How Saudi Crackdowns Fail to Silence Online Dissent⇤

Jennifer Pan† Alexandra A. Siegel‡ September 17, 2019

Abstract

Saudi Arabia has imprisoned and tortured activists, religious leaders, and journalists for voicing dissent online. This reflects a growing worldwide trend in the use of physical repression to censor online speech. In this paper, we systematically examine the consequences of imprisoning well-known Saudis for online dissent by analyzing over 300 million tweets as well as detailed Google search data from 2010 to 2017 using automated text analysis and crowd-sourced human evaluation of content. We find that repression deterred imprisoned Saudis from continuing to dissent online. However, it did not suppress dissent overall. Twitter followers of the imprisoned Saudis engaged in more online dissent, including criticizing the ruling family and calling for regime change. Repression drew public attention to arrested Saudis and their causes, and other prominent figures in Saudi Arabia were not deterred by the repression of their peers and continued to dissent online.

Keywords: repression; social media; online mobilization; censorship; Saudi Arabia

⇤Our thanks to Charles Crabtree, Killian Clarke, Christian Davenport, Martin Dimitrov, Jennifer Earl, Will Hobbs, Holger Kern, Beatriz Magaloni, Elizabeth Nugent, Molly Roberts, Arturas Rozenas, Anton Sobolev, Rory Truex, Lauren Young, Zachary Steinert-Threlkeld, and participants at the 2018 APSA pre- conference on politics and computational social science for their helpful comments and suggestions; to SMaPP Global for making our collaboration possible; to the Stanford King Center on Global Poverty and Development and the National Science Foundation (Award #1647450) for research support. We would also like to thank Twitter for providing us with access to historical data as well as Steve Eglash for facilitating this access. †Assistant Professor, Department of Communication, Building 120, Room 110 450 Serra Mall, Stanford University, Stanford CA 94305-2050; jenpan.com, (650) 725-7326. ‡Postdoctoral Fellow, Immigration Policy Lab, Encina Hall, 616 Serra Street, Stanford University, Stan- ford, CA 94305-2050; alexandra-siegel.com, (781) 454-8867. 1 Introduction

On July 20, 2013, Mohamed al-Arefe, a popular Saudi religious leader afﬁliated with the Sahwa movement, was imprisoned without formal charges. His imprisonment came after he circulated comments and videos to his millions of Twitter and Facebook followers supporting the Muslim Brotherhood, an organization the Saudi regime views as an existential threat (Lacroix 2014). Hundreds of thousands of Saudis identify with the ideas of the Sahwa movement, even though its formal organizations have been repressed and co-opted by the state (Lacroix 2011). Following al-Arefe’s arrest, his online supporters were outraged, and the hashtag #FreeMohamedalArefe quickly went viral. On November 30, 2014, Loujain al-Hathloul live-tweeted her attempt to drive into Saudi Arabia from the United Arab Emirates as part of the #Women2Drive movement. Beginning in 2011, the #Women2Drive social media campaign generated a tide of videos of women defying the Saudi ban on women driving, increasing the domestic and international visibility of protest against these Saudi policies. Al-Hathloul was stopped at the Saudi border, and as we know from her continued tweets, her passport was conﬁscated and she was kept in her car without water overnight. In the morning, al-Hathloul was told to drive into Saudi Arabia, where she was immediately taken into police custody (Mackey 2014). Al-Hathloul’s tweets and the hashtag #FreeLoujain rapidly spread online. Saudi Arabia is an absolute monarchy and theocracy with great oil wealth. It also one of the least free and most repressive countries in the world.1 As a Saudi activist wrote in 2014:

Saudis live under repression, in fact we breathe repression with the air; it haunts us in our dreams. It is our hell before we encounter hell. Even our appearance, streets, and houses are designed by repression. Repression has

1Saudi Arabia ranks 201 out of 211 on Freedom House’s Freedom in the World index (Freedom House 2018). On the Political Terror Scale (PTS), Saudi Arabia has received a score of four in recent years, which means that “civil and political rights violations have expanded to large numbers of the population. Murders, disappearances, and torture are a common part of life” (Gibney et al. 2016). In the most recent year of CIRI Human Rights data (2011), Saudi Arabia is described as a country where torture and disappearances occur frequently, and where many people are imprisoned because of their religious, political, or other beliefs (Cingranelli and Richards 2010). In addition to repression, the Saudi regime also has a long history of selectively co-opting opposition (Wehrey 2015).

1 shaped the media, religion, security services, universities, and institutions (Al-Rasheed 2016, p.119).

In Saudi Arabia, traditional media is tightly controlled. Political dissent is criminalized. Political parties, trade unions, political demonstrations and strikes are banned. All types of organized opposition are suppressed. Formal social movement organizations are largely absent or short-lived.2 When a rare on-the-ground protest occurs, it is violently quashed (Menoret´ 2016). In the past decade, many activists and reformers have turned to online platforms to sustain their social movements. They use social media to distribute information about their causes, to organize and facilitate offline protests, to disseminate online petitions, and to organize online campaigns such as the #Women2Drive movement. The Saudi regime has reacted to this online mobilization by stepping up online censorship— for example, blocking websites promoting Shia rights and those affiliated with the Mus- lim Brotherhood (Ibahrine 2016; Noman, Faris and Kelly 2015).3 But increasingly, the regime has turned to physical repression, which we define following Cingranelli and Richards (2010) as violations of physical integrity rights. Dozens of high-profile individuals connected to diverse social movements in Saudi Arabia have been imprisoned, publicly flogged, and tortured for using social media to criticize the regime and to mobi- lize support for political and social reforms (Alabaster 2018; Calamur 2018; ESHR 2017; Human Rights Watch 2018a). Repression of individuals for their online speech is not limited to Saudi Arabia. In 2017, more than thirty countries—including authoritarian regimes such as China, Russia, and Iran, and democracies such as India, Mexico, and Lebanon—used repression to rein in online expression. The most frequent targets were prominent figures with large online followings—who we refer to as “online opinion leaders”—including journalists and dissi-

2Examples of formal organizations include the Shia Organization for the Islamic Revolution in the Ara- bian Peninsula, which came into existence in 1979 and disappeared around 1993 when its leaders were either arrested or coopted by the regime (Wehrey 2013); and the human rights-oriented Saudi Civil and Political Rights Association (SCPRA), which formed in 2009 and was disbanded in 2013 after the arrests of its leaders. Example alliances include calls for the establishment of a constitutional monarchy in the late 1990s and early 2000s by Sahwa clerics along with liberals and Shia activists. 3High levels of literacy and internet penetration in Saudi Arabia has propelled social media adoption. To date, the Saudi regime has not imposed wholesale blocks of Twitter or Facebook. In addition, while Saudi Arabia can ask Twitter to remove content, Twitter does not comply in all instances (Noman, Faris and Kelly 2015).

2 dents. Political imprisonment and torture were the most common forms of repression, but people in eight countries were executed in 2017 for speaking out about sensitive subjects online (Freedom House 2017). Despite governments’ increased use of repression to control online expression and mobilization, we know very little about what happens when physical repression is used to suppress online dissent. There is a substantial literature on offline repression and its effects on offline mobilization.4 There is also a great deal of research on how online mobilization reinforces offline mobilization and generates new pathways for dissent.5 However, research on efforts by governments to quash online mobilization has focused mainly on online censorship and online disinformation campaigns.6 We provide the first large-scale, systematic study of the effects of offline repression on online dissent. The form of offline repression we focus on is political imprisonment, and the type of online dissent we study are criticisms of the Saudi regime and calls for political and social reform.7 We build on the repression, online mobilization, and censorship literatures to test three mechanisms by which repression might deter dissent: direct deterrence, indirect deterrence, and downstream effects. Direct deterrence occurs when individuals who experience repression rein in their behavior for fear that they will be punished again in the future (Oberschall 1973; Jenkins and Perrow 1977; Tilly 1978). Indirect deterrence occurs when observers of repression—those who see it but are not directly targeted— change their behavior for fear that they too will be subjected to repression (Walter 1969; Durkheim 1984). Repression might also affect those who do not experience or even observe repression through the downstream effects resulting from changes in the behavior

4For overviews of this literature, see Davenport (2007), Davenport and Inman (2012), and Tilly (2005). 5Ayres (1999), Fisher (1998), and Myers (1994) explore how online mobilization reinforces ofﬂine mobilization while Bennett and Segerberg (2012), Bimber, Flanagin and Stohl (2005), Earl and Schussman (2002), Earl and Kimport (2008), Earl et al. (2010), and Tufekci and Wilson (2012) examine how it creates new pathways for dissent. 6Hassanpour (2014), King, Pan and Roberts (2013, 2014), and Roberts (2018) explore how governments use censorship to deter online dissent, while King, Pan and Roberts (2017), Munger et al. (2018), Stukal et al. (2017), and Woolley and Howard (2016) examine governments’ use of disinformation. 7We focus on political imprisonment because it is often a precursor to torture, disappearances, and extrajudicial killings in Saudi Arabia. At times in this paper, we use the term “arrest.” We use this term to denote political imprisonment, not arrests as conceptualized in research on policing and arrest in democratic contexts (Davenport, Soule and Armstrong 2011; Earl 2011; Earl and Soule 2010).

3 of those who are directly repressed. For example, if an online opinion leader becomes supportive rather than critical of the government following repression, this change in sentiment may trickle down and be repeated by their followers. Finally, instead of acting as a deterrent, repression could cause backlash and intensify dissent among those targeted or among supporters or bystanders who are mobilized by observing repression (Sullivan and Davenport 2017; Young 2017). We identify well-known individuals imprisoned by the Saudi regime for online dissent between 2010 and 2017.8 We collect and analyze over 300 million tweets, as well as daily and weekly Google search data for the same time period. These data ﬁrst allow us to disaggregate the effects of repression on different actors and online behaviors. We analyze tweets by imprisoned online opinion leaders, tweets by non-imprisoned online opinion leaders, as well as tweets by individuals who retweeted, mentioned, or replied to the imprisoned leaders on Twitter prior to their arrests, who we refer to as “engaged followers.” We also analyze online search behavior of the general public. Second, these data enable us to measure changes in both the volume and substance of online activity. Third, our data allow us to assess changes in both public expression (tweets) and private interest (Google searches). Finally, our data enables us to examine the immediate consequences of repression as well as its effects up to one year later. We focus on content produced on Twitter because Saudi Arabia has one of the highest levels of Twitter penetration in the world,9 Saudis frequently discuss politics on Twitter (Noman, Faris and Kelly 2015), and Twitter’s networked structure enables us to examine the behavior of diverse actors on the same platform. We ﬁnd that Saudi online opinion leaders who were imprisoned were deterred from dissent. They decreased their online activity, reined in their criticisms of the state, and halted calls for reform after they were released. The altered content of their tweets also had downstream effects on the content of retweets, as well as replies to their tweets and

8We focus on well-known individuals because we are interested in the broader, public effects of repression (e.g., evidence of indirect deterrence), and repression must be publicly known to have these broader, public effects. 9An estimated 41% of the Saudi population uses Twitter (Al-Arabiya 2015), and although most Saudi Twitter users are relatively young, because 70% of the Saudi population is under the age of 30, the Saudi Twittersphere constitutes a large and diverse subset of the population (Glum 2015).

4 mentions. However, when we examine the overall Twitter activity of engaged followers of imprisoned leaders, we find that these public cases of political imprisonment generated backlash. Among followers, repression increased criticisms of the Saudi monarchy, its religious authority, its institutions, and its policies. Repression also increased calls for political and social change, including calls to change the regime from an absolute monarchy to a constitutional monarchy or a democracy. Among other online opinion leaders who were not imprisoned but who had also expressed dissent on social media, repression did not have a deterrent effect. Despite observing the arrests of their peers, these actors continued to tweet and publicly voice support for political and social reform. By showing the varied effects of repression on online dissent, these results tie into the broader literature on the dissent–repression or conflict–repression nexus (Lichbach 1987; Moore 1998; Davenport 2005).10 Our findings also reinforce research on the backlash thesis, showing how repression mobilizes dissent,11 as well as the literature on how censorship can backfire.12 Given that we observe backlash in Saudi Arabia, one of the most repressive countries in the world, we expect these findings may extend to other authoritarian contexts. In particular, we think the patterns we observe may be most likely to occur in countries where social movement organizations are largely absent, where repression is public and overt, where the repression leveled at online opinion leaders does not alter observers’ calculation about their own risk of repression, and where the state lacks fine-grained control over the online sphere. The paper proceeds in three sections: Section 2 describes our unique sources of data and empirical strategy; Section 3 presents our results and discusses their generalizability;

10The dissent-repression nexus literature shows that although dissent consistently increases state repression, the impact of repression on dissent is highly variable (Goldstone and Tilly 2001). In addition to variation by an individual’s level of commitment to a social movement (Davenport, Armstrong and Zeit- zoff 2019; Sullivan and Davenport 2017), effects have been found to vary depending on time frame (Rasler 1996), for violent vs. non-violent forms of dissent (Moore 1998), for different individuals (Opp and Gern 1993), for different organizational categories (Davenport 2015), and for different societal categories (Gold- stein 1978). 11For examples, see Eckstein (1965), Feierabend et al. (1972), Gurr and Duvall (1973), Kuran (1989), Francisco (1996), Khawaja (1993), Kurzman (1996), Lichbach (1998), and Olivier (1991). 12For examples, see Hassanpour (2014), Hobbs and Roberts (2018), Jansen and Martin (2015), Nabi (2014), and Roberts (2018).

5 and Section 4 concludes.

2 Data and Empirical Strategy

Since the 1950s, three broad—and sometimes overlapping—social movements have been present in the Saudi Kingdom: 1) the Sahwa or “Awakening” Sunni Islamist movement with historical ties to the Muslim Brotherhood, 2) a Shia-rights movement calling for rights, protections, and at times secession for the oppressed Shia minority in the oil rich Eastern province, and 3) networks of human rights, women’s rights, and anti-corruption activists calling for social and political reform.13 Many of the imprisoned online opinion leaders that we study—and similar non-imprisoned opinion leaders—are broadly part of the same informal networks, connected by family members, friends, and colleagues.14 All three movements have used social media for mobilization, and from 2010 to 2017, leaders of all three movements—Sahwa clerics, Shia clerics and activists, women’s rights activists, anti-corruption activists, judicial reformers, and human rights activists—were imprisoned and sometimes subsequently tortured. Twitter and Google search data from this period offers detailed digital footprints of the online activity of these arrested individuals, similar non-arrested individuals, the arrested individuals’ followers, and the Saudi public, allowing us to test the effects of repression on these diverse actors. These rich and temporally granular data sources are described in detail below.

2.1 Data

We gathered ﬁve datasets to assess how repression affects online dissent through direct, indirect, and downstream effects:

13For overviews see Menoret´ (2016), Lacroix (2011), and Wehrey (2015). We could also consider the labor movement to be a fourth social movement in this context. 14For example, Loujain al-Hathloul and Mayasa al-Amoudi (two arrested women’s rights activists) were friends who were active in the women’s right to drive online movement, as was Hala al-Dosari, the non- arrested elite that we compared them too in the study. Other activists have family ties—for example, women’s rights activist Samar Badawi is the sister of prominent human rights activist Raif Badawi, an- other arrested opinion leader in our dataset. Raif Badawi’s wife is also a Saudi human rights activist. We also see family ties in among Shia dissidents—Mohammed al-Nimr is the brother of prominent cleric Nimr al-Nimr, and both were arrested during the period under study.

6 1. Tweets by Imprisoned Opinion Leaders: In order to measure the direct effects of physical repression, we began by identifying Saudi opinion leaders who had been imprisoned and were active on Twitter between January 1, 2010 and January 31, 2017.15 We attempted to identify all well-known Saudi individuals who were imprisoned in con- nection with their online activity and who still had active Twitter accounts at the time of our data collection in January 2017 by conducting automated and manual searches of news and human rights reports in Arabic and English. This yielded a list of 49 individuals whose political arrests were widely reported in the Saudi, other Arabic language, or international press. Thirty-six of them had active Twitter accounts at the time of data collection. Appendix A lists all imprisoned opinion leaders, including a brief description of their background, the official justification for their imprisonment, as well as the unofficial reasons for arrest provided by human rights organizations.16 After identifying these 36 opinion leaders, we then collected all of their tweets produced between January 2010 and January 2017 using Twitter’s Historical PowerTrack API. This API provides access to the entire historical archive of public Twitter data—dating back to the first tweet—using a rule-based-filtering system to deliver complete coverage of historical Twitter data. This gave us a dataset of 408,511 tweets.

2. Tweets Engaging with Imprisoned Opinion Leaders: To measure the downstream effects of imprisonment on the content of tweets—how changes in the behavior of imprisoned opinion leaders are spread through the Saudi Twittersphere—we used the Historical PowerTrack API to download all public tweets engaging with the imprisoned opinion leaders using the @ sign (for example, @LoujainHathloul). We then ﬁltered this dataset to only include tweets by individuals who were either geolocated in Saudi Arabia or con-

15We chose January 2010 as a start date because Twitter became increasingly popular across the Arab World in the early days of the Arab Spring protests. We conducted our historical data collection in January 2017, which marks the end of our data collection period. 16Several of the opinion leaders in our dataset were imprisoned on multiple occasions. These arrests tended to be separated by at least a year, and were often in response to different activities. For example, Mohammed al-Arefe was first imprisoned in response to his comments about the Muslim Brotherhood as described in the introduction, and then, over a year later, was imprisoned for his critical tweets about the Saudi Hajj pilgrimage train. In our study, we limited our analysis to opinion leaders’ first arrests in order to keep the analysis consistent across all imprisoned opinion leaders. Our analysis of direct effects is limited to 25 opinion leaders who were released by the time of our data collection. A table of the dates of these first arrests is provided in Appendix A.

7 tained location metadata in the location or timezone ﬁelds of their proﬁles indicating that they were located in Saudi Arabia, resulting in a dataset of 32,504,397 tweets produced by 8,506,400 unique users. We use this dataset to measure the downstream effects of arrests on engagement— retweets, mentions, or replies—with imprisoned opinion leaders.17 We think of these engaged followers as being similar to supporters in the social movements literature (Dav- enport, Armstrong and Zeitzoff 2019).18 The majority of these users (58.2%) engaged both before and after the arrests of the online opinion leaders. Slightly over 10 percent (13.3%) only engaged with arrested opinion leaders prior to their arrests, and slightly less than a third (28.4%) only engaged after the arrests. Our primary analyses of tweet volume includes all engaged followers.19

3. Tweets of Engaged Followers: We measure indirect deterrence by selecting a random sample of about 30,000 of the users who retweeted, mentioned, or replied to the imprisoned opinion leaders at least once preceding and once following their arrests,20 stratiﬁed by imprisoned opinion leader. We then used Twitter’s API to scrape up to 3,200 of each of their most recent tweets for a total of 47,886,355 tweets.21

17We do not include everyone who follows the imprisoned opinion leaders because that would include individuals who were not attentive and may not have observed repression—for example, people who have Twitter accounts but who do not use Twitter, or people who followed these opinion leaders because it was recommended by Twitter’s algorithm but were not actually interested in these individuals. 18Given the absence of social movement organizations in Saudi Arabia, there are no formal organizational leaders or members. We treat the most prominent voices of a social movement as its leaders, and individuals who engage with these leaders as supporters. 19Substantive results of our volume analysis remained unchanged if we subset to the 58.2% of users who engage both before and after arrests, or if we subset to the 71.5% (58.2% + 13.3%) of users who engaged before the arrests. 20When we sampled opinion leaders’ engaged followers, we only sampled users who had engaged with an opinion leader at least once preceding and once following political imprisonment. This ensures that we are only looking at content produced by users that had engaged with the imprisoned opinion leaders prior to their imprisonment, rather than comparing tweets by users who started tweeting about the imprisoned opinion leaders after their imprisonment (potentially bolder or more politically engaged individuals) to tweets produced by individuals who engaged with the opinion leaders pre-arrest. 21For most of these users, 3,200 tweets encompassed all of the tweets they have ever tweeted. In order to use these tweets to assess the sentiment of political content (described below), we first filtered these tweets to include only those that contained the most common political keywords in a random sample of tweets sent by the imprisoned and match opinion leaders coded as relevant to the Saudi regime, politics, or society. This left us with a total of 16,427,785 potentially politically relevant tweets. The full list of these keywords and their translations is provided in Figure A11 in Appendix D. The proportion of political tweets in our data was .34 on average across the entire period and there was no significant difference from the pre-arrest to post-arrest periods.

8 4. Tweets by Non-Imprisoned Opinion Leaders: Collecting tweets of Saudi opinion leaders who are similar to those imprisoned but were not themselves subjected to repression during our period of study enables us to test the indirect effects of arrest on individuals who might have been most likely to be deterred by seeing their peers imprisoned. To identify these individuals, we first used the Historical PowerTrack API to download all tweets sent by Twitter users who had over 10,000 followers located in Saudi Arabia, based on their geo-location and location metadata, between 2013 and 2014, which is approximately the middle of the data collection period for the imprisoned opinion leaders. This resulted in 235,215,314 tweets sent by 1,048,568 unique accounts. We then mea- sured the average cosine similarity between tweets produced by these accounts and our imprisoned opinion leader accounts to find matches for each opinion leader.22 Our matching method matched Sunni clerics with Sunni clerics, Shia clerics with Shia clerics or Shia-rights activists, women’s right activists with women’s rights activists, etc., giving us confidence in the validity of the approach.23 We then used the Historical PowerTrack API to download all of the public tweets of matched non-imprisoned opinion leaders from 2010 to 2017, resulting in a dataset of 365,337 tweets.

5. Google Search Data: To measure private interest in the imprisoned opinion leaders among the general public, we downloaded daily and weekly Saudi Google Search data for the Arabic names24 of each imprisoned opinion leader in the month and year preceding

22Our goal in identifying these matches was to study a theoretically interesting population who might face greater threat of repression than ordinary Saudis, not to conduct matching for causal inference. We did not use a speciﬁc threshold for cosine similarity but simply chose matches that had the highest rates. The average cosine similarity value was .22, and ranged from .09 to .57. As might be expected, the closest matches for many of our imprisoned opinion leaders were other imprisoned opinion leaders, so the closest non-imprisoned match was not necessarily the closest match overall. We found 14 unique matches because several opinion leaders had the same top match. For example, three of our imprisoned women’s rights activists were matched to the women’s rights activist Hala al-Dosari, who was not imprisoned. Several arrested human rights activists were matched to the human rights activist Waleed al-Sulais, who also was not imprisoned. 23See Appendix A for information and account metadata for all imprisoned opinion leaders and matches. 24We manually checked to ensure that these search terms were in fact drawing results related to these opinion leaders by examining the “related queries” provided in the Google Search data. We excluded the names of opinion leaders from our analysis that had too low of a search volume to restrict the analysis to Saudi Arabia.

9 and following their arrests. This enabled us to see how often Saudi Google users privately searched for these individuals.25 This real-time behavioral measure of how much attention everyday Saudis—who the social movements literature refer to as bystanders (Davenport, Armstrong and Zeitzoff 2019)—were privately paying to imprisoned opinion leaders allows us to assess indirect and downstream effects. Because individuals conducting Google searches are generally alone, and there is no obvious record of their activity, they are more likely to express socially and politically taboo thoughts in their searches than they might in more public forums (Conti and Sobiesk 2007; Stephens-Davidowitz 2014, 2017). By contrasting this data to public tweets, we can therefore capture preference falsiﬁcation (Kuran 1997).

2.2 Empirical Strategy

Analyses of Tweet and Search Volume: We first calculate the average change in the volume of both tweets and Google searches from the pre-arrest to the post-arrest (or post- release) period. We then conduct placebo tests to generate a null distribution of changes in tweet and search volume by choosing a placebo intervention date at random, and repeating this procedure 10,000 times. The resulting null distribution of change in volume allows us to conduct a non-parametric test of our hypotheses. We determine whether or not the change in volume we observe using the actual date of imprisonment falls outside the mass of the distribution of changes in volume generated by choosing placebo dates at random. Specifically, we compute a one-sided p-value representing the proportion of simulated differences in volume that are at least the size of the actual observed difference in volume. We also use interrupted time series analysis (ITSA) and event count models (negative binomial autoregressive models) to test the robustness of these results (See Appendix B1 and B2, respectively, for details). Changes in the volume of tweets give us our first measure of direct, indirect, and downstream effects. If we find evidence of a direct deterrent effect of repression we should see a lower volume of tweets from the imprisoned opinion leaders post-release

25We gather daily Google search data, which is available for up to a 90 day period, as well as weekly Google search data, which is available for up to a ﬁve year period.

10 compared to pre-arrest. If we observe an indirect deterrent effect we should see less engagement with imprisoned opinion leaders from their followers and fewer tweets from similar non-imprisoned opinion leaders post-arrest compared to pre-arrest. If we observe direct or indirect backlash effects, we should see the opposite results. Examining private behavior, if we see a decrease in Google Search volume, this might be evidence of a downstream effect whereby people lose interest in the imprisoned opinion leaders and their causes following imprisonment. If we see an increase in Google Search volume, this might be evidence of an indirect backlash effect, whereby the arrest draws greater attention to the imprisoned opinion leaders and their causes.

Crowdsourced Evaluation of Tweet Content: Moving beyond changes in the volume of activity, we also evaluate how the content of tweets produced by imprisoned opinion leaders, their engaged followers, and non-imprisoned opinion leaders changed in the aftermath of repression. In particular, we are interested in the effect of imprisonment on four categories of content: 1) criticism or praise of the regime, 2) criticism or praise of government policies, 3) criticism or praise of Saudi society, and 4) discussion of collective action.26 With regard to criticisms, the ﬁrst category focuses on tweets that express dissatisfaction with or criticize the Saudi monarchy including speciﬁc royal family members, members of the religious establishment such as state-sanctioned clerics, or religious doctrine associated with the monarchy. It also includes tweets calling for democracy, constitutional monarchy, and other changes to the political regime, including rights for the marginalized Shia minority. This category focuses on content that challenges the legitimacy of the religious monarchy, and as such likely represents the most intolerable form of online expression for the Saudi regime. The second category includes tweets that express dissatisfaction with or are critical of Saudi bureaucracy including the judiciary, government ministries, or the religious police.

26The coding categories we used for the content of tweets are designed to be sufﬁciently broad to capture a wide range of politically or socially relevant content. The precise wording of the coding instructions is provided in Appendix D. In order to capture a wide range of topics we did not constrain our coding to particular categories of interest such as women’s driving, the Egyptian coup, government corruption, or disbursement of oil wealth, for example.

11 It also include tweets criticizing or expressing dissatisfaction with policies and policy outcomes such as the state of the economy, corruption, foreign policy, and infrastructure. This category is perhaps less problematic for the regime as it challenges its policies but not its underlying legitimacy. The third category identifies tweets criticizing Saudi society for being too liberal or too conservative, as well as tweets criticizing the role of women in society. Because these tweets focus on Saudi society in general, they may be more likely to be tolerated. The final category includes tweets discussing protest or organized crowd formation on the ground. While rare, these tweets represent a particularly threatening form of dissent for the monarchy in the post-Arab Spring period because they facilitate and spread awareness of offline mobilization. To classify tweets into these categories, we crowdsourced large-scale human coding of tweets via Figure8 (formerly Crowdflower), a platform similar to Mechanical Turk but with more native Arabic speakers. This content analysis gives us a second measure of direct, indirect, and downstream deterrent or backlash effects. If there is a direct deterrent effect then we should see less critical and more supportive content in the tweets of imprisoned opinion leaders post-release. There might also be a downstream deterrent effect if tweets directly mentioning or retweeting imprisoned opinion leaders become more supportive and less critical as a consequence of the direct deterrent effect of repression on the arrested individuals. If there is an indirect deterrent effect we should see content that is less critical or more supportive of the regime in the tweets produced by engaged followers and tweets produced by similar non-imprisoned opinion leaders post-arrest. Backlash would produce the opposite results.

3 Results

We ﬁrst present evidence of direct deterrence of repressed public opinion leaders and its downstream effects, before moving to evidence of indirect backlash effects—increased dissent by engaged online followers of the imprisoned opinion leaders and increased attention from everyday Saudis. We then show a lack of indirect deterrence among non-

12 imprisoned opinion leaders.

3.1 Direct Deterrent Effects on Arrested Opinion Leaders

Opinion leaders who were imprisoned were deterred from dissent following their releases. First, their volume of tweets decreased. This can be seen in Figure 1, which presents the pre-arrest and post-release volume of tweets produced by imprisoned opinion leaders with a loess smoothed trend line for the month before arrest and the month after release (panel a), and for the year before arrest and the year after release (panel b).

a) b)

● 600 ● 600 ● ● Arrest Date Arrest● ● Date ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● 400 ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 400 ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●●● ●● ●● ● ● ● ● ● ● ●● ●● ● ●●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ●●● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ●●● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●●● ●●● ● ●● ●● ●●● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●●● ● ●● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●●●● ● ● ● ● ●●●● ● ● ● ●● ● ● ● ● ● ●●● ● 200 ● ●●● ● ● ● ● ● ● ● ● ●●●●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●●●●● ● ● 200 ● ●●●● ● ● ● ● ● ●● ●● ● ●●● ● ● ● ● ● ●●● ●●●●● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●●● ●●● ● ●● ● ● ● ● ● ● ●●●● ● ● ●● ●●● ● ●●● ●●● ●●● ●● ●● ● ● ● ● ●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●●● ● ●● ●●● ●● ● ● ●● ● ● ● ● ● ●●●● ●●●● ●●● ● ●● ● ●● ●● ● ● ● ●●●●● ●●●● ● ● ● ● ●●●● ● ● ●● ●●●●●● ●●●● ● ● ● ●●● ● ●●● ● ● ● ● ●● ●●●●● ● ●●●● ● ● ●●● ● ● ● ●● ●●●●● ●●●●●●● ●● ● ● ● ● ● ●● ●●● ● ●● ● ● ● ● ● ●● ● ● ●●● ●●● ●● ● ●●●● ● ● ● ● ● ● ● ●●● ●● ●●●● ● ● ●● ● ● ● ● ● of Tweets Daily Volume

Daily Volume of Tweets Daily Volume 0 0 0 400 200 20 0 200 400 20 − − − Days Pre−Arrest and Post−Release Days Pre−Arrest and Post−Release

c) d) 0.020 Observed difference Observed difference (p−value=0.03) (p−value=0.07) 0.015 0.015

0.010 0.010 Density Density 0.005 0.005

0.000 0.000 −200 −100 0 −200 −100 0 Difference in Tweet Volume (Month) Difference in Tweet Volume (Year) Figure 1: The top two panels show pre-arrest and post-release trends in the daily volume of tweets produced by the imprisoned opinion leaders in the month before arrest and month after release (panel a), and in the year before arrest and year after release (panel b) with a loess smoothed trend line. Panels c and d show the results of non-parametric placebo tests comparing the observed difference in volume from the pre-arrest to the post- release period (the dotted line) to a null distribution of placebo dates in the month (panel c) and year (panel d) time frames.

Our placebo tests show that imprisoned opinion leaders tweeted signiﬁcantly less in the post-release period relative to the pre-arrest period. The lower panels (c and d) of

13 Figure 1 present the results of non-parametric placebo tests, which compare the actual difference in tweet volume associated with the arrest to the difference in volume generated by placebo intervention dates chosen at random at the month (panel c) and year (panel d) time frames. The doted vertical line shows the actual average daily difference in tweet volume between the pre-arrest and post-release month (panel c) and year (panel d). Imprisoned opinion leaders tweeted less when comparing the month before arrest to the month after release. Importantly, they also tweeted less when comparing the year before arrest to the year after release, which suggests that the deterrent effect was not a tempo- rary phenomenon that only lasts for a few days or weeks. These results are consistent with the results of our interrupted time series analysis and event count models reported in Ap- pendix B.1 and B.2, which both show statistically signiﬁcant decreases in tweet volume among imprisoned opinion leaders at the .05 level one month and one year after release. These direct deterrent effects were not simply driven by the change of behavior of a speciﬁc subgroup of public opinion leaders. When we disaggregate these effects by the type of opinion leader (Sunni clerics, liberal reformers and activists, and Shia clerics and activists), by the length of the arrests, by whether or not the individual was explicitly arrested for their online dissent,27 by the time period in which he or she was arrested, and by his or her number of followers, we see decreased volume of activity across all of these subgroups (See Appendix C for details). The content of what imprisoned public opinion leaders tweeted also changed. For example, prior to his arrest, one liberal activist in our dataset tweeted his support for Islamists (opposed by the Saudi regime) who gained power after the Arab Spring:

They told me: Why support the Arab spring revolutions if the Islamists have beneﬁted from them? I said: the Arab Spring revolutions have restored dig- nity and trust to the peoples.

27While all of the elites in our study were speculated to have been arrested for their online activity, the ofﬁcial Saudi government rationale for the arrests did not always include online activity. For example, the ofﬁcial reason for the imprisonment of the three judicial reform activists was “disobeying rule / slandering judiciary,” but according to media and observer reports, the “disobedience” and “slander” all took place on Twitter (see Appendix A for details of every arrested individual).

14 : :

By contrast, after he was released, some of his tweets expressed support for the Saudi regime and its leaders:

I followed the al-Arabiya interview with Prince Mohammed bin Salman. The man truly impressed me: ﬂuent in his speech, transparent and practical, knows what he is talking about, his vision is clear, the future is his focus.

: .

More systematic analysis demonstrates that opinion leaders who were very critical of the regime, its policies, and Saudi society, who called for political and social reforms before their imprisonment, changed their tune after their release. As we described in Sec- tion 2.2, Arabic speakers coded the content of tweets as expressing either support (positive sentiment), criticism (negative sentiment), or neutral attitudes toward the Saudi regime, policies, or society.28 The barplots in Figure 2 (panel a) show the average sentiment of these three types of tweets pre-arrest (black bar), one month post-release (light gray bar), and one year post-release (dark gray bar). In the month before imprisonment, the average sentiment of their tweets about the Saudi regime was quite negative (-.7 on a scale ranging from -1 to 1). In the month after their release, the average sentiment became positive (+.15), echoing what we saw in the example tweets. In aggregate, instead of criticizing the regime and calling for change, immediately after their releases from prison, public opinion leaders tweeted more supportive content about the regime. In the year following their releases, average sentiment again became negative, but less negative than it had been in the pre-arrest period. A similar pattern is evident when examining their tweets about Saudi policies and Saudi society, both of which became

28Tweets coded as irrelevant were excluded from the analysis.

15 less negative after release. These pre-arrest and post-release changes in the content of public opinion leaders’ tweets about the regime and policies are statistically significant, as pictured in the coefficient plot displaying the results of t-tests with 95% confidence intervals in Figure 2 (panel b). Although tweets calling for collective action were very

a) b)

More Critical More Supportive More Supportive More Supportive Pre−Arrest Post−Release

Pre−Arrest Mo. Regime Post−Release Mo. Regime Mo. Post−Release Yr. Yr.

Pre−Arrest Mo. ● Policies Post−Release Mo. Policies Mo. Post−Release Yr. Yr.

Pre−Arrest Mo. Society Post−Release Mo. Post−Release Yr. Society Mo. Yr.

−0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 Average Sentiment Change in Sentiment Figure 2: Barplots of the average sentiment of tweets of imprisoned opinion leaders in the month pre-arrest (black bar), the month post-release (light gray bar), and six months to one year post-release (dark gray bar) by content category (panel a). Estimates of the change in average sentiment of tweets by imprisoned opinion leaders before arrests and after release based on t-tests with 95% conﬁdence intervals are displayed in panel b. rare in the Saudi Twittersphere in this period (around 1.5% of arrested opinion leader tweets prior to arrest), they disappeared almost entirely post-release (See Appendix E for details). Together, these results suggest that arrests had a direct deterrent effect on the online dissent of imprisoned opinion leaders. Imprisoned opinion leaders reined in their criticisms of the regime, its policies, and Saudi society, and their already rare online posts about ofﬂine mobilization essentially ceased.

3.2 Downstream Deterrent Effects on Engaged Followers

As a consequence of the direct deterrent effect of arrested online opinion leaders changing how they tweet, we see a downstream deterrent effect in the critical content of tweets directly mentioning, retweeting, or replying to imprisoned opinion leaders. Less than 2% of tweets produced by engaged followers of imprisoned opinion leaders were mentions, replies, or retweets of imprisoned opinion leaders. However, largely because

16 these tweets often contain the text of tweets produced by the imprisoned opinion leaders— which are less critical post-release—this 2% of tweets is also less critical of the regime. Panel a of Figure 3 shows this—the sentiment of retweets, mentions, and replies are very critical in the month pre-arrest (black bar), and become less critical in the month (light gray bar) as well as year (dark gray bar) post-release. The results of t-tests with 95% conﬁdence intervals displayed in panel b of Figure 3 show that retweets, replies, and mentions became more supportive of the regime and policies, and the results are statistically signiﬁcant for tweets about the regime and policies.29

a) b)

More Critical More Supportive More Supportive More Supportive Pre−Arrest Post−Release

Pre−Arrest Mo. Regime Post−Release Mo. Regime Mo. Post−Release Yr. Yr.

Pre−Arrest Mo. ● Policies Post−Release Mo. Policies Mo. Post−Release Yr. Yr.

Pre−Arrest Mo. Society Post−Release Mo. Post−Release Yr. Society Mo. Yr.

−0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 Average Sentiment Change in Sentiment Figure 3: Panel a displays barplots of the average sentiment of retweets, mentions, and replies to imprisoned opinion leaders in the month pre-arrest (black bar), the month post- release (light gray bar), and six months to one year post-release (dark gray bar). Estimates of the change in average sentiment of followers’ retweets, replies, and mentions of imprisoned opinion leaders before the arrest and after the release are displayed with 95% conﬁdence intervals in panel b.

3.3 Indirect Backlash Effects on Engaged Followers

Despite evidence of downstream deterrent effects of repression in the content of the small subset of tweets directly engaging with the imprisoned opinion leaders, when we look at overall levels of engagement with arrested opinion leaders we see no evidence of deterrence. Moreover, when we examine changes in the content of engaged followers’ tweets overall—rather than just those tweets that retweet, reply, or mention imprisoned

29We also see a decrease in the number of tweets calling for collective action, though again the overall volume of these tweets is very small (see Appendix E).

17 opinion leaders—we see evidence of an indirect backlash effect. First, examining the volume of retweets, mentions, and replies by engaged followers we ﬁnd that the arrests did not deter them from interacting with the arrested opinion leaders on Twitter. This can be seen panels a and b of Figure 4, which plots pre- and post- arrest trends in the volume of tweets by engaged followers of imprisoned opinion leaders as local regression lines with loess smoothing for the month before and after arrests (panel a), and for the year before and after arrests (panel b).30 Panels c and d of Figure 4 show

a) b) 80000 80000 Arrest ●Date Arrest Date●

● ● 60000 ● 60000 ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 40000 ● ● ● ● 40000 ● ●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●●● ● ●● ● ● ● ●●● ● ● ●●● ●● ●● ●● ● ● ●● ● ●● ●● ●●●●● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●● ●● ● ● ● ●●● ● ● ●●● ● ● ●●●● ● ● ● ● ● ●● ●● ● ● ●●● ●●● ●● ● ●●●● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●●●● ●●● ● ●● ●●● ●●● ● ● ● ● ● ●● ● ● ●● ● ● ● ●●●● ●● ● ●●●● ●● ● ● ● ●●● ● ●●●● ●●●● ● ● ●● ●● ● ● ● 20000 ●●● ● ●●● ●● ● ●● ● ●●●●●●● ●● ●●●● ●● ●● ●●●● ● ●● ● ● ● ●●●● ●●● ●●● ● ● ● ● ● ●● ● ●●● ●●● ● ● ● ● ●●● ● ●●● ● ● ● ●●● ● ●● ●●●● ●●●●●● ● ●● ●● ●● ● ● ● ● ● ●●●● ●●● ●● ●● ● ● ●●●● ●●●● ●●● ● ●●● ● ●●●● ●●● ● ●● ●● ● ● 20000 ● ● ● ●●● ● ● ●●● ●● ●●●●● ●●● ● ● ● ●●●● ●●●●● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ●●● ● ●●●●●●● ● ●●●● ● ●● ● ●● ● ●●●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●●● ● ●●●●● ● ● ●● ●● ●● ● ● ● ● ● ● ● ●●● ●● ●●●●● ● ●● ● ● ●● ● ● ● ● ● ●●●● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● Daily Volume of Tweets Daily Volume

Daily Volume of Tweets Daily Volume 0 0 0 400 200 20 0 200 400 20 − − − Days Pre−Arrest and Post−Arrest Days Pre−Arrest and Post−Arrest c) d)

0.000100 0.0004 Observed diff. Observed diff. (p−value=0.34) (p−value=0.62) 0.000075 0.0003

0.000050 0.0002 Density Density

0.000025 0.0001

0.000000 0.0000 −20000 −10000 0 10000 20000 −20000 −10000 0 10000 20000 Difference in Tweet Volume (Month) Difference in Tweet Volume (Year) Figure 4: Daily volume of mentions, retweets, and replies of imprisoned opinion leaders in the month pre and post-arrest (panel a) and in the year pre and post-arrest (panel b) with a loess smoothed trend line. Panels c and d show the results of non-parametric placebo tests comparing the observed difference in volume from the pre-arrest to post-arrest period (the dotted line) to a null distribution of placebo dates in the month (panel c) and year (panel d) time frames. the results of placebo tests, which demonstrate that our observed difference falls right

30Figure 4 also shows an uptick in daily mentions approximately ten days before the arrest. Many of the opinion leaders were arrested for their online activities. The uptick may capture some of the tweet(s) that the regime deemed to be problematic and online attention around these tweets.

18 in the middle of the null distribution of volume differences generated by using placebo intervention dates. This suggests that the arrests did not have a deterrent effects on engagement with imprisoned opinion leaders either a month or a year following the arrest. When we analyze these data in other ways—using interrupted time series regressions and event count models—we also do not find any statistically significant declines in volume (See Appendix B.1 and B.2). We also disaggregate users by when they began engaging with the imprisoned opinion leaders. The subset of tweets produced by engaged followers who only engaged with the imprisoned opinion leader pre-arrest contains a spike in volume immediately post-arrest. Similarly, the subset of tweets produced by those who engaged both pre- and post-arrest also shows a spike in volume immediately post-arrest. The volume spikes in both subsets of data means that individuals who actively followed imprisoned opinion leaders were not deterred by their imprisonment. We also find that the spike in tweets by engaged followers who followed the leader prior to arrest is slightly smaller than what we observe in Figure 4. This suggests that the arrest also led to activity from newly engaged followers who had not engaged previously (See Appendix B.3). When we disaggregate this analysis by different characteristics of imprisoned opinion leaders, we do not see statistically significant differences in volume between any subgroup of followers—engaged followers of different types of opinion leaders, engaged followers of opinion leaders who have more or fewer followers, engaged followers of opinion leader arrested in different time periods or for different lengths of time, or engaged followers of opinion leaders who were explicitly arrested for the content of their tweets (See Appendix C for details). Additionally, we observe increased engagement with imprisoned opinion leaders after their release in terms of retweet volume. Engaged followers of imprisoned online opinion leaders retweeted the tweets of imprisoned leaders at higher rates after the leaders were released. As Figure 5 demonstrates, on average, tweets produced by imprisoned opinion leaders garnered more retweets per tweet when comparing the month before the arrests and after the releases, and in the year before the arrests and after the releases, those these

19 results are only statistically signiﬁcant in the year period (p value = .001). The average number of retweets per tweet in the pre-arrest year was 13 and the average number of retweets per tweet in the year post-release was 60. This suggests that physical repression did not scare away ordinary Twitter users from engaging with imprisoned opinion leaders or their causes.

Higher Retweet Ratio Higher Retweet Ratio Pre−Arrest Post−Release

Month

●

Year

−100 −50 0 50 100 Change in Retweet Ratio (Retweets/Tweet)

Figure 5: Change in average number of retweets per tweet made by imprisoned opinion leaders before and after their arrest based on t-test with 95% conﬁdence intervals; compares the month before and after imprisonment and the year before and after imprisonment.

Not only did repression not decrease engagement with imprisoned opinion leaders, we also observe a backlash in the content of tweets produced by engaged followers of the imprisoned opinion leaders. Engaged followers stepped up their dissent by criticizing the regime and calling for reform. Although the liberal activist quoted in Section 3.1 tweeted in support of Saudi leaders following his release from prison, his online followers continued to criticize the regime. For example, one follower tweeted in the post-arrest period:

A society that denies and condemns and calls for the killing of all who do not agree with its ideology, religion and beliefs, is a society that is intellectually sick and the people suffer.

20 When we systematically examine tweets by engaged followers of imprisoned public opinion leaders, we observe the same pattern of increased criticism and dissent. Panel a of Figure 6 displays a barplot of the average sentiment of political tweets produced by Saudi Twitter users who engaged with imprisoned opinion leaders. The average sentiment is always negative, but in the month (light gray bar) and year (dark gray bar) after the releases, online sentiment was more critical toward the regime, policies, and society than before the arrests. Panel b of Figure 6 presents results of t-tests of differences in online sentiment before the arrests and after the releases and shows that online followers were more critical of the regime, its policies, and Saudi society, though these results are not consistently statistically signiﬁcant.

a) b)

More Critical More Supportive More Supportive More Supportive Pre−Arrest Post−Release Pre−Arrest Mo. Regime Post−Release Mo. Regime Mo. Post−Release Yr. Yr.

Pre−Arrest Mo. ● Policies Post−Release Mo. Policies Mo. Post−Release Yr. Yr.

Pre−Arrest Mo. Society Post−Release Mo. Post−Release Yr. Society Mo. Yr.

−0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 Average Sentiment Change in Sentiment Figure 6: Panel a displays barplots of the average sentiment of tweets by those who engaged with imprisoned opinion leaders in the month pre-arrest (black bar), the month post-release (light gray bar), and six months to one year post-release (dark gray bar). Estimates of the change in average sentiment of engaged followers tweets before arrest and after release based on t-tests with 95% conﬁdence intervals are displayed in panel b.

3.4 Indirect Backlash Effects on the Saudi Public

Private interest in the imprisoned opinion leaders by the Saudi public also spiked. Panels a and b of Figure 7 show a very large increase in the popularity of Saudi Google searches for imprisoned opinion leaders in the immediate aftermath of the arrests. The top of Figure 7 plots the pre-arrest and post-arrest trends in our data with loess smoothed trend lines, based on daily search volume for the month prior to and post-arrest (panel

21 a) b)

800 ● ● Arrest Date ● Arrest Date 300

600 ● ● ● ●● ●● ● ● 200 ● ● ● 400 ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 100 ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● 200 ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●● ●●● ●●● ● ●● ● ● ● ●● ●● ● ●●● ●●● ● ●● ●●●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●●●● ●● ● ● ● ●● ● ● ●●● ●● ● ● ● ● ●● ● ●● ●●●●●●●● ● ●● ●●● ● ● ●● ● ●● ● ● ● ●●● ●●●●● ● ●● ●● ●● ● ●● ● ●● ● ● ●●● ●● ● ● ●● ●● ● ● ● ●●●● ● ● ● ●● ●● ●● ●●● ●● ● ●●●●●●●● ●● ●● ● ● ● ● ● ● ●● ●● ●●● ●●● ●●●●●●●●● ● ● ● ●● ● ●●● ●●● ● ●●● ●● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ●● ● ●●● ● ●●●●●●●●●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●●●●● ● ● ●●●● ● ●●● ●●● ●● ● ● ● ●●● ●●●●●●●● ● ●● ● ●●● Daily Search Volume ● ● ● ●● ● ●●●●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●● ●●●● ●● ● ● ● ●● ● ● ●●●● ● ●● ● ●●● ●● ●●● ●● ●●●●●●●● ● ●● ● ● Search Volume Weekly ● ● ● 0 0 ● 0 400 200 20 0 200 400 20 − − − Days Pre− and Post−Arrest Days Pre− and Post−Arrest c) d)

0.0100 Observed diff. Observed diff. (p−value=0.52) 0.06 0.0075 (p−value=0.5)

0.04 0.0050 Density Density

0.0025 0.02

0.0000 0.00 −500 −250 0 250 500 −40 −20 0 20 40 Difference in Search Volume (Daily) Difference in Search Volume (Weekly) Figure 7: Daily relative volume of Google searches for imprisoned opinion leaders in the month pre and post-arrest (panel a) and weekly relative volume of Google searches in the year pre and post-arrest (panel b) with a loess smoothed trend line. Google search data is a relative measure of the popularity of a given search term on Google scaled from 0 to 100. The plots show the daily (or weekly) sums of these relative scores for all imprisoned opinion leaders. Panels c and d show the results of non-parametric placebo tests comparing the observed difference in volume associated with the arrest (the dotted line) to a null distribution of placebo dates in the month (panel c) and year (panel d) time frames. a) and based on weekly search volume for the year prior to and post-arrest (panel b).31 Searches increased signiﬁcantly in the immediate aftermath of arrest and returned to pre- arrest levels soon after. Furthermore, the similar results we observe across the tweets engaging with imprisoned opinion leaders and the Google search data suggest that there is no evidence of preference falsiﬁcation (Kuran 1997) or self-censorship that we might have observed if individuals had been afraid to publicly engage on Twitter but nonetheless

31Google search data is a relative measure of the popularity of a given search term on Google. For each imprisoned opinion leader, the relative popularity is calculated as the total number of searches for that person’s Arabic name divided by the total number of searches from that same geographic region and time window. The resulting numbers for each imprisoned opinion leader are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics. The plots show the daily (or weekly) sums of these relative scores for all imprisoned opinion leaders.

22 continued to search on Google in private. While the uptick in Google searches quickly re- turns to normal and therefore is not captured by our placebo analysis, our interrupted time series analysis shows a statistically signiﬁcant level change in the immediate aftermath of the arrest.32

3.5 No Indirect Deterrent Effect on Similar Opinion Leaders

Turning to individuals who were perhaps most at risk of repression—those who had engaged in similar dissent and also had large online followings—we find no evidence that they were deterred from online dissent. Figure 8 shows that unlike the imprisoned opinion leaders, similar opinion leaders did not decrease their volume of tweets after the arrests of their peers. There was little change in the daily volume of tweets produced by the non-imprisoned opinion leaders in the month before and month after the arrests, or in the year before and year after arrests. When we disaggregate this analysis by different characteristics of imprisoned opinion leaders, we do not see statistically significant declines in volume among any subgroup of matched individuals. If anything, in some subgroups (non-imprisoned opinion leaders matched to imprisoned leaders with long arrest periods and imprisoned leaders with fewer followers), we see an increase in the volume of non- imprisoned opinion leader tweets. The results of interrupted time series analysis and event count models are similar and displayed in Appendix B.1 and B.2 along with the subgroup analysis reported in Appendix C. Similarly, non-imprisoned opinion leaders did not change the content of their tweets. For example, the non-arrested liberal activist matched to the activist quoted in Section 3.1 tweeted equally negative content in the pre-arrest period, and did not reign in his dissent after the first activist’s arrest. Following the arrest, the non-imprisoned activist called for freedom, referencing that Arabs have fought for freedom since pre-Islamic times:

Freedom is an innate state inherent in the primitive existence of man. And therefore Arabs believe in it and have fought for it since the Sa’lek (pre- Islamic) rebellion of the Arabs.

32Once again, these results are similar when disaggregated by subgroup, as we report in detail in Ap- pendix C.

23 a) b) 600 600 ● ● Arrest Date● ● Arrest Date●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● 400 ● ● ● ● ●● ●● ● ● ● ● ● ●● 400 ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ●●● ● ●● ● ● ● ● ●● ●● ● ● ●●● ● ● ●●● ● ●● ● ● ● ●●● ● ● ●●● ● ●●●● ● ● ● ●● ● ● ● ● ● ● ●●●●●●● ● ●●●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ● ● ●● ●● ●● ●●● ● ●● ● ● ● ● ●●●●● ● ●● ●●● ● ● ● ●●● ● ●●●● ●●●●● ● ● ●● ●● ● ●●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●●● ●● ● ● ● ●● ●● ●●● ● ● ● ● ● ● ● ●● ● ● ●●● ●●● ●● ● ● ●●●●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ● ●● ● ●● ● ● ●●●●● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ● ●● ● ●●●● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●●● ●●●● ●● ●● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●● ●●● ●●● ●● ● ● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●●●● ● ●● ● ●● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ●● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● 200 ●● ● ●● ● ● ● ● ●●● ● ● ● ● ●●● ●● ●●●● ● 200 ● ●● ● ● ● ● ● ● Daily Volume of Tweets Daily Volume

Daily Volume of Tweets Daily Volume 0 0 0 400 200 20 0 200 400 20 − − − Days Pre−Arrest and Post−Arrest Days Pre−Arrest and Post−Arrest c) d)

0.03 Observed difference 0.04 Observed diff. (p−value=0.25) (p−value=0.92) 0.03 0.02

0.02

Density 0.01 Density 0.01

0.00 0.00 −50 0 50 100 150 −100 0 100 Difference in Tweet Volume (Month Difference in Tweet Volume (Year) Figure 8: Panels a and b show pre-arrest and post-arrest daily volume of tweets produced by similar opinion leaders who were not imprisoned in the month before arrest and month after arrest (panel a), and in the year before arrest and year after arrest (panel b) with a loess smoothed trend line. Panels c and d show the results of non-parametric placebo tests comparing the observed difference in volume from the pre-arrest to post-arrest period (the dotted line) to a null distribution of placebo dates in the month (panel c) and year (panel d) time frames.

Our systematic content analysis reveals the same lack of deterrence. Figure 9 shows that non-imprisoned opinion leaders continued to express negative sentiment toward the regime, and there were no statistically signiﬁcant changes in the content of their tweets about the Saudi regime, policies, or society. Discussion of ofﬂine collective action by these other opinion leaders also remained unchanged.33

33We do not code more tweets one year out for the non-imprisoned opinion leaders because the chilling effect on the imprisoned opinion leaders diminishes over time, and since we do not observe any change one month post-arrest for non-imprisoned leaders, we are unlikely to observe any further away from the arrests of their peers. Results showing no change in the volume of tweets discussing collective action are displayed

24 a) b)

More Critical More Supportive More Supportive More Supportive Pre−Arrest Post−Release

Pre−Arrest Mo. Regime Post−Release Mo. Regime Mo.

● Policies Pre−Arrest Mo. Post−Release Mo. Policies Mo.

Society Pre−Arrest Mo. Post−Release Mo. Society Mo.

−0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 Average Sentiment Change in Sentiment Figure 9: Barplots in panel a display the average sentiment of tweets of similar, non- imprisoned opinion leaders in the month pre-arrest (black bar) and the month post-release (light gray bar). Panel b displays estimates of the change in average sentiment of tweets of similar, non-imprisoned opinion leaders before arrests and after release based on t-tests with 95% conﬁdence intervals.

Our results also align with what we know anecdotally from this period—that activists and clerics frequently denounced the arrests of their friends and colleagues and did not appear deterred. For example, non-imprisoned women’s rights activist Hala al-Dosari spoke out against the arrests of Loujain al-Hathloul and Mayasa al-Amoudi in 2014 (BBC News 2014). Non-imprisoned clerics denounced the arrests of Mohamad al-Arefe and Mohsen al-Awaji in 2013 and non-imprisoned human rights activists protested the arrests of members of the Saudi Civil and Political Rights Association in 2011 (The Daily Star 2011).

3.6 Discussion

Why did repression deter imprisoned opinion leaders but create backlash among their followers? And why did it it fail to deter similar non-arrested individuals from dissent? Interpreting our results through the lens of the repression, online mobilization, and censorship literatures provides key insights into their generalizability as well as important scope conditions of our study. Our ﬁnding of a direct deterrent effect contrasts with research in democratic contexts in Appendix E.

25 showing that repression can generate backlash among its targets, especially leaders of social movement organizations (Sullivan and Davenport 2017; Davenport 2015). This may be a consequence of the high level of repression in Saudi Arabia, which is virtually uncon- strained by social norms, cultural practices, or law, as well as the absence of formal social mobilization organizations to offer a modicum of protection for dissenters. The future risk of repression is high (likely much higher than in any democratic context), not only for individuals who have been repressed but also for their family members and associates. This direct deterrence may more likely to occur in contexts like Saudi Arabia where social movement organizations are largely absent. In regimes where social movement organizations exist and where offline mobilization is more prevalent, we may see backlash instead of deterrence among repressed individuals in line with Sullivan and Davenport (2017). The lack of indirect deterrence and the backlash we observe among followers of arrested elites aligns with the large repression literature on backlash34 and echoes research on how censorship can backfire. There are two components to the backlash we observe. One is increased engagement and higher levels of criticism by individuals who had already been actively engaging with imprisoned opinion leaders online prior to their arrests. The second is new engaged followers who began interacting with the imprisoned opinion leaders or privately searching for them after their arrests. Backlash in both instances was likely related to the fact that online dissent and mobilization are extremely low cost, allowing large numbers of people to participate. As a result, the risk of repression for any one supporter is very low. This means that publicly visible repression may generate interest without incentivizing individuals to self-censor or to stop supporting a movement online (Earl and Kimport 2011). Given that we observe backlash among engaged followers in this extremely repressive context—and that we find no evidence of preference falsification—we expect these results to generalize to other contexts where social media is used to sustain social movements online. We also expect these backlash results to extend to regimes that are less repressive and those that have freer media. We have no reason to believe these results are unique to

34For example: Eckstein (1965), Feierabend et al. (1972), Gurr and Duvall (1973), Kuran (1989), Fran- cisco (1996), Khawaja (1993), Kurzman (1996), and Lichbach (1998).

26 absolute monarchies, theocracies, or resource-rich dictatorships. There are, however, important scope conditions with regard to these indirect backlash effects. First, the repression we analyze in the Saudi context is publicly visible. Just as censorship is more likely to generate backlash when it is obvious than when it is hidden, when repression of online dissent is more covert, we might not observe backlash. We should therefore expect to see indirect backlash when those targeted by repression are prominent figures. In other contexts, where there have been high-profile cases of repression targeting ordinary social media users,35 backlash might be less likely if online supporters learn new information about their own level of risk by observing the arrest. More generally, we would expect to see similar results in contexts where repression does not alter observers’ calculation about their own risk of repression. In regimes where repression of online activity is rarer, or makes an example of ordinary citizens, repression might be more likely to change peoples’ perceived level of risk and constrain their behavior. Indirect backlash effects of repression may also be limited to regimes that lack fine- grained control over the online sphere. The backlash we observe is likely accelerated by the fact that the Saudi regime cannot quickly, reliably, and selectively censor tweets. Unlike in China, for example, where Chinese social media platforms carry out the censorship directives of the Chinese government to prevent certain online movements from going viral (King, Pan and Roberts 2014), Twitter does not necessarily comply with Saudi government censorship directives. Indeed, the Minister of Information admitted in February 2013 that monitoring Twitter was difficult due to the large volume of users (Al-Rasheed 2013). The increased criticisms and calls for reform we observe suggest that in the time period of our study, online disinformation campaigns by the Saudi government (including large-scale fabrication of pro-government content) also did not stifle critical voices.36 If a regime exercised a high degree of control over social media, it could censor dissent after the arrests. However most authoritarian regimes lack high degrees of control, with the

35For example, in 2013, when China began cracking down against online dissent, not only were prominent ﬁgures arrested but ordinary citizens as well, including the highly publicized story of the arrest of a middle school student from Western China, see https://nyti.ms/2wcIoHI (Accessed May 22, 2019). 36For news reports of Saudi Arabia’s troll army, see https://nyti.ms/2RXKNzP, https://wapo.st/2w8YClj (Accessed May 21, 2019).

27 exception of China and perhaps Russia. The lack of indirect deterrence we observe among online opinion leaders who were not imprisoned is perhaps our most surprising result. First, it differs from the results of Sullivan and Davenport (2017), which show that repression demobilized movement members who did not experience repression. We believe this difference is due to the fact that the dissent we describe in this paper is taking place online instead of offline, and occur- ring within social movements but not in social movement organizations. In Sullivan and Davenport (2017), repression was applied to individuals who participated in an offline protest. Members of this organization who were not present at the protest were not repressed and later withdrew from the organization perhaps because they were perceived by protest participants as lacking commitment and socially stigmatized. Our context differs dramatically. Because of the large and diffuse nature of online mobilization, the Saudi regime could not and did not arrest everyone who participated in online dissent. In contrast to Sullivan and Davenport (2017), other leaders who were not repressed did not lack commitment—they also took risks and spoke out. What else might explain the fact that these non-arrested opinion leaders were not deterred by observing repression despite the seemingly high levels of risk they faced relative to ordinary Saudi social media users? Perhaps these non-imprisoned opinion leaders differed in some way from those who were imprisoned, and did not actually face as high a level of risk. However, our results suggest that non-imprisoned opinion leaders tweeted at a similar rate in general and produced a similar proportion of tweets expressing negative sentiment toward the regime, policies, and society in the pre-arrest period. It therefore seems unlikely that non-imprisoned opinion leaders believed they were immune from state repression because their online activity differed from that of their arrested peers. But there may be other characteristics of non-imprisoned opinion leaders we do not observe—such as the nature of their offline dissent, political connections, or their ability to leave Saudi Arabia—that alter their perceived level of risk and enable them to continue dissenting.37 A final reason we do not observe an indirect deterrent effect on non-imprisoned opin-

37For example, non-imprisoned women’s rights activist Hala al-Dosari currently has an academic position at Harvard University, and human rights defender Waleed Sulais left Saudi Arabia for exile.

28 ion leaders may be that—like ordinary social media users—observing arrests did not alter their calculus about the risk they faced. Because well-known opinion leaders in Saudi Arabia are at risk of arbitrary imprisonment at all times, the political imprisonment of other opinion leaders may not provide any new information to constrain their behavior any more than the daily reality of living under such repressive conditions. This may stand in contrast to other contexts where the repression of a small number of actors might be suf- ﬁcient to alert others to acceptable norms and deter dissent (Link 2002; Stern and Hassid 2012).

4 Conclusion

Analyzing over 300 million tweets and Google search data between 2010 and 2017, this paper offers new temporally granular measures of the direct, indirect, and downstream effects of repression on online dissent. Furthermore, by allowing us to capture both the volume and content of mass and opinion leader messages on the same platform, Twitter data provides novel perspectives on how diverse actors behave in the aftermath of physical repression. Together, our results suggest that while physical repression had a direct deterrent effect on the individuals who were imprisoned, it had an indirect backlash effect on their engaged followers and the public, and did not deter similar opinion leaders who were not imprisoned. Given these results, why would the Saudi regime—or other regimes around the world— use physical repression in response to online dissent? Governments may go through a learning process in how to suppress online mobilization, and perhaps these targeted arrests were one phase in that process. Governments may also default to a particular style of repression depending on their institutional history or who is in power. Indeed Saudi Arabia’s use of physical repression has shifted since our period of analysis. Since 2017, the Saudi Kingdom has moved away from targeted arrests to more indiscriminate forms of physical repression. Examples include larger-scale political imprisonment, such as the late 2017 purge of about 500 business people, princes, government ministers, and activists; an increase in death sentences such as as that of popular cleric Salman al-Oudah; and even

29 the targeting of opponents living abroad such as the recent murder of inﬂuential journalist Jamal Kashoggi (Rauhala 2018; Freedom House 2018; Human Rights Watch 2018b). Our work suggests that—as of 2017—despite the threat of repression, many opinion leaders and everyday Saudis continue to take advantage of Twitter as one of the few avenues of political expression available in the Saudi Kingdom. Future research should examine the extent to which this pattern persists under more recent conditions of intensiﬁed repression in Saudi Arabia. In general, more research is needed to capture how physical repression is being used by authoritarian and democratic regimes in response to online opposition worldwide. We hope the analytical leverage gained by disaggregating the effect of repression on online dissent by type, actor, behavior, and time will be used in future studies examining other regions, regime types, forms of physical repression, and forms of online dissent.

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35 How Saudi Crackdowns Fail to Silence Online Dissent Online Appendix

Jennifer Pan⇤ Alexandra A. Siegel† September 17, 2019

Abstract

Keywords: repression; social media; online mobilization; censorship; Saudi Arabia

⇤Assistant Professor, Department of Communication, Building 120, Room 110 450 Serra Mall, Stanford University, Stanford CA 94305-2050; jenpan.com, (650) 725-7326. †Postdoctoral Fellow, Immigration Policy Lab, Encina Hall, 616 Serra Street, Stanford University, Stan- ford, CA 94305-2050; alexandra-siegel.com, (781) 454-8867.

1 List of Figures

A1 Effect of Imprisonment on Daily Volume of Tweets (Imprisoned Opinion Leaders) ...... 8 A2 Effect of Imprisonment on Daily Volume of Mentions, Retweets, and Replies (Engaged Followers) ...... 10 A3 Effect of Imprisonment on Daily/Weekly Search Volume ...... 11 A4 Effect of Imprisonment on Daily Volume of Tweets (Non-Imprisoned Opinion Leaders) ...... 13 A5 Effect of Imprisonment on Daily Volume of Engagement (excluding post- arrest only users) ...... 15 A6 Disaggregated Effect of Political Imprisonment on Imprisoned Opinion Leader Tweet Volume ...... 17 A7 Disaggregated Effect of Political Imprisonment on Volume of Mentions and Retweets of Imprisoned Opinion Leaders ...... 17 A8 Disaggregated Effect of Political Imprisonment on Google Searches of Imprisoned Opinion Leaders ...... 18 A9 Disaggregated Effect of Political Imprisonment on Non-Imprisoned Opin- ion Leader Tweet Volume ...... 18 A10 Distribution of Tweet Content ...... 22 A11 Top Political Keywords in Opinion Leader Tweets Relevant to Regime, Policies, or Society ...... 23 A12 Change in Average Number of Tweets Calling for Collective Action ... 24 A13 Change in Proportion of Tweets Calling for Collective Action ...... 25

List of Tables

A1 Imprisoned Opinion Leaders Arrest Reasons ...... 3 A2 Opinion Leader Arrest Dates (First Arrest) ...... 4 A3 Imprisoned Opinion Leaders and Non-Imprisoned “Match” Opinion Leaders 5 A4 Effect of Political Imprisonment on Daily Volume of Tweets (Imprisoned Opinion Leaders) One Month Pre-Arrest vs. One Month Post-Release .. 7 A5 Effect of Imprisonment on Daily Volume of Tweets (Imprisoned Opinion Leaders) One Year Pre-Arrest vs. One Year Post-Release ...... 8 A6 Effect of Imprisonments on Daily Volumeof Mentions, Replies, and Retweets One Month Pre-Arrest vs. One Month Post-Arrest ...... 9 A7 Effect of Imprisonments on Daily Volumeof Mentions, Replies, and Retweets One Year Pre-Arrest vs. One Year Post-Arrest ...... 9 A8 Effect of Political Imprisonments on Daily Search Volume One Month Pre-Arrest vs. One Month Post-Arrest ...... 10 A9 Effect of Political Imprisonments on Weekly Search Volume One Year Pre-Arrest vs. One Year Post-Arrest ...... 11 A10 Effect of Political Imprisonments on Daily Volumeof Tweets (Non-Imprisoned Opinion Leaders) One Month Pre-Arrest vs. One Month Post-Arrest ... 12 A11 Effect of Political Imprisonments on Daily Volumeof Tweets (Non-Imprisoned Opinion Leaders) One Year Pre-Arrest vs. One Year Post-Arrest ..... 12

1 A12 Effect of Political Imprisonment on Tweets, Mentions, and Search Vol- ume Negative Binomial Event Count Models ...... 14 A13 Average Intercoder Agreement ...... 22

A Descriptive Data

2 Table A1: Imprisoned Opinion Leaders Arrest Reasons

Name Type Official Arrest Reason Unofficial Arrest Reason 1 Bandar al-Nogaithan Judicial Reform Disobeying ruler / Slandering judiciary Tweets critical of judiciary 2 Abdulrahman al-Subaihi Judicial Reform Disobeying ruler / Slandering judiciary Tweets critical of judiciary 3 Abdulrahman Al-Rumaih Judicial Reform Disobeying ruler / Slandering judiciary Tweets critical of judiciary 4 Raif Badawi Liberal Apostosy /Insulting Islam / Violating cybercrime law Comments on his website debating political and religious issues in KSA 5 Omar al-Saeed Liberal Harming public order / Setting up unliscensed organization Calling for Democracy/Criticizing Saudi HR record 6 Abdullah al Hamid Liberal Sowing Discord and Chaos/Violating Public Safety Calling for prison reform / resignation of Interior Minister 7 Issa al-Nukheifi Liberal Disobedience to the ruler/ Violating cybercrimes law Accused authorities of corruption / Human rights violations 8 Abdulaziz al-Hussan Liberal Providing inaccurate information about the government Representing arrested lawyers / Tweeting about their trial 9 Mohammed al-Bajady Liberal Establishing HR Org/ Distorting state’s reputation / Impugning judicial independence / Instigating relatives of detainees Organized protest against arbitrary detention to protest / Possessing censored books 10 Abdulkarim Al-Khoder Liberal Disobeying the ruler / Inciting disorder / Harming the image of the state / Founding an unlicensed organization Crackdown on Saudi Civil and Political Rights Association 11 Fowzan al-Harbi Liberal Inciting disobedience to the ruler / Describing KSA as a ’police state’ Crackdown on Saudi Civil and Political Rights Association 12 Khaled al-Johani Liberal Being present at a prohibited demonstration/ Distorting the kingdom’s reputation/ Contact with known Saudi dissident Participated in ’Day of Rage’ and spoke to international journalists abroad 13 Mohammad Fahad al-Qahtani Liberal Sowing discord / Disturbing public order / Breaking allegiance with the ruler Calling for prison reform / Resignation of Interior Minister 14 Suliman al-Rashoodi Liberal Breaking Allegiance with ruler / Attempting to distort reputation of kingdom Arrested for publication ’The Legitimacy of Demonstrations in Islamic Law’ 15 Saleh al-Ashwan Liberal Breaking allegiance to and disobeying the ruler/ Questioning the integrity of officials/ Member of unliscensed organiza- Crackdown on SCPRA / Drew attention to Saudi prisoners in Iraq tion 16 Waleed Abul Khair Liberal Disobeying the ruler and seeking to remove his legitimacy/ Insulting the judiciary and questioning the integrity of judges Establishing human rights organization / Criticizing Saudi HR record / Setting up an unlicensed organization / Harming the reputation of the state 17 Zuhair Kutbi Liberal Sowing discord/ Inciting public opinion /Reducing the government’s prestige Calling for Constitutional Monarchy / Combating Repression on TV 18 Alaa Brinji Liberal Insulting rulers / Inciting public opinion Critical tweets about imprisonment of activists and ending the driving ban 19 Hamza Kashagri Liberal Apostosy/ Crossing red lines / Denigrating religious beliefs in God and His Prophet Popular calls for his death online following tweets humanizing Prophet 20 Turki al-Hamad Liberal No public charges Tweets criticizing Saudi interpretation of Islam 21 Hassan Farhan Al-Malki Moderate Cleric Supporting proximity among Islamic sects Defending Shia rights surrounding Nimr al-Nimr’s arrest

3 22 Abdulaziz al-Tarifi Sunni Cleric Calling for Constitutional Monarchy Tweet criticizing monarchy for religious police reform / kowtowing to West 23 Mohammad al-Arefe Sunni Cleric No public charges Supporting Morsi / Muslim Brotherhood / Criticizing Saudi Hajj Trains 24 Mohsen al-Awaji Sunni Cleric No public charges Supporting Morsi /Muslim Brotherhood/ Signing communique 25 Ibrahim al-Sakran Sunni Cleric Damaging fabric of society / Inciting public opinon / Interefering in international affairs Tweets criticizing foreign policy in Yemen / treatment of detainees 26 Adel Ali al-Labbad Shia Activist Disobedience to Ruler/Disturbing Public Order Poems criticizing arrests / treatment of dissidents 27 Mohamed Baqir al-Nimr Shia Activist No public charges Tweeting about Nimr al Nimr’s trial 28 Ahmed al-Musheikhis Shia Activist No public charges Protesting Detentions / Advocating Shia Rights / Belonging to unregistered HR org. 29 Nimr al-Nimr Shia Cleric Disturbing security /Seeking Foreign Meddling /Terrorism Giving anti-regime speeches/ Defending political prisoners / Inciting Protest 30 Tawfiq al-Amer Shia Cleric Defaming ruling system /Ridiculing religious leaders/ Inciting sectarianism/ Calling for change/ Disobeying the ruler Criticizing treatment of Shia / Calling for reforms 31 Sahar Al-Khashrami Anti-University Corruption Defamation / Violating Anti-Cyber Crime Law Hashtag campaign condemning academic fraud, forgery and plagiarism 32 Lujain al-Hathloul Women’s Rights Tried under vague provisions of anti-cybercrime law Comments on social media calling for end to driving ban / guardianship 33 Manal al-Sharif Women’s Rights Disturbing public order / Inciting Public Opinion Social media campaigns calling for protests / filming her violation of driving ban 34 Mayasa al-Amoudi Women’s Rights Tried under vague provisions of anti-cybercrime law Comments on social media calling for end to driving ban / guardianship 35 Samar Badawi Women’s Rights No public charges Women’s Driving Campaign / Managing jailed husband’s Twitter account 36 Souad al-Shammari Women’s Rights Insulting Islam / Inciting rebellion Women’s Driving Campaign / Critcizing Guardianship System Table A2: Opinion Leader Arrest Dates (First Arrest)

Name Type First Arrest Date First Release Date 1 Bandar al-Nogaithan Judicial Reform 10/27/14 4/15/15 2 Abdulrahman al-Subaihi Judicial Reform 10/27/14 5/15/15 3 Abdulrahman Al-Rumaih Judicial Reform 10/27/14 4/15/15 4 Raif Badawi Liberal 6/17/12 not released 5 Omar al-Saeed Liberal 4/30/13 12/24/15 6 Abdullah al Hamid Liberal 9/2/12 not released 7 Issa al-Nukheifi Liberal 9/1/12 4/6/16 8 Abdulaziz al-Hussan Liberal 3/11/13 3/12/13 9 Mohammed al-Bajady Liberal 3/21/11 8/6/13 10 Abdulkarim Al-Khoder Liberal 6/28/13 not released 11 Fowzan al-Harbi Liberal 12/26/13 6/24/14 12 Khaled al-Johani Liberal 3/1/11 8/6/12 13 Mohammad Fahad al-Qahtani Liberal 3/9/13 not released 14 Suliman al-Rashoodi Liberal 12/12/12 12/12/17 15 Saleh al-Ashwan Liberal 7/7/12 not released 16 Waleed Abul Khair Liberal 4/15/14 not released 17 Zuhair Kutbi Liberal 7/15/15 not released 18 Alaa Brinji Liberal 5/12/14 not released 19 Hamza Kashagri Liberal 2/7/12 10/29/13 20 Turki al-Hamad Liberal 12/24/12 6/5/13 21 Hassan Farhan Al-Malki Moderate Cleric 10/14/14 12/24/14 22 Abdulaziz al-Tarifi Sunni Cleric 4/25/16 not released 23 Mohammad al-Arefe Sunni Cleric 7/20/13 7/22/13 24 Mohsen al-Awaji Sunni Cleric 7/20/13 7/22/13 25 Ibrahim al-Sakran Sunni Cleric 6/14/16 not released 26 Adel Ali al-Labbad Shia Activist 10/10/12 not released 27 Mohamed Baqir al-Nimr Shia Activist 10/15/14 11/1/14 28 Ahmed al-Musheikhis Shia Activist 1/5/17 not released 29 Nimr al-Nimr Shia Cleric 7/8/12 executed 1/2/2016 30 Tawfiq al-Amer Shia Cleric 2/27/11 3/6/11 31 Sahar Al-Khashrami University Corruption 4/15/15 4/15/15 32 Lujain al-Hathloul Women’s Rights 12/2/14 2/3/15 33 Manal al-Sharif Women’s Rights 5/21/11 5/30/11 34 Mayasa al-Amoudi Women’s Rights 12/2/14 2/3/15 35 Samar Badawi Women’s Rights 1/1/16 1/13/16 36 Souad al-Shammari Women’s Rights 10/28/14 1/28/15

Those who are “not released” were not yet released at the time of our data collection in January 2017.

4 Table A3: Imprisoned Opinion Leaders and Non-Imprisoned “Match” Opinion Leaders

Name Twitter Handle Imprisoned Match Type 1 Omar al-Saeed 181Umar imprisoned Abdullah al-Nasri Liberal 2 Abdulaziz al-Tarifi abdulaziztarefe imprisoned Suhail bin Mualla al-Mutairi Sunni Cleric 3 Suhail bin Mualla al-Mutairi aborazan2011 match Sunni Cleric 4 Abdullah al Hamid Abubelal 1951 imprisoned Abdullah al-Nasri Liberal 5 Adel Ali al-Labbad adel lobad imprisoned Saeed Abbas Shia Activist 6 Issa al-Nukheifi aesa al nukhifi imprisoned Mujtahidd Liberal 7 Abdulaziz al-Hussan Ahussan imprisoned Abdullah al-Nasri Liberal 8 Mohammed al-Bajady albgadi imprisoned Waleed Sulais Liberal 9 Alaa Brinji albrinji imprisoned Waleed Sulais Liberal 10 Abdullah al-Nasri alnasri1 match Judicial Reform 11 Abbas Said alsaeedabbas match Shia Cleric 12 Abdulrahman al-Subaihi Alsubaihiabdul imprisoned Abdullah al-Nasri Judicial Reform 13 Abdullah Rahman al-Sudais assdais match Sunni Cleric 14 Abdulkarim Al-Khoder drkhdar imprisoned Waleed Sulais Liberal 15 Sadeq al-Jibran DrSadeqMohamed match Judicial Reform 16 Fowzan al-Harbi fowzanm imprisoned Waleed Sulais Liberal 17 Hala al-Dosari Hala Aldosari match Women’s Rights 18 Hamza Kashagri Hmzmz imprisoned Rashad Hassan Liberal

5 19 Hassan Farhan Al-Malki HsnFrhanALmalki imprisoned Abdullah Rahman al-Sudais Moderate Cleric 20 Ibrahim al-Sakran iosakran imprisoned Abdullah Rahman al-Sudais Sunni Cleric 21 Khaled al-Johani KhaledLary imprisoned Mujtahidd Liberal 22 Abdulrahman Al-Rumaih LawyerAMRumaih imprisoned Sadeq al-Jibran Judicial Reform 23 Lujain al-Hathloul LoujainHathloul imprisoned Hala al-Dosari Women’s Rights 24 Mohamad Ali Mahmoud ma573573 match Liberal Writer 25 Manal al-Sharif manal alsharif imprisoned Hala al-Dosari Women’s Rights 26 Mayasa al-Amoudi maysaaX imprisoned Hala al-Dosari Women’s Rights 27 Mohamed Baqir al-Nimr mbanalnemer imprisoned Saeed Abbas Shia Activist 28 Mohammad Fahad al-Qahtani MFQahtani imprisoned Waleed Sulais Liberal 29 Mohammad al-Arefe MohamadAlarefe imprisoned Abdullah Rahman al-Sudais Sunni Cleric 30 Mohsen al-Awaji MohsenAlAwajy imprisoned Yousef Ahmed Qasem Sunni Cleric 31 Ahmed al-Musheikhis mshikhs imprisoned Saeed Abbas Shia Activist 32 mujtahid mujtahidd match Liberal Regime Critic 33 Fawaz al-Ruwaili Muwafig match University Corruption Activist 34 Sahar Al-Khashrami Profsahar imprisoned Fawaz al-Ruwaili University Corruption 35 Raif Badawi raif badawi imprisoned Wadad Khaled Liberal 36 Suliman al-Rashoodi s alrushodi imprisoned Waleed Sulais Liberal 37 Saleh al-Ashwan saleh alashwan imprisoned Taha al-Hajji Liberal 38 Samar Badawi samarbadawi15 imprisoned Hala al-Dosari Women’s Rights 39 Bandar al-Nogaithan SaudiLawyer imprisoned Sadeq al-Jibran Judicial Reform 40 Nimr al-Nimr ShaikhNemer imprisoned Saeed Abbas Shia Cleric 41 Tawfiq al-Amer sk tawfeeq imprisoned Saeed Abbas Shia Cleric 42 Souad al-Shammari SouadALshammary imprisoned Wadad Khaled Women’s Rights 43 Taha al-Hajji tahaalhajji match Liberal 44 Turki al-Hamad TurkiHAlhamad1 imprisoned Mohamad Ali Mohamed Liberal 45 Waleed Abul Khair WaleedAbulkhair imprisoned Waleed Sulais Liberal 46 Waleed Sulais WaleedSulais match Liberal 47 Rashad Hassan watheh1 match Professor 48 Wadad Khaled wdadkhaled match Liberal 49 Yousef Ahmed Qasem Yqasem match Sunni Cleric 50 Zuhair Kutbi zuhairkutbi imprisoned Waleed Sulais Liberal B Interrupted Time Series Analysis, Placebo Tests, and Event Count Models Using Interrupted Time Series Analysis (ITSA), we first model changes in the volume of online behavior as follows:

Yt = 0 + 1(T )+2(Xt)+3(XtT ) (1)

In Equation 1, Yt is the number of tweets (or Google searches) made at time t, T is the time (number of days) since the opinion leader was imprisoned, Xt is a dummy variable representing political imprisonment (for imprisoned opinion leaders the pre-arrest period 1 is coded as 0 and the post-release period is coded as 1 ), and XtT is an interaction term. 0 represents the baseline volume of tweets (or Google searches) produced at t =0, 1 shows the change in the volume of tweets (or Google searches) associated with a one unit time increase, representing the underlying daily pre-arrest trend. 2 captures the immediate effect of the arrest on the volume of tweets (or Google searches) produced, or an intercept change, and 3 captures the slope change in the volume of tweets (or Google searches) following the release, relative to the pre-arrest trend. In other words, ITSA is a segmented regression model. Segmented regression simply refers to a model with different intercept and slope coefficients for the pre and post-intervention time periods. It is used to measure the pre-arrest trend, the immediate change in the volume of tweets (or Google searches) following the release, as well as the change in the slope of the daily volume of tweets (or Google searches) in the post-release period. In order to address serial autocorrelation in our data, we use a first order autoregressive (AR1) model in our analysis instead of the standard OLS ITSA model (Bernal 2016). If repression is followed by increased online activity, then we should see a positive shift immediately after the release 2 or a non-negative immediate effect 2 followed by a positive slope change in the volume of tweets in the post-release period 3. If repression acts as a deterrent, then we should see a negative shift immediately after the release 2 or a non-positive immediate effect 2 followed by a negative slope change in the volume of tweets in the post-release period 3. The results of this interrupted time series analysis are reported in subsection B.1 below. While the advantage of this model is that it enables us to capture both the immediate effect of political imprisonment as well as the longer term effects, it is a linear model and we do not necessarily expect a linear effect. To address this concern, we first conduct placebo tests that offer a non-parametric test of our hypotheses. In particular we estimate the effect of the arrests by choosing “intervention dates” at random over the 30 days preceding and 30 days following the actual arrests in our month analyses and the 365 days preceding and following the arrests in our year analyses. We repeated this procedure 10,000 times for each analysis to generate a null distribution of the parameter estimate. We then computed a p-value by calculating the proportion of simulated coefficient estimates that are at least the size of the actual observed estimate. These results are reported in the main body of the paper. Finally, given that our outcome variable is count data, we also replicate our analyses using event count models—specifically Negative Binomial Autoregressive models—to as-

1If opinion leaders were not released from prison in the period under study they are excluded from the analysis. The release dates, as well as those opinion leaders that were not released, are described in Table A2 in the Appendix.

6 sess the effect of arrests on the volume of tweets produced by imprisoned opinion leaders, mentions and retweets of imprisoned opinion leaders, tweets produced by non-imprisoned opinion leaders, and the Google search data. These results are also consistent with the results of our interrupted time series analysis and are displayed in Table A12.

B.1 Interrupted Time Series Analyses

Table A4: Effect of Political Imprisonment on Daily Volume of Tweets (Imprisoned Opinion Leaders) One Month Pre-Arrest vs. One Month Post-Release

Model 1 Baseline 274.696⇤⇤⇤ (32.129) Pre-Arrest Trend 0.651 (1.798) Post-Release Level Change 190.549⇤⇤⇤ (41.344) Post-Release Slope Change 0.814 (2.693) AIC 626.965 BIC 639.117 Log Likelihood -307.483 Num. obs. 60

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

7 Table A5: Effect of Imprisonment on Daily Volume of Tweets (Imprisoned Opinion Leaders) One Year Pre-Arrest vs. One Year Post-Release

Model 1 Baseline 301.557⇤⇤⇤ (12.440) Pre-Arrest Trend 0.013 (0.059) Post-Release Level Change 165.497⇤⇤⇤ (17.498) Post-Release Slope Change 0.100 (0.084) AIC 8355.648 BIC 8383.174 Log Likelihood -4171.824 Num. obs. 730

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

Figure A1: Effect of Imprisonment on Daily Volume of Tweets (Imprisoned Opinion Leaders)

Month

●

Year

−200 −100 0 100 200 Level Change in Tweet Volume Pre−Arrest vs. Post−Release

8 Table A6: Effect of Imprisonments on Daily Volume of Mentions, Replies, and Retweets One Month Pre-Arrest vs. One Month Post-Arrest

Model 1 Baseline 36865.670⇤⇤⇤ (7590.993) Pre-Arrest Trend 633.552 (425.249) Post-Arrest Level Change 4475.659 (9838.871) Post-Arrest Slope Change 1687.593⇤⇤ (610.016) AIC 1276.251 BIC 1288.509 Log Likelihood -632.126 Num. obs. 61

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

Table A7: Effect of Imprisonments on Daily Volume of Mentions, Replies, and Retweets One Year Pre-Arrest vs. One Year Post-Arrest

Model 1 Baseline 18905.851⇤⇤⇤ (1412.542) Pre-Arrest Trend 6.178 (6.687) Post-Arrest Level Change 1850.453 (1978.944) Post-Arrest Slope Change 7.742 (9.485) AIC 15160.490 BIC 15188.024 Log Likelihood -7574.245 Num. obs. 731

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

9 Figure A2: Effect of Imprisonment on Daily Volume of Mentions, Retweets, and Replies (Engaged Followers)

Month

●

Year

−20000 −10000 0 10000 20000 Level Change in Tweet Volume Pre−Arrest vs. Post−Arrest

Table A8: Effect of Political Imprisonments on Daily Search Volume One Month Pre-Arrest vs. One Month Post-Arrest

Model 1 Baseline 473.727 (11407.545) Pre-Arrest Trend 13.828 (12.752) Post-Arrest Level Change 319.172⇤⇤⇤ (69.856) Post-Arrest Slope Change 36.628⇤ (17.886) AIC 662.689 BIC 674.947 Log Likelihood -325.344 Num. obs. 61

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

10 Table A9: Effect of Political Imprisonments on Weekly Search Volume One Year Pre-Arrest vs. One Year Post-Arrest

Model 1 Baseline 54.476⇤⇤⇤ (5.291) Pre-Arrest Trend 0.113⇤⇤⇤ (0.025) Post-Arrest Level Change 0.020 (7.433) Post-Arrest Slope Change 0.224⇤⇤⇤ (0.035) AIC 5214.140 BIC 5239.754 Log Likelihood -2601.070 Num. obs. 532

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

Figure A3: Effect of Imprisonment on Daily/Weekly Search Volume

Month

●

Year

−400 −200 0 200 400 Level Change in Search Volume Pre−Arrest vs. Post−Arrest

11 Table A10: Effect of Political Imprisonments on Daily Volume of Tweets (Non-Imprisoned Opinion Leaders) One Month Pre-Arrest vs. One Month Post-Arrest

Model 1 Baseline 339.406⇤⇤⇤ (35.448) Pre-Arrest Trend 1.598 (1.986) Post-Arrest Level Change 36.118 (46.206) Post-Arrest Slope Change 3.565 (2.839) AIC 667.534 BIC 679.792 Log Likelihood -327.767 Num. obs. 61

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

Table A11: Effect of Political Imprisonments on Daily Volume of Tweets (Non-Imprisoned Opinion Leaders) One Year Pre-Arrest vs. One Year Post-Arrest

Model 1 Baseline 312.225⇤⇤⇤ (10.560) Pre-Arrest Trend 0.070 (0.050) Post-Arrest Level Change 0.071 (14.813) Post-Arrest Slope Change 0.274⇤⇤⇤ (0.071) AIC 8128.078 BIC 8155.612 Log Likelihood -4058.039 Num. obs. 731

⇤⇤⇤p<0.001, ⇤⇤p<0.01, ⇤p<0.05, ·p<0.1

12 Figure A4: Effect of Imprisonment on Daily Volume of Tweets (Non-Imprisoned Opinion Leaders)

Month

●

Year

−400 −200 0 200 400 Level Change in Tweet Volume Pre−Arrest vs. Post−Arrest

13 B.2 Event Count Models

Table A12: Effect of Political Imprisonment on Tweets, Mentions, and Search Volume Negative Binomial Event Count Models

Arrested Month Arrested Year Match Month Match Year Mentions Month Mentions Year Google Trends Month Google Trends Year

Post-Arrest/Release -1.19⇤⇤⇤ -0.97⇤⇤⇤ 0.08 -0.09⇤⇤⇤ -0.14 0.08⇤⇤⇤ 0.61⇤⇤ -0.003 (0.08) (0.03) (0.05) (0.02) (0.13) (0.03) (0.20) (0.09)

Constant 6.85⇤⇤⇤ 6.69⇤⇤⇤ 5.68⇤⇤⇤ 5.79⇤⇤⇤ 10.39⇤⇤⇤ 9.71⇤⇤⇤ 3.62⇤⇤⇤ 3.52⇤⇤⇤ (0.13) (0.05) (0.08) (0.03) (0.21) (0.05) (0.33) (0.14)

AIC 641.48 8330.97 682.73 8236.51 1327.68 15124.28 711.14 4416.16 Num. obs. 60.00 730.00 61.00 731.00 61.00 731.00 61.00 532.00 14 This table shows the results of our negative binomial event count models. As in all of our analysis, for the arrested opinion leaders, we compare the pre-arrest period to the post-release period. For the rest of the analyses, we compare the pre-arrest period to the post-arrest period. B.3 Engaged Followers Excluding Those Who Tweeted Post-Arrest Only

Figure A5: Effect of Imprisonment on Daily Volume of Engagement (excluding post-arrest only users) a) Users who Engaged Pre-Arrest Only ) 80000 Arrest ●Date ●

● 60000 ●

● ● ● ●

40000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Daily Volume of Tweets Daily Volume 0 20 0 20 − Days Pre−Arrest and Post−Arrest b) Users Who Engaged Pre-Arrest and Post-Arrest 80000 Arrest ●Date ●

● 60000 ●

● ● ● ●

40000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Daily Volume of Tweets Daily Volume 0 20 0 20 − Days Pre−Arrest and Post−Arrest

Daily volume of mentions and retweets of imprisoned opinion leaders in the month pre and post-arrest plotted as local regression lines with loess smoothing. The data in Panel a is subset only to users who engaged with the imprisoned opinion leader at least once in the pre-arrest period and the data in Panel b is subset to users who engaged once in the pre-arrest period and once in the post-arrest period.

C Disaggregated Effects It is possible that the effects we observe are driven by particular opinion leader arrests and that the effects might differ across arrest length, time of the arrest, whether or not the opinion leader was explicitly imprisoned for online activity, follower count, and opinion leader type (liberal reformers, Sunni clerics, and Shia clerics and activists). To test this we conduct subgroup analyses comparing average pre-arrest and post-arrest (or release) tweet volume, engagement with opinion leaders, Google search volume for the imprisoned opinion leaders’ names, and the volume of tweets produced by non-arrested opinion leaders. Overall we see no signiﬁcant differences across these subgrups. These results are reported in Figures A6-A9.

15 We disaggregate individuals into three groups: liberal reformers, Sunni clerics, and Shia reformers. Liberal reforms include human rights activists, judicial reformers, and women’s rights activists. We see very little difference (and none of the differences are statistically significant) in non-arrested elite tweets, mentions or retweets of arrested elites, or Google searches between these three groups (Figures A7-A9). We do see a smaller decrease in tweets by Shia among the imprisoned opinion leaders themselves (Figure A6), but this is a function of the fact that the Shia opinion leaders in our sample tweet less both pre-arrest and post-release than the other actors so the effect (while in the same direction) is smaller. There are no statistically significant differences in tweets by imprisoned opinion leaders or in mentions or retweets by engaged followers, or in tweets sent by similar non- imprisoned opinion leaders for opinion leaders with low numbers of followers compared to opinion leaders with high numbers of followers (Figures A6, A7, or A9).2 However we do see a statistically significantly higher number of Google searches for opinion leaders with high numbers of followers relative to those with low numbers of followers. Perhaps this is due to the fact that more prominent individuals (who likely have more followers on Twitter) got more press coverage following their arrests. For everyday Saudis who don’t necessarily follow these individuals online, arrests of more prominent individuals likely garnered more attention. When we disaggregate by period, for the imprisoned opinion leaders (Figure A6), the decrease in their volume of tweets is statistically significantly greater in 2010-2012 and 2013-2015 relative to 2016-2017. This is likely because although several opinion leaders were arrested in 2016-2017, the only imprisoned opinion leader who was arrested and released before our data collection period ended in January 2017—allowing us to measure these pre-arrest and post-release differences—was Samar Badawi. She is a women’s rights and human rights activist whose brother was also imprisoned in this period, perhaps prompting her to keep tweeting following her release. She was only detained for 3 days in 2016 and may not have been as deterred as the other opinion leaders in part because of her family connections. Looking at changes in the volume of mentions and retweets of imprisoned opinion leaders and tweets by similar non-imprisoned opinion leaders we see no significant differences between the time periods (Figures A7 and A8). With regard to Google searches (Figure A9), it appears that political imprisonment between 2010-2012 garnered slightly more search interest then imprisonment in the other periods—perhaps because these events were of particular interest in the early days of the Arab Spring. While all of the elites in our study were speculated to have been arrested for their online activity, the official Saudi government rationale for the arrests did not always include online activity. For example, the official reason for the imprisonment of the three judicial reform activists was “disobeying rule / slandering judiciary,” but according to media and observer reports, the “disobedience” and “slander” all took place on Twitter (see Table A1 for details for every arrested individual). When we disaggregate based on whether or not they were explicitly imprisoned for online activity, we observe no differences (Fig- ures A6-A9) in imprisoned opinion leader tweets, non-imprisoned opinion leader tweets, mentions or retweets of imprisoned opinion leaders, or Google searches. Perhaps this is because all of the individuals were effectively imprisoned for online activity even though

2Here we compare those with below the median number of followers (21,574) to those with above the median number of followers.

16 this was only made explicit in certain cases. With regard to arrest length, there are no signiﬁcant differences across our analyses of different actors (A6-A9. Here we compare those arrested for below the median number of days (70) to those arrested for above the median number of days (70).

Figure A6: Disaggregated Effect of Political Imprisonment on Imprisoned Opinion Leader Tweet Volume

Long Arrest Short Arrest Not Arrested for Online Arrested for Online Low Followers High Followers ● Shia Liberal Reformers Sunni Clerics 2016−2017 Arrest 2013−2015 Arrest 2010−2012 Arrest −150 −100 −50 0 50 100 150 Change in Arrested Opinion Leader Tweet Count Month Pre−Arrest and Month Post−Release

Figure A7: Disaggregated Effect of Political Imprisonment on Volume of Mentions and Retweets of Imprisoned Opinion Leaders

Long Arrest Short Arrest Not Arrested for Online Arrested for Online Low Followers High Followers ● Shia Liberal Reformers Sunni Clerics 2016−2017 Arrest 2013−2015 Arrest 2010−2012 Arrest −20000 −10000 0 10000 20000 Change in Engagement with Arrested Opinion Leaders Month Pre−Arrest and Month Post−Arrest

17 Figure A8: Disaggregated Effect of Political Imprisonment on Google Searches of Imprisoned Opinion Leaders

Long Arrest Short Arrest Not Arrested for Online Arrested for Online Low Followers High Followers ● Shia Liberal Reformers Sunni Clerics 2016−2017 Arrest 2013−2015 Arrest 2010−2012 Arrest −150 −100 −50 0 50 100 150 Change in Google Searches for Arrested Opinion Leaders Month Pre−Arrest and Month Post−Arrest

Figure A9: Disaggregated Effect of Political Imprisonment on Non-Imprisoned Opinion Leader Tweet Volume

Long Arrest Short Arrest Not Arrested for Online Arrested for Online Low Followers High Followers ● Shia Liberal Reformers Sunni Clerics 2016−2017 Arrest 2013−2015 Arrest 2010−2012 Arrest −150 −100 −50 0 50 100 150 Change in Non−Arrested Opinon Leader Tweet Count Month Pre−Arrest and Month Post−Arrest

D Content Analysis We used Figure8 to code about 10,000 tweets produced by arrested and non-imprisoned opinion leaders and about 15,000 tweets produced by the engaged followers of imprisoned opinion leaders for a total of approximately 25,000 coded tweets. Tweets were selected through stratiﬁed random sampling of all tweets produced by the arrested and non-imprisoned opinion leaders as well as tweets produced by the followers of arrested opinion leaders (both those directly engaging with arrested opinion leaders as well as random samples of all of their tweets). The samples were taken both from the month preceding and following arrest and the six months and one year following arrest, balanced

18 by actor type (clerics, women’s rights activists, Shia rights activists etc.). We did not collect data from a year pre-arrest because substantively we wanted to compare content produced in the lead-up to the arrest (the period during which the regime decided to constrain the opinion leaders’ behavior) with content produced in the immediate aftermath of repression and in the longer term. Because we saw no effects in the month data for the non-imprisoned opinion leaders we did not code their tweets six months to one year out. Three native Arabic speakers assessed each tweet on the Figure8 platform. The coding scheme used by the Figure8 workers is presented in detail in subsection D.1. Across all samples, intercoder agreement was very high, with 94% agreement among coders on average. The reason why intercoder reliability appears so high in our data is that the majority of tweets in our large random sample were coded as not relevant to the Saudi regime, polices, society, or collective action and agreement on relevance (which is easier to assess than sentiment) is very high across the questions. Agreement on whether tweets in each category were positive, negative, or neutral was somewhat lower—about 80% on average—though still a reasonable measure. A table of average intercoder agreement by coding category can be found in Table A13. The majority of tweets about the Saudi regime, policies, and society expressed negative sentiment (62%, 67%, and 58% of relevant tweets respectively) and very few tweets called for collective action (less than 1% of all coded tweets). Histograms of these proportions can be found in Figure A10.

D.1 Figure8 Coding Scheme Overview: In this job you will be presented with Arabic language tweets related to society and politics posted by Saudi Arabian Twitter users. You will answer several brief questions about the content of each tweet. Steps: Read each tweet carefully. • Answer a series of brief questions about the content of each tweet. •

1. What attitude does this tweet express about the Saudi monarchy, ruling regime, leaders, religious establishment, or religious doctrine? Positive • Negative • Neutral • Irrelevant • Unclear • 2. What attitude does this tweet express about Saudi policies or bureaucracy? Positive • Negative • 19 Neutral • Irrelevant • Unclear • 3. What attitude does this tweet express about Saudi society? Positive • Negative • Neutral • Irrelevant • Unclear • 4. Is this tweet calling for collective action (social mobilization to achieve a particular goal)? Yes • No • Unclear • Question 1 Instructions:

Positive tweets include tweets praising or expressing satisfaction with the Saudi • monarchy, ruling regime, leaders, religious establishment, or religious doctrine such as tweets praising speciﬁc royal family members or clerics, tweets supporting the legitimacy of the Saudi regime or religious establishment, or tweets praising Saudi Wahabbi religious doctrine.

Negative tweets include tweets expressing dissatisfaction with or critical of the • Saudi monarchy, ruling regime, leaders, religious establishment, or religious doctrine such as tweets criticizing speciﬁc royal family members or clerics, tweets calling for democracy or other forms of government, or tweets criticizing Saudi Wahabbi religious doctrine.

Neutral tweets neither express satisfaction nor dissatisfaction with the Saudi monar- • chy, ruling regime, leaders, religious establishment, or religious doctrine. These include news articles or factual statements about the regime or religious establishment.

Irrelevant tweets do not mention the Saudi monarchy, ruling regime, leaders, reli- • gious establishment, or religious doctrine.

Question 2 Instructions:

20 Positive tweets include tweets praising or expressing satisfaction with the Saudi • bureaucracy including the judiciary, the ministry of education, or the religious police. They also include tweets praising or expressing satisfaction with policies and policy outcomes including the state of the economy, corruption, foreign policy, or infrastructure.

Negative tweets include tweets expressing dissatisfaction with or critical of the • Saudi bureaucracy including the judiciary, the ministry of education, or the religious police. They also include tweets criticizing or expressing dissatisfaction with policies and policy outcomes including the state of the economy, corruption, foreign policy, and infrastructure.

Neutral tweets neither express satisfaction nor dissatisfaction with Saudi policies • or bureaucracy. These include news articles or factual statements about policies or bureaucracy.

Irrelevant tweets do not mention Saudi policies or bureaucracy. • Question 3 Instructions:

Positive tweets include tweets expressing satisfaction with or praising Saudi society • including the role of women, the piety or industriousness of the population, or youth culture.

Negative tweets include tweets expressing dissatisfaction with or critical of Saudi • society, including tweets criticizing Saudi society for being too liberal or conservative or tweets criticizing the role of women in society or youth culture.

Neutral tweets neither express satisfaction nor dissatisfaction with Saudi society. • These include news articles or factual statements about Saudi society.

Irrelevant tweets do not mention Saudi society. • Question 4 Instructions: Tweets calling for collective action (social mobilization to achieve a speciﬁc goal) • include tweets discussing protest or organized crowd formation.

21 D.2 Intercoder Agreement

Table A13: Average Intercoder Agreement

mean sd policies 0.91 0.16 regime 0.91 0.17 society 0.93 0.15 collective action 0.99 0.05 This table shows average intercoder agreement by category among the three human coders that coded each tweet on Figure8.

Figure A10: Distribution of Tweet Content

What Sentiment Does the Tweet Express About the Regime? What Sentiment Does the Tweet Express About Policies/Bureaucracy? 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 negative neutral positive unclear negative neutral positive unclear

What Sentiment Does the Tweet Express About Saudi Society? Does the Tweet Call for Collective Action? 0.8 0.4 0.4 0.2 0.0 0.0 negative neutral positive unclear no yes

This ﬁgure displays the coding distributions across human coded tweets excluding irrelevant tweets.

22 Figure A11: Top Political Keywords in Opinion Leader Tweets Relevant to Regime, Policies, or Society

keyword translation keyword translation keyword translation women اﻟﺴﯿﺪات prisoners اﻟﺴﺠﻨﺎء King Salman ﺳﻠﻤﺎن fear ﺗﺨﺎف organization ﻣﻨﻈﻤﺔ Interior Ministry اﻟﺪاﺧﻠﯿﺔ the Shia اﻟﺸﯿﻌﺔ citizens اﻟﻤﻮاطﻦ King اﻟﻤﻠﻚ politics ﺳﯿﺎﺳﯿﺎ political ﺳﯿﺎﺳﻲ (Nayef (Interior Minister ﻧﺎﯾﻒ agreement ﻣﻮاﻓﻘﺔ behind/backwards ﺧﻠﻒ the people اﻟﻨﺎس (Al-Nimr (Shia cleric اﻟﻨﻤﺮ (Daesh (ISIS# داﻋﺶ# power اﻟﺴﻠﻄﺔ prince اﻻﻣﯿﺮ citizens اﻟﻤﻮاطﻨﯿﻦ women اﻟﻨﺴﺎء Shia اﻟﺸﯿﻌﻲ prince اﻷﻣﯿﺮ politics اﻟﺴﯿﺎﺳﯿﺔ sector اﻟﻘﻄﺎع rule اﻟﺤﺎﻛﻢ regime اﻟﻨﻈﺎم rights ﺣﻘﻮق judge اﻟﻘﻀﺎء education اﻟﺘﻌﻠﯿﻢ sectarianism اﻟﻄﺎﺋﻔﯿﺔ rights اﻟﺤﻘﻮق ministry وزارة police اﻟﺸﺮطﺔ identity ھﻮﯾﺔ women اﻟﻤﺮأة 23 rights اﻟﺤﻖ decision اﻟﻘﺮار governance اﻟﺤﻜﻢ army اﻟﺠﯿﺶ politics ﺳﯿﺎﺳﺔ minister وزﯾﺮ free اﻟﺤﺮ crown oct26driving oct26driving وﻟﻲ Israel إﺳﺮاﺋﯿﻞ waste ھﺪر the state اﻟﺪوﻟﺔ politics اﻟﺴﯿﺎﺳﺔ private اﻟﺨﺎص the people اﻟﺸﻌﺐ Houthi اﻟﺤﻮﺛﯿﯿﻦ law اﻟﻘﺎﻧﻮن government اﻟﺤﻜﻮﻣﺔ war اﻟﺤﺮب position ﻣﻮﻗﻊ women women clear واﺿﺢ war ﺣﺮب egypt ﻣﺼﺮ# university ﺟﺎﻣﻌﺔ project ﻣﺸﺮوع academic research اﻟﻌﻠﻤﯿﺔ theft اﻟﺴﺮﻗﺎت egypt ﻣﺼﺮ justice اﻟﻌﺪل universities اﻟﺠﺎﻣﻌﺎت university اﻟﺠﻤﻌﺔ society اﻟﻤﺠﺘﻤﻊ killed ﻗﺘﻞ political اﻟﺴﯿﺎﺳﻲ prison ﺳﺠﻦ Yemen اﻟﯿﻤﻦ people اھﻞ corruption اﻟﻔﺴﺎد injustice اﻟﻈﻠﻢ leadership ﻗﯿﺎدة representative وﻛﯿﻞ Syria ﺳﻮرﯾﺎ his excellency ﻣﻌﺎﻟﻲ Egyptian اﻟﻤﺼﺮي they_stole_from_me# ھﻠﻜﻮﻧﻲ# homeland اﻟﻮطﻦ work اﻟﻌﻤﻞ issue ﻣﻠﻒ (Qatif (Shia region اﻟﻘﻄﯿﻒ they_stole_from_me# ﺳﺮﻗﻮﻧﻲ# E Collective Action Results

Figure A12: Change in Average Number of Tweets Calling for Collective Action

Less Collective Action Less Collective Action Pre−Arrest Post−Release Arrested Tweets (Month) ●

Arrested Tweets (Year) ●

Mentions (Month) ●

Mentions (Year) ●

Followers Tweets (Month) ●

Followers Tweets (Year) ●

Match Tweets (Month) ● −0.06 −0.04 −0.02 0.00 0.02 0.04 0.06 Difference in Number of Tweets Calling for Collective Action Pre−Arrest vs. Post−Release

This ﬁgure shows the results of t-tests evaluating the change in the average number of tweets calling for collective action in tweets produced by imprisoned opinion leaders, tweets produced by similar non-imprisoned opinion leaders, tweets mentioning or retweeting imprisoned opinion leaders, and tweets sent by the engaged followers of imprisoned opinion leaders one month before the arrests and one month and one year following the releases. Each tweet was coded by three coders on Figure8 as either containing discussions of collective action or not.

24 Figure A13: Change in Proportion of Tweets Calling for Collective Action a) Arrested Tweets b) Mentions

0.015

0.006

0.010 0.004

0.005 0.002 Average Prop. CA Tweets Prop. Average CA Tweets Prop. Average

0.000 0.000 Month Pre−Arrest Month Post−Release Year Post−Release Month Pre−Arrest Month Post−Release Year Post−Release Pre−Arrest vs. Post−Release Pre−Arrest vs. Post−Release

c) Followers’ Tweets d) Match Tweets

0.0020 0.002

0.0015

0.0010 0.001

0.0005 Average Prop. CA Tweets Prop. Average CA Tweets Prop. Average

0.000 0.0000 Month Pre−Arrest Month Post−Release Year Post−Release Month Pre−Arrest Month Post−Release Pre−Arrest vs. Post−Release Pre−Arrest vs. Post−Release