One: Traditional Media

Stylization: Individuals engage in binary acts of dissent. As a trivial assumption, the thresh-

old of the state as the central node #s = 1. The media is taken to have a threshold of #m which is

close to 1 and is larger than ordinary citizens’ threshold of #c. Media threshold is not related to

action, but is an indicator of the media node’s propensity toward the uprising. The smaller #m is,

the more disparate from the state the media node is. #m’s being close to 1 is a plausible assump-

tion because during a revolutionary situation the traditional media are under direct supervision of

the state. Often they act as the regime’s propaganda machine, and citizens discount their reports.

Among the citizens at each locale there are a few radicals with zero thresholds. This means

they would act irrespective of what others do. The government monitors media’s operations. The

media on the other hand act as intermediaries between the state and its constituents.13 In the

situation depicted in figure (4), citizens’ main source of news are the established media. Because of

their ubiquity and their strong distribution network, the media-individual connections are much

13Assume there are k citizens connected to each media source and there are m media sources, i.e. there are mkcitizens evenly distributed among the news sources. Also Consider the following conditions over the thresholds ofthe media "m and of the citizens "c, 1 > "m > (k + 1)/(k + 2) > k/(k + 1) > "c.

19

stronger than the relations between the citizens. Hence comparatively speaking, interpersonal

links are non-existent. In such a situation, the state and the media’s allegiances are static while

the citizens’ thresholds can change. Ordinary citizens do not act, because all what they see are

controlled sources of information. In the absence of connections with radicals, the majority of

the population is not mobilized. Because of their isolation from the others, radicals’ action does

not amount to much. At a stalemate, citizens learn from static media nodes and become more

risk averse. The result would be a non-action equilibrium with a slight nuisance from the radical

elements. After multiple updates, the thresholds tend to converge to the media’s risk aversion

levels.

Case Two: The Social Media

Decentralized media, e.g. Internet forums for communication, influence citizens’ inclination to

risk in ways di!erent from traditional media. Controlling news propagation on a grass roots level

is much more di"cult than policing newspapers, television, and radio broadcast. The political

inclinations of the social media are often in line with the population majority, not the incumbent.

To stylize the situation I take the configuration of the network to be the same as in figure (4), but

in this case the threshold of the media nodes is the average of the thresholds of their neighboring

citizens.

Assume that the social media represent the average threshold of their correspondents, #m =

(!

i #mi)/N . In line with the dynamics presented in equation (5), take action and thresholds to

be reversely related Ai = 1 % #i. In the stylization, each individual sees the average threshold at

the social media node and decides to act if #i < 1 % media threshold.14 Now according to this

rule, at most half plus one individuals engage in action, while according to the threshold dynamics

in equation (1), all of the thresholds are converging to the average threshold represented by the

social media node.

Note the di!erence between this scenario and a fully connected network of individuals. Here

the social media node provides everybody with a view representing only the “aggregate” threshold.

“Individual” thresholds are not visible to other members of the network.14The calculation behind this decision rule is straight forward: to act "i <

!j Aj/N =

!j(1 % "j)/N =

1 %!

j "j/N = 1 % average threshold.

20

Disruption of the Media, Stylization

Equilibrium Now consider the situation in figure (5) in which the media nodes are not present.

In this case, citizens have to rely on each other for gaining information about the political and

social atmosphere. The incumbent regime is deprived of its propaganda tools; furthermore, it

can not exert influence by supervising newspapers or manipulating the social media. In such a

situation, citizens are influenced by their peers including their radical neighbors in the network.

Dismantling preexisting links among the public and between the media and the population

encourages building new connections. Individuals have to engage in exchanges with their imme-

diate neighbors in order to hear the news, or to estimate the prospects of contention. Extreme

conditions-brought about by disrupting mobile communications as well as the Internet-incite phys-

ical presence instead of online activity (more on the underlying processes in the next section), and

produce new connections on the local level. In the transformed network depicted in figure (5),

highly connected cells of contention start to take hold in di!erent locations, increasing the dis-

persion of the protests and proliferating communal activity throughout the society. Unlike the

situation in the fully connected tree network, action equilibria are possible.15

Dynamics: The static model does not outline the path to widespread participation. For

modeling the transient part of the process, one could implement a dynamic model of threshold

updating and action in a heterogeneous network. For examples during the first round, the insti-

gators act; hence they motivate the rest with low thresholds, and this process is repeated. In each

repetition the rebellion spreads more widely. This was impossible in the case of figure (4), because

each agent was influenced by the media, hence the action of the radicals could not incite universal

dissent.

Each fully connected local network presents a dynamic situation similar to the scenario initially

proposed by Granovetter (1978): acts of each are visible to all. Radicals exert influence and become

opinion leaders, cascades become a possibility. In other words, a disruptive action meant to stop

the rebellion turns to a catalyst for it.

15Note the configuration in figure (5). In this set up, again let’s assume that there are k citizens per eachsubgroup, some of which are radicals with threshold 0, and all others have a threshold of "c. Take r to be theminimum integer z for which z/(k+1) is larger than "c, r = min{z|z/(k+1) > "c}. In this case for all s, k " s " r,“s citizens acting” is an equilibrium in the network threshold game.

21

Dispersion hypothesis: According to the above stylization disrupting the media and mo-

bile communications promotes local mobilization, increasing the dispersion of protests. In

the following section I exploit reports from the Egyptian uprising of 2011 to test the dispersion

hypothesis.

3 Media Disruption and Dissent, Empirical Evidence

In this section, I employ multiple methods to confirm the dispersion hypotheses. I start with

a statistical analysis of revolutionary unrest in relation to media penetration followed by some

suggestive archival evidence. Then I examine the case of the Egyptian uprising of 2011 in greater

detail. In late January 2011, in response to demonstrations, Mubarak’s regime disrupted commu-

nication all together for a few days providing a unique opportunity for studying the role of media

disruption in fostering unrest. The main criterion for testing the theory is the existence of a visible

increase in the dispersion of protests after media interruption.

3.1 Statistical Treatment

The existing statistical studies of mass political violence do not directly address the link between

media and dissent. For example the extensive study of Hibbs (1973) does not consider the media

component among other economic indices. One way of linking media influence to the level of

revolutionary activity is to compare the frequency of revolutionary unrest to the extent of the

reach of centralized media using available country-year data. An index of media influence can be

the number of newspaper copies per capita or prevalence of radio or television use in a country.

According to the above argument I expect that the number of revolutionary resurrections to be a

decreasing function of media penetration. I use country-year data from Banks (2009) to examine

a potential inverse relation between media penetration and revolutionary unrest. Consider the

following plots.16

16Revolutions according to Banks (2009) are “any illegal or forced change in the top government elite, anyattempt at such a change, or any successful or unsuccessful armed rebellion whose aim is independence from thecentral government.” This definition includes more than so called social revolutions and emphasizes the violentnature of political takeover. Nevertheless it contains most of what constitutes a social revolution: its grass-roots

22

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

1

2

3

4

5

6

7

8

9Revolution count vs Newspaper copies per capita (Banks)

Figure 6: Domestic mass political violence for state takeover versus newspaper copies per capita

Now consider the following negative binomial regression (table (2)) of the number of revolutions

over the number of radios, television sets, and newspaper copies per capita controlling for GDP per

capita and a measure of democracy at each country-year point (see table (3) for more information

on the range of the parameters and scaling data).17

As it was expected from the plots, the regression multipliers for printed media influence (news-

paper copies per capita) are negative and strongly significant. Even after controlling for a democ-

racy index and GDP per capita still the pacifying role of media penetration is evident and accounts

for around at least 20 (and up to 40) percent of the dependent variable count (the average revo-

lution count is 0.185, see table (3)).

Low levels of civil disobedience is often linked to high levels of political and economic devel-

opment. Controlling for GDP and a democratic index deals with the very same concern.18

nature, its large scale, and its aim at toppling the incumbent regime.17The democracy index is e!ectiveness of legislature according to the Banks data set.18Huntington (1968) took mass violent takeovers of the polity to be the result of a gap between social and economic

advancements and political modernization. Such an explanation can be improved upon because it does not clearlyspecify what the elements of political modernization are. According to Huntington, political development is a sourceof stability, and political institutionalization is an antidote to major reversals of power such as revolutions. While

23

(1) (2) (3) (4)

Radios per Capita (104) %1.553e % 05 %6.927e % 06(2.448e % 05) (2.057e % 05)

TV sets per Capita (105) 1.055e % 05 9.041e % 06(5.373e % 06)$ (4.875e % 06).

Newspaper Copies per Capita %2.544e % 04 %2.151e % 04 %2.068e % 04 %2.613e % 04(104) (5.549e % 05)$$$ (4.487e % 05)$$$ (4.824e % 05)$$$ (5.438e % 05)$$$

GDP per Capita %1.206e % 04 %1.048e % 04 %1.029e % 04 %1.220e % 04(2.237e % 05)$$$ (1.737e % 05)$$$ (1.879e % 05)$$$ (2.236e % 05)$$$

E!ectiveness of Legislature %5.093e % 01 %5.123e % 01 %5.087e % 01 %5.126e % 01(4.099e % 02)$$$ (3.851e % 02)$$$ (3.908e % 02)$$$ (4.033e % 02)$$$

Observation Count 6278 6832 6799 6285AIC(+ -2 x log-likelihood) 5743.4 6357 6346.8 5743.9

.Signif. codes: 0 ! ! ! 0.001 ! ! 0.01 ! 0.05 . 0.1 1

Table 2: Revolution count, negative binomial count regression

Parameter Average Max MinRevolution Count 0.18528 9 0Radio per Capita for each 104 1650.55 68406 0TV per Capita for each 105 7358.77 226400 0Newspaper Copies per Capita for each 104 1018.23 9000 0GDP per Capita (Factor Cost) 2859.45 44797 18E!ectiveness of Legislature, 0 to 3—3 most e!ective 1.67004 3 0

Table 3: Codebook, range of parameters

24

Controlling for GDP and type of the state, the negative impact of newspaper penetration is

robust and significant.19

3.2 Historical Precedents

Ubiquitous and abrupt changes in media coverage provide an opportunity for studying the role

of connectivity or lack thereof in the course of mass protests. After the introduction of the new

social media such disruptions have become more commonplace. In the face of civil unrest the

authorities have frequently chosen to disrupt communication venues available to protesters. To

gauge the impact of media interruption, I study examples of mass uprisings in which there is

a sharp interruption in service and estimate the e!ects of the change in coverage. To mitigate

the endogeneity concern, it is necessary to control for confounding parameters that alongside

media disruption might have changed the level of protests. As it was mentioned above, the most

visible change that can attest to the validity of the theory is an increase in the dispersion of

protests. There are multiple reasons for adopting such a research design: first, media disruption

has happened frequently during the recent years as a tactic against the the new social media’s

mobilizing capabilities, hence we know of multiple cases that can be used for the purpose of

testing the theory. Second, because of the extent and accuracy of reports in recent years we have

very detailed accounts of protests preceding and ensuing breaks in media/mobile communication

coverage. The same is true of confounding factors. In the course of my study of the Egyptian

uprising of 2011, I will present a showcase of such a design, but before doing so I include some

archival evidence suggesting that the same process may have been at work in other historical

occasions as well.

this is a plausible account, it fails to specify what these political institutions are and what constitutes a measure ofpolitical modernization. Taking development as a repellent of mass political instability is at best tautological. Itscredibility is proved when one finds the elements of political development, demonstrating the mechanisms throughwhich they contribute to political stability.

19One caveat is e!ectiveness of the number of newspaper copies in relation to the rate of literacy in correspondingcountries. If the link between illiteracy and low GDP is strong, the impact of illiteracy is counted in when GDP isincluded as a control parameter.

25

3.2.1 From Pamphlets to Centralized Media

One of the more interesting facts about the French Revolution is the prevalence of pamphleteering

immediately before the revolution (Popkin 1990). While the established periodicals of the time,

e.g. Gazette de France and Gazette de Leyde, mostly ignored the rebellion during the summer

of 1789, many of the leaders of the Third Estate (and later revolutionary leaders during the

struggle of summer 1789) published pamphlets to disseminate news and make their own take on

the situation known to the public. At the beginning of the summer 1789, Louis XVI tried his best

to block the growth of pamphleteering (Popkin 1990), but did not succeed. Later pamphleteers

such as Mirabeau and Abbe sieyes lead the revolution. After victory, the revolution redefined the

relation between the state and media; pamphleteering was discouraged. Instead the printed press

with wider circulation replaced grass roots means of written communication and reporting.

Prior to the culmination of the February 1917 unrest in Petrograd, the city’s newspapers

stopped circulation immediately before the Duma’s dissolution on the 27th. They resumed on

the first of March. Afterwards the Duma tried to maintain a monopoly on the press (Wade 2005,

Hasegawa 1981, Historical New York Times n.d.). Wade (2005) believes the confusion caused by

the absence of printed media might have hastened the collapse of the ancien regime.20

Similarly during the Iranian Revolution of 1978-79, the printed press stopped circulation on

November 6th, 1978 and did not return to normal till two months later (Historical New York Times

n.d., Historical Washington Post 1978, Kayhan 1978-9). The largest protests of the Iranian Revo-

lution happened during the very same period when media coverage was at minimal levels and the

scant broadcast of the state media was widely disregarded in favor of pamphlets, audio cassettes

and foreign radio stations (Historical New York Times n.d., Kayhan 1978-9). Again in this case

the spread of grass-roots rumors had a major impact on the success of revolutionary mobiliza-

tion. Instead of the state setting the general political agenda across the society, the opposition

encouraged repetitive cycles of rebellion.

In both of the above cases it is di"cult to estimate the impact of the absence of media because

20It is necessary to mention that literacy rates among the urban Russian population around 1917 were around 70percent (Mironov 1991), i.e. the printed press as the sole formal news propagation mechanism (prior to televisionand radio broadcast) played a major role in everyday news learning. Their absence might have acted as a catalystfor the revolutionary unrest in Petrograd.

26

of confounding factors. The evidence we have is not accurate enough to test the “dispersion

hypothesis” (see section (2.4)). Specially because the treatment (disruption of the media and

cutting down venues for communication) was not always complete, i.e. did not include all means

of news propagation. In more traditional societies constituents relied on a wide array of traditional

communication processes, hence stopping a number of state controlled channels, e.g. newspapers

might not have had the same impact as cutting all mobile/Internet communications overnight.

That’s exactly what Mubarak’s regime did at the early hours of January 28th, 2011.

3.3 Mubarak’s Natural Experiment in Media Disruption

In response to the opposition staging demonstrations for three consecutive days in Tahrir Square

and promising a yet larger demonstration on a Friday of Rage, Mubarak’s regime shut down the

Internet and cell phone coverage across the country at the early hours of January 28, 2011. In-

stead of stalling demonstration in Tahrir, the consequences caught the regime by surprise. Protests

flared across Cairo and other Egyptian cities including Alexandria and Suez. The protests were

unusually di!use and widespread and overwhelmed Mubarak’s security forces by the end of the

day (Historical New York Times n.d.). Around 7 PM on January 28th the military was brought

into the scene to replace the dysfunctional police force. After deployment of the military, dynamics

of the interaction among the political players (the incumbent, the military, and the opposition)

changed. The military’s inaction, accompanied with unexpected implications of the regime’s bold

experimentation with the mass media in the following days, put an end to Mubarak’s thirty year

rule. At the turning point of January 28th, lack of cell phone coverage and Internet connec-

tion forced the population to find other means of communication, encouraging local mobilization.

Meanwhile apolitical strata of the Egyptian society, aggrieved by the disruption, were pushed into

joining the confrontation. Instead of protests only in and around Tahrir Square, sizable demon-

strations appeared in many locations in Cairo (Historical New York Times n.d., Shehata et al.

2011).

As mentioned above, the Egyptian case o!ers a unique opportunity to test the plausibility of

the dispersion hypothesis in a context similar to a natural experiment (alas run by Mubarak’s

27

regime). The disruption was abrupt and its timing (1:30 AM on January 28th) rules out any

preparation for countering the blackout on the previous day. Between 10 PM and 2 AM, SMS

and Internet communications were shut down, more importantly cell phone communications were

shut down during the 28th, see a timeline at table (5).21 While the regime had experimented with

selectively disrupting network coverage and websites such as Twitter and Facebook, it was the

first time a universal communication blakcout was imposed on the nation.

Dismantling regular venues for communication incited Egyptians to find new ways of staying

online or forgoing online communication altogether. During the social media hiatus, older mobi-

lization tactics were used in conjunction with new means of mass communication. For example

satellite television stations such as Al Jazeera broadcast news communicated to them via landline

phones (Shehata et al. 2011). Al-Arabiya television station also started broadcasting informative

tweets on radio. At the same time tweeting over the phone became a possibility using Google’s

Speak2Tweet (Dunn 2011a). In addition to these spontaneous innovations, the protests prolif-

erated through much more mundane means. On the 28th those worried about their friends and

family members participating in protests, could not reach them via cell phones, and had to join

the crowds in streets to find out about their acquaintances (Shehata et al. 2011). In hazardous

conditions of the ongoing stand o! across the city, focal local points became gathering locations.

Many congregated in local squares, strategic buildings, and mosques instead of trying to reach

Tahrir (Historical New York Times n.d.)22.

To summarize, the disruption of cell phone coverage and Internet on the 28th exacerbated

the unrest in at least three major ways: it implicated many apolitical citizens unaware of or

uninterested in the unrest; it forced more face-to-face communication, i.e. more physical presence

in streets; and finally it e!ectively decentralized the rebellion on the 28th through new hybrid

communication tactics, producing a quagmire much harder to control and repress than one massive

gathering in Tahrir.23

21sources: Ramy Raoof, Egyptian activist and blogger, the Lede Blog, Dunn (2011b), and Renesys.com amongothers (blog posts, video interviews, news reports)).

22http://nyti.ms/gcHvjz23Even in Tahrir there was no single leadership (Shehata et al. 2011): “Nobody was in charge of Tahrir,” and

“a lot of those who joined the protests on January 25th in Tahrir were not aware of the Facebook campaign, theyhad heard about it from the protesters in the square and surrounding streets.” According to Mourtada and Salem

28

3.3.1 Notes on the Dynamics of the Egyptian Unrest

Four important cycles of unrest are evident during the 18 days of the Egyptian uprising, each

of which starts with a significant media event. There are four cycles: January 25-27, January

28-February 01, February 02-07, February 08-11.

a) January 25-27: Initial mobilization on January 25th, the social media campaign

b) January 28-February 01: Disruption of the media on January 28th and the proliferation of

the protests, the military steps in and acts as a game changer during the following clashes between

pro and anti-Mubarak crowds

c) February 02-07: Provocative national address by Mubarak on late night February 01st (stat-

ing he will stay in power till September and will die in Egypt), ensuing clashes on the 2nd and

3rd, the military’s inaction emboldens prevailing opposition crowds, relative calm afterwards till

the 8th

d) February 08-11: Emotional appeal by Wael Ghonim on a late night television show on the

7th and his follow up speech in Tahrir square in the morning of February 08th initiates the final

phase of protests in Tahrir, eventual announcement of Mubarak’s resignation by Suleiman on the

11th.

The above parsing of the events emphasizes the pivotal role of media operations during the

unrest. Setting aside the military’s involvement–itself brought about by the sudden disruption

of all online communication means–the role of the media is in full display. Dismantling the cell

network in particular influenced mobilization on the local level. Egyptians had to revert to local

interactions for gaining information. New local mobilization networks were formed that were

smaller and better connected. Radicals became more e!ective on the local level, because they

could directly contact more people on the ground. Moreover the networks underlying collective

action and news propagation became smaller and more di!use, making it more di"cult to contain

the protests.

To overcome the endogeneity critique (that the government disrupted the media in anticipation

of a major increase in the size of the protests, or that the closing down of the media was simply

(2011) majority of respondents to an online survey on the role of the media disruption on the protests, believed ithad a positive e!ect on the demonstrations.

29

a by-product of the escalating unrest) one could show that the major prediction of the model, i.e.

sharp increase in the dispersion of the protests is manifest and statistically significant even after

controlling for other confounding factors. The fact that there were calls for attending a protest in

Tahrir during the previous days does not account for a sharp jump in the number of “locations”

in which the clashes on the 28th took place. The dependent variable here is the dispersion of the

protests, not their total size. Furthermore, such proliferation never happened again during the

course of the protests, in spite of the the return of the media and enduring contention in Tahrir,

even when the numbers in Tahrir were unprecedented (e.g. upon the restoration of the Internet

on February 2nd).

In the following I outline the procedure for extracting protest location data using available

journalistic reports (photos, wires, and blog posts), describe the media interruption timeline and

present a statistical analysis for singling out the e!ect of disruption on protest dispersion. I show

that the Mubarak experiment is yet the most convincing evidence on the dispersion hypothesis we

have on file.

3.4 Protest Dispersion in Cairo and Media Disruption

In the following I outline the data on protest dispersion and media disruption during the 18 days

of the Egyptian uprising of 2011. I include four variable in the OLS regressions. The dependent

variable is protest dispersion defined as the number of locations in Cairo where protests were

happening each day. As independent variables I include a dummy for the treatment, i.e. media

disruption, controlling for Fridays and national addresses via television. Controlling for Fridays is

necessary because Friday is the Muslim weekly holiday and more people have the time and latitude

for demonstration. Friday prayers also act as focal events. Media announcements are included as

a control to estimate and control for the influence of mainstream media reporting. The details of

protest locations are included in table (4), a log of state interruptions of media/cell coverage is

included in table (5).

A di!erence-in-di!erence strategy Here I regress daily di!erences in protest dispersion

in Cairo on media disruption, controlling for Fridays and media announcements. First note the

30

Date Dispersion: Protest Locations

January 25 1: TahrirJanuary 26 1: TahrirJanuary 27 1: TahrirJanuary 28 Friday 8: Tahrir-NDP headquarters-Egyptian National Museum/Kasr al-Nil bridge/

6 October bridge/TV headquarters/Al Azhar mosque/Mohandeseen/Mustafa Mahmoud Mosque/l Istiqama Mosque

January 29 4: Tahrir-NDP headquarters-Egyptian National Museum/Interior Ministry/Corniche al-Nil/Abu Zaabal

January 30 3: Tahrir/Heliopolis/Abu ZaabalJanuary 31 3: Tahrir/ Mohandeseen/Arkadia Shopping CenterFebruary 01 2: Tahrir/Kasr al-Nil BridgeFebruary 02 3: Tahrir-Egyptian National Museum/Mohandeseen/Corniche al-NilFebruary 03 1: Tahrir-Egyptian National MuseumFebruary 04 Friday 1: Tahrir-Egyptian National MuseumFebruary 05 1: Tahrir-Egyptian National MuseumFebruary 06 1: Tahrir-Egyptian National MuseumFebruary 07 1: Tahrir-MugammaFebruary 08 2: Tahrir/ Egyptian ParliamentFebruary 09 4: Tahrir/ Zamalek/ Ministry of Health-Egyptian Parliament/

Dokki(organized labor protests)February 10 4: Tahrir/ TV Headquarters/ Egyptian Parliament/ Abdin PalaceFebruary 11 Friday 5: Tahrir/ TV Headquarters/Presidential Palace/

Mustafa Mahmoud Mosque/ Egyptian Parliament

Table 4: Source: The New York Times, The Lede Blog, See Section (5.1)

31

Date Type of Disruption or Restoration

January 25 Twitter.com blockedBambuser.com (Live video streaming) blocked at 02:00 PMActivists’ mobile lines shut downNetwork coverage shut down in Tahrir

January 26 Facebook.com blockedBlackberry services shut down 7:00 PM

January 27 SMS shut down 10:00 PMJanuary 28 Internet shut down-except one ISP, 1:30 AM

Mobile phone calls shut down for one dayLandlines shut down in some areas

January 30 Al-jazeera Cairo bureau shut downJanuary 31 Last ISP shut downFebruary 01 State media campaign against protesters (text messages)February 02 Internet service restored 12:30 PMFebruary 05 SMS restored 12:35 AM

Table 5: All times are Egyptian local time, i.e. EST+7 in January/February 2011–Source: RamyRaoof, Egyptian Activist and Blogger, Alix Dunn Blogger, Renesys.com–23.51 Million Internetusers (30% penetration), 71.46 Million Mobile subscribers (90% penetration) in January 2011

sharp jump in the number of protest locations on the 28th in figure (7). 28th was the first day of

blanket disruption and the only day in which cell phone communications, Internet services, SMS,

and occasionally landlines were unavailable (see table (5)). Lack of cell phone coverage is more far

reaching among the population than the absence of the Internet. In January 2011, there were 23.51

million Internet users compared to 71.46 million mobile subscribers.24 Hence compared to Internet

outage, any disruption in mobile communications directly implicates a much larger portion of the

Egyptian population. Also mobile phones are more e!ective means of communication on the fly

during protests in streets. On the other hand, Internet disruption complicates longer term planning

and organization. Cutting cell coverage has an immediate impact on personal communication in

streets. Considering these issues and the fact that dismantling Egyptian online network came

as a surprise during the very first day of the outage, I code the treatment as a dummy on the

28th (discon-di!)-Later I consider an extended version of the disruption variable. National media

addresses by the heads of the state and the opposition (see appendix 5.2) are also coded as a

24 23.51 Million Internet users (30% penetration), 71.46 Million Mobile subscribers (90% penetra-tion) in January 2011, from report by Egypt’s ministry of Communications and Information Technologyhttp://slidesha.re/mtrPuM

32

control variable in table (6). Using a di!erence-in-di!erence strategy, I regress the daily change

in dispersion over the treatment (disruption) controlling for other variables. As can be seen in

table (6) media interruption on the 28th had a statistically significant and relatively e!ective role

in proliferating the protests in Cairo.25

Figure 7: Top: Protest Dispersion (Number of Distinct Protest Locations According to Table 4),Bottom: All Google Products, Egypt Tra"c, from http://bit.ly/h3Q1FZ

In addition to the di!erence-in-di!erence strategy in table (6), I also code the disruption across

the 5 day period of 28th to 01st. Taking the relative penetration of mobile devices to the Internet

into account, I implement the treatment vector [3 1 1 1 1]. Note that this is an underestimation

of the importance of the disruption on the 28th, because on the first day full disruption came as

an utter surprise, hence its e!ect was more pronounced in the first day compared to the following

25Similar results are obtained when the variable “television announcement” is excluded.

33

Estimate Std. Error t value Pr(> |t|)(Intercept) %0.08333 0.40886 %0.204 0.84143discon-di! 5.91667 1.96080 3.017 0.00923$$

fridays 1.16667 1.29291 0.902 0.38213announc %0.58333 0.91423 %0.638 0.53373

.Signif. codes: 0 ! ! ! 0.001 ! ! 0.01 ! 0.05 . 0.1 1

Residual standard error: 1.416 on 14 degrees of freedomMultiple R-squared: 0.6382, Adjusted R-squared: 0.5606

F-statistic: 8.23 on 3 and 14 DF, p-value: 0.002109

Table 6: Protest Dispersion in Cairo, OLS over treatment (disruption on 28th), controlling forFridays, and media announcements

four days. In fact as it was mentioned above, Egyptians found their way around the blockage soon,

hence the decentralizing influence from the type discussed above was the most pronounced on the

very first day of disruption. Nevertheless even with the new coding, the impact of the disruption

on protest dispersion is large and statistically significant. In this case, the dependent variable is

the dispersion per se (not daily di!erences). The results are included in table (7).

Estimate Std. Error t value Pr(> |t|)(Intercept) 1.2845 0.3980 3.227 0.006083$$

discon 1.8223 0.4328 4.210 0.000873$$$

fridays 0.6526 0.9048 0.721 0.482635announc 1.3610 0.6818 1.996 0.065752.

.Signif. codes: 0 ! ! ! 0.001 ! ! 0.01 ! 0.05 . 0.1 1

Residual standard error: 1.205 on 14 degrees of freedomMultiple R-squared: 0.6746, Adjusted R-squared: 0.6049

F-statistic: 9.676 on 3 and 14 DF, p-value: 0.001025

Table 7: Protest Dispersion in Cairo, OLS over treatment (disruption on 28th-01st), controllingfor Fridays, and Television announcements

Note the positive, large and statistically significant impact of social media/mobile communica-

tion disruption. In addition to media disruption, the influence of nationally broadcast television

addresses is also manifest here (although not as statistically significant). Witnesses present at

the unrest have repeatedly cited the role of satellite television stations in proliferating the rebel-

lion (Shehata et al. (2011) among others). The above results confirm those speculations. Major

television addresses preceded unrest in multiple occasions during the 18 days of the uprising (see

34

appendix (5.2)). The contrast with the common view on the singular importance of the new social

media in generating unrest in Cairo is thought provoking.

3.4.1 Blocking the New Social Media for Stopping a Rebellion?

From the above it is clear that disrupting the media (and to a lesser degree the ever present satellite

television coverage of the events) charged the population and overwhelmed the authorities; but

what are the conditions under which such disruptions might not be as mobilizing?

An overview of the press at revolutionary times reveals that there are varying degrees of a"nity

between the press and the people. At times the press completely ignore the protests even till the

very moment the ancien regime is about to collapse (Petrograd Gazette 1917, Gazette de France

1789, Al-Ahram 2011). Sometimes they cautiously report on the unrest and sympathize with the

protesters in a subtle tone (Kayhan, Iran 1978). Finally at times they fully support the protesters,

reporting on their actions in detail, Al Jazeera Egypt 2011). There are multiple cases of failed

rebellion during which access to the social media was highly restricted e.g. the post-election unrest

in Iran 2009 and the Thailand Red Shirts rebellion of 2010. At the height of the protests in Tehran

in June 2009, mobile communication was cut o! and press reporters were banned. Opposition

leaders’ communication with the outside world was very limited (Historical New York Times n.d.).

In spite of limited access to social media at the time of the unrest, there was never a blanket

interruption, but limitations were targeted, local, and temporary. Similarly in Thailand, the

supervised flow of the opposition press allowed the government to suppress the opposition. The

largest fatalities in one single protest event happened when the Red Shirts tried to take over

the building housing the Peoples Television Station (a television station sympathizing with the

Red Shirts). After an initial retreat from the premises, the government allowed for the station’s

operation but under strict supervisory26. In both cases a slow flow of supervised communication

was allowed; on the other hand, face-to-face means of mobilization were actively disrupted.

26Reports on the incident here http://nyti.ms/bP55zl and http://bbc.in/oMA7Wo

35

4 Conclusion: too many in too many places...

In the above I showed that the favorable portrayal of social media in fostering unrest in the

context of heterogeneous networks should be reconsidered. In other words, in the presence of

a risk-averse majority and a radical minority, adding more links among the majority does not

necessarily help mobilization. In the absence of centralized media, crowds’ risk-taking behavior

becomes independent of the state’s intentions. Note that even the most authoritarian regimes

prefer not to systematically bomb their own population, they instead use a threat of forceful

military action in order to deter. When it is impossible to communicate the possibility of a

painful military retaliation, the state is unable to dissuade the crowds. In fact protests proliferate

when such threatening measures fail. In reaction to a shock similar to the one exerted on the

Egyptian society on January 28th, the population can overwhelm the incumbent apparatus. The

consequences of such an action were evident in the aftermath of the Egyptian media blockage.

The Lede Blog reported from Alexandria on January 28th (emphasis is mine):

“It’s clear that the very extensive police force in Egypt is no longer able to control

these crowds. There are too many protests in too many places... said Peter Bouck-

aert, emergencies director of Human Rights Watch, who observed the street battle in

Alexandria on Friday[01/28/2011].”

5 Appendix 1: Event Data Collection: Egyptian Uprising

of 2011

5.1 Event Data Collection, Methodology

In this appendix I outline the data collection procedure for the Egyptian uprising of 2011. In my

reconstruction of the events, as for the location of protests, I used graphic designs from the New

York Times’ website in addition to accounts from the Lede blog,27 for media disruption I have

27Links to visual representations on The New York Times’ website:a. http://nyti.ms/gf4hzJ b. http://nyti.ms/g3LanI c. http://nyti.ms/fh5Ypb

36

used online information from Egyptian bloggers28 and coded the data after crosschecks.

For confirming the hypothesis on the role of the Egyptian media shut down on the unrest and

the eventual ouster of Mubarak, I used the closest log to news wires I could find i.e. the Times’

Lede Blog and the aforementioned graphic designs to reconstruct a fine-grained description of the

events. News blogs often can be valuable sources of information on protests. My reconstruction of

the Cairo events has been mostly based on my readings of various blogs and inferences from news

photos. The real-time evidence provided by blogs, twitter and Facebook updates, in addition to

event photos provide an unprecedented level of accuracy in event reconstruction and enforce new

ways of studying decentralized mass protests.

5.2 Major Media Announcements During the Egyptian Protests

Announcements are major addresses to the nation, broadcast from the state television or cable

TV channels with a national audience. I have collected the following accounts from The New York

Times’ Lede Blog. All times are Egyptian local time (EST+7 in January and February 2011).

1. January 29th: at around 12:30 AM, Mubarak’s late night address (coded as 29th), he gives

concessions, dismisses the government, appoints a Vice President/ Obama talks to the press

immediately after Mubarak’s speech.

2. February 01st: at around 10 PM (earlier that day the largest protest to date in Tahrir),

Mubarak gives another speech on the state television. He announces he intends to remain

in o"ce until the end of his current term. Obama again speaks after Mubarak’s speech.

Note: Worst clashes start on February 02nd and last for two days (2nd and 3rd of February)-

by 5th or 6th the situation has almost returned to normal.

3. February 04th: Suleiman makes TV appearances the day before–total of two television

appearances before Friday 04th.

4. February 08th: Wael Ghonim gives an emotional interview on Monday night (07th) on a

28See a timeline at: http://bit.ly/pNRvEH

37

cable TV channel and appears in Tahrir the next morning (08th) to give a speech addressing

the massive crowd in the square.

5. February 10th: Around 7 pm Egyptian state TV announces an address by Mubarak to be

broadcast soon-Obama gives a speech around 8:30 PM, “Egyptians are making history.”

Around 10 PM, Mubarak gives a speech on the state television, asserting that he is not

stepping down and will remain in o"ce till September.

6. February 11th: Around 6 PM Suleiman gives an address on the state TV announcing that

“President Hosni Mubarak has resigned and handed over power to the country’s military.”

Obama gives a speech at 10 PM.

6 Appendix 2: Dynamic Models

6.1 First Dynamic Model, Asymptotics

We would like to find final thresholds #i(') for the first dynamic model. In any connected

network, the elements of the stationary transition matrix are

$% =di + 1!j(dj + 1)

.

In the case of the example in section (2.3.1) for the star network, v = [3/(3 + 2 + 2) 2/(3 + 2 +

2) 2/(3 + 2 + 2)]T = [3/7 2/7 2/7]T and the final thresholds will be [3/7 2/7 2/7].[0 ! ! ]T =

4!/7 + 0.57! .

Also as the number of elements in a star network increases, the threshold & 2/3: using the

same technique as above, the final threshold for a k + 1-star (the above example was a 2-star.)

is (2k)/(3k + 1) which is always smaller than 2/3, but approaches 2/3 when k becomes large.

Interestingly this is the final threshold for the fully connected graph with the number of nodes

n = 3 (i.e. triangle).

38

6.2 Action Based Dynamics, Examples

The general equilibrium solution was outlined in section (2.3.3). Here I include the details for a

number of well-known topologies of networks under the action-based dynamics.

6.2.1 Star Networks

Starting at t = 0, the central node’s threshold is 0, the peripheral nodes’ thresholds are all taken

to be ! . In a star network with n agents, at time t,

• if t is odd, the central node’s threshold is

$1 =n % 1

n(1 +

1

n2+ . . . +

1

(n2)t!1

2

),

and the central node does not act (N). The peripheral nodes will have a threshold of

$i =!

2t+

1

4+ . . . +

1

4t!1

2

,

and act, (A).

• If t is even (larger than 0), then the central node’s threshold is

$1 =n % 1

n2(1 +

1

n2+ . . . +

1

(n2)t!2

2

)

and the central node acts (A). The peripheral nodes will have a threshold of

$i =!

2t+

1

2(1 +

1

4+ . . . +

1

4t!2

2

),

and do not act (N).

Note the asymptotic cases, when t & '. When t is odd, the central node does not act (N),

and the peripheral ones act (A).

$1 &n

n + 1, $i & 1/3

39

when t is even, the central node acts (A), and the peripheral ones do not act (N).

$1 &1

n + 1, $i & 2/3

6.2.2 Fully Connected Networks

Consider the case in which the network is fully connected. If there is only one radical agent, then

there will be no action starting from t = 1, and thresholds are

n % 1

n(1 +

1

n+

1

n2+ . . . +

1

nt&1) & 1

for the radical node. For the others, if t = 1, $k(1) = !n

+ n&2

n, and for t " 2, $k(t) is

1

nt&1(!

n+

n % 2

n) +

n % 1

n(1 +

1

n+

1

n2+ . . . +

1

nt&2) & 1.

The size of critical mass in a fully connected network in this model is m > n/2. In this case

at time t the thresholds are

$k =1

nt&1(n % m

n)

$k =1

nt&1(!

n+

n % 1 % m

n)

for the critical mass and the ordinary agents respectively. Both of the above go to zero as t & '.

From t = 1 onwards both groups act (A).

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