metadata: authors: 0: author: Arezki,Rabah
1: author: Dama,Alou Adesse
2: author: Djankov,Simeon
3: author: Nguyen,Ha Minh
count: World
docna: 0: docna: Contagious Protests
docty: Policy Research Working Paper
subtopic: ICT Applications,Armed Conflict,Economic Conditions and Volatility,Disease Control & Prevention
teratopic: Macroeconomics and Economic Growth,Information and Communication Technologies,Conflict and Development,Health, Nutrition and Population
abstracts: cdata!: This paper explores the spillover of
            protests across countries using data on nonviolent and
            spontaneous demonstrations for 200 countries from 2000 to
            2020. Using an autoregressive spatial model, the analysis
            finds strong evidence of "contagious protests,"
            with a catalyzing role of social media. In particular,
            social media penetration in the source and destination of
            protests leads to protest spillovers between countries.
            There is evidence of parallel learning between streets of
            nations alongside the already documented learning between governments.
repnme: repnme: Contagious Protests
display_title: Contagious Protests
listing_relative_url: /research/2020/07/32241238/contagious-protests
topicv3: Macroeconomic Vulnerability and Debt
            m1326057 1104,Social Media m1327099 662,Political Violence and War m1327310 2462,Injury m1327524 1428,Revolutionary Violence m1327774 2465
publishtoextweb_dt: 2020-07-14T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Information and Communication Technologies,Conflict and Development,Health, Nutrition and Population
origu: Office of the Chief Economist (MNACE)
no_of_pages: 27
author_keys: 0: author_key: 1596791
1: author_key: 1862779
2: author_key: 124574
3: author_key: 1057980
model_extractions: Policy Research Working Paper 9321

# Contagious Protests

_Rabah Arezki Alou Adessé Dama Simeon Djankov Ha Nguyen_ Middle East and North Africa Region Office of the Chief Economist July 2020
Policy Research Working Paper 9321

# **Abstract**

This paper explores the spillover of protests across countries using data on nonviolent and spontaneous demonstrations for 200 countries from 2000 to 2020. Using an autoregressive spatial model, the analysis finds strong evidence of “contagious protests,” with a catalyzing role of social media.

In particular, social media penetration in the source and destination of protests leads to protest spillovers between countries. There is evidence of parallel learning between streets of nations alongside the already documented learning between governments.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rarezki@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Contagious Protests**

Rabah Arezki, Alou Adessé Dama, Simeon Djankov, and Ha Nguyen<sup>1</sup>

**_JEL Codes_ :** C21, D74, D8, E71

**Keywords:** street protest; contagion; social media.

> 1 Rabah Arezki is the Chief Economist for the Middle East and North Africa at the World Bank and a Senior Fellow at Harvard’s Kennedy School of Government. Alou Adessé Dama is PhD candidate at CERDI University of ClermontAuvergne. Simeon Djankov is Director for Policy with the Financial Markets Group at the London School of Economics and Senior Fellow at the Peterson Institute for International Economics. Ha Nguyen is a Senior Economist in the Chief Economist Office of the Middle East and North Africa at the World Bank. We thank Rick van der Ploeg and Tarek Masoud for helping comments and suggestions. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. All remaining errors are ours.
# **I. Introduction**

History provides examples of media incentivizing street protests, which spread across borders. The popular discontent that ended communism in Eastern Europe <mark>began in Poland in the early 1980s, and swept through Hungary, East Germany, Bulgaria, Czechoslovakia, and Romania.</mark> Radio Free Europe played a critical role as a vehicle for the spread of the information about protests and catalyzed its spillovers across borders (Puddington 2000), as did a network of radio stations of the Catholic Church (Stehle 1982). Fast forward 30 years to the wave of street protests that sparked the Arab Spring in 2011 following the death of a Tunisian street vendor who set himself ablaze— after having been harassed and humiliated by municipal officials. These protests established social media as the new conduit for the spread of protests. In 2019, a second wave of protests that started in Sudan and Algeria spread to other Arab countries, including the Arab Republic of Egypt, Lebanon and Iraq, eventually resulting in a global contagion of protests spanning from Chile to the Russian Federation to Hong Kong SAR, China. Just as the global pandemic put a damper on street protests, the death of George Floyd in US police custody powered a global protest movement against police brutality and racial injustice. Again, social media was the main channel of protest engagement.

In this paper we systematically explore spillovers of spontaneous street protests across countries, with social media acting as the catalyst. We use data on nonviolent and unorganized demonstrations for 200 countries for the period 2000 to 2020. Using an autoregressive spatial model, we find strong evidence for contagious protests with a catalyzing role of social media. Social media penetration in the source and destination of protests leads to protest spillovers between countries. There is evidence for parallel learning between streets of nations alongside the previously documented learning between governments.

Our research is related to three strands of previous work. First, our paper is related to the emerging literature on social media and protests. Battaglini (2017) argues that protests are a way to signal private information to policy makers, in which social media is a good medium for policy makers to aggregate information. <mark>A field experiment in Hong Kong SAR shows that o</mark> ne’s turnout decision depends on others’ turnout decisions (Cantoni et al, 2019). Broadband internet is shown to be positively associated with local online grassroots protest movements in Italy (Campante et al, 2018). The diffusion of VK, Russia’s dominant online social network, increased protest turnout in Russia (Enikolopov et al, forthcoming). Where internet coverage is low, such as in Africa, mobile phones’ diffusion increased protest turnout (Manacorda and Tesei, 2020).

Our paper is also related to the literature on learning and networks. Buera et al. (2011) study how countries learn from the successes of neighbors’ past economic policies. Chen and Suen (2016) theorize that a successful revolution could <mark>dramatically lead to revisions in beliefs of both protesters and governments and hence may lead to a series of revolutions in other countries.</mark> König et al. (2017) examine how networks (of military alliances and enmities) affect the intensity of the conflict in the Democratic Republic of Congo. Harari and Ferrara (2018) examine how local conflicts in one gridded cell in Africa spill over to neighboring cells.

2
A strand of the literature studies spillovers from protests. Several papers have documented that protests in Tunisia and Egypt have inspired protesters elsewhere (Bamert et al, 2015; Hale, 2013; Lynch, 2013; Saideman, 2012). Similar spillovers were observed from other revolutionary periods, such as the Eastern European democratization in the 1990s and the revolutionary wave of 1848 in Europe (Hale, 2013; Weyland, 2010, 2012).

The remainder of the paper is organized as follows. Section II presents the data and the empirical strategy. Section III shows the results. Section IV presents robustness checks. Section V concludes.

# **II. Data and Empirical Strategy**

Data on protests are from the Armed Conflict Location & Event Data (ACLED) Project. Protests are defined in the data set as _“non-violent demonstrations, involving typically unorganized action by members of society.”_ This category covers peaceful protests, protests with interventions (e.g. arrests), and protests in which excessive force was used against protesters (e.g. causing injuries and deaths). Note that this definition does not include violent demonstrations and mob violence. The data coverage starts in 1997 but is severely imbalanced, rendering difficult the use of earlier years to conduct the spatial analysis. We aggregate the number of protests at the monthly (and quarterly) level.

Figure 1 shows the evolution of the number of protests at a monthly frequency as recorded by ACLED—in absolute number (Panel A) and in per million capita (Panel B). Note that starting in 2010 and subsequent years more countries were added to the data set—see Panel A of Appendix Table A1 for the list of countries included in the data set. That explains the big jump in the series in the year 2010. We hence restrict our sample to start in January 2010 when using the ACLED data set.<sup>2</sup>

**Figure 1: Evolution of the Total Number of Protests**

**Panel A: Protests in levels Panel B: Protests in per capita**

<!-- Start of picture text --> 1995m1 2000m1 2005m1 2010m1 2015m1 2020m1 1995m1 2000m1 2005m1 2010m1 2015m1 2020m1<br>Sources: Authors' calculation using ACLED dataset Sources: Authors' calculation using ACLED dataset<br>1.2<br>6000<br>1<br>.8<br>4000<br>.6<br>.4<br>2000<br>Monthly number of protests<br>.2<br>Monthly number of protests (per million persons)<br>0 0<br><!-- End of picture text --> 2 Note that for countries added to the data set after 2010, we consider these country-year pairs as missing observations (and not zeros) before they are added to the data set. However, our results remain robust when using data from ACLED starting in January 1997.

3
For the sample starting in January 2010 to January 2020, the average number of protests per month per country is 21. In 34 percent of country-month pairs (2,910 of 8,549), there is no occurrence of protest. Panel A of Appendix Figure A1 presents the histogram of the entire distribution of available occurrence of protests from ACLED (Appendix Table A2 for summary statistics of actual protests). The distribution supports the use of the Zero-Inflated Poisson estimator, which we discuss in the next section.

We construct an alternative measure of protests capturing the intensity of media chatter around protests. The rationale for the news-based measure of protests is first and foremost that it allows to significantly expand the scope of the data on protests over space and time. This measure accounts for the saliency of specific protests. For instance, a protest sparked by a popular figure is likely reported and discussed more extensively in the news media. The news data are from Dow Jones FACTIVA, which is a global repository of over one billion articles published across the world and reported in 28 different languages. For each language, the following words are translated: (i) “street” and (ii) “protests” or “protest”. The search for each language is constructed such that the word “street” and at least one relating to “protest” appear. For example, in English, the precise search query is: _[(“street”) AND (“protest” OR “protests”)]_ .

This search is performed for all countries and all languages in the Dow Jones database, over the period from January 1, 2000 to December 31, 2019. In addition, we gather the number of total articles in the Dow Jones FACTIVA for each country and each month. A measure of normalized article counts is then constructed by taking the ratio between the number of articles mentioning “street protests” and the total number of articles for each country-month pair multiplied by 100. Formally, for country _i_ in month _t_ the intensity of media chatter around protests is as follows: The news-based protest data provide us with a balanced panel for 207 countries from January 1, 2000 to December 19, 2019 (see Panel B of Appendix Table A1 for the list of countries). The coverage is more comprehensive than for ACLED and allows us to exploit time and space variation. Panel B of Appendix Figure A1 presents the histogram of the entire distribution of newsbased protests. In 33.4 percent of country-month pairs (16,568 of 49,678), there is zero news about protest (Appendix Table A2 for summary statistics). The distribution is also skewed toward zero, which supports the use of the Zero-Inflated Poisson estimator.

The evolution of the measure is presented in Figure 2 in monthly frequency (Panel A). We also illustrate the evolution of the measure for countries in the Middle East and North Africa (Panel B). Both panels show a clear uptick in the chatter about protests around the Arab Spring in 2011 and the second wave of protests that started in 2019. The uptick in Panel A around 2003 reflects global protests against the war in Iraq.

4
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international’s Corruption Perception Index (CPI). The data cover 189 countries. The _Common Corruption_ dummy takes the value of 1 if both countries under consideration are relatively corrupt, i.e., the corruption indices of both countries for the last two years are less than the median value. Appendix Table A2 presents summary statistics of actual protests, news-based protests, polity IV, corruption perception index, and social media penetration in 2018.

We next present the test as to whether protests in period, _t-1,_ in the rest of world (i.e., for all other countries _j_ in the rest of the world) lead to protests in the current period, _t_ , in country _i_ . The number of protests per capita is skewed to the left, with a majority of countries having no protest (see Table 1).

The framework is an autoregressive spatial model following Lesage and Pace (2009). The ZeroInflated Poisson (ZIP) model mixes two zero generating processes. The first process generates zeros. The second process is governed by a Poisson distribution with an estimation as follows: ) (1) where is an N-by-1 vector that contains the number of protests tt tt−1 tt−ττ ii tt tt _per capita_ in N countries at yy = NNeepp (ρWWyy + δδIIyy + FFEE + FFEE + cc current month . is an N-by-1 vector that contains the number of protests _per capita_ in N tt yy countries in the past month. In subsequent robustness checks, we use quarterly frequency instead tt−1 of monthly. In addition to the Zero-Inflated Poisson estimator, we use the standard Poisson as an PP yy alternative estimator. We also use an alternative measure of protests based on the intensity of media chatter around protests. is an N-by-N spatial weight matrix that captures geographic proximity between countries. In the baseline specification, we measure the spatial spillovers using geographical distance. The WWdiagonal elements are zero. The off-diagonal elements are the inverse of the distance between 1 country capitals (i.e., ~~)~~ . Next, the weight matrix is standardized so that the sum of row is unity. ρ captures the importance of spatial dependence in protest spillover from foreign countries. ddiiddttd ddeeii,jj is an N-by-N identity matrix. captures the autoregressive effects of domestic protests _per capita_ in previous months. and are vectors of country and month fixed effects. Data on II δδ<sup>ττ</sup> distance and language are obtained from CEPII. ii tt FFEE FFEE To account for the role that social media could play as a catalyst for protests, we use information about social media penetration in both source and destination of protests. Specifically, we test whether source countries with higher social media penetration are more likely to send stronger protest spillover to other countries. The econometric specification is as follows: ) (2) The only difference between (2) and (1) is the spatial weight matrix. tt tt−1 tt−1 ii tt tt now is also an N-by-N yy = NNeepp (ρZZyy + δδIIyy + FFEE + FFEE + cc spatial weight matrix but is different from . The diagonal elements are zero. The off-diagonal ZZ elements are the inverse of the distance multiplied by common social media penetration (i.e., 1 WW a ddiiddttdsum of row is unity. The idea is to account for protests in countries with a common level of social ddeeii,jj × aaPPc PPcc NNPPa ccNNmma ii,jj<sup>). The weight matrix is then also standardized so that the</sup> 7
media penetration. a is a dummy which takes the value of 1 if both countries _i_ and _j_ have relatively large penetration of social media and 0 if at least one country’s ii,jj aaPPc PPcc NNPPa ccNNmma social media penetration is below that threshold. The hypothesis we test is that the set of countries subject to potential protest spillover is limited to countries with social media penetration beyond a certain threshold.

To account for similarities between political systems and other factors that could play an important role in driving spillovers, we use the following dummies in the estimation:

- role in driving spillovers, we use the following dummies in the estimation: • a is a dummy which takes the value of 1 if country _i_ and country j are either both democracies or both autocracies. The hypothesis we want to test is whether ii,jj

- CCPPc PPcc P PPyy protest spillovers take place between countries with “close” polity.

- • is a dummy which takes the value of 1 if both countries _i_ and j have a high level of corruption. The hypothesis we want to test is whether protest spillovers ii,jj

- CCPPc PPcc CCP PPccppPPaaPPcc take place between countries with “close” levels of perceived corruption.

- • is a dummy which takes the value of 1 if both countries _i_ and _j_ have the same language. The hypothesis we want to test is whether protest spillovers take ii,jj

- CCPPc PPcc LLaaccggccaaggNN place between countries with similar languages.

# **III. Results**

The results for geographic proximity point to statistically insignificant protest spillovers from neighboring countries for both measurement of geographic proximity (see columns [1] and [2] of Table 1). Column [1] captures the spillovers of protests from neighboring countries (i.e., sharing borders) where protests are measured as the simple average of the number of protests per million capita. After controlling for domestic protests in the previous months, protests in neighboring countries are found to have a statistically insignificant effect on protests in the country under consideration. Similarly, column [2] indicates that the spillovers from inverse distance-weighted global protests in the previous months are statistically insignificant.

8
# **Table 1: Contagion, Distance and Social Media—Actual Protests**

|Zero-Inflated Poisson||Protests|per millionp|ersons(t)||
|---|---|---|---|---|---|
||Distanc|e Only|Distance|*Common So|cial Media|
||||Social Me<br>|dia Penetratio<br>|n Threshold<br>|
||||**> 10%**|**> 20%**|**> 30%**|
||[1]|[2]|[3]|[4]|[5]|
|Protests pc (t-1)|0.033***|0.031***|0.031***|0.032***|0.032***|
||(0.006)|(0.004)|(0.004)|(0.004)|(0.004)|
|Neighbor's simple average protests pc*(t-1)|0.012<br>(0.015)|||||
|Inverse-distance weighted protests pc*(t-1)||0.022<br>(0.022)||||
|Inverse-Distance-Common Social Media weighted||||||
|Protest pc*(t-1)|||0.030|0.044*|0.047**|
||||(0.026)|(0.023)|(0.022)|
|Constant|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|7,728|8,256|5,340|3,324|2,652|
|Number of countries|92|97|67|47|40|
|N_zero|2776|2822|1584|781|626|
|Chi-square|18514|22090|16763|11038|9742|
|Loglikelihood|-5150|-5865|-4595|-3466|-2836|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _Protests pc_ captures the number of protests per million persons. * denotes foreign protests. In columns [3],[4],[5], the threshold for a country to be defined to have large Facebook penetration is 10%, 20% and 30% respectively. Data are from January 2010 to January 2020.

The effects of protest spillovers become significant when the commonality in social media penetration between countries is considered as well. Columns [3],[4],[5] of Table 1 differ in the threshold of the definition for large social media penetration. In column [3], a country is defined to have large social media penetration if it has more than 10% of its population subscribed to Facebook in 2018. In columns [4] and [5], the threshold is 20% and 30% respectively. The dummy a penetrations and 0 otherwise. ii,jj<sup>takes the value of 1 if both countries have large social media</sup> aaPPc PPcc NNPPa ccNNmma The larger the threshold, the smaller the number of countries included in the sample—which by construction only includes countries with social media above the threshold. We can interpret the results as protest spillovers within a selected number of “connected” countries with high social media penetration. The three columns show that commonality in social media penetration between source and destination country of protests plays an important role in protest spillovers. The coefficients for (inverse distance-social media) weighted average global protests in the previous 9
month now are statistically significant. The coefficient is more significant with larger magnitude for larger common social media thresholds, and largest at the 30% threshold (see column [5]).<sup>4</sup> The coefficient of 0.047 implies that a one standard deviation increase in foreign protests, which is 3.1 more protests per million persons, leads to 1.15 more domestic protests per million persons. This is 37% of the standard deviation of a country’s monthly protests. To put this in perspective, at the onset of the Arab Spring, protests in Tunisia jumped from zero in November 2010 to close to 15 protests per million persons in January 2011, which is 5 times the standard deviation of protests in our sample.

Social media is found to play a significant role in driving spillover of protests using the newsbased measure. News-based protest about neighboring countries in the previous month has statistically significant effect on current month domestic news-based protest (see Column [1] of Table 2), but global protest using inverse distance-weighted in previous month has statistically insignificant effect (Column [2]). We observe large and consistent news-based protest spillovers for countries with strong media penetrations. This is true for all three thresholds of social media (columns [3] to [5]). The coefficient of 0.912 in column [5] implies that one standard deviation increase in previous month’s foreign news-based protests, which is 0.632 percentage point increase, leads to a 1.78 percentage points increase in the contemporaneous month of its own newsbased protests. This is equivalent to about 3 times the standard deviation of news-based protests.<sup>5,6</sup> Our results are consistent with the social media being a catalyst for both push and pull factors behind protests, i.e. social media makes it much easier for protesters in one country to raise their voice, and for sympathizers in other countries to read, learn and emulate.

> 4 When we run the regressions with distance alone on the samples in columns [3],[4],[5], the coefficients of foreign protests (per million persons) are not significant (Appendix Table A3, Panel A). Appendix Table A4 shows that our main results are robust to using an earlier cross-section of data for Facebook penetration for the year 2010 for 61 countries for both actual protests (Panel A) and news-based protests (Panel B).

> 5 Note that changes in sample size are not driving the significance of the role of social media. Indeed, Appendix Table A3 Panel B shows that coefficients associated with distance-weighted foreign protests remain insignificant and are smaller when using the samples presented in Columns [3] to [5] in Table 1.

> 6 The samples used in Columns [3] to [5] in Table 1 only include countries above the social media thresholds—weights are not defined otherwise. Results remain robust when we impose undefined weights as zeros when countries having relatively low social media penetration are included in the samples (Appendix Table A5).

10
# **Table 2: Contagion, Distance and Social Media—News based Protests**

|Zero-Inflated Poisson||Ne|ws-basedprote|sts||
|---|---|---|---|---|---|
||Distanc|e Only|Distance*|Common Soci|al Media|
||||Social Med|ia Penetration|Threshold|
||||**> 10%**|**> 20%**|**> 30%**|
||[1]|[2]|[3]|[4]|[5]|
|News-based protests(t-1)|0.238***|0.160***|0.234***|0.374***|0.372***|
||(0.014)|(0.012)|(0.014)|(0.021)|(0.022)|
|Neighbor's simple news-based protests*(t-1)|0.098***<br>(0.034)|||||
|Inverse distance weighted news-based<br>protests*(t-1)||0.128<br>(0.152)||||
|Inverse-Distance-Common Social Media<br>weighted News-based protests*(t-1)|||0.889***<br>(0.137)|0.872***<br>(0.155)|0.912***<br>(0.156)|
|Constant|-1.461***|-1.80***|-2.079***|-2.152***|-2.14***|
||(0.177)|(0.321)|(0.277)|(0.255)|(0.258)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|36,806|49,469|31,348|26,218|24,318|
|Number of countries|154|207|165|138|128|
|N_zero|8045|16469|9050|7273|6495|
|Chi-square|26983|23141|29855|17864|17386|
|Log likelihood|-16695|-22067|-12989|-10529|-9772|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _News-based protests_ captures the number of articles mentioning street protests in a country as percentage of total number of articles covering the country. * denotes foreign _news-based protests._ In columns [3],[4],[5], the threshold for a country to be defined to have large. Facebook penetration is 10%, 20% and 30% respectively. Since Facebook was launched in 2004, data in these 3 columns are restricted from January 2004 to December 2019.

We examine the role of _Polity_ characteristics, _Corruption_ level and _Language_ in protest spillovers. They are found to play an insignificant role in the spillover of actual protests (Columns [1] to [3] of Table 3), except for the spillover of news-based protests where they have some importance (columns [4] to [6]).

11
# **Table 3: Contagion, Polity, Corruption, and Language**

|Zero-Inflated Poisson|Protest|sper million|persons|Ne|ws-basedpro|tests|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Protest pc (t-1)|0.031***<br>(0.005)|0.031***<br>(0.005)|0.032***<br>(0.004)||||
|News-based protests (t-1)||||0.236***<br>(0.013)|0.234***<br>(0.014)|0.164***<br>(0.012)|
|Inverse-distance-Common Polity<br>weighted Protest pc*(t-1)|0.009<br>(0.018)||||||
|Inverse-distance-Corruption weighted|||||||
|Protest pc*(t-1)||0.002<br>(0.015)|||||
|Inverse-distance-Common Language<br>weighted Protest pc*(t-1)|||0.018*<br>(0.010)||||
|Inverse-distance-CommonPolity<br>weighted news-based protests*(t-1)||||0.467***<br>(0.078)|||
|Inverse-distance-Corruption weighted<br>news-based protests*(t-1))|||||0.224***<br>(0.074)||
|Inverse-distance-Common Language<br>weighted news-based protests*(t-1)||||||0.174***<br>(0.051)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|8,184|6,048|6,936|38,718|18,499|41,582|
|Number of countries|95|72|76|162|124|174|
|N_zero|2822|2117|2602|8241|4023|14946|
|Chi-square|21384|13219|18698|31139|19957|19197|
|Log likelihood|-5697|-3658|-4730|-509.1|-289.6|-527.0|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _Protests pc_ capture the number of protests per million persons. _News-based protests_ captures the number of articles mentioning street protests in a country as percentage of total number of articles covering the country. * denotes foreign.

Regarding the news discussions of street protests, polity, corruption, and common language are found to have significant effects on the spillover of global protest discussions (Panel B). Between them, _Common Polity_ seems to have the largest effects (columns [4] to [6] of Table 3). The coefficient of 0.467 implies that one standard deviation increase in previous month’s foreign newsbased protests, which is 0.632 percentage point increase, leads to 1.34 percentage points increase in the contemporaneous month of its own news-based protests.

12
# **IV. Robustness Checks**

We conduct several robustness checks. First, we examine protest spillover at the quarterly frequency. Second, we employ the Standard Poisson estimator. Our results are robust to these checks.

We find that the spillovers are weaker when using quarterly frequency. This is true for both measures of protests. Table 4 shows that past foreign protests, even with large social media penetration, are no longer having spillover effects on domestic protests (columns [1] to [3] of Table 4). The coefficients remain positive but are not statistically significant.

In terms of news-based protests, foreign protests are found to have statistically significant spillover effects on domestic protests at all three thresholds of social media penetration (see columns [4] to [6]). This finding could reflect the stronger tendency for news-based protests to spread than actual protests do. However, the coefficients associated with the effects for the last quarter’s discussions about foreign protests are smaller than those of the last month’s (see Table 2 for a comparison). The effect using quarterly frequency tends to “dilute” the effect captured at the monthly frequency.

**Table 4: Contagion, Distance, and Social Media—Quarterly Frequency**

|Zero-Inflated Poisson|Protest|sper million|persons|N|ews-basedpr|otests|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Social Media Penetration Threshold|**> 10%**|**> 20%**|**> 30%**|**> 10%**|**> 20%**|**> 30%**|
|Protest pc (t-1)|0.010***<br>(0.002)|0.010***<br>(0.002)|0.010***<br>(0.002)||||
|News-based protests (t-1)||||0.193***<br>(0.032)|0.413***<br>(0.033)|0.412***<br>(0.035)|
|Inverse-distance-Common Social|||||||
|Media weighted Protest pc*(t-1)|0.039|0.040|0.038||||
||(0.029)|(0.025)|(0.024)||||
|Inverse-distance-Common Social|||||||
|Media weighted news-based<br>protests*(t-1)||||0.440**<br>(0.208)|0.536**<br>(0.237)|0.662***<br>(0.233)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,780|1,108|884|10,395|8,694|8,064|
|Number of countries|67|47|40|165|138|128|
|N_zero|310|174|145|1813|1477|1291|
|Chi-square|11224|7293|6535|14666|8477|8142|
|Loglikelihood|-2805|-2117|-1751|-4286|-3466|-3224|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _Protests pc_ capture the number of protests per million persons. Data are from January 2010 to January 2020. _News-based protests_ captures the number of articles mentioning street protests in a country as percentage of total number of articles covering the country. Since Facebook was launched in 2004, data in columns [4] to [6] are restricted from January 2004 to December 2019. *denotes foreign.

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We also test the presence for spillover of last month’s foreign protests in both actual protests and news-based protests, using the Standard Poisson estimator. A standard Poisson estimator is less suitable to the Zero-Inflated Poisson Estimator because the former does not address the issue of excessive zeros in the dependent variable. Nevertheless, the standard Poisson estimator yields a similar magnitude for the actual protest spillovers to that by the Zero-Inflated Poisson Estimator (see columns [2] and [3] of Table 5). The Standard Poisson Estimator also yields similar magnitude for the spillover of news-based protests (columns [4] to [6] of Table 5). At all thresholds of common social media, the coefficients are significant.

**Table 5: Contagion, Distance, and Social Media—Poisson Estimator**

|Zero-Inflated Poisson|Protest|sper million|persons|Ne|ws-basedpro|tests|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Social Media Penetration Threshold|**> 10%**|**> 20%**|**> 30%**|**> 10%**|**> 20%**|**> 30%**|
|Protest pc (t-1)|0.031***<br>(0.001)|0.032***<br>(0.002)|0.032***<br>(0.002)||||
|News-based protests (t-1)||||0.234***<br>(0.020)|0.374***<br>(0.027)|0.372***<br>(0.028)|
|Inverse-distance-Common Social|||||||
|Media weighted Protest pc*(t-1)|0.029<br>(0.032)|0.031***<br>(0.028)|0.046*<br>(0.026)||||
|Inverse-distance-Common Social<br>Media weighted news-based<br>protests*(t-1)||||0.889***<br>(0.175)|0.872***<br>(0.214)|0.912***<br>(0.219)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|5,340|3,324|2,652|31,348|26,218|24,318|
|Number of countries|67|47|40|165|138|128|
|Chi-square|221625|397000|164076|117912|33160|53553|
|Log likelihood|-4418|-3339|-2729|-12563|-10174|-9443|

Note: Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). _Protests pc_ capture the number of protests per million persons. _News-based protests_ captures the number of articles mentioning street protests in a country as percentage of total number of articles covering the country. Since Facebook was found in 2004, data in columns [4] to [6] are restricted from January 2004 to December 2019. *denotes foreign.

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# **V. Conclusions**

The frequent shutdown of internet by governments in times of protests illuminates the catalyzing role of social media. Governments often argue that limited social media access is necessary for public safety or curbing the spread of misinformation. It seems, however, that a bigger concern is the organizing role of social media in anti-government protests.

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