Scaling Authoritarian Information Control How China Adjusts the Level of Online Censorship

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/348429732

Article in Political Research Quarterly · January 2021

CITATIONS READS 0 102

2 authors:

Rongbin Han Li Shao University of Georgia Zhejiang University

19 PUBLICATIONS 254 CITATIONS 7 PUBLICATIONS 11 CITATIONS

SEE PROFILE SEE PROFILE

Some of the authors of this publication are also working on these related projects:

Self-censorship View project

Cyber Criticism of Chinese Public Intellectuals View project

All content following this page was uploaded by Li Shao on 19 January 2021.

The user has requested enhancement of the downloaded file.

Scaling Authoritarian Information Control

How China Adjusts the Level of Online Censorship

Rongbin Han

Department of International Affairs, University of Georgia

Address: 322 Candler Hall, University of Georgia, Athens GA 30602

Phone: 706-542-6705

Email: [email protected]

Li Shao (Corresponding Author)

Department of Political Science, School of Public Affairs, Zhejiang University

Address: 634 School of Public Affairs, Zhejiang U Zijingang, Hangzhou, China 310068

Phone: 86-0592-5633-6986

Email: [email protected]

Abstract

Autocracies can conduct “strategic censorship" online by selectively targeting different types of content, and by adjusting the level of information control. While studies have confirmed the state’s selective targeting behaviour in censorship, few have empirically examined how the autocracies may adjust the control level. Using data with a 6-year span, this paper tests whether the Chinese state scales up control over citizenry complaints in reaction to a series of socio-political events. The results show that instead of responding to mass protests and major disasters as previous studies have suggested, the state tend to adjust the control level because of political ceremonies, policy shifts, or leadership changes. The findings help refine the strategic censorship theory and offer a granular understanding of the motives and tactics of authoritarian information control.

Keywords

Authoritarian information control, strategic censorship, Internet, China

(Political Research Quarterly, forthcoming)

The literature on censorship in autocracies has explored the selective targeting

phenomenon extensively (e.g. Bamman, O’Connor, and Smith 2012; Fu, Chan, and Chau

2013; King, Pan, and Roberts 2013; Qin, Strömberg, and Wu 2017), with the focus on

why the government blocks certain types of content but not the others. However, the level of censorship may not be constant over a specific type of content. For example, to celebrate the 70th Anniversary of the founding of the People’s Republic, Chinese authorities banned “overly entertaining” TV shows for a hundred days from August 1,

2019 onwards, while promoting only 86 selected mainstream propaganda TV series.1

Such a ban instead of targeting a specific type of taboo topic represents a scale-up of the level of information control, albeit for a limited time period. How to explain such

temporary “scale-up of control” on certain content during special time periods? Building

on the strategic censorship model by Peter Lorentzen (2014), this article provides

evidence on how the Chinese state adjusts the level of control over online information

across the time. More specifically, we address the question: under what circumstances

does the Chinese government scale up the level of information control?

This article differentiates selective targeting in state censorship, which has been heavily

studied, from adjustments in the information control level and focuses only on the latter.

If selective targeting is about sorting out what type of content is tolerated or censored,

thus concerns primarily with mapping the boundaries of expression, adjusting the level of

control indicates the scenario in which the state shrinks or expands the zone of

permissible expression. Using a metaphor of highway patrol here, we are not studying

whether traffic police catching and punishing drivers going above the speed limit of 65

miles per hour but when and why the traffic authorities temporarily or permanently adjust

the speed limit from 65 to 45 miles per hour. We operationalize adjustments in the

control level as changes in the volume of a broad category of online expression—

citizenry complaints about local problems (again, not specific cases of such complaints).

We then test whether a series of socio-political events have caused fluctuation in this category of expression by comparing the daily counts of posts on China’s largest online

forum Tianya.cn to the state-sponsored forum Local Leadership Message Board between

July 2009 and July 2015. We find that the Chinese government tends to scale up the

control level at politically symbolic moments such as national anniversaries or political

meetings but less so in cases of crisis events like protests, foreign revolutions, disasters, or accidents.

This research adds to studies on information control and authoritarian politics in several ways. First, while scholars argue that the state may adjust the overall control level

(Lorentzen 2014), only a few have empirically explored the conditions under which the state makes such adjustments (e.g., Cairns and Carlson 2016; Repnikova 2017; Ruan et al. 2020). This paper contributes to the burgeoning literature by testing whether certain socio-political events cause changes in the overall control level over a broad category of content (citizenry complaints) that include multiple specific topics. Second, analysis in

this article reveals the complicated and mixed motives of authoritarian information

control: adding to studies that highlight the Party-state’s motives to preserve stability

(e.g. King, Pan, and Roberts 2013; Lorentzen 2014; Roberts 2018), we find the symbolic

concerns and the will of the leadership are strong drives for information control, where

the state is more likely to adjust the control level in response to political ceremonies and

the leadership change than to shocks such as protests and accidents. Finally, by refining

the “strategic censorship” model (Lorentzen 2014) and providing a more granular

understanding of the motives and tactics of authoritarian information control, the article

enriches the literature on authoritarian resilience in the digital age (e.g. Gilley 2003;

Nathan 2003; Shambaugh 2008). The fact that the Chinese state not only scales the level

of censorship over time, but also differentiates such adjustments to distinctive socio-

political events shows that authoritarian information control can be customized by the

state for different purposes, revealing the state’s capacity to adjust to the digital

challenges.

Authoritarian Information Control: Categorizing and Scaling

Though the conventional wisdom suggests that authoritarianism is incompatible with free information (e.g. Friedrich and Brzezinski 1965; Gainous, Wagner, and Ziegler 2018;

Levitsky and Way 2010; McMillan and Zoido 2004), recent studies show that

authoritarian states may not suppress information and media completely because critical

expression can serve a safety valve (Hassid 2012), provide necessary policy feedback and

check local agents (Lorentzen 2013), and allow prediction of events such as protests and

corruption charges (Qin, Strömberg, and Wu 2017). Along this line, Peter Lorentzen

(2014) argues that authoritarian states may employ “strategic censorship” to benefit from freer information without taking the risk of being overthrown. Here “strategic censorship” means the government can (1) selectively target critical information such as allowing exposure of local scandals but not criticism on top leaders and (2) adjust the amount of criticism depending on the level of social tensions.

The notion of “selective targeting” is easy to understand, which boils down to the

reasoning and practice of categorizing content into two types, with one type to be

removed and the other type kept. Since not all types of critical information are equally

threatening and some may even be beneficial, autocracies like Chinese government can

suppress more threatening types while tolerating less harmful types. As Lorentzen (2014)

reasons, the Chinese state may suppress criticism of top leaders while tolerating

disclosure of local scandals. Along this line, through analysis of censored online posts

and experimental research, King, Pan, and Roberts (2013) find that China prioritizes

expression related to collective action over general criticism of the government. Others, while may contest what types of content are more censored, confirm the selective targeting argument by showing the state prioritizing various types of content over other types when censoring (e.g. Bamman, O’Connor, and Smith 2012; Fu, Chan, and Chau

2013; Qin, Strömberg, and Wu 2017; Shao 2018; Q. Tai 2014). Tai and Fu (2020), through examining censored articles on the popular social media platform WeChat, further find that in addition to “sensitive” or “problematic” topics, state censorship may selectively target articles that contain a higher number of key specific terms, especially

those signalling conflicts or tensions. In other words, the state may be driven by the logic

of suppressing online focal points, i.e. hot-button issues perceived as crises.

In addition to selective targeting, scholars increasingly realize that “sensitiveness” of

online expression as well as the censorship system are both contextually contingent

(King, Pan, and Roberts 2013; Y. Tai and Fu 2020). More specifically, the strategic

censorship model expects the censors to adjust the acceptable boundaries of reporting

depending on the overall level of social tensions: “When social tensions are high, more

news must be censored, whereas when tensions are relatively low, censorship can be

loosened” (Lorentzen 2014, 403). This means in addition to categorizing content into

different types to censor or to keep, the state may also scale the level of control over a

specific type of content or online information overall. This makes immediate sense

because autocracies have a rich tool kit to control the Internet and have learned to apply

different levels of control in different circumstances ranging from completely shutting

down the Internet access to targeting very specific types of online expression only while

allowing free expression on other topics (e.g. Deibert et al. 2008, 2010; Howard and

Hussain 2013).2 Scholars studying China have confirmed that the state may fine tune control over specific online crises or during important political events. For instance,

Christopher Cairns (2019) find that during the 2012 scandal surrounding former politburo member and Chongqing Party Chief Bo Xilai, the state not only differentiates between critical and supportive voices on the topic, but also changes the censorship rate over such

types of expression over the time. This and similar studies (e.g., Cairns and Carlson 2016;

Cairns and Plantan 2017; Repnikova 2017; Shao 2018) reveal temporal and situational

variations in state censorship, yet focus only on specific crises and often only map

information management over the specific topics (such as the Bo Xilai scandal itself),

thus cannot tell us whether the state adjusts overall level of control beyond these crises

per se. In other words, it is unclear whether the scandal, which supposedly increased

social tensions, had caused the state to scale up control, as the “strategic censorship” model conceptualizes.

Scholars at the Citizen Lab have found the Chinese state adjusting the control level

during important political events such as the 19th National Communist Party Congress

(Crete-Nishihata et al. 2017; Ruan et al. 2020). They find that censorship on WeChat in

the form of keyword blocking increased before the Congress, peaked during the event, and then gradually receded. Such studies, echoing earlier scholarship on “calendar of activism” and “dissident calendars” (Stern and Hassid 2012; Truex 2019), cleverly reveal how political events may affect online censorship. However, as Ruan et al (2020) acknowledge, they focus only on real-time pre-emptive censorship of a unique event on one social media platform. Moreover, it remains opaque whether spikes in blocked keywords observed in these studies truly indicate the scaling up of censorship beyond the specific event, or simply reflect increased deviant expression directly relevant to the

Party Congress itself.

In short, besides selectively targeting different types of content, authoritarian regimes like

China may adjust the level of control by redefining the scope of censorship. But more systemic empirical research has yet to be done on how the state adjusts the control level over time. The following sections explore under what conditions the state may scale up the control level. In doing so, we provide a granular understanding of authoritarian information control strategy.

Motives of Control Level Adjustments and Testable Hypotheses

The difference between the categorizing logic (selective targeting) and the scaling logic

(adjusting the control level) in censorship can be illustrated by two leaked official directives from China. On February 20, 2010, websites in Hunan Province were decreed to remove the article “Only democracy can save China.”3 On September 14, 2009, the

government asked local websites “not to cover any disruptive news in order to control negative publicity (kongfu, 控负).”4 Between the two directives, the first identifies a

specific taboo topic to be censored and removing it is primarily to patrol the boundaries

of permissible expression just like traffic police enforcing a specific speech limit; the

second reflects scaling up the overall level of information control, as illustrated in Figure

1 with the shift from the left panel to the right panel (light green zones represent topics

that are tolerated at a particular moment). In the latter scenario, the state does not simply remove the taboo topics, but rather adjusts, and in this specific case shrinks, how much total criticism is allowed, resembling a reset of the speed limit from a higher speed to a lower speed.

[Here Insert Figure 1]

When will the Chinese state and other autocracies adjust the level of control? First, according to Lorentzen (2014), the state shall optimize the level of control relative to the level of social tensions in order to keep the amount of negative publicity and public discontent constant. Evidently, social tensions tend to increase during socio-political crises which not only signal to the public that there is widespread dissatisfaction, but also suggest an opportunity for mobilization. In this regard, authoritarian states shall respond to socio-political crises, which will increase the amount of negative publicity. In these cases, besides suppressing expression directly related to the crises, the state may escalate the overall level of control, thus shrink the zone of permissible expression. So we propose:

H1: Outbreaks of socio-political crises that may lead to heightened social

tensions and criticism toward the regime will increase the level of control.

Previous studies (Bamman, O’Connor, and Smith 2012; Truex 2019) suggest the following four types of crises that may trigger tightened state control: political struggles,

disasters or accidents, domestic protests, and foreign revolutions. As Rory Truex (2019,

1041) nicely summarizes existing studies (Beissinger 2002; Danneman and Ritter 2014;

Meyer 2004; Opp 2000), these types of events may all beget mobilization and repression

because they might give dissidents “the impression that they have elite allies,” “signal

poor governance,” give people “something to mobilize around,” or bear “a

demonstration and diffusion effect.” While due to different natures, dynamics, and scopes of influence, crises can be further differentiated, we group them together given our

research purpose. Even in cases such as natural disasters when the state may attempt to

turn the crisis into a driver of regime support by prompting pro-regime voices online,

critical voices would be suppressed, thus we expect the censorship effects to be similar as

compared to other types of crises. Since such events are often unexpected and fast-

developing, state control in such cases is crises-management and contingent in nature,

regardless whether it is to maintain stability or to protect legitimacy.

H2: Ritualistic events of the state increase the level of control.

Second, existing studies suggest that state control may correspond with events like

national celebrations, anniversaries, and meetings. Being the natural focal points (Truex

2019), these events may function as “political opportunities” for contention (McAdam,

Tarrow, and Tilly 2001; Meyer 2004; Tilly and Tarrow 2006). Information control at

such moments is not responding to socio-political crises that shock the state. Rather it is

preemptive in nature because enhanced control here, instead of conditioning on the actual level of social tensions, reflects more a decrease in the state’s tolerance of social tensions.

In addition, such events often are also of symbolic significance as the state intends to

either create a political atmosphere of unity, harmony, and happiness under authoritarian

rule, or to signal its invincibility to citizens (Huang 2015; Magaloni 2006). Whether the

state is driven by the desire to maintain stability, symbolic motives, or signaling concerns,

control at such moments can be seen as ritualistic. We propose:

Research Design and Data

Assessing adjustment of censorship

To study changes in the level of control, the first challenge is to detect and measure such

changes. Current empirical studies either rely on leaked censorship directives (e.g., King,

Pan, and Roberts 2017; Kuang 2018) or adopt a “state-reflected-in-society” approach by

detecting the posts removed by the state (e.g., Fu, Chan, and Chau 2013; King, Pan, and

Roberts 2013). The nature of this research makes it inadequate to directly apply neither of

the approaches. On the one hand, leaked censorship directives, while valuable to study

state motives, are limited in quantity and often paint an incomplete picture of censorship.

On the other hand, observing posts removed by the state allows scholars to directly

examine the state’s censorship behavior, especially selective targeting in censorship, but

it is not an ideal direct measure of adjustments in the level of control. Admittedly, if one

aggregates the number of posts censored at a time, as King-wa Fu and his team at Hong

Kong University have done in their Weiboscope project,5 this approach has the promise

to gauge the intensity of censorship, thus the level of control. Even so, variation in the

aggregated number of censored posts may well reflect the bursts of discussion rather than

changes in the level of control, not to mention that it fails to measure users’ self- censorship or platforms’ filtering effects.

In this research, we propose a simple yet effective approach that better suits the purpose

of studying changes in the level of control. We deem this is possible by observing

unnatural fluctuations in the volume of citizenry complaints on a major platform. The

logic is straightforward: when controlling other variables, the volume of citizenry

complaints is a function of citizens’ enthusiasm to post and the level of state control. By

controlling citizens’ enthusiasm to post, we can identify scale-ups of the control level,

which should cause dramatic drops in the discussion volume as a portion of previously-

tolerated complaints are suddenly no longer permitted (whether due to pre-emptive

control or post hoc censorship). In comparison, regular censorship patrol that eliminates

specific taboo topics, which the state constantly removes, normally would not cause

observable changes in the discussion volume. Though there are obstacles to measure the

drops and control alternative explanations, we believe they can be addressed, as

elaborated below.

We focus on one type of online content—citizenry complaints about local governments,

because we expect the state to adjust its levels of tolerance under different circumstances, which in turn render the volume of such expression an effective indicator of the information control level. From the state’s perspective, online complaints about local ills can provide policy feedback and improve governance, but may also promulgate negative publicity, encourage social activism, and erode regime legitimacy. The ambiguous implications of citizenry complaints mean that such expression is precisely the type of content over which the authoritarian state may scale up control in response to changes in

the level of social tensions (Lorentzen 2014), to conduct preemptive suppression during

political ceremonies (Truex 2019), or for other state motives yet to be identified.

Dependent Variables

We chose “Grassroots Voices” (Baixing Shengyin, 百姓声音, or Complaints hereafter)

on Tianya (tianya.cn) and Local Leader Message Board (LLMB hereafter) on

People.com.cn (People’s Daily Online) as our main platforms to detect control adjustment. As China’s most influential and vibrant online forum, private-owned Tianya hosts hundreds of thematic boards and more than two million simultaneous visitors during its peak hours every day. As of January 2018, it boasts over 135 million registered users, meaning one of every six Internet users frequent the forum in China. While it is not as popular as platforms such as the Twitter-like Weibo, Tianya is one of the national focal points of online expression and censorship, thus should be sensitive to adjustments in the levels of state control. As a thematic discussion board on Tianya, Complaints was established specifically for netizens to voice complaints of local government and social ills. Since the forum administrators will patrol the board and remove irrelevant posts regularly, Complaints features relatively homogenous content, thus reducing the noise we must deal with.6 We have scraped the threads posted on Complaints starting from its

establishment in June 2009 to July 2015, providing a time-series dataset to examine the

daily fluctuation in the volume of citizenry complaints.

As one may question whether the discussion volume on Complaints alone can serve as a

dependent variable independently, we introduce the number of discussion threads on

LLMB as a baseline.7 LLMB, as a state-sponsored “online petition forum that allows

citizens to directly register complaints to party and government leaders in their localities

(Jiang, Meng, and Zhang 2019, 2),” serves as an adequate comparative baseline here for

the following reasons. First, the daily number of posts on LLMB can serve as a proxy of

Chinese citizens’ overall enthusiasm to complain about local governments because the

forum carries the same content as Complaints. Second, LLMB shall be influenced

relatively less than Tianya when the state adjusts the control level because it is a

“privileged” platform—it is argued certain groups of elites and government controlled forums enjoys more room for free expression (Congressional-Executive Commission on

China n.d.; Creemers 2016). As a platform directly affiliated to the Party’s mouthpiece,

People’s Daily Online, LLMB is not just more trustworthy from the state’s perspective.

To the regime, the forum is also less threatening, not just in that it knows where the

official red line is, but also because the petitioners, with a clearer understanding of the

opening, would behave accordingly by avoiding deviant expression. More importantly,

LLMB helps the state to display its responsiveness—by tolerating and responding to non-

threatening citizenry complaints, the regime may enhance its image and legitimacy (e.g.,

Lorentzen 2013; O’Brien and Li 2006). It would be unwise for the state to tighten up

control abruptly on LLMB in this sense. And as the state directly runs the forum, it cannot

even shift blame to intermediary actors as in the case of Tianya. The reasons are

confirmed by sporadic evidence. We find that LLMB suffers a very low level of post hoc

censorship, with only less than 1% of posts deemed as irrelevant to local grievances

being deleted.8 In addition, the forum displays a very high level of responsiveness

(typically over 80 per cent of the complaints would receive a response across the sub- forums), confirming its showcase function. Overall, even though we cannot rule out the

possibility that the state may instruct LLMB to screen its posts more tightly at some

points in time, should be able to help us better discern when the state would adjust the

level of information control as the baseline.

[Here Insert Figure 2]

Using LLMB as the baseline, we construct two indicators to measure the level of control

as our dependent variables. First, we use the difference of post percentage to make them

comparable (Equation 1). The dependent variable y R is the adjusted percentage change

𝑐𝑐𝑐𝑐 of posts on Complaints, with c denoting the board Complaints and t denoting the date t in the dataset. p denotes the number of posts on Complaints on date t, and is the mean

𝑐𝑐𝑐𝑐 𝑐𝑐 number of posts on Complaints. Likewise, p is the number of posts on LL𝑃𝑃�MB on date t,

𝑡𝑡 and is the mean number of posts on LLMB. Because LLMB is the proxy for people’s

𝑙𝑙 enthusiasm𝑃𝑃� to complain, the smaller y , the fewer posts Complaints have on date t

𝑐𝑐𝑐𝑐 compared to LLMB, thus the higher level of control over Complaints. This “Percent” measurement reflects the daily relative volume on Complaints compared to LLMB.

y = (1) p𝑐𝑐𝑐𝑐 p𝑙𝑙𝑙𝑙 𝑐𝑐𝑐𝑐 𝑃𝑃�𝑐𝑐 − 𝑃𝑃�𝑙𝑙 Second, we use the difference of standardized measurement of posts between the two

boards (Equation 2). and denote the standard deviation of daily Complaints posts

𝜎𝜎𝑐𝑐 𝜎𝜎𝑙𝑙 and daily LLMB posts, respectively. reflects the standardized volume of p𝑐𝑐𝑐𝑐−𝑃𝑃�𝑐𝑐 𝜎𝜎𝑐𝑐 Complaints posts on date t, and applies the same to LLMB. Similarly, the smaller p𝑙𝑙𝑙𝑙−𝑃𝑃�𝑙𝑙 𝜎𝜎𝑙𝑙 y is, the higher level of control over Complaints.

𝑐𝑐𝑐𝑐

y = (2) p𝑐𝑐𝑐𝑐−𝑃𝑃�𝑐𝑐 p𝑙𝑙𝑙𝑙−𝑃𝑃�𝑙𝑙 𝑐𝑐𝑐𝑐 𝜎𝜎𝑐𝑐 − 𝜎𝜎𝑙𝑙 14

Control Variables

Other than citizens’ enthusiasm to complain online, we acknowledge that several

additional confounding factors may have an impact on our dependent variables, thus are

controlled in the analysis. For example, the discussion on Complaints may drop not

because citizens are less likely to complain online, but because they have shifted to other

platforms such as Weibo. For this reason, we control the popularity of the Tianya. Since

the daily traffic of the website is inaccessible, we use daily thread counts on Tianya’s second most popular board “Free Talk” (Tianya Zatan, 天涯杂谈, or General hereafter) as a proxy [Tianya General].9 As the title suggests, General hosts all sorts of political and non-political topics, thus the public attention bias (as reflected in Tianya’s popularity)

shall have little impact on the board (as Appendix 2.b for a snapshot of sample posts).10

We do not use Tianya’s most popular board “Funinfo” as the baseline because the board

is susceptible to the highly volatile agenda of the entertainment industry.

We also control the following non-political fact that may affect the number of posts on

Complaints and LLMB: the weekends, national holidays, four seasons of the year as well as the change of yearly patterns. The following political processes which are not of primary interest to us but may affect the level of control over Complaints (variable names in square brackets) are controlled as well. During the period we analyze, two official campaigns might have led to suppression of citizenry complaints: (1) the crackdown of

Chinese spinoffs of I-Paid-A-Bribe in 2011 [IPAB_Crack]11 and (2) the anti-rumor

campaign that targeted anti-corruption social media influencers in 2013 [Anti-rumor]. We

assume the effects of both campaigns last two weeks, similar to how we coded our

independent variables of interests (other than ritualistic ones), as will be discussed

below,12 because studies in China and elsewhere show that is generally the time span of

public and media attention to online events (e.g., Leetaru 2015; Yu 2010, 25). In fact,

different coding schemes return similar results. Finally, we include a binary variable on

President Xi Jinping (Xi_Term, from March 15, 2013) since Xi has changed China’s

policy course in many ways (Economy 2018).

Independent Variables

Since the data for our dependent variables cover the period from July 15, 2009 to July 31,

2015, we select a series of events that fall within this period as our independent variables

based on the discussion of the censorship motives discussed above.

We categorized four types of contingent events that may lead the state to scale up control

over citizenry complaints: political struggles, disasters or accidents, foreign revolutions,

and domestic protests.13 For the first three types, we have selected events that are of the most significant impact during the period we cover as they are limited in number. First, political struggles are moments when authoritarian rule is challenged from within at the top level. Amidst such crises, the state may enhance control over citizenry complaints to prevent challenges arising from the public. We include the Wang Lijun incident when the

Chongqing police chief sought asylum from the U.S. Consulate [Wang], the purge and verdict of former Politburo member and Chongqing Party Chief Bo Xilai [Bo_Purge and

Bo_Verdict], and the verdict of former Politburo Standing Committee member Zhou

Yongkang [Zhou_Verdict]. Second, natural disasters and large-scale accidents may lead to criticism of the regime and cast doubt on the state’s ability to govern, thus increasing

the level of social tensions and subsequently overall control over negative information.

We include the three most prominent disasters or accidents in the time period covered:

the 2010 earthquake in Yushu [Yushu], the high-speed train crash in 2011 [Train], and

the Shanghai New Year’s Eve stampede on December 31, 2014 [Stampede]. Third,

foreign revolutions may generate demonstration effects and inspire Chinese citizens to

mobilize for regime transition, triggering the state to enhance the control over negative

information such as citizenry complaints. In this study, we include the Arab Spring

(starting from the step-down of Ben Ali) in 2011 [Ben_Ali], the Chinese Jasmine

Revolution inspired by the Arab Spring [Jasmine], and the Hong Kong Umbrella

Revolution in 2014 [Umbrella].

Compared to the other three sub-categories of contingent events, mass protests are larger in number. We include cases only with national influence as they were more likely to cause the state to scale up the control level. To sample these events, (1) we gathered all collective mobilization cases between 2010 and 2015 from Wikipedia Chinese as events captured by the site are mostly of national impact; (2) we excluded cases that were not citizen-initiated or not targeting the state; (3) we then randomly selected five cases from the remaining pool. The sampled cases are the Qian Yunhui Incident [Qian], the Dalian

PX (Para-Xylene) protest [Dalian], the Wukan Incident [Wukan], the Qidong waste water system protest [Qidong], and the Maoming PX protest [Maoming] (Appendix 4 briefly introduces these cases). As these cases were covered in international media (e.g.,

Bradsher 2011; Lee and Ho 2014; Perlez 2012; Wines 2011; Yang and Wong 2010), they are the most likely cases if the state indeed would scale up control strategically to mass protests (other than simply removing discussion on these topics per se).

As mentioned above, studies show that public and media attention to online events (thus the period of state intervention) normally lasts for about two weeks (Leetaru 2015; Yu

2010, 25). Thus we code all contingent events from the date they happened and the next

14 days as their effective periods.14

For ritualistic control, we include the following events that are politically symbolic: (1) the annual meetings of the National People’s Congress (NPC) and the People’s Political

Consultative Conference (CPPCC), which are normally convened together in March

[Two Sessions]; (2) the Chinese Communist Party’s National Congress Plenaries

[CCP_Plen]; and (3) two specific major political events, the Republic’s 60th Anniversary

[PRC60] and the Party’s 18th National Congress [CCP18]. The former witnessed a huge celebration with a military parade, and the latter was the power transition from Hu Jintao to Xi Jinping. We also include anniversaries of the Tiananmen democratic movement

[June4] here as they are likely to cause symbolic damage to the regime. Since these events are on fixed dates, adjustments of control, if present, should be routine and predictable. To prepare or maintain the ceremonial atmosphere, state control may extend both before and after these events. We have coded two weeks before and after as their time range of influence for all events. To check the robustness, we also run the analysis by setting the influential period to ± one week and ± two days (see Appendix 1), and the results remain similar.

Again, we do not intend to examine whether the regime censored posts related to the events themselves. Rather, we are interested in whether these events may cause the state to scale up the control and reduce the permissible zone of citizenry complaints online in general. After controlling the factors that may affect the audiences’ enthusiasm to post

complaints, the reduction of number can only be affected by increased control, i.e. the

expansion of temporary censorship.

Analysis and Results

The regression model we used is provided below as Equation 3:

y = 0 + 1 + 2 + 3 + 4 + 5 + 6 + 7 + 𝑐𝑐𝑐𝑐 (3) 𝛽𝛽 𝑅𝑅𝑅𝑅𝑡𝑡𝛽𝛽 𝐶𝐶𝐶𝐶𝑡𝑡𝛽𝛽 𝐺𝐺𝑡𝑡𝛽𝛽 𝐶𝐶𝑡𝑡𝛽𝛽 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡𝛽𝛽 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡𝛽𝛽 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑡𝑡𝛽𝛽 𝜀𝜀When𝑡𝑡 y denotes the dependent variable, the adjusted discussion volume on Complaints

𝑐𝑐𝑐𝑐 on date t, denotes the vector including all ritualistic events on the same date.

𝑡𝑡 𝑡𝑡 denotes the𝑅𝑅𝑅𝑅 vector including all contingent events. Gt denotes the count of posts on𝐶𝐶𝐶𝐶 Board

General that proxies the traffic of Tianya.cn. denotes the vector of control variables:

𝑡𝑡 IPAB_Crack, Anti-rumor and Xi_Term. The dummy𝐶𝐶 variable of whether date t was a weekend and fixed effects on seasons and years were also added to the model. Since the dataset is time-series, we use Newey West standard errors that are robust to heteroskedasticity and autocorrelation. We set our maximum lag as 7(days) while we also run the tests by changing the maximum lag from 1 to 6. The significance level does not change (See Online Appendices 7-10).

Contingent Cases

Figure 3 shows the coefficient plots of contingent events (in these models ritualistic events are coded ±two weeks; see Appendix 3 for the detailed coding). Overall, the results show that these cases had not reduced the volume of citizenry complaints consistently. In fact, compared to LLMB, discussion on Complaints did not decrease for

Ben Ali stepping down and Jasmine revolution, and even increased during the Umbrella

Revolution. Given that the Party-state was clearly concerned with these revolutions and

19 their impact online (Fallows 2011; Jacobs 2014; Wong and Barboza 2011), the results run against our expectation.

While further studies are needed to investigate the puzzling observation, there are two possible explanations. First, since citizenry complaints do not seek to topple the regime after all, the regime does not scale up control over such negative expression upon foreign revolutions to project a benevolent image and to vent pressure. This reasoning resonates with studies that find popular contention may help reinforce authoritarian rule (Lorentzen

2013; Tsai 2015), but contradicts the strategic censorship prediction (Lorentzen 2014) as in such cases allowing venting will push up the level of social tensions. Second, revolutions might have made the state nervous, but the state was preoccupied with other control priorities and ignored platforms like Complaints, turning the board into a safe haven for expression. This explanation implies a different control mechanism that may help refine the strategic censorship model: when adjusting the level of control, the state is concerned with not only the total amount, but also the type of negative information given the nature of crises.

[Here Insert Figure 3]

Among other contingent cases, the Maoming Protest, Wukan incident (domestic protest), the Wang Lijun incident and the purge of Bo Xilai (political struggle) had resulted in statistically significant drops in the volume of complaints. The Maoming case recorded a drop by 56.8% (109% standardized). Wukan case recorded a drop by 44.9% (88.2% standardized). Wang Lijun recorded by 27.9% (53.5% standardized) and the purge of Bo by 38.4% (76.3% standardized), as compared to LLMB. Results for other cases are either

statistically not significant or positive (as in the cased of Hong Kong Umbrella

Revolution, Qidong water waste system protest, and the high-speed train crash). Overall, the results for the contingent cases are at best mixed, thus Hypothesis 1 is not fully supported.

Ritualistic Cases

Figure 4 shows the coefficient plots of five ritualistic events when the influential periods

of these events were set to ±2 weeks, ±1 weeks and ±2 days. The left panel plots the

coefficients of ritualistic events when coded ±2 weeks. The results reveal that the national

or Party conferences and anniversaries had led to significantly tighter levels of control

over citizenry complaints, and the Tiananmen movement anniversaries showed an effect

that indicates possible scaled-up control, but is quite weak.

[Here Insert Figure 4]

More specifically, during the annual CPPCC and NPC meetings, the volume of citizenry

complaints dropped 29.2% (56.2% in standardized measurement) compared to LLMB.

Similarly, during annual plenaries of the Party Congress, the volume of citizenry

complaints dropped 24.0% (47.5% standardized) with LLMB as the baseline. In addition,

politically more significant and symbolic meetings or ceremonies triggered even tighter

levels of control. Discussion on Complaints dropped 34.1% (77.0% standardized) relative

to LLMB during the 60th Anniversary of the People’s Republic, and 34.1% (67.1%

standardized) during 18th Party Congress. All results are statistically significant at a 95% confidence level.

During the Tiananmen democratic movement anniversaries, the daily number of posts on

Complaints dropped by 9.5% (18.2% standardized) compared to LLMB. This is consistent with Hypothesis 3. However, the effect is relatively weaker compared to other ritualistic events.

Overall, these results support Hypothesis 2 that ritualistic events may lead to enhanced control over citizenry complaints. In addition, the political importance of the events seems to affect the degree of adjustments, with more symbolic moments having a more dramatic increase in the control level as compared to less significant and more routinized events. Though the increased control during the Tiananmen movement anniversaries was weak, it is unlikely that the movement is no longer sensitive since the state still tries hard to silence democratic activists, victim families, and related expression. We believe this is because the movement mobilizes more dissident communities than ordinary citizens, thus the state feels less need to enhance control over citizenry complaints that make no political claims related to the June 4th movement.

Results of Control Variables

Some control variables show noteworthy results (Online Appendix 7). The crackdown on

IPAB spinoffs generated significant reduction of citizenry complaints by 32.1% (64.4% in standardized measurement). This is expected because Complaints features similar types of content as IPAB spinoffs (citizenry disclosure of local problems), thus was logically affected by a campaign targeting the latter. What is worth noting is Complaints survived the campaign, despite experiencing a high level of impact similar to that of the

60th Anniversary of the PRC and the 18th Party Congress and much larger than other routine ritualistic events. In addition, under President Xi’s rule, discussion volume on

Complaints dropped 55.1% (109.2% standardized) with LLMB as the baseline, an impact

more dramatic than all our other independent variables.

Discussion

Overall, our findings allow us to refine the strategic censorship model by empirically

testing whether and when the Chinese state scales up the control level (Lorentzen 2014).

First, the state is clearly less tolerant of citizen complaints during politically symbolic

events like the Party’s national conferences and anniversaries. This is worth noting

because though preventing dissent and social unrest may be a concern (e.g., Truex 2019),

control at such times is also about the regime’s symbolic image and its overall legitimacy.

Leaked state censorship directives support this latter reasoning. For instance, before the

2009 Spring Festival, the Hunan Provincial News Office relayed a directive to all media

outlets under its jurisdiction, asking them “to tighten up control over negative reports to

create a merry and harmonious atmosphere.”15 If control over critical information during

ceremonial occasions can still be driven by the state’s concern over stability maintenance,

the following instances confirm the ritualistic motive further. First, the state circular that

announces the ban of “overly entertaining” TV shows in 2019 mentioned above in the

introduction explicitly states that the purpose is to maintain the propaganda atmosphere

of the 70th Anniversary of the PRC. This stated purpose is unlikely just rhetorical but rather makes sense because banning popular entertaining shows does not help prevent but

may rather stimulate collective action. Similarly, after the 2010 earthquake in Qinghai

Province, the Chinese state designated April 21, 2010 as a “national mourning day.” In

response, Tianya not only turned its webpages into black and white just like other portal

websites, but it also had to suspend its most popular board, Funinfo. Again, the state was

not driven by the concern of collective action since entertainment news is practically of

no harm. The motive was purely ritualistic: allowing citizens to have fun as usual is

incompatible with the mourning atmosphere that the state intended to maintain.

Second, while we confirm that an authoritarian state may adjust the level of control, there

are mixed results with contingent cases such as foreign revolutions, domestic protests,

political struggles, and natural disasters and accidents. We find that the state only scaled

up the level control in response to selected crisis events, but not all of them. The patterns

of control adjustment vary across these sub-categories of contingent cases and within

these sub-categories. While further research is needed to explain why the state have

responded differently to such shocks, it is safe to conclude that not all crises would shock the state enough to scale up the control level over critical information online. Please note that the finding that the state did not scale up control in response to some contingent cases does not indicate that it tolerates expression on these cases, especially those of

collective action nature. Rather, we argue that state censorship of these cases would not spill over to other citizenry complaints, i.e. not triggering strategic censorship as

Lorentzen(2014) argues. Overall, given the more consistent evidence of the state adjusting control level during political ceremonies, authoritarian information control is not just about responding to threats of collective action or calculating social tensions, but may be more shaped by the regime’s symbolic motives.

Third, our findings confirm the impact of leadership and policy changes on everyday

online complaints. President Xi’s impact exemplifies the leadership factor. Our analysis,

by offering one of the first quantitative measures of the impact, reveals the alarming scale

of increased control, which is larger than any other independent variables we have

examined. The IPAB crackdown in 2011 represent the impact of policy shifts on

information control, which helps update the strategic censorship model (Lorentzen 2014):

Since IPAB spinoffs had been there for several months before being shut down and it

took a few weeks for the campaign to hit Complaints, it is reasonable to argue that

adjustments in the control level often reflect a dynamic process through which the state

assesses the challenge, formulates its response, and exercises its control.

Robustness Check

Admittedly, our observational approach is not a direct measure of state adjustments in the

control level, but rather a state-reflected-in-society one (King, Pan, and Roberts 2013;

Stern and O’Brien 2011). Therefore, we run further tests to address several major

concerns. First, our finding may be influenced by unknown factors that affected LLMB

that we did not include it into our model. We run further tests using two other measures

of dependent variable, including the ratio of Complaints itself, and the ratio of

Complaints compared to General. The results turn out to be very similar.16 We also run

LLMB as the dependent variable independently with the same specification of the model, and find that LLMB remain more stable during the events we explored. The results have much smaller coefficients and most insignificant.

Second, the coding of the influential periods of the events may affect the results. To check the robustness, we have also coded one week and two days for ritualistic events.

The results turn out to be highly similar. The only difference is that for some ritualistic events, the effects become smaller as the coding period narrows. As Figure 4 shows, for

CCP’s annual plenaries, discussion volume on Complaints decreased by 24.0% when coded as ± 2 weeks. When coded ± 1 week and ± 2 days, the effects dropped to 22.5%,

then 10.7% and lost statistical significance. This is the evidence of our coding of 2 weeks being proper because shorter coding spans are more vulnerable to noises and random factors.

Third, our analysis does not differentiate self-censorship (by users or the forum) and state censorship, or ex ante and post-hoc censorship. This issue of self-censorship is unlikely to bias our detection of the changes in scale of control. Existing studies suggest that the socio-political crises and ritualistic events tend to serve as “dissident calendars” that encourage mobilization (Stern and Hassid 2012; Truex 2019) rather than self-censorship.

Self-censorship by individual users, even if it exists, will not cause a significant drop in the discussion volume unless it is systematically induced, which is unlikely but for the regime’s intimidation. Similarly, Tianya was unlikely to voluntarily increase the level of platform self-censorship without the state’s pressure because studies show that platforms are incentivized to resist state pressure to censor (Han 2018; Miller 2018). And other platforms are also affected by the same events that have impacted Tianya (e.g. Ruan et al.

2020; Y. Tai and Fu 2020). The 60th Anniversary of PRC, for instance, which led to the temporary shutdown of Complaints, reportedly caused an all-around escalation of online censorship, including blockade of tens of thousands of websites (Ma 2009). Nevertheless, further studies should be conducted to differentiate the nuances between self-censorship and direct state censorship. Similarly, whether the state scales up the control level by systematically preventing citizens to post complaints or by removing their complaints en masse afterwards makes no difference given our research purpose.

Our analysis does not differentiate the role of the central and the local authorities. This shall not invalidate our findings because in the cases we have analyzed local authorities

26 are unlikely to make qualitatively different decisions from the central government though they may overreact or underreact by controlling more or less. Although we do not intend to discuss the structure of censorship system, our findings likely reflect more the central preferences for two reasons: first, local authorities, though can remove individual threads, often lack the incentive and/or the ability to drastically scale up overall control over platforms beyond their jurisdiction, especially those have national influence like Tianya.

The fact that all our cases are of national or international influence, and none is related to

Hainan Province where Tianya is based, makes it even less likely that our results are caused by local authorities. Moreover, ritualistic events and policy or leadership changes that triggered scaling up of control are of interest to the central government. In contrast, domestic protest cases, while of national influence, often put more pressure on local authorities.

Our results are supported by circumstantial evidence, too. For instance, in cases of the

60th Anniversary of PRC and the 18th Party Congress, discussion volume on Complaints not only free fell, but also remained low for a period that covers the two events almost perfectly (see Online Appendix 5). The Citizen Lab’s research confirms our argument showing that the state increased the control level as the 19th Party Congress approached

(Ruan et al. 2020). All the evidence adds to our confidence in our findings.

Conclusion

This article examines when the state adjusts the level of control over online expression, which has been understudied. We find that the Chinese state has scaled up the control level over citizenry complaints in cases of politically symbolic moments and policy or

leadership changes, while accidents, disasters, foreign revolutions, political struggles, and

domestic protests show mixed results. The findings, which add to our understanding of

authoritarian information control, both confirm and challenge what Lorentzen (2014)

articulates as “strategic censorship”: while the state clearly adjusts the level of control over critical online information to changes in socio-political conditions; such adjustments often happen at the time of symbolic events, policy and leadership changes than in response to socio-political crises such as popular protests, accidents, and foreign revolutions that may catch the state by surprise and cause higher level of social tensions.

We did not find consistent evidence that the state scales up control in response to

revolutions and protests. This does not negate previous findings of the state prioritizing

stability maintenance (e.g., King, Pan, and Roberts 2013), but rather suggest that it is

productive to further contextualize the analysis of the state’s control motives in two

senses. On the one hand, authoritarian states like China attach much importance to

stability maintenance (e.g., Chen and Xu 2015; Wang and Minzner 2015; J. Wright and

Escribà-Folch 2012), thus may have censored collective action expression diligently. But

stability maintenance does not capture all forms of state censorship motives. In this paper,

we propose that adjustment of the censorship level may follow a logic that emphasizes

more the ceremonial moments and ritualistic needs. At least, the state is not simply driven

by its concern over collective action during such moments as citizenry complaints are

much less tolerated during politically symbolic events. On the other hand, we find that

even when the state is driven by the motive of stability maintenance, authoritarian

regimes may exert different levels of control in different circumstances. When

responding to a particular incidence of collective action, the state’s action may range

from targeting only topics directly related to the specific incidence, to scaling up the

control level over online expression in general.

The research is explorative in nature, thus limited in several ways. Since we focus only

on comparing two online platforms, namely the most popular private forum Tianya and

the most popular official petition site LLMB—though we believe doing so is both

justified and brings methodological and conceptual benefits given our research purpose—

further studies are needed to generalize our findings. In addition, the events or days of the

independent variables can be considered exogenous to the change in discussion volume.

We also have only chosen the events that are most influential. Socio-political events

happen every day, and we cannot exhaust all events that may potentially have an impact.

Therefore, the results should be interpreted as suggestive to demonstrate a causal relation.

We hope to draw more academic attention to examine how authoritarian states may scale

censorship constantly to understand information control in a deeper and more dynamic sense.

Acknowledgements

We wish to thank Dr. Tianguang Meng for sharing the LLMB dataset. We are also grateful for the constructive feedback from Maria Repnikova, Suzanne Scoggins, John

Givens, John Yasuda, Jonas Nahm, and participants of the 15th China Internet Research

Conference and the anonymous reviewers. Juan Du, Steve Kaszycki and Jenica Moore made valuable contributions to this research as excellent research assistants.

Notes

1 https://www.sohu.com/a/330659108_656330

2 We acknowledge that there is a huge literature on how autocracies may use the Internet to promulgate pro-government voices (Han 2018; Roberts 2018; Toepfl 2012).

3 See https://chinadigitaltimes.net/chinese/2010/02/【真理部】唯有民主才能救中国一

文-2/

4 See https://chinadigitaltimes.net/chinese/2009/09/【真理部】锡矿山 10 万矿工罢工/

5 The team has constructed the Weibo censorship index based on data from China’s

popular micro-blog platform Sina Weibo. See https://weiboscope.jmsc.hku.hk/wsr/.

6 While one may wonder if Complaints also hosts posts supportive or neutral to the government, close examination of the discussion on the board suggests that this shall not bias our measurement. Appendix 2 provides snapshots of discussion from several boards on Tianya taken on a randomly selected date, including Complaints. The results show that most of the posts on Complaints are critical. This is again confirmed by our topic modeling analysis in Appendix 11. The Online Appendix will be found as supplemental

materials in the electronic version of this paper at http://prq.sagepub.com.

7 The authors thank Dr. Tianguang Meng for generously sharing their data.

8 Researchers who collected the data reached this conclusion by repeatedly scraping

LLMB data and comparing the differences, as authors of this paper are informed in person.

9 Calculated as a percentage [0-1], the number on that day divided by its mean daily

number during the time range we investigate

10 See Appendix 11 for results of topic modeling analysis of the post titles on Complaints

and General, which confirm the differences of the content between the two boards.

11 I-Paid-A-Bribe is an Indian corruption-reporting site. For more on how Chinese spinoffs of the site were suppressed, see Ang (2014).

12 August 29 is picked as the starting date of our coding because it was when Tianya

announced that users must verify their identity before posting on Complaints. See

“Gonggao: Baixing Shengyin Fatie Renzheng Tongzhi” (Announcement: Circular on

Identity Verification for Posting on Complaints), Tianya, August 29, 2011,

http://bbs.tianya.cn/post-828-223380-1.shtml. The anti-rumor campaign started in early

2013. We pick August 26 when People’s Daily Online put up a collection of reports on

local enforcement of the campaign. See http://bit.ly/2Haoyn5

13 We acknowledge that not all these four types of events directly speak to Lorentzen

(2014), which sees the state’s primary motive as to prevent citizens from learning about

how many other citizens are unhappy due to government failures. Instead the events are

more like the high-risk focal point times discussed in Truex (2019). However, regardless

of whether such socio-political crises would reveal the pervasiveness of citizenry

discontent or not, they are proper indicators of increased social tension, which according

to Lorentzen (2014) shall lead to enhanced control over negative publicity. In other

words, during such crises, the state shall worry about citizens expressing dissatisfaction

on Complaints more as such expression (not just the socio-political crises) would help

reveal that many are not quite happy about the government.

14 The coding scheme is not perfect especially given its inability to tell how long the

scaled-up control level may maintain. However, since there is no better methodological

option and we are primarily interested in whether a particular event triggers heightened

control level, we deem such a coding strategy acceptable.

15 http://bit.ly/2H7q6ya, accessed on 10-25-2020

16 Check Online Appendix 1 for detailed results.

References

Ang, Yuen Yuen. 2014. “Authoritarian Restraints on Online Activism Revisited: Why ‘I-

Paid-A-Bribe’ Worked in India but Failed in China.” Comparative Politics 47(1):

21–40.

Bamman, David, Brendan O’Connor, and Noah A. Smith. 2012. “Censorship and

Deletion Practices in Chinese Social Media.” First Monday 17(3).

https://doi.org/10.5210/fm.v17i3.3943.

Beissinger, Mark R. 2002. Nationalist Mobilization and the Collapse of the Soviet State.

Cambridge, UK: Cambridge University Press.

Bradsher, Keith. 2011. “China Moves Swiftly to Close Chemical Plant After Protests.”

New York Times, August 15.

Cairns, Chirstopher. 2019. “Microblog Dissent and Censorship during the 2012 Bo Xilai

Scandal.” In Handbook of Protest and Resistance in China, edited by Teressa

Wright, 361–77. Northampton, MA: Edward Elgar Publishing.

Cairns, Chirstopher, and Allen Carlson. 2016. “Real-World Islands in a Social Media Sea:

Nationalism and Censorship on Weibo during the 2012 Diaoyu/Senkaku Crisis.” The

China Quarterly 225: 23-49.

Cairns, Chirstopher, and Elizabeth Plantan. 2017. “Why Autocrats Sometimes Relax

Censorship: Signaling Government Responsiveness on Chinese Social Media.”

http://www.chrismcairns.com/uploads/3/0/2/2/30226899/why_autocrats_sometimes_

relax_censorship.pdf.

Chen, Jidong, and Yiqing Xu. 2017. “Information Manipulation and Reform in

Authoritarian Regimes.” Political Science Research and Methods 5 (1): 163–78.

Congressional-Executive Commission on China. “Freedom of Expression in China: A

Privilege, Not a Right.” Congressional-Executive Commission on China.

https://www.cecc.gov/freedom-of-expression-in-china-a-privilege-not-a-right.

Creemers, Rogier. 2016. “The Privilege of Speech and New Media: Conceptualizing

China’s Communications Law in the Internet Age.” In The Internet, Social Media,

and a Changing China, edited by Jacques DeLisle, Avery Goldstein, and Guobin

Yang, 86–105. University of Pennsylvania Press.

Crete-Nishihata, Masashi, Lotus Ruan, Jakub Dalek, and Jeffrey Knockel. 2017.

“Managing the Message: What You Can’t Say about the 19th National Communist

Party Congress on WeChat.” Citizen Lab. https://citizenlab.ca/2017/11/managing-

message-censorship-19th-national-communist-party-congress-wechat/.

Danneman, Nathan, and Emily Hencken Ritter. 2014. “Contagious Rebellion and

Preemptive Repression.” Journal of Conflict Resolution 58(2): 254–79.

Deibert, Ronald, John Palfrey, Rafal Rohozinski, and Jonathan Zittrain, eds. 2008. Access

Denied: The Practice and Policy of Global Internet Filtering. Cambridge, MA: MIT

Press.

———. 2010. Access Controlled: The Shaping of Power, Rights, and Rule in

Cyberspace. Cambridge, MA: MIT Press.

Economy, Elizabeth. 2018. The Third Revolution: Xi Jinping and the New Chinese State.

New York: Oxford University Press.

Fallows, James. 2011. “Arab Spring, Chinese Winter.” The Atlantic, September 2011, 50–

58.

Friedrich, Carl Joachim, and Zbigniew K. Brzezinski. 1965. Totalitarian Dictatorship

and Autocracy. Cambridge, MA: Harvard University Press.

Fu, King Wa, Chung Hong Chan, and Michael Chau. 2013. “Assessing Censorship on

Microblogs in China: Discriminatory Keyword Analysis and the Real-Name

Registration Policy.” IEEE Internet Computing 17(3): 42–50.

Gainous, Jason, Kevin M. Wagner, and Charles E. Ziegler. 2018. “Digital Media and

Political Opposition in Authoritarian Systems: Russia’s 2011 and 2016 Duma

Elections.” Democratization 25(2): 209–26.

Gilley, Bruce. 2003. “The Limits of Authoritarian Resilience.” Journal of Democracy

14(1): 18-26.

Han, Rongbin. 2018. Contesting Cyberspace in China: Online Expression and

Authoritarian Resilience. New York: Columbia University Press.

Hassid, Jonathan. 2012. “Safety Valve or Pressure Cooker? Blogs in Chinese Political

Life.” Journal of Communication 62(2): 212–30.

Howard, Philip N, and Muzammil M Hussain. 2013. Democracy’s Fourth Wave?: Digital

Media and the Arab Spring. New York: Oxford University Press.

Huang, Haifeng. 2015. “Propaganda as Signaling.” Comparative Politics 47(4): 419–44.

Jacobs, Andrew. 2014. “Chinese Web Censors Struggle With Hong Kong Protest.” New

York Times. October 30.

Jiang, Junyan, Tianguang Meng, and Qing Zhang. 2019. “From Internet to Social Safety

Net: The Policy Consequences of Online Participation in China.” Governance 32(3):

531–46.

King, Gary, Jennifer Pan, and Margaret E. Roberts. 2013. “How Censorship in China

Allows Government Criticism but Silences Collective Expression.” American

Political Science Review 107(2): 1–18.

———. 2017. “How the Chinese Government Fabricates Social Media Posts for

Strategic Distraction, Not Engaged Argument.” American Political Science Review

111(3): 484–501.

Kuang, Xianwen. 2018. “Central State vs. Local Levels of Government: Understanding

News Media Censorship in China.” Chinese Political Science Review 3(2): 154–71.

Lee, Kingsyhon, and Ming-sho Ho. 2014. “The Maoming Anti-PX Protest of 2014: An

Environmental Movement in Contemporary China.” China Perspectives (3): 33–39.

Leetaru, Kalev H. 2015. “The World’s Short Attention Span, Quantified.” The Atlantic.

September 14. https://www.theatlantic.com/international/archive/2015/09/nepal-

earthquake-media-natural-disaster/404647/.

Levitsky, Steven, and Lucan A. Way. 2010. Competitive Authoritarianism: Hybrid

Regimes After the Cold War. Cambridge University Press.

Lorentzen, Peter. 2013. “Regularizing Rioting: Permitting Public Protest in an

Authoritarian Regime.” Quarterly Journal of Political Science 8(2): 127–58.

———. 2014. “China’s Strategic Censorship.” American Journal of Political Science

58(2): 402–14.

Ma, Ding. 2009. “Zhonggong Dangju Guoqing Qianxi Fengsuo Shuwan Wangzhan”

(Chinese Authorities Block Tens of Thousands of Websites on National Day Eve).

Radio France Internationale. September 29.

http://www1.rfi.fr/actucn/articles/117/article_16472.asp.

Magaloni, Beatriz. 2006. Voting for Autocracy: Hegemonic Party Survival and Its Demise

in Mexico. Cambridge: Cambridge University Press.

McAdam, Doug, Sidney G Tarrow, and Charles Tilly. 2001. Dynamics of Contention.

New York: Cambridge University Press.

McMillan, John, and Pablo Zoido. 2004. “How to Subvert Democracy: Montesinos in

Peru.” Journal of Economic Perspectives 18(4): 69–92.

Meyer, David S. 2004. “Protest and Political Opportunities.” Annual Review of Sociology

30: 125–45.

Nathan, Andrew. 2003. “Authoritarian Resilience.” Journal of Democracy 14(1): 6–17.

O’Brien, Kevin J., and Lianjiang Li. 2006. Rightful Resistance in Rural China. New

York: Cambridge University Press.

Opp, Karl-Dieter. 2000. “Adverse Living Conditions, Grievances, and Political Protest

after Communism: The Example of East Germany.” Social Forces 79(1): 29–65.

Perlez, Jane. 2012. “Waste Project Is Abandoned Following Protests in China.” The New

York Times. July 28. https://www.nytimes.com/2012/07/29/world/asia/after-protests-

in-qidong-china-plans-for-water-discharge-plant-are-abandoned.html

Qin, Bei, David Strömberg, and Yanhui Wu. 2017. “Why Does China Allow Freer Social

Media? Protests versus Surveillance and Propaganda.” Journal of Economic

Perspectives 31(1): 117–40.

Repnikova, Maria. 2017. “Information Management During Crisis Events: A Case Study

of Beijing Floods of 2012.” Journal of Contemporary China 26(107): 711–25.

Roberts, Margaret E. 2018. Censored: Distraction and Diversion Inside China’s Great

Firewall. Princeton, NJ: Princeton University Press.

Ruan, Lotus et al. 2020. “The Intermingling of State and Private Companies: Analysing

Censorship of the 19th National Communist Party Congress on WeChat.” The China

Quarterly, 1–30. https://doi.org/10.1017/S0305741020000491.

Shambaugh, David L. 2008. China’s Communist Party: Atrophy and Adaptation.

Berkeley, CA: University of California Press.

Shao, Li. 2018. “The Dilemma of Criticism: Disentangling the Determinants of Media

Censorship in China.” Journal of East Asian Studies 18(3): 279–97.

Stern, Rachel E., and Jonathan Hassid. 2012. “Amplifying Silence: Uncertainty and

Control Parables in Contemporary China.” Comparative Political Studies 45(10):

1230–54.

Stern, Rachel E., and Kevin J. O’Brien. 2011. “Politics at the Boundary: Mixed Signals

and the Chinese State.” Modern China 38(2): 174–98.

Tai, Qiuqing. 2014. “China’s Media Censorship: A Dynamic and Diversified Regime.”

Journal of East Asian Studies 14 (2): 185–210.

Tai, Yun, and King-wa Fu. 2020. “Specificity, Conflict, and Focal Point: A Systematic

Investigation into Social Media Censorship in China.” Journal of Communication 70

(6): 842–867.

Tilly, Charles, and Sidney Tarrow. 2006. Contentious Politics. New York: Oxford

University Press.

Toepfl, Florian. 2012. “Blogging for the Sake of the President: The Online-Diaries of

Russian Governors.” Europe-Asia Studies 64(8): 1435–59.

Truex, Rory. 2019. “Focal Points, Dissident Calendars, and Preemptive Repression.”

Journal of Conflict Resolution 63(4): 1032–1052.

Tsai, Lily L. 2015. “Constructive Noncompliance.” Comparative Politics 47(3): 253–79.

Wang, Yuhua, and Carl Minzner. 2015. “The Rise of the Chinese Security State.” The

China Quarterly 222: 339–59.

Wines, Michael. 2011. “A Village In Revolt Could Be A Harbinger.” The New York Times.

December 25. http://www.nytimes.com/2011/12/26/world/asia/in-china-the-wukan-

revolt-could-be-a-harbinger.html.

Wong, Edward, and David Barboza. 2011. “Wary of Egypt Unrest, China Censors Web.”

New York Times. February 1.

https://www.nytimes.com/2011/02/01/world/asia/01beijing.html.

Wright, Joseph, and Abel Escribà-Folch. 2012. “Authoritarian Institutions and Regime

Survival: Transitions to Democracy and Subsequent Autocracy.” British Journal of

Political Science 42(2): 283–309.

Yang, Xiyun, and Edward Wong. 2010. “Villager’s Suspicious Death Ignites Fury in

China.” New York Times. December 28.

https://www.nytimes.com/2010/12/29/world/asia/29china.html.

Yu, Guoming. 2010. “Wangluo Yuqing Redian Shijian de Tezheng ji Tongji Fenxi”

(Features of Online Hot-Button Public Opinion Events and the Statistical Analysis).

People’s Forum (11):24-26.

Taboo Taboo Topics Topics

Non-taboo Non-taboo Critical Critical Topics Topics

Permissible Permissible Topics Topics

Relaxed Control Enhanced Control

Figure 1： Adjusting the level of control

Figure 2: Daily number of posts on two boards per year (error bar CI=95%)

Figure 3: Coefficient Plots of Contingent Events

Figure 4: Coefficient Plots of Ritualistic Events

Online Appendix of "Scaling Authoritarian Information Control: How China Adjusts the Level of Online Censorship"

Contents 1. Robustness Check 2. A snapshot of posts (with English Translation) 3. Coding dates for each event 4. A brief introduction to popular protests included 5. The change of post rate during CCP 18th conference and PRC 60th anniversary 6. Descriptive Statistics of the data 7. Regression Results I (Complaints relative to LLMB, the main results) 8. Regression Results II (Complaints itself as DV) 9. Regression Results III (LLMB as DV) 10. Regression Results IV (Complaints relative to the General) 11. LDA topic models of the Complaints and the General

Note for regression tables: All regressions adopts Newey-West estimator with lag(1-7). *p<0.1, **p<0.05, ***p<0.01. “Percent” refers to the relative measurement of number of post divided by the mean. “Standardized” refers to the standardized number of post(number of post minus mean and then divided by the standard deviation). Please refer to the main text for the specific algorithm of the dependent variable. Part 1 Robustness Check We first run the estimation on Complaints without LLMB as the baseline. The results turn

out to be highly similar (Figure A1.1 and Figure A1.2). We then run the estimation on

LLMB independently. As an official petition platform, we expect the discussion volume to be relatively stable or show opposite traits as compared to Complaints because (1)

LLMB should be relatively immune from state adjustments in the control level, as we reasoned above, and (2) citizens may shift to and away from the official petition platform depending on whether private forums such as Tianya.com are more or less suppressed, especially in certain cases. The results (see Figures A1.3 and A1.4) generally confirm our expectations. During all ritualistic events (Figure A1.4), daily post number on LLMB generally remained stable, or even increased, indicating that the censorship level on

LLMB barely increased during these periods. For contingent cases, the daily posts on

LLMB were significantly fewer during the Dalian PX protest, the Yushu Earthquake, the

High Speed Train crash and the Bo Verdict. While such results warrant further explanation—it was likely that citizens were distracted from LLMB during these events,

we at least can safely conclude that the state did not scaled up control over Complaints in these cases as the discussion volume on the board did not drop compared to its own mean

(see Figure A1.2). The overall results show again that ritualistic events have caused higher control level over Complaints and contingent events have not.

FigureA1 1 Contingent Events (Complaint Alone)

FigureA1 2 Ritualistic Events (Complaints alone) FigureA1 3 Contingent Event (LLMB only)

FigureA1 4 Ritualistic Event(LLMB only) In addition, we change the baseline of LLMB to General of Tianya.cn and run the same tests. Here, we follow the same procedure to construct dependent variables (Percent and

Standardized) as described above when using LLMB as the baseline and estimate with the same models. The results remain mostly similar(Figure A1.5 and Figure A1.6): the number of daily posts on Complaints significantly dropped when compared to General during all ritualistic events, while effects of contingent events are not quite consistent.

The only major difference is that this time as compared to the models using LLMB as the baseline, discussion volume on Complaints increased during all three cases of foreign revolutions and Wang Lijun incident. The reason could be that such topics were discussed much more on General, and state censorship of such topics had resulted in lower measured discussion volume of the board. In comparison, Complaints was affected less by these events, which were not quite related to citizenry complaints in the first place. Again, such results confirm earlier findings of ritualistic events constantly leading to higher control level over citizenry complaints and contingent events showing mixed results. FigureA1 5 Contingent Event (General as Baseline)

FigureA1 6 Ritualistic Event (General as Baseline) Appendix Part 2.a Snapshot of Grassroots Voices Posts(July 11-12, 2014) Topic Topic (English) Time 给中央第一督导组、第八巡视组正副组 Got no response after 17 letters to directors/deputy directors of 2014/7/12 长共寄快件 17 封都石沉大海无声无息 the 1st Supervision Group and the 8th Inspection Team. 7:45 2014/7/12 四川通江有个“私人法院” There is a private court in Tongjiang, Sichuan Province 7:17 数百村民深夜捧烛祈福沉痛哀悼殉职 Hundreds of villagers pray at night with candles in hand, 2014/7/12 村干部（悲戚） mourning village cadre perished in the line of duty (sad) 2:41 顺德容桂华夏新城 G 座业主围堵停车 Homeowners of Huaxia New City in Ronggui, Shunde City 2014/7/12 场，求关注求帮助 blocked the parking garage, seeking attention and help 0:53 2014/7/12 向 zy 巡视组告状管用吗 Does petitioning to Central Inspection Team work? 0:44 道德良知何在？农民工人怎么活？ Where is morality and conscience? How can peasants and 2014/7/11 workers survive? 23:48 护士被逼辞职，编制被顶替，无人管。 Nursed forced to resign and position being replaced. Nobody 2014/7/11 因病急需手术求捐助(转载) cared. Sick and needs surgery, please donate (repost). 23:31 大连公交集团 405 路司机无故辱骂老人， Route 405 Bus Driver of Dalian Transit Group insulted the 2014/7/11 无人管 elderly for no reason. Nobody did anything about it. 23:10 恳请铜陵市人民政府对我实名举报环境 Tongling City Government please disclose investigation results 2014/7/11 污染的调查信息予以公开 of the environment pollution that I reported in real name 23:09 佳木斯市长与港龙东方城一些不为人知 Unknown relations between Jamusi Mayor and Ganglong 2014/7/11 的事（违法强拆补偿不公） Eastern City (illegal forced demolition & unfair compensation) 23:03 2014/7/11 挥金如土的账条。 Accounts and bills of lavish spending 23:01 广东省肇庆市端州区黄岗镇岩前村村官 Cadres in Yanqian Village, Huanggang, Duanzhou, Zhaoqing, 2014/7/11 只手遮天，法理何在？ Guangdong rule like a monarch. Where is law or justice? 22:40 我们今天摊上这样的地方党政领导真该 We are really unlucky to have such local governmental and 2014/7/11 我们倒霉 party leaders 22:40 2014/7/11 看 btv 生活广角 Take a look at Beijing TV's Wide-angle of Life program 22:08 跪求转发~让警察不作为的真面目大白 Pleading on knees for dissemination~ Expose the police's 2014/7/11 于天下~让打人的人得到应有的惩罚 dereliction of duty ~ let the aggressor get due punishment 21:32 Village head of Nanzhai Village, Dongshe Township, 山西省太原市万柏林区东社乡南寨村村 2014/7/11 Wanbailin District, Taiyuan City, Shanxi Province ignored law, 长无视国法强占耕地还抓人 21:24 occupied farmland, and even detained people Due process absent; announcement failed due process; 程序不合法公告程序不合法重庆市人 2014/7/11 Chongqing Municipal Government's approval failed due 民政府批复不合法上访人违法吗 21:23 process; are us petitioners violating the law? 俄罗斯全境运输服务国际快递专线一 Shipping across Russia; International express delivery; 2014/7/11 条龙服务 All-in-one service 20:51 Billionaire in Zhenan County, Shanxi Province exploits the 陕西省镇安县亿万富翁吃低保，路虎车 2014/7/11 social insurance. His Land Rover plate number is 99999 号 99999【自称九五之尊】. 20:32 [self-proclaimed as King of Kings] 2014/7/11 求党办事是有多难 It is so difficult to petition the Party to do anything 20:38 Source: http://bbs.tianya.cn/list.jsp?item=828&sub=1&order=1&nextid=730934.

1 Appendix Part 2.b Snapshot of Free Posts (July 11, 2014) Topic Topic (English) Time 劳作起止时，汗淋未断丝。文司来电询， Labor began and ended; the sweat never stopped. Wensi 2014/7/11 秀购两衣试。 called to greet; showed him the two apparels I purchased. 23:52 Geek Chen Shifeng: "Entrepreneurship is My 极客陈诗峰：“创业是我的梦想”－－摘 2014/7/11 Dream"—Guangdong Business Magazine 2014, no. 07 自《粤商》杂志 2014 年 07 期(转载) 23:52 (Repost) 伪造宅基证霸占他人土地 Fabricated rural house slot ownership certificate and 2014/7/11 grabbed other's land 23:44 2014/7/11 帮帮白血病护士 Please help the nurse diagnosed with leukemia 23:43 2014/7/11 现在佛教怎么了 What happened to today's Buddhism 23:33 强势的婆婆，凶狠的公公，无能的老公， Dominating mother-in-law, fierce father-in-law, impotent 2014/7/11 悲催的楼主 husband, and poor OP 23:36 新黑社会的滋生：乐着了那些借贷主， The breeding of a new underworld: creditors are happy, 2014/7/11 忙着了那些马仔，死了那些老板 their muscles are busy, and business owners are dead 23:45 难道只有我一个觉得《小苹果》很神烦 2014/7/11 Am I the only who get extremely sick of Little Apple 吗？ 23:42 心情烦躁，请经验丰富的朋友帮忙分析 Feeling fretful. Friends with rich experiences please help 2014/7/11 一下 me sort things out 23:28 爆啦爆啦！！！今天居然发现这个 Breaking news!!! I actually found this [bee hives and 2014/7/11 honey] today 23:28 2014/7/11 改善高铁服务迫在眉睫 It is urgent to improve the service on High speed trains 23:37 那些年内心的 os（特别是看到网络上那 Overlapping sounds in my heart in all those years (esp. 2014/7/11 些流行的评论转发） after seeing the popular comments and retweets online) 23:25 2014/7/11 苦逼小孩成绩(转载) My poor kid's exam scores (Repost) 23:21 Meimei [Guo], how come you got caught? I am so sad. 2014/7/11 美美，你怎么被抓了，我好伤心，加油！ Come on! 23:21 之前发的帖子可能说得不太清楚怀疑 The original post was probably unclear. Those who doubt 2014/7/11 和有疑问的朋友进来看看 or have questions please read this! 23:20

大家瞧瞧霸气的 RAV4 是如何强行变 Everyone, look at this bullish RAV4 has forcibly changed 2014/7/11 道、刮擦后车、辱骂后车车主的！ lane, scratched the next car and insulted its owner 23:17

黑河航道局局长顶风上 Heihe waterway bureau chief misconduct amidst 2014/7/11 anti-corruption campaign 23:16 2014/7/11 苦逼小孩成绩 My poor kid's exam scores 23:15 2014/7/11 村姑指南 How to become a country girl 23:15 2014/7/11 成都移动,你让我喜又让我忧 Chengdu Mobile, you makes me happy and worried 23:12 Source: http://bbs.tianya.cn/list.jsp?item=free&order=1&nextid=4475258.

2 Appendix Part 2.c Snapshot of Outlook Posts (July 11-12, 2014) Topic Topic (English) Time 乌克兰军方称不会对顿涅茨克发动空 Ukrainian military said it would not launch an air strike 2014/7/12 袭(转载) against Donetsk (Repost) 1:18 我的好友詹姆斯刚刚告我，他将离开热 My good friend James just told me that he will leave 2014/7/12 火重返骑士 Miami Heat and return to the Cavaliers 1:16 美国继续悬赏 1000 万美元缉拿伊极端 U.S. continues to offer $ 10 Million reward for Islamic 2014/7/12 组织首领(转载) extremist organization leaders (Repost) 1:14 伊拉克库尔德自治区武装占领 2 个大 Armed forces of the Iraqi Kurdistan Autonomous Region 2014/7/12 型油田(转载) occupied two large oil fields (Repost) 1:09 报告称中国军事威慑力列第三梯队正 Report says China's military deterrent force in the third tier 2014/7/12 在强势崛起(转载) but rising with strong momentum (Repost) 1:06 2014/7/12 迅速缩小贫富差距的手段之一 One way to quickly narrow the income gap 1:00 深度：停止研制的一代先进战机超七 In-depth: An advanced fighter that ceased development, 2014/7/12 战机前世今生(转载) the past and present of Super Seven (Repost) 0:57 Foreign companies to extend oil and gas drills in Reed 外国公司延长礼乐滩油气开发中方： 2014/7/12 Bank [South China Sea]. China: illegal and invalid 非法且无效转载 0:53 ( ) (Repost) 2014/7/12 有中国特色的中国安保公司 Chinese security companies with Chinese characteristics 0:48 美称希望中国勿顶撞亚太秩序未对中 U.S. claims that it hopes China not to disrupt order in 2014/7/12 国手下留情(转载) Asia-Pacific but has shown no mercy to China (Repost) 0:32 日媒：中美强硬派都想选择战争中国 Japanese media: Hardliners in both China and the U.S. 2014/7/12 要展现诚意(转载) want a war and China has to show sincerity (Repost) 0:49 澳外长指责中国不会尊重弱者称将对 Australian Foreign Minister says China doesn’t respect 2014/7/12 中国更强硬(转载) weakness and claims to be tougher toward China (Repost) 0:43 Philippine Media: China steadfastly improves nuclear 菲媒：中国加紧提升对美核威慑令美 2014/7/12 deterrence capacity against the U.S., preventing the U.S. 不敢援助菲转载 0:39 ( ) from aiding Philippines (Repost) 一阴一阳看世界，中美俄兴衰 See the world from the Yin-Yang perspective: the rise and 2014/7/12 decline of China, the U.S., and Russia 0:34 德国限制情报部门和国防部与美国之 German restricted intelligence and defense cooperation 2014/7/12 间合作(转载) with the United States (Repost) 0:26 五岳散人长微博：郭美美、中红博爱与 Wuyue Sanren’s Long Tweet: The affairs between Meimei 2014/7/12 红会那点儿事！ Guo, Red Cross Commerce, and China Red Cross Society 0:26 媒体:应依法剥夺徐才厚上将军衔(转 Media: Xu Caihou's title of Major General should be 2014/7/12 载) deprived according to law (Repost) 0:23 2014/7/12 奇怪，很少见日本人吃蘑菇 Weird, I rarely see Japanese eat mushrooms 0:24 2014/7/12 穷人命贱，死不足惜？ Poor people's lives are cheap; nobody cares if you die? 0:20 Being a power of copy-catting, if [China] copy-cat [the 做为山寨大国，山寨着给它两个蘑菇， 2014/7/12 U.S.] and drop two a-bombs [in Japan], will that allow us 是不是也能随心所欲滴当回干爹 0:17 to become [Japan's] god-father and do whatever we like Source: http://bbs.tianya.cn/list.jsp?item=worldlook&order=1&nextid=1185107.

3 Appendix Part 2.d Snapshot of Funinfo Posts(July 11, 2014) Topic Topic (English) Time 男生会因为什么找到女朋友外形看起 How come boys always find girlfriends that look quite 2018/7/11 来都差不多？ similar to each other 23:54 大家就这么讨厌老师吗？说说工资低 Does everybody really hate teachers that much? Should 2018/7/11 就要被骂？ [teachers] be because of complaints about low salaries 23:53 我想问一下一个男人除了对你好之 I want to ask: A man, besides being very nice to you, 2018/7/11 外其他都不符合你对男朋友的标准 meets none other standard you set for a boyfriend, will you 23:52 还会继续在一起吗 continue to be with him? 什么狗屁一个人说走就走的旅行？！糟 What the fuck about the one-man trip on the go?! It is 2018/7/11 糕透了 awful 23:52 出去玩定位发微博很装逼吗！？ Is it showing off if you tweet on Weibo showing your 2018/7/11 position when you are traveling? 23:50 有木有人和我一样，好心疼羊立方 Are there people like me? Cube Yang [referring to the 2018/7/11 actor Yangyang Yang] makes my heart ache 23:48 Dad 看这一期爸爸去哪儿掉了两次眼泪 I broke into tears twice watching this episode of 2018/7/11 Where Are We Going 23:45 湖南长沙市枫林三路海天大厦对面黄 Got maggots in dishes at Huangchunhe Restaurant facing 2018/7/11 春和吃到蛆! Haitian Building, Fenglin Third Rd, Changsha, Hunan 23:45 大家说几个自己混的小众圈子，比如中 Let’s talk about the small circles you are in, like the 2018/7/11 抓圈电竞圈什么的... Chinese Drama Circle, the Gaming Circle, and so forth 23:44 看到老爸要落水的时候，费曼的表现真 Feynman's performance was really… when he saw his 2018/7/11 是。。。 daddy about falling into the river 23:44 2018/7/11 大家都是怎么从失恋中走出来的？ How does everyone recover from the breakup? 23:43 2018/7/11 爆一爆 sally ~~~~~~~~~~~~~~~~~~~ Let me expose sally~~~~ 23:41 大家来说说不再爱一个人是什么感 Everyone, please share how you feel when you no longer 2018/7/11 觉。。。 love someone… 23:35 2018/7/11 不得不说我飞曼实在是鸡汁 I have to say that my Feynman is really smart 23:34 今天发生的不愉快，和女友冷战中，各 Had an unhappy day and got into a cold war with my 2018/7/11 位进来说下看法 girlfriend. Please come and share your opinions. 23:33 Starting wearing dental braces at the age of 26, will it still 2018/7/11 26 岁带牙套，还会有作用吗。 work? 23:32 急求，本人痘痘肌，用什么牌子的彩妆 Urgent! I have acne skin. Which brand of makeup will hurt 2018/7/11 对痘痘伤害最小啊？ least? 23:31 一直知道事出有因有因必有果，但始料 I always knew that there would be consequences, but never 2018/7/11 未及人生会这么惨淡。 thought that my life would be so miserable 23:29 被椅子砸，被烟头烫嘴，被送精神病 Hit by a chair, burned by cigarette butts in the mouth, and 2018/7/11 院……这就是我的少女时代 sent to mental hospitals ... This was my girlhood 23:29 你出邮费，我赠送。不是什么大牌子。 You pay the postage; I give away for free [old clothes]. 2018/7/11 也不想转了。 They are not big brands and I do not want to resell them. 23:28 Source: http://bbs.tianya.cn/list.jsp?item=funinfo&order=1&nextid=5613584.

4 Appendix Part 2.e LLMB topic samples Source： http://liuyan.people.com.cn/threads/list?fid=1337 http://liuyan.people.com.cn/threads/list?fid=539

Topic Topic（English） Time

在北京交警 app 上不能办理 Cannot apply for the Beijing Entry Pass 2020/5/13

进京证 on the Beijing Traffic Police App 13:38

来自大别山革命老区一企业 An enterprise in the old revolutionary 2020/5/13

的紧急求助 base of Dabieshan needs emergency help 13:32

京心相助上退票后无法再次 Tickets on the Help Beijing People App 2020/5/13

订票 cannot be booked again after refund 12:50

Issue of underage children who have not 关于北京市双职工家庭有未 2020/5/13 yet started school in Beijing's dual- 成年孩子未开学的问题 12:47 worker families

The Housekeepers Help App still has not

管家帮半年协调仍不退费，极 refunded our money after half a year’s 2020/5/13

其恶性行为 coordination ， which is extremely 12:47

disgusting

Issues of refund from Deep Sea 2020/5/13 深海教育退费问题 Education 12:40

Xicheng Exhibition Road Neighborhood 西城展览路居委会物业要求 2020/5/13 Committee requires home renovations to 家庭装修必须全楼签字 12:27 be signed by the whole building

2020/5/13 电梯消毒问题 Elevator disinfection problem 12:14

Children in the old district of

天通苑老区孩子无法就近入 Tiantongyuan cannot attend school 2020/5/13

学（幼升小） nearby (kindergarten to elementary 12:01

schools)

Taiwanese children enter public 2020/5/13 台胞子女入上公立幼儿园 kindergarten 12:01

Topic Topic（English） Time

Against the illegal establishment of 反对万科魅力之城五期 10 栋 2020/05/12 Dianjian Hospital in Phase 5 Building 10 违规建立滇健医院 17:01 of Vanke's Charming City

Bus stops are not enough in Country 盘龙区白沙街碧桂园璟台小 2020/05/12 Garden Jingtai Community, Baisha 区公交车停靠少 16:35 Street, Panlong District

云南鸿云科技有限公司拖欠 Yunnan Hongyun Technology Co., Ltd. 2020/05/12

学生工资 owes student wages 15:24

2020/05/12 我要我的工资 I want my salary 11:53

Construction disturbs people after 12 2020/05/12 夜间 12 点后施工扰民 o'clock at night 00:29

百姓苦不堪言，请政府为民做 People are miserable, and we need the 2020/05/12

主 government to help us 20:21

Advertisements are everywhere in public 2020/05/11 小区公共区域遍地广告 areas of the community 17:54

The developer does not give a stamp to 2020/05/11 办理房产证开发商不给盖章 the real estate certificate 17:54

Purchase the house in the Grape Street 买葡萄街天宇康苑全款结清 2020/05/11 Tianyukang Garden at full price but it 房拖欠房产证 13:29 defaults our property certificate

昆明友和道通物流有限公司 Kunming Youhe Daotong Logistics Co., 2020/05/11

拖欠 8 个月工资 Ltd. owes me wages for 8 months 10:59

Appendix 3: Coding for each event variable Event Variable Name Start End June 4 Tiananmen June4(2 Weeks) 05-23-[Every Year] 06-18-[Every Year] June4(1 Weeks) 05-28-[Every Year] 06-11-[Every Year] June4(2 Days) 06-02-[Every Year] 06-06-[Every Year]

Two Conference Two Conf.(2 Weeks) 02-17-[Every Year] 03-29-[Every Year] Two Conf.(1 Weeks) 02-24-[Every Year] 03-22-[Every Year] Two Conf.(2 Days) 03-01-[Every Year] 03-17-[Every Year]

PRC 60th Anniversary PRC60(2 Weeks) 09-17-2009 10-15-2009 PRC60(1 Weeks) 09-24-2009 10-08-2009 PRC60(2 Days) 09-29-2009 10-03-2009

CCP 18th Conference CCP18(2 Weeks) 10-25-2012 11-29-2012 CCP18(1 Weeks) 11-01-2012 11-22-2012 CCP18(2 Days) 11-06-2012 11-17-2012

CCP Central Committee Plenaries CCP_Plen(2 Weeks) Varied for each year CCP_Plen(1 Weeks) Varied for each year CCP_Plen(2 Days) Varied for each year

Striking down "I pay a bribe" IPAB_Crack 08-29-2011 09-12-2011 Sirking down rumors Anti-rumor 08-26-2013 09-09-2013

Ben Ali flee Ben_Ali 01-14-2011 01-28-2011 Jasmine Revolution Jasmine 02-20-2011 03-06-2011 Umbrella Revolution Umbrella 09-26-2014 12-29-2014

Qian Yunhui Qian 12-25-2010 01-08-2011 Dalian PX Protest Dalian 08-14-2011 08-28-2011 Qidong Protest Qidong 07-28-2012 08-11-2012 Maoming Protest Maoming 03-30-2014 04-18-2014 Wukan Incident Wukan 12-09-2011 12-21-2011

Yushu Earthquake Yushu 04-21-2010 05-04-2010 Wenzhou Train Accident Train 07-23-2011 08-06-2011 Shanghai Stampede Stampede 12-31-2014 01-21-2015

Wang Lijun Incident Wang 02-06-2012 02-20-2012 Purge of Bo Xilai Bo_Purge 15-03-2012 04-24-2012 Verdict Zhou Yongkang Zhou_Verdict 06-11-2015 06-25-2015 Verdict Bo Xilai Bo_Verdict 09-22-2013 10-06-2013 Appendix 4: Brief Introduction to the Popular Protests included

The Qian Yunhui Incident Qian was the village head of Zhaiqiao Village in East China’s Zhejiang Province. He had a long history of leading petitions against local governments for abuses in land appropriation. He died in a suspicious traffic accident on December 25, 2010. Fellow villagers claimed that Qian was murdered with four men in uninforms held Qian on the ground while the truck drove over him. Local residents as well as netizens in China got infuriated and started to protest both online and offline.

The Dalian PX Protest This was a protest against the p-Xylene project in Dalian City, Liaoning Province. Facilitated by online mobilization through Weibo, Twitter, and Internet forums, tens of thousands of local residents demonstrated on the People’s Square of Dalian on August 14, 2011, forcing local authorities to suspend the project.

The Maoming PX Protest The protest took place in early 2014 in Maoming City located in western Guangdong Province against a proposed PX project. It started in the morning of March 30, 2014 when tens of thousands of local residents gathered in front the city government. The confrontation escalated into violent clash in the evening and last until the next day. Smaller scale of protests continued for several days after that. The Internet and social media played an important role in the mobilization.

The Qidong Waste Water System Protest The protest took place on July 28, 2012 in Qidong, Jiangsu Province. Thousands of local residents rose against the proposed waste water pipeline project as part of a joint-venture paper factory out of the concern of pollution. Protesters destroyed government vehicles, stormed government buildings, and stripped off the mayor’s shirt. Under the popular pressure, local authorities promised to abandon the project permanently.

The Wukan Incident The protest in Wukan, a village in Guangdong Province, began on September 21, 2011 as an anti-corruption protest: grassroots officials sold the village’s land to real estate developers but did not compensate the villagers properly. The event escalated in December 2011 when a protesting villager representative died in custody. As a result, villagers expulsed the cadres and confronted the police that sieged the town. The confrontation drew much attention from the media, public, and upper levels of the state. Ultimately, villagers reached a peaceful agreement with the authorities under the intervention of provincial government, resulting in investigation and punishment of responsible officials, redistribution of land, and election of new village cadres in 2012. What is worth noting is that in July 2016, another round of protest took place in Wukan because of the arrest of protest leader and elected village cadre Lin Zulian. The protest was subsequently suppressed.

6 Part 5.a Post rate changes during CCP 18th Conference

2.0

1.5

1.0 Posts Percentage of Mean Percentage Posts

0.5 Board bxsy outlook zatan

Oct−01−2012 Nov−01−2012 Dec−01−2012 Jan−01−2013 10−01 to 12−31, 2012 Part5.b Post rate changes during PRC 60th Anniversary

0.75

0.50 Posts Percentage of Mean Percentage Posts

0.25

Board bxsy outlook 0.00 zatan

Sep−01−2009 Oct−01−2009 Nov−01−2009 08−15 to 11−15, 2009 Appendix Part 6 Descriptive Statistics Complaints LLMB General (1) (2) (3) 07-15-2009 to 07-31-2015 mean sd days mean sd days mean sd count 2009 99.24 73.21 170 102.14 50.14 170 477.41 109.78 170 2010 259.18 67.31 365 183.32 102.91 365 469.69 241.96 365 2011 165.41 66.36 365 223.52 91.15 365 429.67 112.39 365 2012 94.13 38.30 366 226.80 92.86 366 1113.87 353.29 366 2013 140.17 42.66 365 306.23 110.66 365 1458.77 357.74 365 2014 119.43 46.22 365 342.63 147.59 365 1166.62 293.97 365 2015 157.25 36.48 212 359.49 146.78 212 878.33 247.84 212 Total 151.44 76.20 254.49 134.56 888.39 475.80 Observations 2208 2208 2208 Part 7 Results: Complaints relative to LLMB

Regression Results, Ritual events coded as ±2 weeks, DV=Percent (1) (2) (3) (4) (5) (6) (7)

June4_2w ‐0.0953** ‐0.0953** ‐0.0953** ‐0.0953** ‐0.0953** ‐0.0953** ‐0.0953** (‐2.94) (‐2.75) (‐2.60) (‐2.48) (‐2.39) (‐2.32) (‐2.25) Two_conf._2w ‐0.292** ‐0.292** ‐0.292** ‐0.292** ‐0.292** ‐0.292** ‐0.292** (‐5.59) (‐4.98) (‐4.58) (‐4.29) (‐4.06) (‐3.88) (‐3.74) PRC60 ‐0.382** ‐0.382** ‐0.382** ‐0.382** ‐0.382** ‐0.382** ‐0.382** (‐6.55) (‐5.89) (‐5.49) (‐5.23) (‐5.06) (‐4.89) (‐4.71) CCP18 ‐0.341** ‐0.341** ‐0.341** ‐0.341** ‐0.341** ‐0.341** ‐0.341** (‐4.06) (‐3.76) (‐3.60) (‐3.49) (‐3.42) (‐3.32) (‐3.21) CCP_Plen_2w ‐0.240** ‐0.240** ‐0.240** ‐0.240** ‐0.240** ‐0.240** ‐0.240** (‐5.33) (‐4.83) (‐4.47) (‐4.22) (‐4.04) (‐3.91) (‐3.81) Ben_Ali 0.0483 0.0483 0.0483 0.0483 0.0483 0.0483 0.0483 (0.52) (0.51) (0.52) (0.54) (0.56) (0.57) (0.57) Jasmine ‐0.0735 ‐0.0735 ‐0.0735 ‐0.0735 ‐0.0735 ‐0.0735 ‐0.0735 (‐0.49) (‐0.47) (‐0.46) (‐0.46) (‐0.46) (‐0.47) (‐0.49) Umbrella 0.570** 0.570** 0.570** 0.570** 0.570** 0.570** 0.570** (8.20) (7.71) (7.25) (6.89) (6.67) (6.53) (6.38) Qian ‐0.0722 ‐0.0722 ‐0.0722 ‐0.0722 ‐0.0722 ‐0.0722 ‐0.0722 (‐0.29) (‐0.29) (‐0.32) (‐0.35) (‐0.38) (‐0.40) (‐0.40) Dalian ‐0.172 ‐0.172 ‐0.172 ‐0.172 ‐0.172 ‐0.172 ‐0.172 (‐0.91) (‐0.80) (‐0.75) (‐0.73) (‐0.72) (‐0.73) (‐0.75) Qidong 0.208** 0.208** 0.208* 0.208* 0.208* 0.208* 0.208* (2.09) (1.96) (1.91) (1.89) (1.89) (1.89) (1.89) Maoming ‐0.568** ‐0.568** ‐0.568** ‐0.568** ‐0.568** ‐0.568** ‐0.568** (‐4.67) (‐4.84) (‐5.04) (‐5.27) (‐5.50) (‐5.79) (‐5.79) Wukan ‐0.449** ‐0.449** ‐0.449** ‐0.449** ‐0.449** ‐0.449** ‐0.449** (‐6.89) (‐6.09) (‐5.56) (‐5.23) (‐5.01) (‐4.87) (‐4.78) Yushu 0.147* 0.147* 0.147 0.147 0.147 0.147 0.147 (1.80) (1.71) (1.62) (1.53) (1.50) (1.47) (1.45) Train 0.456** 0.456** 0.456** 0.456** 0.456** 0.456** 0.456** (5.24) (5.25) (5.36) (5.66) (6.01) (6.03) (6.04) Stampede 0.0179 0.0179 0.0179 0.0179 0.0179 0.0179 0.0179 (0.17) (0.16) (0.15) (0.14) (0.13) (0.12) (0.12) Wang ‐0.279** ‐0.279** ‐0.279** ‐0.279** ‐0.279** ‐0.279** ‐0.279** (‐3.52) (‐3.12) (‐2.89) (‐2.76) (‐2.69) (‐2.65) (‐2.65) Bo_Purge ‐0.384** ‐0.384** ‐0.384** ‐0.384** ‐0.384** ‐0.384** ‐0.384** (‐5.99) (‐5.34) (‐4.96) (‐4.66) (‐4.43) (‐4.25) (‐4.07) Bo_Verdict 0.209** 0.209** 0.209** 0.209** 0.209** 0.209** 0.209** (3.39) (3.33) (3.31) (3.29) (3.36) (3.35) (3.28) Zhou_Verdict ‐0.105 ‐0.105 ‐0.105 ‐0.105 ‐0.105 ‐0.105 ‐0.105 (‐0.88) (‐0.90) (‐0.94) (‐1.01) (‐1.03) (‐1.08) (‐1.07) IPAB_Crack ‐0.321** ‐0.321** ‐0.321** ‐0.321** ‐0.321** ‐0.321** ‐0.321** (‐4.73) (‐4.57) (‐4.27) (‐4.15) (‐4.07) (‐3.94) (‐3.87) Anti‐rumor 0.0552 0.0552 0.0552 0.0552 0.0552 0.0552 0.0552 (0.75) (0.76) (0.80) (0.85) (0.91) (0.94) (0.95) Tianya General 0.0343 0.0343 0.0343 0.0343 0.0343 0.0343 0.0343 (0.56) (0.51) (0.49) (0.48) (0.46) (0.46) (0.45) Xi_Term ‐0.551** ‐0.551** ‐0.551** ‐0.551** ‐0.551** ‐0.551** ‐0.551** (‐8.26) (‐7.79) (‐7.43) (‐7.21) (‐6.98) (‐6.83) (‐6.69) weekend 0.141** 0.141** 0.141** 0.141** 0.141** 0.141** 0.141** (5.46) (5.32) (5.32) (5.33) (5.36) (5.35) (5.25) holiday 0.155** 0.155** 0.155** 0.155** 0.155** 0.155** 0.155** (2.96) (2.65) (2.48) (2.36) (2.28) (2.24) (2.21) Spring 0.0461 0.0461 0.0461 0.0461 0.0461 0.0461 0.0461 (0.99) (0.88) (0.81) (0.77) (0.73) (0.70) (0.68) Summer ‐0.0214 ‐0.0214 ‐0.0214 ‐0.0214 ‐0.0214 ‐0.0214 ‐0.0214 (‐0.50) (‐0.44) (‐0.40) (‐0.38) (‐0.36) (‐0.34) (‐0.32) Autumn ‐0.172** ‐0.172** ‐0.172** ‐0.172** ‐0.172** ‐0.172** ‐0.172** (‐3.57) (‐3.12) (‐2.84) (‐2.65) (‐2.51) (‐2.40) (‐2.31) year=2010 0.621** 0.621** 0.621** 0.621** 0.621** 0.621** 0.621** (11.94) (10.37) (9.39) (8.72) (8.23) (7.84) (7.53) year=2011 ‐0.123** ‐0.123** ‐0.123* ‐0.123* ‐0.123* ‐0.123 ‐0.123 (‐2.44) (‐2.14) (‐1.95) (‐1.82) (‐1.72) (‐1.64) (‐1.58) year=2012 ‐0.597** ‐0.597** ‐0.597** ‐0.597** ‐0.597** ‐0.597** ‐0.597** (‐9.81) (‐8.78) (‐8.16) (‐7.71) (‐7.36) (‐7.09) (‐6.87) year=2013 ‐0.236** ‐0.236** ‐0.236** ‐0.236** ‐0.236** ‐0.236** ‐0.236* (‐2.67) (‐2.44) (‐2.28) (‐2.17) (‐2.07) (‐2.01) (‐1.95) year=2014 ‐0.522** ‐0.522** ‐0.522** ‐0.522** ‐0.522** ‐0.522** ‐0.522** (‐5.91) (‐5.43) (‐5.08) (‐4.83) (‐4.61) (‐4.46) (‐4.33) year=2015 ‐0.247** ‐0.247** ‐0.247** ‐0.247** ‐0.247** ‐0.247* ‐0.247* (‐2.60) (‐2.37) (‐2.19) (‐2.07) (‐1.97) (‐1.89) (‐1.83) Constant 0.398** 0.398** 0.398** 0.398** 0.398** 0.398** 0.398** (7.01) (6.26) (5.78) (5.44) (5.18) (4.97) (4.79) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Regression Results, Ritual events coded as ±2 weeks, DV=Standardized N‐W Lag (1) (2) (3) (4) (5) (6) (7)

June4_2w ‐0.182** ‐0.182** ‐0.182** ‐0.182** ‐0.182** ‐0.182** ‐0.182** (‐2.92) (‐2.72) (‐2.57) (‐2.45) (‐2.36) (‐2.29) (‐2.22) Two_conf._2w ‐0.562** ‐0.562** ‐0.562** ‐0.562** ‐0.562** ‐0.562** ‐0.562** (‐5.63) (‐5.02) (‐4.62) (‐4.32) (‐4.09) (‐3.91) (‐3.77) PRC60 ‐0.770** ‐0.770** ‐0.770** ‐0.770** ‐0.770** ‐0.770** ‐0.770** (‐6.79) (‐6.08) (‐5.65) (‐5.37) (‐5.18) (‐4.99) (‐4.81) CCP18 ‐0.671** ‐0.671** ‐0.671** ‐0.671** ‐0.671** ‐0.671** ‐0.671** (‐4.16) (‐3.85) (‐3.68) (‐3.57) (‐3.49) (‐3.39) (‐3.28) CCP_Plen_2w ‐0.475** ‐0.475** ‐0.475** ‐0.475** ‐0.475** ‐0.475** ‐0.475** (‐5.45) (‐4.93) (‐4.56) (‐4.30) (‐4.11) (‐3.97) (‐3.86) Ben_Ali 0.109 0.109 0.109 0.109 0.109 0.109 0.109 (0.62) (0.60) (0.62) (0.65) (0.66) (0.67) (0.67) Jasmine ‐0.108 ‐0.108 ‐0.108 ‐0.108 ‐0.108 ‐0.108 ‐0.108 (‐0.38) (‐0.37) (‐0.36) (‐0.36) (‐0.36) (‐0.37) (‐0.38) Umbrella 1.131** 1.131** 1.131** 1.131** 1.131** 1.131** 1.131** (8.50) (7.98) (7.50) (7.13) (6.90) (6.75) (6.59) Qian ‐0.0858 ‐0.0858 ‐0.0858 ‐0.0858 ‐0.0858 ‐0.0858 ‐0.0858 (‐0.18) (‐0.18) (‐0.20) (‐0.22) (‐0.24) (‐0.25) (‐0.25) Dalian ‐0.353 ‐0.353 ‐0.353 ‐0.353 ‐0.353 ‐0.353 ‐0.353 (‐0.95) (‐0.83) (‐0.78) (‐0.75) (‐0.75) (‐0.76) (‐0.77) Qidong 0.405** 0.405** 0.405* 0.405* 0.405* 0.405* 0.405* (2.09) (1.97) (1.92) (1.90) (1.90) (1.90) (1.91) Maoming ‐1.090** ‐1.090** ‐1.090** ‐1.090** ‐1.090** ‐1.090** ‐1.090** (‐4.69) (‐4.85) (‐5.04) (‐5.27) (‐5.50) (‐5.78) (‐5.78) Wukan ‐0.882** ‐0.882** ‐0.882** ‐0.882** ‐0.882** ‐0.882** ‐0.882** (‐6.86) (‐6.04) (‐5.52) (‐5.19) (‐4.98) (‐4.84) (‐4.75) Yushu 0.270* 0.270 0.270 0.270 0.270 0.270 0.270 (1.69) (1.60) (1.52) (1.44) (1.42) (1.38) (1.36) Train 0.887** 0.887** 0.887** 0.887** 0.887** 0.887** 0.887** (5.25) (5.25) (5.36) (5.66) (6.01) (6.04) (6.05) Stampede 0.0594 0.0594 0.0594 0.0594 0.0594 0.0594 0.0594 (0.29) (0.27) (0.25) (0.24) (0.22) (0.21) (0.21) Wang ‐0.535** ‐0.535** ‐0.535** ‐0.535** ‐0.535** ‐0.535** ‐0.535** (‐3.53) (‐3.13) (‐2.90) (‐2.77) (‐2.70) (‐2.65) (‐2.65) Bo_Purge ‐0.763** ‐0.763** ‐0.763** ‐0.763** ‐0.763** ‐0.763** ‐0.763** (‐6.21) (‐5.53) (‐5.13) (‐4.81) (‐4.58) (‐4.38) (‐4.20) Bo_Verdict 0.393** 0.393** 0.393** 0.393** 0.393** 0.393** 0.393** (3.33) (3.28) (3.26) (3.23) (3.30) (3.29) (3.22) Zhou_Verdict ‐0.194 ‐0.194 ‐0.194 ‐0.194 ‐0.194 ‐0.194 ‐0.194 (‐0.86) (‐0.87) (‐0.91) (‐0.98) (‐1.00) (‐1.04) (‐1.03) IPAB_Crack ‐0.644** ‐0.644** ‐0.644** ‐0.644** ‐0.644** ‐0.644** ‐0.644** (‐4.85) (‐4.64) (‐4.33) (‐4.19) (‐4.10) (‐3.98) (‐3.91) Anti‐rumor 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 (0.60) (0.61) (0.64) (0.68) (0.73) (0.75) (0.76) Tianya General 0.0837 0.0837 0.0837 0.0837 0.0837 0.0837 0.0837 (0.70) (0.65) (0.62) (0.60) (0.59) (0.58) (0.57) Xi_Term ‐1.092** ‐1.092** ‐1.092** ‐1.092** ‐1.092** ‐1.092** ‐1.092** (‐8.53) (‐8.03) (‐7.65) (‐7.41) (‐7.17) (‐7.01) (‐6.85) weekend 0.257** 0.257** 0.257** 0.257** 0.257** 0.257** 0.257** (5.16) (5.03) (5.03) (5.05) (5.08) (5.07) (4.98) holiday 0.267** 0.267** 0.267** 0.267** 0.267** 0.267** 0.267* (2.63) (2.35) (2.19) (2.09) (2.02) (1.98) (1.96) Spring 0.100 0.100 0.100 0.100 0.100 0.100 0.100 (1.13) (1.00) (0.92) (0.87) (0.82) (0.79) (0.76) Summer ‐0.0305 ‐0.0305 ‐0.0305 ‐0.0305 ‐0.0305 ‐0.0305 ‐0.0305 (‐0.37) (‐0.33) (‐0.30) (‐0.28) (‐0.26) (‐0.25) (‐0.24) Autumn ‐0.333** ‐0.333** ‐0.333** ‐0.333** ‐0.333** ‐0.333** ‐0.333** (‐3.58) (‐3.13) (‐2.84) (‐2.65) (‐2.51) (‐2.40) (‐2.31) year=2010 1.261** 1.261** 1.261** 1.261** 1.261** 1.261** 1.261** (12.36) (10.72) (9.70) (9.00) (8.48) (8.08) (7.75) year=2011 ‐0.201** ‐0.201* ‐0.201 ‐0.201 ‐0.201 ‐0.201 ‐0.201 (‐2.03) (‐1.78) (‐1.62) (‐1.50) (‐1.42) (‐1.35) (‐1.30) year=2012 ‐1.155** ‐1.155** ‐1.155** ‐1.155** ‐1.155** ‐1.155** ‐1.155** (‐9.72) (‐8.68) (‐8.05) (‐7.59) (‐7.25) (‐6.97) (‐6.74) year=2013 ‐0.412** ‐0.412** ‐0.412** ‐0.412* ‐0.412* ‐0.412* ‐0.412* (‐2.41) (‐2.20) (‐2.05) (‐1.95) (‐1.86) (‐1.80) (‐1.75) year=2014 ‐0.966** ‐0.966** ‐0.966** ‐0.966** ‐0.966** ‐0.966** ‐0.966** (‐5.67) (‐5.19) (‐4.84) (‐4.60) (‐4.38) (‐4.23) (‐4.11) year=2015 ‐0.412** ‐0.412** ‐0.412* ‐0.412* ‐0.412* ‐0.412 ‐0.412 (‐2.25) (‐2.04) (‐1.89) (‐1.78) (‐1.69) (‐1.63) (‐1.57) Constant 0.726** 0.726** 0.726** 0.726** 0.726** 0.726** 0.726** (6.52) (5.81) (5.36) (5.03) (4.79) (4.59) (4.42) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Regression Results, Ritual events coded as ±1 weeks, DV=Percent N‐W Lag (1) (2) (3) (4) (5) (6) (7)

June4_1w ‐0.0904** ‐0.0904** ‐0.0904* ‐0.0904* ‐0.0904* ‐0.0904* ‐0.0904 (‐2.13) (‐1.99) (‐1.89) (‐1.80) (‐1.72) (‐1.67) (‐1.63) Two_conf._1w ‐0.336** ‐0.336** ‐0.336** ‐0.336** ‐0.336** ‐0.336** ‐0.336** (‐5.19) (‐4.67) (‐4.31) (‐4.03) (‐3.82) (‐3.66) (‐3.52) PRC60_1w ‐0.422** ‐0.422** ‐0.422** ‐0.422** ‐0.422** ‐0.422** ‐0.422** (‐6.06) (‐5.64) (‐5.50) (‐5.43) (‐5.34) (‐5.20) (‐5.05) CCP18_1w ‐0.238** ‐0.238** ‐0.238** ‐0.238** ‐0.238** ‐0.238** ‐0.238** (‐2.42) (‐2.33) (‐2.25) (‐2.20) (‐2.17) (‐2.16) (‐2.12) CCP_Plen_1w ‐0.225** ‐0.225** ‐0.225** ‐0.225** ‐0.225** ‐0.225** ‐0.225** (‐4.24) (‐3.79) (‐3.52) (‐3.34) (‐3.22) (‐3.14) (‐3.07) Ben_Ali 0.0656 0.0656 0.0656 0.0656 0.0656 0.0656 0.0656 (0.71) (0.69) (0.71) (0.74) (0.76) (0.78) (0.77) Jasmine ‐0.102 ‐0.102 ‐0.102 ‐0.102 ‐0.102 ‐0.102 ‐0.102 (‐0.74) (‐0.72) (‐0.73) (‐0.72) (‐0.71) (‐0.71) (‐0.72) Umbrella 0.575** 0.575** 0.575** 0.575** 0.575** 0.575** 0.575** (8.13) (7.58) (7.09) (6.72) (6.49) (6.33) (6.18) Qian ‐0.0517 ‐0.0517 ‐0.0517 ‐0.0517 ‐0.0517 ‐0.0517 ‐0.0517 (‐0.21) (‐0.21) (‐0.23) (‐0.25) (‐0.27) (‐0.29) (‐0.29) Dalian ‐0.166 ‐0.166 ‐0.166 ‐0.166 ‐0.166 ‐0.166 ‐0.166 (‐0.88) (‐0.77) (‐0.72) (‐0.70) (‐0.70) (‐0.71) (‐0.72) Qidong 0.217** 0.217** 0.217** 0.217** 0.217** 0.217** 0.217** (2.17) (2.04) (1.98) (1.97) (1.96) (1.97) (1.97) Maoming ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** (‐4.51) (‐4.68) (‐4.88) (‐5.12) (‐5.38) (‐5.69) (‐5.71) Wukan ‐0.429** ‐0.429** ‐0.429** ‐0.429** ‐0.429** ‐0.429** ‐0.429** (‐6.54) (‐5.77) (‐5.27) (‐4.96) (‐4.76) (‐4.62) (‐4.54) Yushu 0.163** 0.163* 0.163* 0.163* 0.163* 0.163 0.163 (2.01) (1.91) (1.81) (1.72) (1.68) (1.64) (1.62) Train 0.463** 0.463** 0.463** 0.463** 0.463** 0.463** 0.463** (5.34) (5.36) (5.48) (5.80) (6.18) (6.21) (6.23) Stampede 0.0435 0.0435 0.0435 0.0435 0.0435 0.0435 0.0435 (0.42) (0.38) (0.36) (0.34) (0.32) (0.30) (0.29) Wang ‐0.321** ‐0.321** ‐0.321** ‐0.321** ‐0.321** ‐0.321** ‐0.321** (‐4.78) (‐4.33) (‐4.05) (‐3.88) (‐3.74) (‐3.64) (‐3.59) Bo_Purge ‐0.403** ‐0.403** ‐0.403** ‐0.403** ‐0.403** ‐0.403** ‐0.403** (‐6.67) (‐6.00) (‐5.63) (‐5.34) (‐5.13) (‐4.95) (‐4.75) Bo_Verdict 0.271** 0.271** 0.271** 0.271** 0.271** 0.271** 0.271** (4.41) (4.34) (4.32) (4.31) (4.41) (4.41) (4.31) Zhou_Verdict ‐0.137 ‐0.137 ‐0.137 ‐0.137 ‐0.137 ‐0.137 ‐0.137 (‐1.16) (‐1.18) (‐1.22) (‐1.31) (‐1.33) (‐1.38) (‐1.35) IPAB_Crack ‐0.272** ‐0.272** ‐0.272** ‐0.272** ‐0.272** ‐0.272** ‐0.272** (‐3.86) (‐3.70) (‐3.48) (‐3.39) (‐3.33) (‐3.24) (‐3.18) Anti‐rumor 0.0921 0.0921 0.0921 0.0921 0.0921 0.0921* 0.0921* (1.29) (1.31) (1.41) (1.52) (1.64) (1.70) (1.70) Tianya General 0.0275 0.0275 0.0275 0.0275 0.0275 0.0275 0.0275 (0.44) (0.40) (0.38) (0.37) (0.36) (0.36) (0.35) Xi_Term ‐0.504** ‐0.504** ‐0.504** ‐0.504** ‐0.504** ‐0.504** ‐0.504** (‐7.47) (‐6.99) (‐6.65) (‐6.43) (‐6.23) (‐6.11) (‐6.02) weekend 0.140** 0.140** 0.140** 0.140** 0.140** 0.140** 0.140** (5.35) (5.22) (5.21) (5.24) (5.28) (5.27) (5.17) holiday 0.137** 0.137** 0.137** 0.137** 0.137* 0.137* 0.137* (2.53) (2.26) (2.11) (2.01) (1.94) (1.90) (1.87) Spring 0.0452 0.0452 0.0452 0.0452 0.0452 0.0452 0.0452 (0.97) (0.87) (0.80) (0.75) (0.71) (0.68) (0.66) Summer ‐0.00904 ‐0.00904 ‐0.00904 ‐0.00904 ‐0.00904 ‐0.00904 ‐0.00904 (‐0.21) (‐0.19) (‐0.17) (‐0.16) (‐0.15) (‐0.14) (‐0.14) Autumn ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** (‐4.53) (‐3.95) (‐3.59) (‐3.34) (‐3.16) (‐3.02) (‐2.91) year=2010 0.637** 0.637** 0.637** 0.637** 0.637** 0.637** 0.637** (12.32) (10.66) (9.64) (8.94) (8.42) (8.02) (7.69) year=2011 ‐0.108** ‐0.108* ‐0.108* ‐0.108 ‐0.108 ‐0.108 ‐0.108 (‐2.22) (‐1.94) (‐1.77) (‐1.64) (‐1.55) (‐1.48) (‐1.42) year=2012 ‐0.581** ‐0.581** ‐0.581** ‐0.581** ‐0.581** ‐0.581** ‐0.581** (‐9.22) (‐8.24) (‐7.66) (‐7.24) (‐6.92) (‐6.66) (‐6.44) year=2013 ‐0.262** ‐0.262** ‐0.262** ‐0.262** ‐0.262** ‐0.262** ‐0.262** (‐2.95) (‐2.68) (‐2.51) (‐2.39) (‐2.29) (‐2.22) (‐2.17) year=2014 ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** (‐6.21) (‐5.67) (‐5.29) (‐5.03) (‐4.81) (‐4.66) (‐4.54) year=2015 ‐0.281** ‐0.281** ‐0.281** ‐0.281** ‐0.281** ‐0.281** ‐0.281** (‐2.97) (‐2.69) (‐2.49) (‐2.35) (‐2.23) (‐2.15) (‐2.07) Constant 0.369** 0.369** 0.369** 0.369** 0.369** 0.369** 0.369** (6.38) (5.67) (5.23) (4.91) (4.67) (4.47) (4.31) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Regression Results, Ritual events coded as ±1 weeks, DV=Standardized N‐W Lag (1) (2) (3) (4) (5) (6) (7)

June4_1w ‐0.170** ‐0.170* ‐0.170* ‐0.170* ‐0.170* ‐0.170 ‐0.170 (‐2.08) (‐1.94) (‐1.83) (‐1.74) (‐1.67) (‐1.63) (‐1.58) Two_conf._1w ‐0.644** ‐0.644** ‐0.644** ‐0.644** ‐0.644** ‐0.644** ‐0.644** (‐5.25) (‐4.72) (‐4.36) (‐4.07) (‐3.86) (‐3.69) (‐3.56) PRC60_1w ‐0.842** ‐0.842** ‐0.842** ‐0.842** ‐0.842** ‐0.842** ‐0.842** (‐6.27) (‐5.80) (‐5.63) (‐5.53) (‐5.43) (‐5.28) (‐5.12) CCP18_1w ‐0.469** ‐0.469** ‐0.469** ‐0.469** ‐0.469** ‐0.469** ‐0.469** (‐2.49) (‐2.40) (‐2.31) (‐2.26) (‐2.23) (‐2.22) (‐2.18) CCP_Plen_1w ‐0.442** ‐0.442** ‐0.442** ‐0.442** ‐0.442** ‐0.442** ‐0.442** (‐4.29) (‐3.83) (‐3.54) (‐3.35) (‐3.23) (‐3.14) (‐3.07) Ben_Ali 0.142 0.142 0.142 0.142 0.142 0.142 0.142 (0.81) (0.78) (0.80) (0.84) (0.86) (0.88) (0.87) Jasmine ‐0.163 ‐0.163 ‐0.163 ‐0.163 ‐0.163 ‐0.163 ‐0.163 (‐0.63) (‐0.62) (‐0.62) (‐0.61) (‐0.61) (‐0.60) (‐0.61) Umbrella 1.140** 1.140** 1.140** 1.140** 1.140** 1.140** 1.140** (8.40) (7.82) (7.31) (6.92) (6.68) (6.51) (6.35) Qian ‐0.0467 ‐0.0467 ‐0.0467 ‐0.0467 ‐0.0467 ‐0.0467 ‐0.0467 (‐0.10) (‐0.10) (‐0.11) (‐0.12) (‐0.13) (‐0.13) (‐0.13) Dalian ‐0.342 ‐0.342 ‐0.342 ‐0.342 ‐0.342 ‐0.342 ‐0.342 (‐0.92) (‐0.80) (‐0.75) (‐0.73) (‐0.73) (‐0.73) (‐0.75) Qidong 0.422** 0.422** 0.422** 0.422** 0.422** 0.422** 0.422** (2.17) (2.04) (1.99) (1.98) (1.97) (1.98) (1.98) Maoming ‐1.052** ‐1.052** ‐1.052** ‐1.052** ‐1.052** ‐1.052** ‐1.052** (‐4.52) (‐4.68) (‐4.88) (‐5.12) (‐5.37) (‐5.68) (‐5.70) Wukan ‐0.845** ‐0.845** ‐0.845** ‐0.845** ‐0.845** ‐0.845** ‐0.845** (‐6.51) (‐5.73) (‐5.24) (‐4.93) (‐4.73) (‐4.60) (‐4.52) Yushu 0.302* 0.302* 0.302* 0.302 0.302 0.302 0.302 (1.89) (1.80) (1.71) (1.62) (1.59) (1.55) (1.53) Train 0.900** 0.900** 0.900** 0.900** 0.900** 0.900** 0.900** (5.35) (5.36) (5.47) (5.80) (6.18) (6.22) (6.24) Stampede 0.108 0.108 0.108 0.108 0.108 0.108 0.108 (0.54) (0.49) (0.46) (0.43) (0.41) (0.39) (0.38) Wang ‐0.616** ‐0.616** ‐0.616** ‐0.616** ‐0.616** ‐0.616** ‐0.616** (‐4.81) (‐4.36) (‐4.07) (‐3.89) (‐3.76) (‐3.65) (‐3.59) Bo_Purge ‐0.799** ‐0.799** ‐0.799** ‐0.799** ‐0.799** ‐0.799** ‐0.799** (‐6.89) (‐6.19) (‐5.80) (‐5.50) (‐5.28) (‐5.09) (‐4.88) Bo_Verdict 0.516** 0.516** 0.516** 0.516** 0.516** 0.516** 0.516** (4.39) (4.33) (4.31) (4.28) (4.38) (4.38) (4.28) Zhou_Verdict ‐0.255 ‐0.255 ‐0.255 ‐0.255 ‐0.255 ‐0.255 ‐0.255 (‐1.14) (‐1.15) (‐1.19) (‐1.27) (‐1.29) (‐1.33) (‐1.31) IPAB_Crack ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** ‐0.548** (‐3.96) (‐3.77) (‐3.52) (‐3.43) (‐3.36) (‐3.27) (‐3.22) Anti‐rumor 0.157 0.157 0.157 0.157 0.157 0.157 0.157 (1.16) (1.18) (1.26) (1.37) (1.47) (1.52) (1.52) Tianya General 0.0706 0.0706 0.0706 0.0706 0.0706 0.0706 0.0706 (0.58) (0.53) (0.51) (0.49) (0.48) (0.47) (0.46) Xi_Term ‐0.997** ‐0.997** ‐0.997** ‐0.997** ‐0.997** ‐0.997** ‐0.997** (‐7.70) (‐7.19) (‐6.83) (‐6.59) (‐6.38) (‐6.25) (‐6.15) weekend 0.254** 0.254** 0.254** 0.254** 0.254** 0.254** 0.254** (5.05) (4.93) (4.93) (4.95) (5.00) (4.99) (4.90) holiday 0.230** 0.230** 0.230* 0.230* 0.230* 0.230 0.230 (2.20) (1.96) (1.83) (1.74) (1.68) (1.64) (1.62) Spring 0.0978 0.0978 0.0978 0.0978 0.0978 0.0978 0.0978 (1.10) (0.98) (0.90) (0.84) (0.80) (0.77) (0.74) Summer ‐0.00727 ‐0.00727 ‐0.00727 ‐0.00727 ‐0.00727 ‐0.00727 ‐0.00727 (‐0.09) (‐0.08) (‐0.07) (‐0.07) (‐0.06) (‐0.06) (‐0.06) Autumn ‐0.407** ‐0.407** ‐0.407** ‐0.407** ‐0.407** ‐0.407** ‐0.407** (‐4.57) (‐3.98) (‐3.61) (‐3.36) (‐3.18) (‐3.03) (‐2.92) year=2010 1.296** 1.296** 1.296** 1.296** 1.296** 1.296** 1.296** (12.76) (11.03) (9.96) (9.22) (8.68) (8.25) (7.91) year=2011 ‐0.171* ‐0.171 ‐0.171 ‐0.171 ‐0.171 ‐0.171 ‐0.171 (‐1.78) (‐1.55) (‐1.41) (‐1.30) (‐1.23) (‐1.17) (‐1.12) year=2012 ‐1.120** ‐1.120** ‐1.120** ‐1.120** ‐1.120** ‐1.120** ‐1.120** (‐9.11) (‐8.13) (‐7.54) (‐7.12) (‐6.79) (‐6.54) (‐6.32) year=2013 ‐0.463** ‐0.463** ‐0.463** ‐0.463** ‐0.463** ‐0.463** ‐0.463* (‐2.69) (‐2.44) (‐2.27) (‐2.16) (‐2.07) (‐2.01) (‐1.95) year=2014 ‐1.017** ‐1.017** ‐1.017** ‐1.017** ‐1.017** ‐1.017** ‐1.017** (‐5.95) (‐5.42) (‐5.05) (‐4.79) (‐4.57) (‐4.42) (‐4.30) year=2015 ‐0.478** ‐0.478** ‐0.478** ‐0.478** ‐0.478* ‐0.478* ‐0.478* (‐2.62) (‐2.36) (‐2.18) (‐2.05) (‐1.95) (‐1.88) (‐1.81) Constant 0.669** 0.669** 0.669** 0.669** 0.669** 0.669** 0.669** (5.90) (5.23) (4.81) (4.51) (4.28) (4.10) (3.94) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Regression Results, Ritual events coded as ±2 Days, DV=Percent N‐W Lag (1) (2) (3) (4) (5) (6) (7)

Jun4 ‐0.153** ‐0.153** ‐0.153** ‐0.153** ‐0.153** ‐0.153** ‐0.153** (‐2.41) (‐2.23) (‐2.15) (‐2.11) (‐2.08) (‐2.06) (‐2.04) Two_conf. ‐0.305** ‐0.305** ‐0.305** ‐0.305** ‐0.305** ‐0.305** ‐0.305** (‐4.04) (‐3.69) (‐3.47) (‐3.29) (‐3.15) (‐3.04) (‐2.96) PRC60_2d ‐0.396** ‐0.396** ‐0.396** ‐0.396** ‐0.396** ‐0.396** ‐0.396** (‐7.13) (‐6.80) (‐6.37) (‐5.94) (‐5.60) (‐5.33) (‐5.13) CCP18_2d ‐0.130 ‐0.130 ‐0.130 ‐0.130 ‐0.130 ‐0.130 ‐0.130 (‐1.41) (‐1.42) (‐1.45) (‐1.47) (‐1.44) (‐1.40) (‐1.40) CCP_Plen ‐0.107 ‐0.107 ‐0.107 ‐0.107 ‐0.107 ‐0.107 ‐0.107 (‐1.56) (‐1.44) (‐1.36) (‐1.31) (‐1.26) (‐1.22) (‐1.19) Ben_Ali 0.0807 0.0807 0.0807 0.0807 0.0807 0.0807 0.0807 (0.87) (0.84) (0.87) (0.91) (0.93) (0.95) (0.94) Jasmine ‐0.207 ‐0.207 ‐0.207 ‐0.207 ‐0.207 ‐0.207 ‐0.207 (‐1.25) (‐1.18) (‐1.15) (‐1.14) (‐1.14) (‐1.16) (‐1.20) Umbrella 0.580** 0.580** 0.580** 0.580** 0.580** 0.580** 0.580** (7.79) (7.17) (6.66) (6.28) (6.03) (5.86) (5.69) Qian ‐0.0357 ‐0.0357 ‐0.0357 ‐0.0357 ‐0.0357 ‐0.0357 ‐0.0357 (‐0.14) (‐0.15) (‐0.16) (‐0.17) (‐0.19) (‐0.20) (‐0.20) Dalian ‐0.167 ‐0.167 ‐0.167 ‐0.167 ‐0.167 ‐0.167 ‐0.167 (‐0.88) (‐0.78) (‐0.73) (‐0.71) (‐0.70) (‐0.71) (‐0.73) Qidong 0.216** 0.216** 0.216** 0.216* 0.216* 0.216* 0.216* (2.16) (2.02) (1.96) (1.95) (1.94) (1.94) (1.94) Maoming ‐0.523** ‐0.523** ‐0.523** ‐0.523** ‐0.523** ‐0.523** ‐0.523** (‐4.31) (‐4.47) (‐4.65) (‐4.86) (‐5.08) (‐5.35) (‐5.35) Wukan ‐0.416** ‐0.416** ‐0.416** ‐0.416** ‐0.416** ‐0.416** ‐0.416** (‐6.38) (‐5.63) (‐5.15) (‐4.84) (‐4.63) (‐4.50) (‐4.41) Yushu 0.189** 0.189** 0.189** 0.189** 0.189* 0.189* 0.189* (2.33) (2.22) (2.10) (1.99) (1.95) (1.91) (1.88) Train 0.464** 0.464** 0.464** 0.464** 0.464** 0.464** 0.464** (5.38) (5.40) (5.53) (5.87) (6.28) (6.33) (6.36) Stampede 0.0709 0.0709 0.0709 0.0709 0.0709 0.0709 0.0709 (0.67) (0.62) (0.58) (0.54) (0.51) (0.48) (0.47) Wang ‐0.313** ‐0.313** ‐0.313** ‐0.313** ‐0.313** ‐0.313** ‐0.313** (‐4.56) (‐4.13) (‐3.85) (‐3.68) (‐3.55) (‐3.45) (‐3.39) Bo_Purge ‐0.419** ‐0.419** ‐0.419** ‐0.419** ‐0.419** ‐0.419** ‐0.419** (‐7.12) (‐6.49) (‐6.18) (‐5.93) (‐5.76) (‐5.62) (‐5.43) Bo_Verdict 0.310** 0.310** 0.310** 0.310** 0.310** 0.310** 0.310** (5.09) (5.00) (4.96) (4.94) (5.05) (5.06) (4.96) Zhou_Verdict ‐0.138 ‐0.138 ‐0.138 ‐0.138 ‐0.138 ‐0.138 ‐0.138 (‐1.16) (‐1.18) (‐1.22) (‐1.30) (‐1.31) (‐1.35) (‐1.33) IPAB_Crack ‐0.240** ‐0.240** ‐0.240** ‐0.240** ‐0.240** ‐0.240** ‐0.240** (‐3.31) (‐3.17) (‐2.99) (‐2.92) (‐2.88) (‐2.81) (‐2.76) Anti‐rumor 0.121* 0.121* 0.121* 0.121** 0.121** 0.121** 0.121** (1.73) (1.77) (1.90) (2.08) (2.26) (2.34) (2.34) Tianya General 0.0116 0.0116 0.0116 0.0116 0.0116 0.0116 0.0116 (0.18) (0.16) (0.16) (0.15) (0.15) (0.14) (0.14) Xi_Term ‐0.445** ‐0.445** ‐0.445** ‐0.445** ‐0.445** ‐0.445** ‐0.445** (‐6.78) (‐6.41) (‐6.15) (‐5.99) (‐5.84) (‐5.75) (‐5.67) weekend 0.136** 0.136** 0.136** 0.136** 0.136** 0.136** 0.136** (5.10) (4.97) (4.97) (5.00) (5.05) (5.05) (4.96) holiday 0.144** 0.144** 0.144** 0.144** 0.144** 0.144** 0.144** (2.63) (2.36) (2.20) (2.10) (2.03) (1.99) (1.96) Spring 0.0373 0.0373 0.0373 0.0373 0.0373 0.0373 0.0373 (0.79) (0.70) (0.64) (0.59) (0.56) (0.53) (0.51) Summer 0.00913 0.00913 0.00913 0.00913 0.00913 0.00913 0.00913 (0.21) (0.18) (0.16) (0.15) (0.14) (0.14) (0.13) Autumn ‐0.235** ‐0.235** ‐0.235** ‐0.235** ‐0.235** ‐0.235** ‐0.235** (‐5.08) (‐4.42) (‐4.00) (‐3.71) (‐3.50) (‐3.34) (‐3.20) year=2010 0.654** 0.654** 0.654** 0.654** 0.654** 0.654** 0.654** (12.41) (10.70) (9.65) (8.93) (8.40) (7.98) (7.63) year=2011 ‐0.0897* ‐0.0897 ‐0.0897 ‐0.0897 ‐0.0897 ‐0.0897 ‐0.0897 (‐1.82) (‐1.59) (‐1.44) (‐1.33) (‐1.26) (‐1.19) (‐1.14) year=2012 ‐0.551** ‐0.551** ‐0.551** ‐0.551** ‐0.551** ‐0.551** ‐0.551** (‐8.41) (‐7.51) (‐6.97) (‐6.57) (‐6.27) (‐6.03) (‐5.82) year=2013 ‐0.285** ‐0.285** ‐0.285** ‐0.285** ‐0.285** ‐0.285** ‐0.285** (‐3.18) (‐2.89) (‐2.70) (‐2.57) (‐2.46) (‐2.39) (‐2.32) year=2014 ‐0.578** ‐0.578** ‐0.578** ‐0.578** ‐0.578** ‐0.578** ‐0.578** (‐6.54) (‐5.98) (‐5.59) (‐5.31) (‐5.08) (‐4.91) (‐4.78) year=2015 ‐0.324** ‐0.324** ‐0.324** ‐0.324** ‐0.324** ‐0.324** ‐0.324** (‐3.45) (‐3.13) (‐2.90) (‐2.73) (‐2.60) (‐2.49) (‐2.40) Constant 0.343** 0.343** 0.343** 0.343** 0.343** 0.343** 0.343** (5.72) (5.06) (4.64) (4.35) (4.12) (3.93) (3.77) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Regression Results, Ritual events coded as ±2 Days, DV=Standardized N‐W Lag (1) (2) (3) (4) (5) (6) (7)

Jun4 ‐0.293** ‐0.293** ‐0.293** ‐0.293** ‐0.293** ‐0.293** ‐0.293** (‐2.39) (‐2.20) (‐2.12) (‐2.08) (‐2.05) (‐2.03) (‐2.01) Two_conf. ‐0.590** ‐0.590** ‐0.590** ‐0.590** ‐0.590** ‐0.590** ‐0.590** (‐4.10) (‐3.75) (‐3.52) (‐3.34) (‐3.20) (‐3.09) (‐3.00) PRC60_2d ‐0.785** ‐0.785** ‐0.785** ‐0.785** ‐0.785** ‐0.785** ‐0.785** (‐7.28) (‐6.92) (‐6.44) (‐5.98) (‐5.62) (‐5.35) (‐5.14) CCP18_2d ‐0.263 ‐0.263 ‐0.263 ‐0.263 ‐0.263 ‐0.263 ‐0.263 (‐1.49) (‐1.50) (‐1.52) (‐1.54) (‐1.51) (‐1.47) (‐1.46) CCP_Plen ‐0.213 ‐0.213 ‐0.213 ‐0.213 ‐0.213 ‐0.213 ‐0.213 (‐1.59) (‐1.46) (‐1.38) (‐1.32) (‐1.27) (‐1.23) (‐1.20) Ben_Ali 0.171 0.171 0.171 0.171 0.171 0.171 0.171 (0.97) (0.94) (0.96) (1.01) (1.03) (1.05) (1.04) Jasmine ‐0.364 ‐0.364 ‐0.364 ‐0.364 ‐0.364 ‐0.364 ‐0.364 (‐1.17) (‐1.10) (‐1.07) (‐1.06) (‐1.06) (‐1.08) (‐1.12) Umbrella 1.151** 1.151** 1.151** 1.151** 1.151** 1.151** 1.151** (8.04) (7.39) (6.86) (6.46) (6.20) (6.01) (5.84) Qian ‐0.0160 ‐0.0160 ‐0.0160 ‐0.0160 ‐0.0160 ‐0.0160 ‐0.0160 (‐0.03) (‐0.03) (‐0.04) (‐0.04) (‐0.04) (‐0.05) (‐0.05) Dalian ‐0.344 ‐0.344 ‐0.344 ‐0.344 ‐0.344 ‐0.344 ‐0.344 (‐0.92) (‐0.81) (‐0.76) (‐0.73) (‐0.73) (‐0.74) (‐0.76) Qidong 0.420** 0.420** 0.420** 0.420* 0.420* 0.420* 0.420* (2.15) (2.02) (1.97) (1.95) (1.95) (1.95) (1.95) Maoming ‐1.004** ‐1.004** ‐1.004** ‐1.004** ‐1.004** ‐1.004** ‐1.004** (‐4.33) (‐4.48) (‐4.65) (‐4.86) (‐5.08) (‐5.35) (‐5.35) Wukan ‐0.820** ‐0.820** ‐0.820** ‐0.820** ‐0.820** ‐0.820** ‐0.820** (‐6.37) (‐5.61) (‐5.12) (‐4.81) (‐4.61) (‐4.48) (‐4.40) Yushu 0.351** 0.351** 0.351** 0.351* 0.351* 0.351* 0.351* (2.21) (2.10) (1.99) (1.89) (1.85) (1.81) (1.78) Train 0.902** 0.902** 0.902** 0.902** 0.902** 0.902** 0.902** (5.39) (5.40) (5.53) (5.87) (6.27) (6.33) (6.37) Stampede 0.161 0.161 0.161 0.161 0.161 0.161 0.161 (0.79) (0.72) (0.67) (0.63) (0.59) (0.56) (0.55) Wang ‐0.602** ‐0.602** ‐0.602** ‐0.602** ‐0.602** ‐0.602** ‐0.602** (‐4.59) (‐4.15) (‐3.87) (‐3.70) (‐3.56) (‐3.45) (‐3.39) Bo_Purge ‐0.831** ‐0.831** ‐0.831** ‐0.831** ‐0.831** ‐0.831** ‐0.831** (‐7.35) (‐6.69) (‐6.37) (‐6.11) (‐5.93) (‐5.78) (‐5.58) Bo_Verdict 0.592** 0.592** 0.592** 0.592** 0.592** 0.592** 0.592** (5.10) (5.01) (4.98) (4.95) (5.06) (5.07) (4.96) Zhou_Verdict ‐0.258 ‐0.258 ‐0.258 ‐0.258 ‐0.258 ‐0.258 ‐0.258 (‐1.13) (‐1.15) (‐1.19) (‐1.27) (‐1.27) (‐1.31) (‐1.29) IPAB_Crack ‐0.485** ‐0.485** ‐0.485** ‐0.485** ‐0.485** ‐0.485** ‐0.485** (‐3.40) (‐3.23) (‐3.04) (‐2.97) (‐2.92) (‐2.84) (‐2.80) Anti‐rumor 0.213 0.213 0.213* 0.213* 0.213** 0.213** 0.213** (1.60) (1.64) (1.77) (1.93) (2.09) (2.16) (2.15) Tianya General 0.0403 0.0403 0.0403 0.0403 0.0403 0.0403 0.0403 (0.32) (0.29) (0.28) (0.27) (0.26) (0.26) (0.25) Xi_Term ‐0.882** ‐0.882** ‐0.882** ‐0.882** ‐0.882** ‐0.882** ‐0.882** (‐7.01) (‐6.61) (‐6.34) (‐6.17) (‐6.00) (‐5.90) (‐5.82) weekend 0.248** 0.248** 0.248** 0.248** 0.248** 0.248** 0.248** (4.80) (4.69) (4.69) (4.72) (4.77) (4.78) (4.69) holiday 0.243** 0.243** 0.243* 0.243* 0.243* 0.243* 0.243* (2.30) (2.05) (1.92) (1.83) (1.77) (1.73) (1.71) Spring 0.0835 0.0835 0.0835 0.0835 0.0835 0.0835 0.0835 (0.91) (0.81) (0.74) (0.69) (0.65) (0.62) (0.59) Summer 0.0279 0.0279 0.0279 0.0279 0.0279 0.0279 0.0279 (0.33) (0.29) (0.26) (0.24) (0.23) (0.22) (0.21) Autumn ‐0.459** ‐0.459** ‐0.459** ‐0.459** ‐0.459** ‐0.459** ‐0.459** (‐5.14) (‐4.46) (‐4.04) (‐3.74) (‐3.53) (‐3.36) (‐3.23) year=2010 1.331** 1.331** 1.331** 1.331** 1.331** 1.331** 1.331** (12.85) (11.07) (9.97) (9.21) (8.65) (8.21) (7.85) year=2011 ‐0.133 ‐0.133 ‐0.133 ‐0.133 ‐0.133 ‐0.133 ‐0.133 (‐1.37) (‐1.19) (‐1.08) (‐1.00) (‐0.94) (‐0.89) (‐0.85) year=2012 ‐1.061** ‐1.061** ‐1.061** ‐1.061** ‐1.061** ‐1.061** ‐1.061** (‐8.30) (‐7.40) (‐6.85) (‐6.46) (‐6.16) (‐5.91) (‐5.70) year=2013 ‐0.506** ‐0.506** ‐0.506** ‐0.506** ‐0.506** ‐0.506** ‐0.506** (‐2.91) (‐2.64) (‐2.46) (‐2.34) (‐2.24) (‐2.17) (‐2.11) year=2014 ‐1.074** ‐1.074** ‐1.074** ‐1.074** ‐1.074** ‐1.074** ‐1.074** (‐6.28) (‐5.72) (‐5.33) (‐5.06) (‐4.83) (‐4.66) (‐4.53) year=2015 ‐0.558** ‐0.558** ‐0.558** ‐0.558** ‐0.558** ‐0.558** ‐0.558** (‐3.08) (‐2.79) (‐2.58) (‐2.43) (‐2.30) (‐2.21) (‐2.13) Constant 0.616** 0.616** 0.616** 0.616** 0.616** 0.616** 0.616** (5.24) (4.62) (4.24) (3.96) (3.75) (3.58) (3.43) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Part 8 Results: Complaints itself

Percent; Two Weeks (1) (2) (3) (4) (5) (6) (7)

June4_2w ‐0.0241 ‐0.0241 ‐0.0241 ‐0.0241 ‐0.0241 ‐0.0241 ‐0.0241 (‐1.10) (‐0.97) (‐0.89) (‐0.84) (‐0.80) (‐0.77) (‐0.74) Twocon_2w ‐0.0954** ‐0.0954** ‐0.0954** ‐0.0954** ‐0.0954** ‐0.0954** ‐0.0954* (‐3.24) (‐2.76) (‐2.47) (‐2.28) (‐2.13) (‐2.02) (‐1.92) PRC60 ‐0.491** ‐0.491** ‐0.491** ‐0.491** ‐0.491** ‐0.491** ‐0.491** (‐8.45) (‐7.23) (‐6.50) (‐6.00) (‐5.64) (‐5.38) (‐5.19) CCP18 ‐0.265** ‐0.265** ‐0.265** ‐0.265** ‐0.265** ‐0.265** ‐0.265** (‐6.90) (‐6.16) (‐5.69) (‐5.37) (‐5.16) (‐4.99) (‐4.81) CCP_C_2w ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** (‐5.77) (‐4.90) (‐4.38) (‐4.01) (‐3.74) (‐3.54) (‐3.37) Ben_Ali 0.187** 0.187** 0.187** 0.187** 0.187** 0.187** 0.187** (4.05) (3.69) (3.42) (3.27) (3.16) (3.06) (2.97) Jasmine 0.319** 0.319** 0.319** 0.319** 0.319** 0.319** 0.319** (5.92) (5.37) (5.02) (4.75) (4.51) (4.35) (4.24) Umbrella 0.557** 0.557** 0.557** 0.557** 0.557** 0.557** 0.557** (12.85) (11.07) (9.95) (9.15) (8.54) (8.05) (7.65) Qian 0.527** 0.527** 0.527** 0.527** 0.527** 0.527** 0.527** (2.95) (3.23) (3.86) (4.52) (4.99) (5.37) (5.51) Dalian ‐0.293* ‐0.293 ‐0.293 ‐0.293 ‐0.293 ‐0.293 ‐0.293 (‐1.65) (‐1.41) (‐1.31) (‐1.25) (‐1.23) (‐1.23) (‐1.24) Qidong 0.116 0.116* 0.116* 0.116** 0.116** 0.116** 0.116** (1.61) (1.75) (1.89) (2.00) (2.05) (2.09) (2.10) Maoming ‐0.167** ‐0.167** ‐0.167** ‐0.167** ‐0.167** ‐0.167** ‐0.167** (‐4.04) (‐3.65) (‐3.54) (‐3.50) (‐3.52) (‐3.51) (‐3.47) Wukan ‐0.348** ‐0.348** ‐0.348** ‐0.348** ‐0.348** ‐0.348** ‐0.348** (‐4.71) (‐4.13) (‐3.83) (‐3.67) (‐3.58) (‐3.52) (‐3.47) Yushu ‐0.0800 ‐0.0800 ‐0.0800 ‐0.0800 ‐0.0800 ‐0.0800 ‐0.0800 (‐1.08) (‐1.00) (‐1.00) (‐0.97) (‐0.95) (‐0.93) (‐0.93) Train 0.247** 0.247** 0.247** 0.247** 0.247** 0.247** 0.247** (4.16) (3.89) (3.93) (4.10) (4.34) (4.45) (4.41) Stampede 0.265** 0.265** 0.265** 0.265** 0.265** 0.265** 0.265** (3.91) (3.43) (3.11) (2.92) (2.75) (2.63) (2.54) Wang ‐0.0833** ‐0.0833* ‐0.0833* ‐0.0833* ‐0.0833 ‐0.0833 ‐0.0833 (‐2.08) (‐1.89) (‐1.77) (‐1.69) (‐1.63) (‐1.58) (‐1.54) Bo_Purge ‐0.389** ‐0.389** ‐0.389** ‐0.389** ‐0.389** ‐0.389** ‐0.389** (‐12.69) (‐11.00) (‐9.96) (‐9.25) (‐8.70) (‐8.24) (‐7.84) Bo_Verdict ‐0.0338 ‐0.0338 ‐0.0338 ‐0.0338 ‐0.0338 ‐0.0338 ‐0.0338 (‐0.82) (‐0.77) (‐0.72) (‐0.67) (‐0.64) (‐0.62) (‐0.61) Zhou_Verdict 0.0456 0.0456 0.0456 0.0456 0.0456 0.0456 0.0456 (0.82) (0.79) (0.79) (0.76) (0.74) (0.75) (0.76) IPAB_Crack ‐0.392** ‐0.392** ‐0.392** ‐0.392** ‐0.392** ‐0.392** ‐0.392** (‐5.32) (‐4.79) (‐4.46) (‐4.25) (‐4.10) (‐3.99) (‐3.91) Anti‐rumor ‐0.212** ‐0.212** ‐0.212** ‐0.212** ‐0.212** ‐0.212** ‐0.212** (‐5.26) (‐5.04) (‐5.05) (‐4.88) (‐4.77) (‐4.70) (‐4.65) Tianya General 0.197** 0.197** 0.197** 0.197** 0.197** 0.197** 0.197** (5.31) (4.92) (4.71) (4.58) (4.49) (4.40) (4.30) Xi_Term ‐0.509** ‐0.509** ‐0.509** ‐0.509** ‐0.509** ‐0.509** ‐0.509** (‐11.67) (‐10.17) (‐9.24) (‐8.55) (‐8.03) (‐7.62) (‐7.28) weekend ‐0.104** ‐0.104** ‐0.104** ‐0.104** ‐0.104** ‐0.104** ‐0.104** (‐6.17) (‐6.21) (‐6.41) (‐6.66) (‐6.99) (‐7.15) (‐6.96) holiday ‐0.279** ‐0.279** ‐0.279** ‐0.279** ‐0.279** ‐0.279** ‐0.279** (‐7.20) (‐6.31) (‐5.83) (‐5.54) (‐5.35) (‐5.22) (‐5.13) Spring 0.138** 0.138** 0.138** 0.138** 0.138** 0.138** 0.138** (4.87) (4.16) (3.73) (3.42) (3.19) (3.01) (2.86) Summer 0.103** 0.103** 0.103** 0.103** 0.103** 0.103** 0.103** (3.66) (3.12) (2.80) (2.57) (2.40) (2.27) (2.16) Autumn ‐0.0793** ‐0.0793* ‐0.0793* ‐0.0793 ‐0.0793 ‐0.0793 ‐0.0793 (‐2.22) (‐1.88) (‐1.67) (‐1.53) (‐1.43) (‐1.35) (‐1.29) year=2010 0.907** 0.907** 0.907** 0.907** 0.907** 0.907** 0.907** (17.24) (14.46) (12.77) (11.61) (10.75) (10.06) (9.51) year=2011 0.319** 0.319** 0.319** 0.319** 0.319** 0.319** 0.319** (6.13) (5.15) (4.56) (4.15) (3.84) (3.60) (3.41) year=2012 ‐0.265** ‐0.265** ‐0.265** ‐0.265** ‐0.265** ‐0.265** ‐0.265** (‐4.98) (‐4.25) (‐3.80) (‐3.49) (‐3.26) (‐3.07) (‐2.92) year=2013 0.352** 0.352** 0.352** 0.352** 0.352** 0.352** 0.352** (5.04) (4.32) (3.86) (3.54) (3.31) (3.12) (2.97) year=2014 0.213** 0.213** 0.213** 0.213** 0.213* 0.213* 0.213* (3.04) (2.58) (2.30) (2.10) (1.95) (1.84) (1.75) year=2015 0.583** 0.583** 0.583** 0.583** 0.583** 0.583** 0.583** (8.46) (7.18) (6.40) (5.86) (5.45) (5.14) (4.88) Constant 0.727** 0.727** 0.727** 0.727** 0.727** 0.727** 0.727** (13.22) (11.25) (10.04) (9.20) (8.57) (8.06) (7.65) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Standardized; Two Weeks (1) (2) (3) (4) (5) (6) (7)

June4_2w ‐0.0479 ‐0.0479 ‐0.0479 ‐0.0479 ‐0.0479 ‐0.0479 ‐0.0479 (‐1.10) (‐0.97) (‐0.89) (‐0.84) (‐0.80) (‐0.77) (‐0.74) Twocon_2w ‐0.190** ‐0.190** ‐0.190** ‐0.190** ‐0.190** ‐0.190** ‐0.190* (‐3.24) (‐2.76) (‐2.47) (‐2.28) (‐2.13) (‐2.02) (‐1.92) PRC60 ‐0.975** ‐0.975** ‐0.975** ‐0.975** ‐0.975** ‐0.975** ‐0.975** (‐8.45) (‐7.23) (‐6.50) (‐6.00) (‐5.64) (‐5.38) (‐5.19) CCP18 ‐0.527** ‐0.527** ‐0.527** ‐0.527** ‐0.527** ‐0.527** ‐0.527** (‐6.90) (‐6.16) (‐5.69) (‐5.37) (‐5.16) (‐4.99) (‐4.81) CCP_C_2w ‐0.416** ‐0.416** ‐0.416** ‐0.416** ‐0.416** ‐0.416** ‐0.416** (‐5.77) (‐4.90) (‐4.38) (‐4.01) (‐3.74) (‐3.54) (‐3.37) Ben_Ali 0.372** 0.372** 0.372** 0.372** 0.372** 0.372** 0.372** (4.05) (3.69) (3.42) (3.27) (3.16) (3.06) (2.97) Jasmine 0.635** 0.635** 0.635** 0.635** 0.635** 0.635** 0.635** (5.92) (5.37) (5.02) (4.75) (4.51) (4.35) (4.24) Umbrella 1.107** 1.107** 1.107** 1.107** 1.107** 1.107** 1.107** (12.85) (11.07) (9.95) (9.15) (8.54) (8.05) (7.65) Qian 1.048** 1.048** 1.048** 1.048** 1.048** 1.048** 1.048** (2.95) (3.23) (3.86) (4.52) (4.99) (5.37) (5.51) Dalian ‐0.582* ‐0.582 ‐0.582 ‐0.582 ‐0.582 ‐0.582 ‐0.582 (‐1.65) (‐1.41) (‐1.31) (‐1.25) (‐1.23) (‐1.23) (‐1.24) Qidong 0.230 0.230* 0.230* 0.230** 0.230** 0.230** 0.230** (1.61) (1.75) (1.89) (2.00) (2.05) (2.09) (2.10) Maoming ‐0.332** ‐0.332** ‐0.332** ‐0.332** ‐0.332** ‐0.332** ‐0.332** (‐4.04) (‐3.65) (‐3.54) (‐3.50) (‐3.52) (‐3.51) (‐3.47) Wukan ‐0.691** ‐0.691** ‐0.691** ‐0.691** ‐0.691** ‐0.691** ‐0.691** (‐4.71) (‐4.13) (‐3.83) (‐3.67) (‐3.58) (‐3.52) (‐3.47) Yushu ‐0.159 ‐0.159 ‐0.159 ‐0.159 ‐0.159 ‐0.159 ‐0.159 (‐1.08) (‐1.00) (‐1.00) (‐0.97) (‐0.95) (‐0.93) (‐0.93) Train 0.490** 0.490** 0.490** 0.490** 0.490** 0.490** 0.490** (4.16) (3.89) (3.93) (4.10) (4.34) (4.45) (4.41) Stampede 0.526** 0.526** 0.526** 0.526** 0.526** 0.526** 0.526** (3.91) (3.43) (3.11) (2.92) (2.75) (2.63) (2.54) Wang ‐0.166** ‐0.166* ‐0.166* ‐0.166* ‐0.166 ‐0.166 ‐0.166 (‐2.08) (‐1.89) (‐1.77) (‐1.69) (‐1.63) (‐1.58) (‐1.54) Bo_Purge ‐0.773** ‐0.773** ‐0.773** ‐0.773** ‐0.773** ‐0.773** ‐0.773** (‐12.69) (‐11.00) (‐9.96) (‐9.25) (‐8.70) (‐8.24) (‐7.84) Bo_Verdict ‐0.0672 ‐0.0672 ‐0.0672 ‐0.0672 ‐0.0672 ‐0.0672 ‐0.0672 (‐0.82) (‐0.77) (‐0.72) (‐0.67) (‐0.64) (‐0.62) (‐0.61) Zhou_Verdict 0.0907 0.0907 0.0907 0.0907 0.0907 0.0907 0.0907 (0.82) (0.79) (0.79) (0.76) (0.74) (0.75) (0.76) IPAB_Crack ‐0.780** ‐0.780** ‐0.780** ‐0.780** ‐0.780** ‐0.780** ‐0.780** (‐5.32) (‐4.79) (‐4.46) (‐4.25) (‐4.10) (‐3.99) (‐3.91) Anti‐rumor ‐0.421** ‐0.421** ‐0.421** ‐0.421** ‐0.421** ‐0.421** ‐0.421** (‐5.26) (‐5.04) (‐5.05) (‐4.88) (‐4.77) (‐4.70) (‐4.65) Tianya General 0.392** 0.392** 0.392** 0.392** 0.392** 0.392** 0.392** (5.31) (4.92) (4.71) (4.58) (4.49) (4.40) (4.30) Xi_Term ‐1.011** ‐1.011** ‐1.011** ‐1.011** ‐1.011** ‐1.011** ‐1.011** (‐11.67) (‐10.17) (‐9.24) (‐8.55) (‐8.03) (‐7.62) (‐7.28) weekend ‐0.207** ‐0.207** ‐0.207** ‐0.207** ‐0.207** ‐0.207** ‐0.207** (‐6.17) (‐6.21) (‐6.41) (‐6.66) (‐6.99) (‐7.15) (‐6.96) holiday ‐0.554** ‐0.554** ‐0.554** ‐0.554** ‐0.554** ‐0.554** ‐0.554** (‐7.20) (‐6.31) (‐5.83) (‐5.54) (‐5.35) (‐5.22) (‐5.13) Spring 0.274** 0.274** 0.274** 0.274** 0.274** 0.274** 0.274** (4.87) (4.16) (3.73) (3.42) (3.19) (3.01) (2.86) Summer 0.205** 0.205** 0.205** 0.205** 0.205** 0.205** 0.205** (3.66) (3.12) (2.80) (2.57) (2.40) (2.27) (2.16) Autumn ‐0.158** ‐0.158* ‐0.158* ‐0.158 ‐0.158 ‐0.158 ‐0.158 (‐2.22) (‐1.88) (‐1.67) (‐1.53) (‐1.43) (‐1.35) (‐1.29) year=2010 1.803** 1.803** 1.803** 1.803** 1.803** 1.803** 1.803** (17.24) (14.46) (12.77) (11.61) (10.75) (10.06) (9.51) year=2011 0.634** 0.634** 0.634** 0.634** 0.634** 0.634** 0.634** (6.13) (5.15) (4.56) (4.15) (3.84) (3.60) (3.41) year=2012 ‐0.526** ‐0.526** ‐0.526** ‐0.526** ‐0.526** ‐0.526** ‐0.526** (‐4.98) (‐4.25) (‐3.80) (‐3.49) (‐3.26) (‐3.07) (‐2.92) year=2013 0.699** 0.699** 0.699** 0.699** 0.699** 0.699** 0.699** (5.04) (4.32) (3.86) (3.54) (3.31) (3.12) (2.97) year=2014 0.423** 0.423** 0.423** 0.423** 0.423* 0.423* 0.423* (3.04) (2.58) (2.30) (2.10) (1.95) (1.84) (1.75) year=2015 1.158** 1.158** 1.158** 1.158** 1.158** 1.158** 1.158** (8.46) (7.18) (6.40) (5.86) (5.45) (5.14) (4.88) Constant ‐0.543** ‐0.543** ‐0.543** ‐0.543** ‐0.543** ‐0.543** ‐0.543** (‐4.98) (‐4.23) (‐3.78) (‐3.46) (‐3.22) (‐3.03) (‐2.88) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Percent; One Weeks (1) (2) (3) (4) (5) (6) (7)

June4_1w 0.00596 0.00596 0.00596 0.00596 0.00596 0.00596 0.00596 (0.21) (0.18) (0.17) (0.16) (0.15) (0.15) (0.15) Twocon_1w ‐0.0964** ‐0.0964** ‐0.0964** ‐0.0964** ‐0.0964** ‐0.0964** ‐0.0964** (‐3.26) (‐2.82) (‐2.55) (‐2.37) (‐2.24) (‐2.14) (‐2.06) PRC60_1w ‐0.445** ‐0.445** ‐0.445** ‐0.445** ‐0.445** ‐0.445** ‐0.445** (‐6.57) (‐5.67) (‐5.19) (‐4.92) (‐4.75) (‐4.64) (‐4.55) CCP18_1w ‐0.198** ‐0.198** ‐0.198** ‐0.198** ‐0.198** ‐0.198** ‐0.198** (‐4.94) (‐4.55) (‐4.26) (‐4.04) (‐3.90) (‐3.78) (‐3.64) CCP_C_1w ‐0.170** ‐0.170** ‐0.170** ‐0.170** ‐0.170** ‐0.170** ‐0.170** (‐3.52) (‐2.96) (‐2.62) (‐2.39) (‐2.23) (‐2.10) (‐2.00) Ben_Ali 0.188** 0.188** 0.188** 0.188** 0.188** 0.188** 0.188** (4.05) (3.68) (3.41) (3.26) (3.15) (3.05) (2.96) Jasmine 0.299** 0.299** 0.299** 0.299** 0.299** 0.299** 0.299** (5.62) (5.08) (4.75) (4.54) (4.36) (4.28) (4.24) Umbrella 0.555** 0.555** 0.555** 0.555** 0.555** 0.555** 0.555** (12.03) (10.30) (9.20) (8.44) (7.87) (7.41) (7.04) Qian 0.531** 0.531** 0.531** 0.531** 0.531** 0.531** 0.531** (2.96) (3.24) (3.87) (4.51) (4.96) (5.32) (5.45) Dalian ‐0.291 ‐0.291 ‐0.291 ‐0.291 ‐0.291 ‐0.291 ‐0.291 (‐1.64) (‐1.41) (‐1.30) (‐1.24) (‐1.22) (‐1.22) (‐1.23) Qidong 0.120* 0.120* 0.120* 0.120** 0.120** 0.120** 0.120** (1.67) (1.81) (1.95) (2.07) (2.12) (2.16) (2.17) Maoming ‐0.156** ‐0.156** ‐0.156** ‐0.156** ‐0.156** ‐0.156** ‐0.156** (‐3.73) (‐3.38) (‐3.30) (‐3.27) (‐3.32) (‐3.32) (‐3.30) Wukan ‐0.346** ‐0.346** ‐0.346** ‐0.346** ‐0.346** ‐0.346** ‐0.346** (‐4.62) (‐4.05) (‐3.76) (‐3.60) (‐3.51) (‐3.45) (‐3.41) Yushu ‐0.0702 ‐0.0702 ‐0.0702 ‐0.0702 ‐0.0702 ‐0.0702 ‐0.0702 (‐0.94) (‐0.88) (‐0.87) (‐0.85) (‐0.83) (‐0.81) (‐0.81) Train 0.249** 0.249** 0.249** 0.249** 0.249** 0.249** 0.249** (4.22) (3.94) (3.99) (4.17) (4.44) (4.55) (4.52) Stampede 0.271** 0.271** 0.271** 0.271** 0.271** 0.271** 0.271** (3.97) (3.48) (3.15) (2.96) (2.79) (2.66) (2.57) Wang ‐0.101** ‐0.101** ‐0.101** ‐0.101** ‐0.101** ‐0.101** ‐0.101** (‐2.76) (‐2.51) (‐2.33) (‐2.20) (‐2.11) (‐2.04) (‐1.98) Bo_Purge ‐0.392** ‐0.392** ‐0.392** ‐0.392** ‐0.392** ‐0.392** ‐0.392** (‐12.94) (‐11.22) (‐10.19) (‐9.51) (‐8.97) (‐8.52) (‐8.13) Bo_Verdict 0.0248 0.0248 0.0248 0.0248 0.0248 0.0248 0.0248 (0.61) (0.58) (0.54) (0.50) (0.48) (0.47) (0.46) Zhou_Verdict 0.0404 0.0404 0.0404 0.0404 0.0404 0.0404 0.0404 (0.68) (0.66) (0.65) (0.62) (0.61) (0.61) (0.62) IPAB_Crack ‐0.348** ‐0.348** ‐0.348** ‐0.348** ‐0.348** ‐0.348** ‐0.348** (‐4.50) (‐4.04) (‐3.78) (‐3.62) (‐3.50) (‐3.41) (‐3.34) Anti‐rumor ‐0.178** ‐0.178** ‐0.178** ‐0.178** ‐0.178** ‐0.178** ‐0.178** (‐4.06) (‐3.79) (‐3.73) (‐3.60) (‐3.54) (‐3.51) (‐3.50) Tianya General 0.194** 0.194** 0.194** 0.194** 0.194** 0.194** 0.194** (5.00) (4.61) (4.40) (4.27) (4.17) (4.08) (3.98) Xi_Term ‐0.455** ‐0.455** ‐0.455** ‐0.455** ‐0.455** ‐0.455** ‐0.455** (‐10.25) (‐8.87) (‐8.02) (‐7.41) (‐6.97) (‐6.62) (‐6.34) weekend ‐0.105** ‐0.105** ‐0.105** ‐0.105** ‐0.105** ‐0.105** ‐0.105** (‐5.99) (‐6.04) (‐6.23) (‐6.48) (‐6.81) (‐6.97) (‐6.77) holiday ‐0.294** ‐0.294** ‐0.294** ‐0.294** ‐0.294** ‐0.294** ‐0.294** (‐7.15) (‐6.28) (‐5.81) (‐5.52) (‐5.33) (‐5.21) (‐5.12) Spring 0.128** 0.128** 0.128** 0.128** 0.128** 0.128** 0.128** (4.55) (3.90) (3.49) (3.21) (3.00) (2.83) (2.69) Summer 0.102** 0.102** 0.102** 0.102** 0.102** 0.102** 0.102** (3.56) (3.03) (2.71) (2.49) (2.32) (2.19) (2.09) Autumn ‐0.130** ‐0.130** ‐0.130** ‐0.130** ‐0.130** ‐0.130** ‐0.130** (‐3.73) (‐3.15) (‐2.80) (‐2.56) (‐2.38) (‐2.24) (‐2.13) year=2010 0.945** 0.945** 0.945** 0.945** 0.945** 0.945** 0.945** (17.88) (14.96) (13.20) (11.99) (11.08) (10.37) (9.79) year=2011 0.356** 0.356** 0.356** 0.356** 0.356** 0.356** 0.356** (6.94) (5.82) (5.14) (4.67) (4.32) (4.05) (3.83) year=2012 ‐0.228** ‐0.228** ‐0.228** ‐0.228** ‐0.228** ‐0.228** ‐0.228** (‐4.23) (‐3.62) (‐3.24) (‐2.97) (‐2.77) (‐2.61) (‐2.49) year=2013 0.337** 0.337** 0.337** 0.337** 0.337** 0.337** 0.337** (4.64) (3.96) (3.54) (3.25) (3.04) (2.87) (2.74) year=2014 0.201** 0.201** 0.201** 0.201* 0.201* 0.201* 0.201 (2.78) (2.35) (2.09) (1.91) (1.78) (1.67) (1.59) year=2015 0.564** 0.564** 0.564** 0.564** 0.564** 0.564** 0.564** (8.06) (6.81) (6.06) (5.54) (5.15) (4.86) (4.62) Constant 0.691** 0.691** 0.691** 0.691** 0.691** 0.691** 0.691** (12.43) (10.56) (9.40) (8.60) (8.00) (7.52) (7.12) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Standardized; One Weeks (1) (2) (3) (4) (5) (6) (7)

June4_1w 0.0118 0.0118 0.0118 0.0118 0.0118 0.0118 0.0118 (0.21) (0.18) (0.17) (0.16) (0.15) (0.15) (0.15) Twocon_1w ‐0.192** ‐0.192** ‐0.192** ‐0.192** ‐0.192** ‐0.192** ‐0.192** (‐3.26) (‐2.82) (‐2.55) (‐2.37) (‐2.24) (‐2.14) (‐2.06) PRC60_1w ‐0.884** ‐0.884** ‐0.884** ‐0.884** ‐0.884** ‐0.884** ‐0.884** (‐6.57) (‐5.67) (‐5.19) (‐4.92) (‐4.75) (‐4.64) (‐4.55) CCP18_1w ‐0.394** ‐0.394** ‐0.394** ‐0.394** ‐0.394** ‐0.394** ‐0.394** (‐4.94) (‐4.55) (‐4.26) (‐4.04) (‐3.90) (‐3.78) (‐3.64) CCP_C_1w ‐0.338** ‐0.338** ‐0.338** ‐0.338** ‐0.338** ‐0.338** ‐0.338** (‐3.52) (‐2.96) (‐2.62) (‐2.39) (‐2.23) (‐2.10) (‐2.00) Ben_Ali 0.373** 0.373** 0.373** 0.373** 0.373** 0.373** 0.373** (4.05) (3.68) (3.41) (3.26) (3.15) (3.05) (2.96) Jasmine 0.594** 0.594** 0.594** 0.594** 0.594** 0.594** 0.594** (5.62) (5.08) (4.75) (4.54) (4.36) (4.28) (4.24) Umbrella 1.103** 1.103** 1.103** 1.103** 1.103** 1.103** 1.103** (12.03) (10.30) (9.20) (8.44) (7.87) (7.41) (7.04) Qian 1.055** 1.055** 1.055** 1.055** 1.055** 1.055** 1.055** (2.96) (3.24) (3.87) (4.51) (4.96) (5.32) (5.45) Dalian ‐0.579 ‐0.579 ‐0.579 ‐0.579 ‐0.579 ‐0.579 ‐0.579 (‐1.64) (‐1.41) (‐1.30) (‐1.24) (‐1.22) (‐1.22) (‐1.23) Qidong 0.238* 0.238* 0.238* 0.238** 0.238** 0.238** 0.238** (1.67) (1.81) (1.95) (2.07) (2.12) (2.16) (2.17) Maoming ‐0.310** ‐0.310** ‐0.310** ‐0.310** ‐0.310** ‐0.310** ‐0.310** (‐3.73) (‐3.38) (‐3.30) (‐3.27) (‐3.32) (‐3.32) (‐3.30) Wukan ‐0.687** ‐0.687** ‐0.687** ‐0.687** ‐0.687** ‐0.687** ‐0.687** (‐4.62) (‐4.05) (‐3.76) (‐3.60) (‐3.51) (‐3.45) (‐3.41) Yushu ‐0.140 ‐0.140 ‐0.140 ‐0.140 ‐0.140 ‐0.140 ‐0.140 (‐0.94) (‐0.88) (‐0.87) (‐0.85) (‐0.83) (‐0.81) (‐0.81) Train 0.494** 0.494** 0.494** 0.494** 0.494** 0.494** 0.494** (4.22) (3.94) (3.99) (4.17) (4.44) (4.55) (4.52) Stampede 0.539** 0.539** 0.539** 0.539** 0.539** 0.539** 0.539** (3.97) (3.48) (3.15) (2.96) (2.79) (2.66) (2.57) Wang ‐0.201** ‐0.201** ‐0.201** ‐0.201** ‐0.201** ‐0.201** ‐0.201** (‐2.76) (‐2.51) (‐2.33) (‐2.20) (‐2.11) (‐2.04) (‐1.98) Bo_Purge ‐0.779** ‐0.779** ‐0.779** ‐0.779** ‐0.779** ‐0.779** ‐0.779** (‐12.94) (‐11.22) (‐10.19) (‐9.51) (‐8.97) (‐8.52) (‐8.13) Bo_Verdict 0.0494 0.0494 0.0494 0.0494 0.0494 0.0494 0.0494 (0.61) (0.58) (0.54) (0.50) (0.48) (0.47) (0.46) Zhou_Verdict 0.0803 0.0803 0.0803 0.0803 0.0803 0.0803 0.0803 (0.68) (0.66) (0.65) (0.62) (0.61) (0.61) (0.62) IPAB_Crack ‐0.692** ‐0.692** ‐0.692** ‐0.692** ‐0.692** ‐0.692** ‐0.692** (‐4.50) (‐4.04) (‐3.78) (‐3.62) (‐3.50) (‐3.41) (‐3.34) Anti‐rumor ‐0.355** ‐0.355** ‐0.355** ‐0.355** ‐0.355** ‐0.355** ‐0.355** (‐4.06) (‐3.79) (‐3.73) (‐3.60) (‐3.54) (‐3.51) (‐3.50) Tianya General 0.386** 0.386** 0.386** 0.386** 0.386** 0.386** 0.386** (5.00) (4.61) (4.40) (4.27) (4.17) (4.08) (3.98) Xi_Term ‐0.905** ‐0.905** ‐0.905** ‐0.905** ‐0.905** ‐0.905** ‐0.905** (‐10.25) (‐8.87) (‐8.02) (‐7.41) (‐6.97) (‐6.62) (‐6.34) weekend ‐0.208** ‐0.208** ‐0.208** ‐0.208** ‐0.208** ‐0.208** ‐0.208** (‐5.99) (‐6.04) (‐6.23) (‐6.48) (‐6.81) (‐6.97) (‐6.77) holiday ‐0.585** ‐0.585** ‐0.585** ‐0.585** ‐0.585** ‐0.585** ‐0.585** (‐7.15) (‐6.28) (‐5.81) (‐5.52) (‐5.33) (‐5.21) (‐5.12) Spring 0.254** 0.254** 0.254** 0.254** 0.254** 0.254** 0.254** (4.55) (3.90) (3.49) (3.21) (3.00) (2.83) (2.69) Summer 0.203** 0.203** 0.203** 0.203** 0.203** 0.203** 0.203** (3.56) (3.03) (2.71) (2.49) (2.32) (2.19) (2.09) Autumn ‐0.259** ‐0.259** ‐0.259** ‐0.259** ‐0.259** ‐0.259** ‐0.259** (‐3.73) (‐3.15) (‐2.80) (‐2.56) (‐2.38) (‐2.24) (‐2.13) year=2010 1.878** 1.878** 1.878** 1.878** 1.878** 1.878** 1.878** (17.88) (14.96) (13.20) (11.99) (11.08) (10.37) (9.79) year=2011 0.708** 0.708** 0.708** 0.708** 0.708** 0.708** 0.708** (6.94) (5.82) (5.14) (4.67) (4.32) (4.05) (3.83) year=2012 ‐0.453** ‐0.453** ‐0.453** ‐0.453** ‐0.453** ‐0.453** ‐0.453** (‐4.23) (‐3.62) (‐3.24) (‐2.97) (‐2.77) (‐2.61) (‐2.49) year=2013 0.670** 0.670** 0.670** 0.670** 0.670** 0.670** 0.670** (4.64) (3.96) (3.54) (3.25) (3.04) (2.87) (2.74) year=2014 0.399** 0.399** 0.399** 0.399* 0.399* 0.399* 0.399 (2.78) (2.35) (2.09) (1.91) (1.78) (1.67) (1.59) year=2015 1.121** 1.121** 1.121** 1.121** 1.121** 1.121** 1.121** (8.06) (6.81) (6.06) (5.54) (5.15) (4.86) (4.62) Constant ‐0.615** ‐0.615** ‐0.615** ‐0.615** ‐0.615** ‐0.615** ‐0.615** (‐5.57) (‐4.73) (‐4.21) (‐3.85) (‐3.58) (‐3.37) (‐3.19) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Percent; Two Days (1) (2) (3) (4) (5) (6) (7)

Jun4 ‐0.0308 ‐0.0308 ‐0.0308 ‐0.0308 ‐0.0308 ‐0.0308 ‐0.0308 (‐0.77) (‐0.71) (‐0.68) (‐0.67) (‐0.66) (‐0.66) (‐0.65) Two_con ‐0.132** ‐0.132** ‐0.132** ‐0.132** ‐0.132** ‐0.132** ‐0.132** (‐3.98) (‐3.48) (‐3.20) (‐3.00) (‐2.85) (‐2.72) (‐2.61) PRC60_2d ‐0.370** ‐0.370** ‐0.370** ‐0.370** ‐0.370** ‐0.370** ‐0.370** (‐3.51) (‐3.37) (‐3.44) (‐3.55) (‐3.58) (‐3.58) (‐3.55) CCP18_2d ‐0.176** ‐0.176** ‐0.176** ‐0.176** ‐0.176** ‐0.176** ‐0.176** (‐4.54) (‐4.18) (‐3.91) (‐3.65) (‐3.47) (‐3.32) (‐3.18) CCP_Plen ‐0.120* ‐0.120 ‐0.120 ‐0.120 ‐0.120 ‐0.120 ‐0.120 (‐1.67) (‐1.43) (‐1.29) (‐1.19) (‐1.13) (‐1.09) (‐1.06) Ben_Ali 0.191** 0.191** 0.191** 0.191** 0.191** 0.191** 0.191** (4.10) (3.73) (3.45) (3.30) (3.19) (3.08) (2.99) Jasmine 0.282** 0.282** 0.282** 0.282** 0.282** 0.282** 0.282** (6.21) (5.73) (5.40) (5.13) (4.85) (4.68) (4.59) Umbrella 0.555** 0.555** 0.555** 0.555** 0.555** 0.555** 0.555** (11.77) (10.05) (8.96) (8.19) (7.61) (7.16) (6.78) Qian 0.535** 0.535** 0.535** 0.535** 0.535** 0.535** 0.535** (2.98) (3.26) (3.89) (4.53) (4.98) (5.33) (5.45) Dalian ‐0.296* ‐0.296 ‐0.296 ‐0.296 ‐0.296 ‐0.296 ‐0.296 (‐1.67) (‐1.43) (‐1.32) (‐1.27) (‐1.25) (‐1.24) (‐1.25) Qidong 0.113 0.113* 0.113* 0.113* 0.113** 0.113** 0.113** (1.58) (1.71) (1.84) (1.96) (2.00) (2.04) (2.04) Maoming ‐0.159** ‐0.159** ‐0.159** ‐0.159** ‐0.159** ‐0.159** ‐0.159** (‐3.77) (‐3.41) (‐3.32) (‐3.29) (‐3.32) (‐3.32) (‐3.29) Wukan ‐0.343** ‐0.343** ‐0.343** ‐0.343** ‐0.343** ‐0.343** ‐0.343** (‐4.58) (‐4.01) (‐3.72) (‐3.57) (‐3.48) (‐3.42) (‐3.37) Yushu ‐0.0732 ‐0.0732 ‐0.0732 ‐0.0732 ‐0.0732 ‐0.0732 ‐0.0732 (‐0.98) (‐0.91) (‐0.90) (‐0.88) (‐0.86) (‐0.84) (‐0.84) Train 0.244** 0.244** 0.244** 0.244** 0.244** 0.244** 0.244** (4.15) (3.88) (3.93) (4.12) (4.39) (4.51) (4.49) Stampede 0.278** 0.278** 0.278** 0.278** 0.278** 0.278** 0.278** (4.06) (3.55) (3.22) (3.02) (2.84) (2.72) (2.62) Wang ‐0.102** ‐0.102** ‐0.102** ‐0.102** ‐0.102** ‐0.102** ‐0.102* (‐2.72) (‐2.47) (‐2.30) (‐2.17) (‐2.07) (‐2.00) (‐1.94) Bo_Purge ‐0.405** ‐0.405** ‐0.405** ‐0.405** ‐0.405** ‐0.405** ‐0.405** (‐13.40) (‐11.72) (‐10.74) (‐10.09) (‐9.58) (‐9.14) (‐8.73) Bo_Verdict 0.0575 0.0575 0.0575 0.0575 0.0575 0.0575 0.0575 (1.44) (1.36) (1.29) (1.20) (1.15) (1.13) (1.11) Zhou_Verdict 0.0383 0.0383 0.0383 0.0383 0.0383 0.0383 0.0383 (0.65) (0.62) (0.61) (0.58) (0.57) (0.58) (0.59) IPAB_Crack ‐0.326** ‐0.326** ‐0.326** ‐0.326** ‐0.326** ‐0.326** ‐0.326** (‐4.09) (‐3.68) (‐3.44) (‐3.30) (‐3.20) (‐3.12) (‐3.06) Anti‐rumor ‐0.160** ‐0.160** ‐0.160** ‐0.160** ‐0.160** ‐0.160** ‐0.160** (‐3.39) (‐3.13) (‐3.05) (‐2.94) (‐2.90) (‐2.88) (‐2.88) Tianya General 0.191** 0.191** 0.191** 0.191** 0.191** 0.191** 0.191** (4.81) (4.44) (4.23) (4.10) (4.01) (3.92) (3.83) Xi_Term ‐0.432** ‐0.432** ‐0.432** ‐0.432** ‐0.432** ‐0.432** ‐0.432** (‐10.22) (‐8.89) (‐8.06) (‐7.46) (‐7.02) (‐6.67) (‐6.38) weekend ‐0.105** ‐0.105** ‐0.105** ‐0.105** ‐0.105** ‐0.105** ‐0.105** (‐5.97) (‐6.01) (‐6.20) (‐6.45) (‐6.79) (‐6.94) (‐6.73) holiday ‐0.298** ‐0.298** ‐0.298** ‐0.298** ‐0.298** ‐0.298** ‐0.298** (‐7.26) (‐6.38) (‐5.92) (‐5.63) (‐5.44) (‐5.32) (‐5.23) Spring 0.134** 0.134** 0.134** 0.134** 0.134** 0.134** 0.134** (4.56) (3.90) (3.49) (3.21) (2.99) (2.82) (2.68) Summer 0.111** 0.111** 0.111** 0.111** 0.111** 0.111** 0.111** (3.82) (3.25) (2.90) (2.66) (2.48) (2.34) (2.23) Autumn ‐0.155** ‐0.155** ‐0.155** ‐0.155** ‐0.155** ‐0.155** ‐0.155** (‐4.49) (‐3.78) (‐3.35) (‐3.06) (‐2.85) (‐2.68) (‐2.54) year=2010 0.971** 0.971** 0.971** 0.971** 0.971** 0.971** 0.971** (18.19) (15.20) (13.40) (12.15) (11.23) (10.50) (9.91) year=2011 0.382** 0.382** 0.382** 0.382** 0.382** 0.382** 0.382** (7.42) (6.22) (5.49) (4.98) (4.60) (4.31) (4.07) year=2012 ‐0.198** ‐0.198** ‐0.198** ‐0.198** ‐0.198** ‐0.198** ‐0.198** (‐3.63) (‐3.10) (‐2.78) (‐2.55) (‐2.38) (‐2.24) (‐2.13) year=2013 0.341** 0.341** 0.341** 0.341** 0.341** 0.341** 0.341** (4.75) (4.06) (3.63) (3.33) (3.11) (2.93) (2.79) year=2014 0.207** 0.207** 0.207** 0.207** 0.207* 0.207* 0.207 (2.88) (2.44) (2.17) (1.98) (1.84) (1.73) (1.64) year=2015 0.566** 0.566** 0.566** 0.566** 0.566** 0.566** 0.566** (8.20) (6.93) (6.16) (5.62) (5.23) (4.92) (4.67) Constant 0.663** 0.663** 0.663** 0.663** 0.663** 0.663** 0.663** (11.69) (9.90) (8.81) (8.05) (7.48) (7.03) (6.65) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Standardized; Two Days (1) (2) (3) (4) (5) (6) (7)

Jun4 ‐0.0612 ‐0.0612 ‐0.0612 ‐0.0612 ‐0.0612 ‐0.0612 ‐0.0612 (‐0.77) (‐0.71) (‐0.68) (‐0.67) (‐0.66) (‐0.66) (‐0.65) Two_con ‐0.262** ‐0.262** ‐0.262** ‐0.262** ‐0.262** ‐0.262** ‐0.262** (‐3.98) (‐3.48) (‐3.20) (‐3.00) (‐2.85) (‐2.72) (‐2.61) PRC60_2d ‐0.734** ‐0.734** ‐0.734** ‐0.734** ‐0.734** ‐0.734** ‐0.734** (‐3.51) (‐3.37) (‐3.44) (‐3.55) (‐3.58) (‐3.58) (‐3.55) CCP18_2d ‐0.350** ‐0.350** ‐0.350** ‐0.350** ‐0.350** ‐0.350** ‐0.350** (‐4.54) (‐4.18) (‐3.91) (‐3.65) (‐3.47) (‐3.32) (‐3.18) CCP_Plen ‐0.238* ‐0.238 ‐0.238 ‐0.238 ‐0.238 ‐0.238 ‐0.238 (‐1.67) (‐1.43) (‐1.29) (‐1.19) (‐1.13) (‐1.09) (‐1.06) Ben_Ali 0.379** 0.379** 0.379** 0.379** 0.379** 0.379** 0.379** (4.10) (3.73) (3.45) (3.30) (3.19) (3.08) (2.99) Jasmine 0.559** 0.559** 0.559** 0.559** 0.559** 0.559** 0.559** (6.21) (5.73) (5.40) (5.13) (4.85) (4.68) (4.59) Umbrella 1.102** 1.102** 1.102** 1.102** 1.102** 1.102** 1.102** (11.77) (10.05) (8.96) (8.19) (7.61) (7.16) (6.78) Qian 1.063** 1.063** 1.063** 1.063** 1.063** 1.063** 1.063** (2.98) (3.26) (3.89) (4.53) (4.98) (5.33) (5.45) Dalian ‐0.589* ‐0.589 ‐0.589 ‐0.589 ‐0.589 ‐0.589 ‐0.589 (‐1.67) (‐1.43) (‐1.32) (‐1.27) (‐1.25) (‐1.24) (‐1.25) Qidong 0.225 0.225* 0.225* 0.225* 0.225** 0.225** 0.225** (1.58) (1.71) (1.84) (1.96) (2.00) (2.04) (2.04) Maoming ‐0.316** ‐0.316** ‐0.316** ‐0.316** ‐0.316** ‐0.316** ‐0.316** (‐3.77) (‐3.41) (‐3.32) (‐3.29) (‐3.32) (‐3.32) (‐3.29) Wukan ‐0.681** ‐0.681** ‐0.681** ‐0.681** ‐0.681** ‐0.681** ‐0.681** (‐4.58) (‐4.01) (‐3.72) (‐3.57) (‐3.48) (‐3.42) (‐3.37) Yushu ‐0.146 ‐0.146 ‐0.146 ‐0.146 ‐0.146 ‐0.146 ‐0.146 (‐0.98) (‐0.91) (‐0.90) (‐0.88) (‐0.86) (‐0.84) (‐0.84) Train 0.485** 0.485** 0.485** 0.485** 0.485** 0.485** 0.485** (4.15) (3.88) (3.93) (4.12) (4.39) (4.51) (4.49) Stampede 0.553** 0.553** 0.553** 0.553** 0.553** 0.553** 0.553** (4.06) (3.55) (3.22) (3.02) (2.84) (2.72) (2.62) Wang ‐0.202** ‐0.202** ‐0.202** ‐0.202** ‐0.202** ‐0.202** ‐0.202* (‐2.72) (‐2.47) (‐2.30) (‐2.17) (‐2.07) (‐2.00) (‐1.94) Bo_Purge ‐0.804** ‐0.804** ‐0.804** ‐0.804** ‐0.804** ‐0.804** ‐0.804** (‐13.40) (‐11.72) (‐10.74) (‐10.09) (‐9.58) (‐9.14) (‐8.73) Bo_Verdict 0.114 0.114 0.114 0.114 0.114 0.114 0.114 (1.44) (1.36) (1.29) (1.20) (1.15) (1.13) (1.11) Zhou_Verdict 0.0761 0.0761 0.0761 0.0761 0.0761 0.0761 0.0761 (0.65) (0.62) (0.61) (0.58) (0.57) (0.58) (0.59) IPAB_Crack ‐0.647** ‐0.647** ‐0.647** ‐0.647** ‐0.647** ‐0.647** ‐0.647** (‐4.09) (‐3.68) (‐3.44) (‐3.30) (‐3.20) (‐3.12) (‐3.06) Anti‐rumor ‐0.318** ‐0.318** ‐0.318** ‐0.318** ‐0.318** ‐0.318** ‐0.318** (‐3.39) (‐3.13) (‐3.05) (‐2.94) (‐2.90) (‐2.88) (‐2.88) Tianya General 0.379** 0.379** 0.379** 0.379** 0.379** 0.379** 0.379** (4.81) (4.44) (4.23) (4.10) (4.01) (3.92) (3.83) Xi_Term ‐0.858** ‐0.858** ‐0.858** ‐0.858** ‐0.858** ‐0.858** ‐0.858** (‐10.22) (‐8.89) (‐8.06) (‐7.46) (‐7.02) (‐6.67) (‐6.38) weekend ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** ‐0.209** (‐5.97) (‐6.01) (‐6.20) (‐6.45) (‐6.79) (‐6.94) (‐6.73) holiday ‐0.592** ‐0.592** ‐0.592** ‐0.592** ‐0.592** ‐0.592** ‐0.592** (‐7.26) (‐6.38) (‐5.92) (‐5.63) (‐5.44) (‐5.32) (‐5.23) Spring 0.266** 0.266** 0.266** 0.266** 0.266** 0.266** 0.266** (4.56) (3.90) (3.49) (3.21) (2.99) (2.82) (2.68) Summer 0.220** 0.220** 0.220** 0.220** 0.220** 0.220** 0.220** (3.82) (3.25) (2.90) (2.66) (2.48) (2.34) (2.23) Autumn ‐0.309** ‐0.309** ‐0.309** ‐0.309** ‐0.309** ‐0.309** ‐0.309** (‐4.49) (‐3.78) (‐3.35) (‐3.06) (‐2.85) (‐2.68) (‐2.54) year=2010 1.930** 1.930** 1.930** 1.930** 1.930** 1.930** 1.930** (18.19) (15.20) (13.40) (12.15) (11.23) (10.50) (9.91) year=2011 0.759** 0.759** 0.759** 0.759** 0.759** 0.759** 0.759** (7.42) (6.22) (5.49) (4.98) (4.60) (4.31) (4.07) year=2012 ‐0.393** ‐0.393** ‐0.393** ‐0.393** ‐0.393** ‐0.393** ‐0.393** (‐3.63) (‐3.10) (‐2.78) (‐2.55) (‐2.38) (‐2.24) (‐2.13) year=2013 0.678** 0.678** 0.678** 0.678** 0.678** 0.678** 0.678** (4.75) (4.06) (3.63) (3.33) (3.11) (2.93) (2.79) year=2014 0.411** 0.411** 0.411** 0.411** 0.411* 0.411* 0.411 (2.88) (2.44) (2.17) (1.98) (1.84) (1.73) (1.64) year=2015 1.124** 1.124** 1.124** 1.124** 1.124** 1.124** 1.124** (8.20) (6.93) (6.16) (5.62) (5.23) (4.92) (4.67) Constant ‐0.669** ‐0.669** ‐0.669** ‐0.669** ‐0.669** ‐0.669** ‐0.669** (‐5.93) (‐5.03) (‐4.47) (‐4.09) (‐3.80) (‐3.57) (‐3.38) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Part9: Results, LLMB itself

LLMB, 2 Weeks (1) (2) (3) (4) (5) (6) (7)

June4_2w 0.0764** 0.0764** 0.0764** 0.0764** 0.0764** 0.0764** 0.0764** (2.85) (2.79) (2.70) (2.63) (2.58) (2.54) (2.50) Twocon_2w 0.204** 0.204** 0.204** 0.204** 0.204** 0.204** 0.204** (3.86) (3.41) (3.13) (2.91) (2.75) (2.63) (2.53) PRC60 ‐0.116* ‐0.116* ‐0.116* ‐0.116* ‐0.116* ‐0.116* ‐0.116 (‐1.93) (‐1.80) (‐1.72) (‐1.68) (‐1.67) (‐1.66) (‐1.64) CCP18 0.126* 0.126* 0.126* 0.126 0.126 0.126 0.126 (1.79) (1.70) (1.65) (1.61) (1.58) (1.53) (1.47) CCP_C_2w 0.0230 0.0230 0.0230 0.0230 0.0230 0.0230 0.0230 (0.56) (0.51) (0.47) (0.45) (0.43) (0.42) (0.41) Ben_Ali 0.138 0.138 0.138 0.138 0.138 0.138 0.138 (1.52) (1.45) (1.45) (1.49) (1.54) (1.57) (1.58) Jasmine 0.378** 0.378** 0.378** 0.378** 0.378** 0.378** 0.378** (2.17) (2.06) (2.01) (1.99) (2.00) (2.04) (2.13) Umbrella ‐0.0273 ‐0.0273 ‐0.0273 ‐0.0273 ‐0.0273 ‐0.0273 ‐0.0273 (‐0.41) (‐0.38) (‐0.35) (‐0.33) (‐0.31) (‐0.30) (‐0.29) Qian 0.624** 0.624** 0.624** 0.624** 0.624** 0.624** 0.624** (3.96) (3.84) (3.89) (4.01) (4.08) (4.08) (4.13) Dalian ‐0.118** ‐0.118* ‐0.118** ‐0.118** ‐0.118** ‐0.118** ‐0.118** (‐2.04) (‐1.96) (‐2.08) (‐2.16) (‐2.18) (‐2.24) (‐2.28) Qidong ‐0.0919* ‐0.0919* ‐0.0919 ‐0.0919 ‐0.0919 ‐0.0919 ‐0.0919 (‐1.84) (‐1.68) (‐1.62) (‐1.59) (‐1.56) (‐1.53) (‐1.52) Maoming 0.379** 0.379** 0.379** 0.379** 0.379** 0.379** 0.379** (3.66) (3.99) (4.20) (4.31) (4.46) (4.71) (4.78) Wukan 0.0967** 0.0967** 0.0967** 0.0967* 0.0967* 0.0967* 0.0967* (2.08) (1.99) (1.97) (1.94) (1.87) (1.82) (1.76) Yushu ‐0.248** ‐0.248** ‐0.248** ‐0.248** ‐0.248** ‐0.248** ‐0.248** (‐4.42) (‐4.53) (‐4.25) (‐3.94) (‐3.81) (‐3.72) (‐3.59) Train ‐0.186** ‐0.186** ‐0.186** ‐0.186** ‐0.186** ‐0.186** ‐0.186** (‐3.03) (‐3.08) (‐3.13) (‐3.27) (‐3.34) (‐3.30) (‐3.26) Stampede 0.300** 0.300** 0.300** 0.300** 0.300** 0.300** 0.300** (3.36) (3.50) (3.60) (3.62) (3.54) (3.45) (3.42) Wang 0.108 0.108 0.108 0.108 0.108 0.108 0.108 (1.62) (1.44) (1.33) (1.28) (1.27) (1.27) (1.30) Bo_Purge 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Bo_Verdict ‐0.251** ‐0.251** ‐0.251** ‐0.251** ‐0.251** ‐0.251** ‐0.251** (‐3.80) (‐3.67) (‐3.62) (‐3.58) (‐3.58) (‐3.55) (‐3.48) Zhou_Verdict 0.139 0.139 0.139 0.139 0.139 0.139 0.139 (1.17) (1.17) (1.22) (1.38) (1.45) (1.52) (1.51) IPAB_Crack ‐0.0675 ‐0.0675 ‐0.0675 ‐0.0675 ‐0.0675 ‐0.0675 ‐0.0675 (‐1.04) (‐1.10) (‐1.13) (‐1.14) (‐1.15) (‐1.10) (‐1.06) Anti‐rumor ‐0.254** ‐0.254** ‐0.254** ‐0.254** ‐0.254** ‐0.254** ‐0.254** (‐3.48) (‐3.50) (‐3.66) (‐3.90) (‐4.22) (‐4.51) (‐4.67) Xi_Term 0.0985 0.0985 0.0985 0.0985 0.0985 0.0985 0.0985 (1.53) (1.43) (1.35) (1.30) (1.26) (1.24) (1.21) weekend ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** (‐15.35) (‐15.07) (‐15.09) (‐15.18) (‐15.34) (‐15.26) (‐14.84) holiday ‐0.495** ‐0.495** ‐0.495** ‐0.495** ‐0.495** ‐0.495** ‐0.495** (‐12.15) (‐11.02) (‐10.33) (‐9.84) (‐9.51) (‐9.30) (‐9.17) Spring 0.118** 0.118** 0.118** 0.118** 0.118** 0.118** 0.118** (2.86) (2.55) (2.37) (2.24) (2.14) (2.07) (2.01) Summer 0.145** 0.145** 0.145** 0.145** 0.145** 0.145** 0.145** (4.18) (3.71) (3.40) (3.18) (3.01) (2.88) (2.77) Autumn 0.111** 0.111** 0.111** 0.111** 0.111* 0.111* 0.111* (2.72) (2.39) (2.17) (2.03) (1.92) (1.84) (1.77) year=2010 0.282** 0.282** 0.282** 0.282** 0.282** 0.282** 0.282** (7.04) (6.07) (5.48) (5.08) (4.78) (4.54) (4.33) year=2011 0.430** 0.430** 0.430** 0.430** 0.430** 0.430** 0.430** (12.57) (11.14) (10.24) (9.61) (9.13) (8.73) (8.37) year=2012 0.443** 0.443** 0.443** 0.443** 0.443** 0.443** 0.443** (13.04) (11.53) (10.57) (9.87) (9.33) (8.89) (8.48) year=2013 0.722** 0.722** 0.722** 0.722** 0.722** 0.722** 0.722** (11.03) (10.12) (9.48) (9.08) (8.74) (8.49) (8.25) year=2014 0.808** 0.808** 0.808** 0.808** 0.808** 0.808** 0.808** (10.48) (9.70) (9.13) (8.75) (8.42) (8.18) (7.96) year=2015 0.839** 0.839** 0.839** 0.839** 0.839** 0.839** 0.839** (9.78) (8.94) (8.33) (7.91) (7.57) (7.31) (7.06) Constant 0.418** 0.418** 0.418** 0.418** 0.418** 0.418** 0.418** (11.48) (10.07) (9.18) (8.55) (8.08) (7.69) (7.36) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 LLMB Standardized, 2 Weeks (1) (2) (3) (4) (5) (6) (7)

June4_2w 0.145** 0.145** 0.145** 0.145** 0.145** 0.145** 0.145** (2.85) (2.79) (2.70) (2.63) (2.58) (2.54) (2.50) Twocon_2w 0.387** 0.387** 0.387** 0.387** 0.387** 0.387** 0.387** (3.86) (3.41) (3.13) (2.91) (2.75) (2.63) (2.53) PRC60 ‐0.219* ‐0.219* ‐0.219* ‐0.219* ‐0.219* ‐0.219* ‐0.219 (‐1.93) (‐1.80) (‐1.72) (‐1.68) (‐1.67) (‐1.66) (‐1.64) CCP18 0.237* 0.237* 0.237* 0.237 0.237 0.237 0.237 (1.79) (1.70) (1.65) (1.61) (1.58) (1.53) (1.47) CCP_C_2w 0.0436 0.0436 0.0436 0.0436 0.0436 0.0436 0.0436 (0.56) (0.51) (0.47) (0.45) (0.43) (0.42) (0.41) Ben_Ali 0.261 0.261 0.261 0.261 0.261 0.261 0.261 (1.52) (1.45) (1.45) (1.49) (1.54) (1.57) (1.58) Jasmine 0.714** 0.714** 0.714** 0.714** 0.714** 0.714** 0.714** (2.17) (2.06) (2.01) (1.99) (2.00) (2.04) (2.13) Umbrella ‐0.0516 ‐0.0516 ‐0.0516 ‐0.0516 ‐0.0516 ‐0.0516 ‐0.0516 (‐0.41) (‐0.38) (‐0.35) (‐0.33) (‐0.31) (‐0.30) (‐0.29) Qian 1.180** 1.180** 1.180** 1.180** 1.180** 1.180** 1.180** (3.96) (3.84) (3.89) (4.01) (4.08) (4.08) (4.13) Dalian ‐0.223** ‐0.223* ‐0.223** ‐0.223** ‐0.223** ‐0.223** ‐0.223** (‐2.04) (‐1.96) (‐2.08) (‐2.16) (‐2.18) (‐2.24) (‐2.28) Qidong ‐0.174* ‐0.174* ‐0.174 ‐0.174 ‐0.174 ‐0.174 ‐0.174 (‐1.84) (‐1.68) (‐1.62) (‐1.59) (‐1.56) (‐1.53) (‐1.52) Maoming 0.717** 0.717** 0.717** 0.717** 0.717** 0.717** 0.717** (3.66) (3.99) (4.20) (4.31) (4.46) (4.71) (4.78) Wukan 0.183** 0.183** 0.183** 0.183* 0.183* 0.183* 0.183* (2.08) (1.99) (1.97) (1.94) (1.87) (1.82) (1.76) Yushu ‐0.470** ‐0.470** ‐0.470** ‐0.470** ‐0.470** ‐0.470** ‐0.470** (‐4.42) (‐4.53) (‐4.25) (‐3.94) (‐3.81) (‐3.72) (‐3.59) Train ‐0.351** ‐0.351** ‐0.351** ‐0.351** ‐0.351** ‐0.351** ‐0.351** (‐3.03) (‐3.08) (‐3.13) (‐3.27) (‐3.34) (‐3.30) (‐3.26) Stampede 0.567** 0.567** 0.567** 0.567** 0.567** 0.567** 0.567** (3.36) (3.50) (3.60) (3.62) (3.54) (3.45) (3.42) Wang 0.204 0.204 0.204 0.204 0.204 0.204 0.204 (1.62) (1.44) (1.33) (1.28) (1.27) (1.27) (1.30) Bo_Purge 0000000 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Bo_Verdict ‐0.475** ‐0.475** ‐0.475** ‐0.475** ‐0.475** ‐0.475** ‐0.475** (‐3.80) (‐3.67) (‐3.62) (‐3.58) (‐3.58) (‐3.55) (‐3.48) Zhou_Verdict 0.263 0.263 0.263 0.263 0.263 0.263 0.263 (1.17) (1.17) (1.22) (1.38) (1.45) (1.52) (1.51) IPAB_Crack ‐0.128 ‐0.128 ‐0.128 ‐0.128 ‐0.128 ‐0.128 ‐0.128 (‐1.04) (‐1.10) (‐1.13) (‐1.14) (‐1.15) (‐1.10) (‐1.06) Anti‐rumor ‐0.480** ‐0.480** ‐0.480** ‐0.480** ‐0.480** ‐0.480** ‐0.480** (‐3.48) (‐3.50) (‐3.66) (‐3.90) (‐4.22) (‐4.51) (‐4.67) Xi_Term 0.186 0.186 0.186 0.186 0.186 0.186 0.186 (1.53) (1.43) (1.35) (1.30) (1.26) (1.24) (1.21) weekend ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** (‐15.35) (‐15.07) (‐15.09) (‐15.18) (‐15.34) (‐15.26) (‐14.84) holiday ‐0.936** ‐0.936** ‐0.936** ‐0.936** ‐0.936** ‐0.936** ‐0.936** (‐12.15) (‐11.02) (‐10.33) (‐9.84) (‐9.51) (‐9.30) (‐9.17) Spring 0.223** 0.223** 0.223** 0.223** 0.223** 0.223** 0.223** (2.86) (2.55) (2.37) (2.24) (2.14) (2.07) (2.01) Summer 0.274** 0.274** 0.274** 0.274** 0.274** 0.274** 0.274** (4.18) (3.71) (3.40) (3.18) (3.01) (2.88) (2.77) Autumn 0.210** 0.210** 0.210** 0.210** 0.210* 0.210* 0.210* (2.72) (2.39) (2.17) (2.03) (1.92) (1.84) (1.77) year=2010 0.533** 0.533** 0.533** 0.533** 0.533** 0.533** 0.533** (7.04) (6.07) (5.48) (5.08) (4.78) (4.54) (4.33) year=2011 0.814** 0.814** 0.814** 0.814** 0.814** 0.814** 0.814** (12.57) (11.14) (10.24) (9.61) (9.13) (8.73) (8.37) year=2012 0.838** 0.838** 0.838** 0.838** 0.838** 0.838** 0.838** (13.04) (11.53) (10.57) (9.87) (9.33) (8.89) (8.48) year=2013 1.365** 1.365** 1.365** 1.365** 1.365** 1.365** 1.365** (11.03) (10.12) (9.48) (9.08) (8.74) (8.49) (8.25) year=2014 1.528** 1.528** 1.528** 1.528** 1.528** 1.528** 1.528** (10.48) (9.70) (9.13) (8.75) (8.42) (8.18) (7.96) year=2015 1.588** 1.588** 1.588** 1.588** 1.588** 1.588** 1.588** (9.78) (8.94) (8.33) (7.91) (7.57) (7.31) (7.06) Constant ‐1.102** ‐1.102** ‐1.102** ‐1.102** ‐1.102** ‐1.102** ‐1.102** (‐16.01) (‐14.05) (‐12.80) (‐11.93) (‐11.27) (‐10.73) (‐10.26) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 LLMB Percent, 1 Weeks (1) (2) (3) (4) (5) (6) (7)

June4_1w 0.103** 0.103** 0.103** 0.103** 0.103** 0.103** 0.103** (3.16) (3.18) (3.11) (3.02) (2.95) (2.92) (2.89) Twocon_1w 0.249** 0.249** 0.249** 0.249** 0.249** 0.249** 0.249** (3.67) (3.28) (3.02) (2.82) (2.67) (2.56) (2.47) PRC60_1w ‐0.0270 ‐0.0270 ‐0.0270 ‐0.0270 ‐0.0270 ‐0.0270 ‐0.0270 (‐0.32) (‐0.30) (‐0.30) (‐0.31) (‐0.31) (‐0.31) (‐0.32) CCP18_1w 0.0789 0.0789 0.0789 0.0789 0.0789 0.0789 0.0789 (0.91) (0.90) (0.88) (0.86) (0.85) (0.85) (0.84) CCP_C_1w 0.0441 0.0441 0.0441 0.0441 0.0441 0.0441 0.0441 (0.93) (0.86) (0.80) (0.77) (0.75) (0.73) (0.72) Ben_Ali 0.120 0.120 0.120 0.120 0.120 0.120 0.120 (1.33) (1.27) (1.26) (1.30) (1.34) (1.36) (1.37) Jasmine 0.385** 0.385** 0.385** 0.385** 0.385** 0.385** 0.385** (2.37) (2.29) (2.26) (2.22) (2.21) (2.23) (2.30) Umbrella ‐0.0364 ‐0.0364 ‐0.0364 ‐0.0364 ‐0.0364 ‐0.0364 ‐0.0364 (‐0.55) (‐0.51) (‐0.47) (‐0.45) (‐0.43) (‐0.41) (‐0.39) Qian 0.607** 0.607** 0.607** 0.607** 0.607** 0.607** 0.607** (3.85) (3.74) (3.78) (3.90) (3.97) (3.97) (4.02) Dalian ‐0.122** ‐0.122** ‐0.122** ‐0.122** ‐0.122** ‐0.122** ‐0.122** (‐2.11) (‐2.04) (‐2.16) (‐2.25) (‐2.27) (‐2.33) (‐2.37) Qidong ‐0.0993** ‐0.0993* ‐0.0993* ‐0.0993* ‐0.0993* ‐0.0993* ‐0.0993* (‐1.99) (‐1.82) (‐1.76) (‐1.72) (‐1.69) (‐1.67) (‐1.66) Maoming 0.370** 0.370** 0.370** 0.370** 0.370** 0.370** 0.370** (3.60) (3.95) (4.17) (4.30) (4.47) (4.75) (4.84) Wukan 0.0786* 0.0786 0.0786 0.0786 0.0786 0.0786 0.0786 (1.69) (1.62) (1.60) (1.57) (1.52) (1.47) (1.43) Yushu ‐0.255** ‐0.255** ‐0.255** ‐0.255** ‐0.255** ‐0.255** ‐0.255** (‐4.62) (‐4.78) (‐4.51) (‐4.18) (‐4.05) (‐3.96) (‐3.82) Train ‐0.189** ‐0.189** ‐0.189** ‐0.189** ‐0.189** ‐0.189** ‐0.189** (‐3.11) (‐3.16) (‐3.21) (‐3.37) (‐3.43) (‐3.40) (‐3.35) Stampede 0.281** 0.281** 0.281** 0.281** 0.281** 0.281** 0.281** (3.17) (3.30) (3.42) (3.45) (3.37) (3.30) (3.28) Wang 0.128** 0.128* 0.128* 0.128* 0.128* 0.128* 0.128* (2.07) (1.87) (1.76) (1.71) (1.70) (1.70) (1.71) Bo_Purge 0.0141 0.0141 0.0141 0.0141 0.0141 0.0141 0.0141 (0.29) (0.27) (0.26) (0.25) (0.24) (0.23) (0.23) Bo_Verdict ‐0.255** ‐0.255** ‐0.255** ‐0.255** ‐0.255** ‐0.255** ‐0.255** (‐3.92) (‐3.80) (‐3.77) (‐3.75) (‐3.76) (‐3.74) (‐3.67) Zhou_Verdict 0.168 0.168 0.168 0.168* 0.168* 0.168** 0.168* (1.47) (1.48) (1.56) (1.80) (1.89) (2.00) (1.95) IPAB_Crack ‐0.0719 ‐0.0719 ‐0.0719 ‐0.0719 ‐0.0719 ‐0.0719 ‐0.0719 (‐1.12) (‐1.19) (‐1.23) (‐1.25) (‐1.26) (‐1.21) (‐1.16) Anti‐rumor ‐0.257** ‐0.257** ‐0.257** ‐0.257** ‐0.257** ‐0.257** ‐0.257** (‐3.54) (‐3.57) (‐3.74) (‐4.00) (‐4.34) (‐4.66) (‐4.83) Xi_Term 0.106* 0.106 0.106 0.106 0.106 0.106 0.106 (1.65) (1.54) (1.46) (1.41) (1.37) (1.35) (1.33) weekend ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** (‐15.35) (‐15.06) (‐15.09) (‐15.18) (‐15.34) (‐15.26) (‐14.84) holiday ‐0.495** ‐0.495** ‐0.495** ‐0.495** ‐0.495** ‐0.495** ‐0.495** (‐12.47) (‐11.40) (‐10.73) (‐10.24) (‐9.90) (‐9.69) (‐9.55) Spring 0.109** 0.109** 0.109** 0.109** 0.109** 0.109* 0.109* (2.63) (2.36) (2.19) (2.07) (1.98) (1.92) (1.86) Summer 0.131** 0.131** 0.131** 0.131** 0.131** 0.131** 0.131** (3.76) (3.33) (3.05) (2.85) (2.70) (2.58) (2.48) Autumn 0.0977** 0.0977** 0.0977** 0.0977* 0.0977* 0.0977* 0.0977* (2.57) (2.25) (2.06) (1.92) (1.82) (1.74) (1.67) year=2010 0.305** 0.305** 0.305** 0.305** 0.305** 0.305** 0.305** (7.73) (6.67) (6.03) (5.59) (5.27) (5.00) (4.77) year=2011 0.455** 0.455** 0.455** 0.455** 0.455** 0.455** 0.455** (13.59) (12.06) (11.09) (10.41) (9.89) (9.45) (9.05) year=2012 0.471** 0.471** 0.471** 0.471** 0.471** 0.471** 0.471** (14.09) (12.50) (11.51) (10.78) (10.19) (9.72) (9.28) year=2013 0.738** 0.738** 0.738** 0.738** 0.738** 0.738** 0.738** (11.49) (10.57) (9.94) (9.54) (9.21) (8.98) (8.76) year=2014 0.825** 0.825** 0.825** 0.825** 0.825** 0.825** 0.825** (10.90) (10.12) (9.57) (9.19) (8.87) (8.66) (8.46) year=2015 0.857** 0.857** 0.857** 0.857** 0.857** 0.857** 0.857** (10.13) (9.28) (8.66) (8.23) (7.88) (7.62) (7.38) Constant 0.411** 0.411** 0.411** 0.411** 0.411** 0.411** 0.411** (11.33) (9.96) (9.08) (8.46) (8.00) (7.62) (7.28) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 LLMB Std, 1 Weeks (1) (2) (3) (4) (5) (6) (7)

June4_1w 0.194** 0.194** 0.194** 0.194** 0.194** 0.194** 0.194** (3.16) (3.18) (3.11) (3.02) (2.95) (2.92) (2.89) Twocon_1w 0.471** 0.471** 0.471** 0.471** 0.471** 0.471** 0.471** (3.67) (3.28) (3.02) (2.82) (2.67) (2.56) (2.47) PRC60_1w ‐0.0510 ‐0.0510 ‐0.0510 ‐0.0510 ‐0.0510 ‐0.0510 ‐0.0510 (‐0.32) (‐0.30) (‐0.30) (‐0.31) (‐0.31) (‐0.31) (‐0.32) CCP18_1w 0.149 0.149 0.149 0.149 0.149 0.149 0.149 (0.91) (0.90) (0.88) (0.86) (0.85) (0.85) (0.84) CCP_C_1w 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 (0.93) (0.86) (0.80) (0.77) (0.75) (0.73) (0.72) Ben_Ali 0.228 0.228 0.228 0.228 0.228 0.228 0.228 (1.33) (1.27) (1.26) (1.30) (1.34) (1.36) (1.37) Jasmine 0.729** 0.729** 0.729** 0.729** 0.729** 0.729** 0.729** (2.37) (2.29) (2.26) (2.22) (2.21) (2.23) (2.30) Umbrella ‐0.0688 ‐0.0688 ‐0.0688 ‐0.0688 ‐0.0688 ‐0.0688 ‐0.0688 (‐0.55) (‐0.51) (‐0.47) (‐0.45) (‐0.43) (‐0.41) (‐0.39) Qian 1.148** 1.148** 1.148** 1.148** 1.148** 1.148** 1.148** (3.85) (3.74) (3.78) (3.90) (3.97) (3.97) (4.02) Dalian ‐0.230** ‐0.230** ‐0.230** ‐0.230** ‐0.230** ‐0.230** ‐0.230** (‐2.11) (‐2.04) (‐2.16) (‐2.25) (‐2.27) (‐2.33) (‐2.37) Qidong ‐0.188** ‐0.188* ‐0.188* ‐0.188* ‐0.188* ‐0.188* ‐0.188* (‐1.99) (‐1.82) (‐1.76) (‐1.72) (‐1.69) (‐1.67) (‐1.66) Maoming 0.699** 0.699** 0.699** 0.699** 0.699** 0.699** 0.699** (3.60) (3.95) (4.17) (4.30) (4.47) (4.75) (4.84) Wukan 0.149* 0.149 0.149 0.149 0.149 0.149 0.149 (1.69) (1.62) (1.60) (1.57) (1.52) (1.47) (1.43) Yushu ‐0.483** ‐0.483** ‐0.483** ‐0.483** ‐0.483** ‐0.483** ‐0.483** (‐4.62) (‐4.78) (‐4.51) (‐4.18) (‐4.05) (‐3.96) (‐3.82) Train ‐0.358** ‐0.358** ‐0.358** ‐0.358** ‐0.358** ‐0.358** ‐0.358** (‐3.11) (‐3.16) (‐3.21) (‐3.37) (‐3.43) (‐3.40) (‐3.35) Stampede 0.532** 0.532** 0.532** 0.532** 0.532** 0.532** 0.532** (3.17) (3.30) (3.42) (3.45) (3.37) (3.30) (3.28) Wang 0.241** 0.241* 0.241* 0.241* 0.241* 0.241* 0.241* (2.07) (1.87) (1.76) (1.71) (1.70) (1.70) (1.71) Bo_Purge 0.0266 0.0266 0.0266 0.0266 0.0266 0.0266 0.0266 (0.29) (0.27) (0.26) (0.25) (0.24) (0.23) (0.23) Bo_Verdict ‐0.482** ‐0.482** ‐0.482** ‐0.482** ‐0.482** ‐0.482** ‐0.482** (‐3.92) (‐3.80) (‐3.77) (‐3.75) (‐3.76) (‐3.74) (‐3.67) Zhou_Verdict 0.318 0.318 0.318 0.318* 0.318* 0.318** 0.318* (1.47) (1.48) (1.56) (1.80) (1.89) (2.00) (1.95) IPAB_Crack ‐0.136 ‐0.136 ‐0.136 ‐0.136 ‐0.136 ‐0.136 ‐0.136 (‐1.12) (‐1.19) (‐1.23) (‐1.25) (‐1.26) (‐1.21) (‐1.16) Anti‐rumor ‐0.487** ‐0.487** ‐0.487** ‐0.487** ‐0.487** ‐0.487** ‐0.487** (‐3.54) (‐3.57) (‐3.74) (‐4.00) (‐4.34) (‐4.66) (‐4.83) Xi_Term 0.201* 0.201 0.201 0.201 0.201 0.201 0.201 (1.65) (1.54) (1.46) (1.41) (1.37) (1.35) (1.33) weekend ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** (‐15.35) (‐15.06) (‐15.09) (‐15.18) (‐15.34) (‐15.26) (‐14.84) holiday ‐0.936** ‐0.936** ‐0.936** ‐0.936** ‐0.936** ‐0.936** ‐0.936** (‐12.47) (‐11.40) (‐10.73) (‐10.24) (‐9.90) (‐9.69) (‐9.55) Spring 0.206** 0.206** 0.206** 0.206** 0.206** 0.206* 0.206* (2.63) (2.36) (2.19) (2.07) (1.98) (1.92) (1.86) Summer 0.248** 0.248** 0.248** 0.248** 0.248** 0.248** 0.248** (3.76) (3.33) (3.05) (2.85) (2.70) (2.58) (2.48) Autumn 0.185** 0.185** 0.185** 0.185* 0.185* 0.185* 0.185* (2.57) (2.25) (2.06) (1.92) (1.82) (1.74) (1.67) year=2010 0.576** 0.576** 0.576** 0.576** 0.576** 0.576** 0.576** (7.73) (6.67) (6.03) (5.59) (5.27) (5.00) (4.77) year=2011 0.860** 0.860** 0.860** 0.860** 0.860** 0.860** 0.860** (13.59) (12.06) (11.09) (10.41) (9.89) (9.45) (9.05) year=2012 0.891** 0.891** 0.891** 0.891** 0.891** 0.891** 0.891** (14.09) (12.50) (11.51) (10.78) (10.19) (9.72) (9.28) year=2013 1.395** 1.395** 1.395** 1.395** 1.395** 1.395** 1.395** (11.49) (10.57) (9.94) (9.54) (9.21) (8.98) (8.76) year=2014 1.561** 1.561** 1.561** 1.561** 1.561** 1.561** 1.561** (10.90) (10.12) (9.57) (9.19) (8.87) (8.66) (8.46) year=2015 1.621** 1.621** 1.621** 1.621** 1.621** 1.621** 1.621** (10.13) (9.28) (8.66) (8.23) (7.88) (7.62) (7.38) Constant ‐1.114** ‐1.114** ‐1.114** ‐1.114** ‐1.114** ‐1.114** ‐1.114** (‐16.25) (‐14.29) (‐13.03) (‐12.14) (‐11.48) (‐10.92) (‐10.45) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 LLMB Percent, 2 days (1) (2) (3) (4) (5) (6) (7)

June4_1w 0.123** 0.123** 0.123** 0.123** 0.123** 0.123** 0.123** (2.51) (2.57) (2.61) (2.73) (2.79) (2.83) (2.84) Twocon_1w 0.175** 0.175** 0.175** 0.175* 0.175* 0.175* 0.175* (2.38) (2.16) (2.02) (1.92) (1.83) (1.76) (1.70) PRC60_1w 0.0187 0.0187 0.0187 0.0187 0.0187 0.0187 0.0187 (0.15) (0.15) (0.16) (0.17) (0.19) (0.20) (0.21) CCP18_1w ‐0.0154 ‐0.0154 ‐0.0154 ‐0.0154 ‐0.0154 ‐0.0154 ‐0.0154 (‐0.19) (‐0.20) (‐0.21) (‐0.21) (‐0.21) (‐0.21) (‐0.23) CCP_C_1w ‐0.0290 ‐0.0290 ‐0.0290 ‐0.0290 ‐0.0290 ‐0.0290 ‐0.0290 (‐0.47) (‐0.45) (‐0.43) (‐0.43) (‐0.42) (‐0.42) (‐0.42) Ben_Ali 0.108 0.108 0.108 0.108 0.108 0.108 0.108 (1.19) (1.14) (1.13) (1.17) (1.20) (1.22) (1.23) Jasmine 0.479** 0.479** 0.479** 0.479** 0.479** 0.479** 0.479** (2.63) (2.47) (2.41) (2.37) (2.39) (2.45) (2.56) Umbrella ‐0.0454 ‐0.0454 ‐0.0454 ‐0.0454 ‐0.0454 ‐0.0454 ‐0.0454 (‐0.67) (‐0.62) (‐0.58) (‐0.54) (‐0.52) (‐0.50) (‐0.48) Qian 0.597** 0.597** 0.597** 0.597** 0.597** 0.597** 0.597** (3.79) (3.68) (3.73) (3.85) (3.91) (3.91) (3.95) Dalian ‐0.126** ‐0.126** ‐0.126** ‐0.126** ‐0.126** ‐0.126** ‐0.126** (‐2.19) (‐2.11) (‐2.24) (‐2.34) (‐2.36) (‐2.43) (‐2.48) Qidong ‐0.108** ‐0.108** ‐0.108* ‐0.108* ‐0.108* ‐0.108* ‐0.108* (‐2.17) (‐1.98) (‐1.91) (‐1.87) (‐1.83) (‐1.80) (‐1.78) Maoming 0.337** 0.337** 0.337** 0.337** 0.337** 0.337** 0.337** (3.28) (3.59) (3.77) (3.86) (3.99) (4.21) (4.26) Wukan 0.0677 0.0677 0.0677 0.0677 0.0677 0.0677 0.0677 (1.43) (1.37) (1.35) (1.32) (1.28) (1.24) (1.20) Yushu ‐0.288** ‐0.288** ‐0.288** ‐0.288** ‐0.288** ‐0.288** ‐0.288** (‐5.36) (‐5.61) (‐5.28) (‐4.88) (‐4.72) (‐4.60) (‐4.43) Train ‐0.194** ‐0.194** ‐0.194** ‐0.194** ‐0.194** ‐0.194** ‐0.194** (‐3.19) (‐3.24) (‐3.30) (‐3.46) (‐3.53) (‐3.50) (‐3.45) Stampede 0.264** 0.264** 0.264** 0.264** 0.264** 0.264** 0.264** (2.91) (3.01) (3.10) (3.10) (3.02) (2.95) (2.92) Wang 0.111* 0.111 0.111 0.111 0.111 0.111 0.111 (1.77) (1.59) (1.50) (1.45) (1.44) (1.43) (1.44) Bo_Purge 0.0152 0.0152 0.0152 0.0152 0.0152 0.0152 0.0152 (0.31) (0.29) (0.28) (0.26) (0.26) (0.25) (0.24) Bo_Verdict ‐0.261** ‐0.261** ‐0.261** ‐0.261** ‐0.261** ‐0.261** ‐0.261** (‐4.04) (‐3.93) (‐3.89) (‐3.86) (‐3.88) (‐3.87) (‐3.79) Zhou_Verdict 0.166 0.166 0.166 0.166* 0.166* 0.166* 0.166* (1.43) (1.44) (1.52) (1.75) (1.83) (1.93) (1.89) IPAB_Crack ‐0.0809 ‐0.0809 ‐0.0809 ‐0.0809 ‐0.0809 ‐0.0809 ‐0.0809 (‐1.24) (‐1.32) (‐1.37) (‐1.39) (‐1.41) (‐1.36) (‐1.30) Anti‐rumor ‐0.266** ‐0.266** ‐0.266** ‐0.266** ‐0.266** ‐0.266** ‐0.266** (‐3.70) (‐3.74) (‐3.94) (‐4.22) (‐4.60) (‐4.96) (‐5.14) Xi_Term 0.0736 0.0736 0.0736 0.0736 0.0736 0.0736 0.0736 (1.17) (1.09) (1.04) (1.00) (0.97) (0.96) (0.94) weekend ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** ‐0.286** (‐15.19) (‐14.91) (‐14.95) (‐15.04) (‐15.20) (‐15.12) (‐14.71) holiday ‐0.511** ‐0.511** ‐0.511** ‐0.511** ‐0.511** ‐0.511** ‐0.511** (‐12.87) (‐11.74) (‐11.03) (‐10.52) (‐10.17) (‐9.96) (‐9.82) Spring 0.127** 0.127** 0.127** 0.127** 0.127** 0.127** 0.127** (3.26) (2.91) (2.68) (2.51) (2.38) (2.28) (2.20) Summer 0.123** 0.123** 0.123** 0.123** 0.123** 0.123** 0.123** (3.42) (3.02) (2.75) (2.57) (2.43) (2.32) (2.23) Autumn 0.0999** 0.0999** 0.0999** 0.0999** 0.0999* 0.0999* 0.0999* (2.65) (2.32) (2.12) (1.97) (1.87) (1.78) (1.71) year=2010 0.315** 0.315** 0.315** 0.315** 0.315** 0.315** 0.315** (8.09) (6.99) (6.33) (5.87) (5.54) (5.27) (5.03) year=2011 0.462** 0.462** 0.462** 0.462** 0.462** 0.462** 0.462** (14.07) (12.53) (11.54) (10.86) (10.34) (9.91) (9.51) year=2012 0.483** 0.483** 0.483** 0.483** 0.483** 0.483** 0.483** (14.47) (12.83) (11.80) (11.04) (10.44) (9.94) (9.48) year=2013 0.776** 0.776** 0.776** 0.776** 0.776** 0.776** 0.776** (12.37) (11.41) (10.76) (10.35) (10.00) (9.76) (9.53) year=2014 0.870** 0.870** 0.870** 0.870** 0.870** 0.870** 0.870** (11.52) (10.69) (10.12) (9.74) (9.42) (9.19) (8.97) year=2015 0.905** 0.905** 0.905** 0.905** 0.905** 0.905** 0.905** (10.78) (9.91) (9.28) (8.83) (8.46) (8.18) (7.92) Constant 0.416** 0.416** 0.416** 0.416** 0.416** 0.416** 0.416** (11.26) (9.90) (9.02) (8.41) (7.95) (7.58) (7.25) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 LLMB Std, 2 days (1) (2) (3) (4) (5) (6) (7)

June4_1w 0.233** 0.233** 0.233** 0.233** 0.233** 0.233** 0.233** (2.51) (2.57) (2.61) (2.73) (2.79) (2.83) (2.84) Twocon_1w 0.332** 0.332** 0.332** 0.332* 0.332* 0.332* 0.332* (2.38) (2.16) (2.02) (1.92) (1.83) (1.76) (1.70) PRC60_1w 0.0354 0.0354 0.0354 0.0354 0.0354 0.0354 0.0354 (0.15) (0.15) (0.16) (0.17) (0.19) (0.20) (0.21) CCP18_1w ‐0.0291 ‐0.0291 ‐0.0291 ‐0.0291 ‐0.0291 ‐0.0291 ‐0.0291 (‐0.19) (‐0.20) (‐0.21) (‐0.21) (‐0.21) (‐0.21) (‐0.23) CCP_C_1w ‐0.0548 ‐0.0548 ‐0.0548 ‐0.0548 ‐0.0548 ‐0.0548 ‐0.0548 (‐0.47) (‐0.45) (‐0.43) (‐0.43) (‐0.42) (‐0.42) (‐0.42) Ben_Ali 0.205 0.205 0.205 0.205 0.205 0.205 0.205 (1.19) (1.14) (1.13) (1.17) (1.20) (1.22) (1.23) Jasmine 0.905** 0.905** 0.905** 0.905** 0.905** 0.905** 0.905** (2.63) (2.47) (2.41) (2.37) (2.39) (2.45) (2.56) Umbrella ‐0.0859 ‐0.0859 ‐0.0859 ‐0.0859 ‐0.0859 ‐0.0859 ‐0.0859 (‐0.67) (‐0.62) (‐0.58) (‐0.54) (‐0.52) (‐0.50) (‐0.48) Qian 1.129** 1.129** 1.129** 1.129** 1.129** 1.129** 1.129** (3.79) (3.68) (3.73) (3.85) (3.91) (3.91) (3.95) Dalian ‐0.238** ‐0.238** ‐0.238** ‐0.238** ‐0.238** ‐0.238** ‐0.238** (‐2.19) (‐2.11) (‐2.24) (‐2.34) (‐2.36) (‐2.43) (‐2.48) Qidong ‐0.205** ‐0.205** ‐0.205* ‐0.205* ‐0.205* ‐0.205* ‐0.205* (‐2.17) (‐1.98) (‐1.91) (‐1.87) (‐1.83) (‐1.80) (‐1.78) Maoming 0.637** 0.637** 0.637** 0.637** 0.637** 0.637** 0.637** (3.28) (3.59) (3.77) (3.86) (3.99) (4.21) (4.26) Wukan 0.128 0.128 0.128 0.128 0.128 0.128 0.128 (1.43) (1.37) (1.35) (1.32) (1.28) (1.24) (1.20) Yushu ‐0.545** ‐0.545** ‐0.545** ‐0.545** ‐0.545** ‐0.545** ‐0.545** (‐5.36) (‐5.61) (‐5.28) (‐4.88) (‐4.72) (‐4.60) (‐4.43) Train ‐0.367** ‐0.367** ‐0.367** ‐0.367** ‐0.367** ‐0.367** ‐0.367** (‐3.19) (‐3.24) (‐3.30) (‐3.46) (‐3.53) (‐3.50) (‐3.45) Stampede 0.499** 0.499** 0.499** 0.499** 0.499** 0.499** 0.499** (2.91) (3.01) (3.10) (3.10) (3.02) (2.95) (2.92) Wang 0.210* 0.210 0.210 0.210 0.210 0.210 0.210 (1.77) (1.59) (1.50) (1.45) (1.44) (1.43) (1.44) Bo_Purge 0.0287 0.0287 0.0287 0.0287 0.0287 0.0287 0.0287 (0.31) (0.29) (0.28) (0.26) (0.26) (0.25) (0.24) Bo_Verdict ‐0.494** ‐0.494** ‐0.494** ‐0.494** ‐0.494** ‐0.494** ‐0.494** (‐4.04) (‐3.93) (‐3.89) (‐3.86) (‐3.88) (‐3.87) (‐3.79) Zhou_Verdict 0.314 0.314 0.314 0.314* 0.314* 0.314* 0.314* (1.43) (1.44) (1.52) (1.75) (1.83) (1.93) (1.89) IPAB_Crack ‐0.153 ‐0.153 ‐0.153 ‐0.153 ‐0.153 ‐0.153 ‐0.153 (‐1.24) (‐1.32) (‐1.37) (‐1.39) (‐1.41) (‐1.36) (‐1.30) Anti‐rumor ‐0.503** ‐0.503** ‐0.503** ‐0.503** ‐0.503** ‐0.503** ‐0.503** (‐3.70) (‐3.74) (‐3.94) (‐4.22) (‐4.60) (‐4.96) (‐5.14) Xi_Term 0.139 0.139 0.139 0.139 0.139 0.139 0.139 (1.17) (1.09) (1.04) (1.00) (0.97) (0.96) (0.94) weekend ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** ‐0.540** (‐15.19) (‐14.91) (‐14.95) (‐15.04) (‐15.20) (‐15.12) (‐14.71) holiday ‐0.966** ‐0.966** ‐0.966** ‐0.966** ‐0.966** ‐0.966** ‐0.966** (‐12.87) (‐11.74) (‐11.03) (‐10.52) (‐10.17) (‐9.96) (‐9.82) Spring 0.240** 0.240** 0.240** 0.240** 0.240** 0.240** 0.240** (3.26) (2.91) (2.68) (2.51) (2.38) (2.28) (2.20) Summer 0.233** 0.233** 0.233** 0.233** 0.233** 0.233** 0.233** (3.42) (3.02) (2.75) (2.57) (2.43) (2.32) (2.23) Autumn 0.189** 0.189** 0.189** 0.189** 0.189* 0.189* 0.189* (2.65) (2.32) (2.12) (1.97) (1.87) (1.78) (1.71) year=2010 0.595** 0.595** 0.595** 0.595** 0.595** 0.595** 0.595** (8.09) (6.99) (6.33) (5.87) (5.54) (5.27) (5.03) year=2011 0.873** 0.873** 0.873** 0.873** 0.873** 0.873** 0.873** (14.07) (12.53) (11.54) (10.86) (10.34) (9.91) (9.51) year=2012 0.914** 0.914** 0.914** 0.914** 0.914** 0.914** 0.914** (14.47) (12.83) (11.80) (11.04) (10.44) (9.94) (9.48) year=2013 1.467** 1.467** 1.467** 1.467** 1.467** 1.467** 1.467** (12.37) (11.41) (10.76) (10.35) (10.00) (9.76) (9.53) year=2014 1.646** 1.646** 1.646** 1.646** 1.646** 1.646** 1.646** (11.52) (10.69) (10.12) (9.74) (9.42) (9.19) (8.97) year=2015 1.711** 1.711** 1.711** 1.711** 1.711** 1.711** 1.711** (10.78) (9.91) (9.28) (8.83) (8.46) (8.18) (7.92) Constant ‐1.105** ‐1.105** ‐1.105** ‐1.105** ‐1.105** ‐1.105** ‐1.105** (‐15.84) (‐13.93) (‐12.69) (‐11.83) (‐11.19) (‐10.66) (‐10.20) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey‐West regression with lag from 1 to 7. * p<0.1 ** p<0.05 *** p<0.01 Part 10 Results: Complaints relative to General

Regression Results (±2 Weeks, DV=percentage) # of Max. Lag (1) (2) (3) (4) (5) (6) (7) June4 -0.0501* -0.0501 -0.0501 -0.0501 -0.0501 -0.0501 -0.0501 (-1.76) (-1.57) (-1.46) (-1.38) (-1.32) (-1.27) (-1.22) Two Conf. -0.134** -0.134** -0.134** -0.134** -0.134** -0.134** -0.134** (-4.03) (-3.47) (-3.13) (-2.89) (-2.72) (-2.58) (-2.46) PRC60 -0.455** -0.455** -0.455** -0.455** -0.455** -0.455** -0.455** (-7.65) (-6.55) (-5.90) (-5.45) (-5.13) (-4.87) (-4.67) CCP18 -0.510** -0.510** -0.510** -0.510** -0.510** -0.510** -0.510** (-9.34) (-8.54) (-7.99) (-7.59) (-7.34) (-7.11) (-6.87) CCP_Plen -0.169** -0.169** -0.169** -0.169** -0.169** -0.169** -0.169** (-4.00) (-3.43) (-3.07) (-2.81) (-2.62) (-2.46) (-2.34) Ben_Ali 0.193** 0.193** 0.193** 0.193** 0.193** 0.193** 0.193** (3.57) (3.40) (3.14) (2.99) (2.92) (2.85) (2.78) Jasmine 0.394** 0.394** 0.394** 0.394** 0.394** 0.394** 0.394** (6.30) (5.96) (5.66) (5.37) (5.10) (4.89) (4.74) Umbrella 0.629** 0.629** 0.629** 0.629** 0.629** 0.629** 0.629** (13.38) (11.79) (10.75) (10.00) (9.42) (8.95) (8.53) Qian 0.409** 0.409** 0.409** 0.409** 0.409** 0.409** 0.409** (2.12) (2.26) (2.64) (3.11) (3.60) (4.03) (4.10) Dalian -0.311* -0.311 -0.311 -0.311 -0.311 -0.311 -0.311 (-1.76) (-1.51) (-1.40) (-1.34) (-1.32) (-1.32) (-1.33) Qidong 0.112 0.112 0.112* 0.112* 0.112* 0.112* 0.112* (1.37) (1.59) (1.67) (1.69) (1.67) (1.72) (1.76) Maoming -0.0598 -0.0598 -0.0598 -0.0598 -0.0598 -0.0598 -0.0598 (-1.04) (-0.96) (-0.93) (-0.92) (-0.93) (-0.93) (-0.93) Wukan -0.326** -0.326** -0.326** -0.326** -0.326** -0.326** -0.326** (-2.92) (-2.55) (-2.37) (-2.29) (-2.24) (-2.22) (-2.21) Yushu 0.0252 0.0252 0.0252 0.0252 0.0252 0.0252 0.0252 (0.40) (0.39) (0.40) (0.40) (0.39) (0.38) (0.38) Train 0.127* 0.127* 0.127* 0.127* 0.127* 0.127* 0.127* (1.76) (1.68) (1.71) (1.76) (1.86) (1.87) (1.82) Stampede 0.00451 0.00451 0.00451 0.00451 0.00451 0.00451 0.00451 (0.07) (0.06) (0.06) (0.05) (0.05) (0.05) (0.05) Wang 0.348** 0.348** 0.348** 0.348** 0.348** 0.348** 0.348** (7.38) (6.63) (6.02) (5.54) (5.16) (4.91) (4.71) Bo_Purge -0.416** -0.416** -0.416** -0.416** -0.416** -0.416** -0.416** (-9.77) (-8.68) (-8.04) (-7.63) (-7.28) (-6.98) (-6.72) Bo_Verdict 0.00424 0.00424 0.00424 0.00424 0.00424 0.00424 0.00424 (0.04) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) Zhou_Verdict 0.102 0.102 0.102 0.102 0.102 0.102 0.102 (1.46) (1.51) (1.55) (1.54) (1.51) (1.54) (1.57) IPAB_Crack -0.415** -0.415** -0.415** -0.415** -0.415** -0.415** -0.415** (-9.32) (-8.64) (-7.82) (-7.41) (-6.96) (-6.58) (-6.31) Anti-rumor -0.278** -0.278** -0.278** -0.278* -0.278* -0.278* -0.278* (-2.40) (-2.13) (-2.02) (-1.94) (-1.90) (-1.88) (-1.89) Xi_Term -0.783** -0.783** -0.783** -0.783** -0.783** -0.783** -0.783** (-18.10) (-16.06) (-14.83) (-13.85) (-13.06) (-12.36) (-11.77) Weekend 0.0943** 0.0943** 0.0943** 0.0943** 0.0943** 0.0943** 0.0943** (5.45) (5.49) (5.65) (5.85) (6.09) (6.12) (5.82) Holiday 0.0215 0.0215 0.0215 0.0215 0.0215 0.0215 0.0215 (0.47) (0.41) (0.38) (0.35) (0.34) (0.33) (0.32) Spring 0.00908 0.00908 0.00908 0.00908 0.00908 0.00908 0.00908 (0.29) (0.25) (0.22) (0.20) (0.19) (0.18) (0.17) Summer 0.00485 0.00485 0.00485 0.00485 0.00485 0.00485 0.00485 (0.15) (0.13) (0.11) (0.11) (0.10) (0.09) (0.09) Autumn -0.169** -0.169** -0.169** -0.169** -0.169** -0.169** -0.169** (-4.12) (-3.54) (-3.18) (-2.93) (-2.75) (-2.61) (-2.49) year=2010 0.929** 0.929** 0.929** 0.929** 0.929** 0.929** 0.929** (16.81) (14.24) (12.68) (11.58) (10.75) (10.09) (9.54) year=2011 0.375** 0.375** 0.375** 0.375** 0.375** 0.375** 0.375** (6.95) (5.89) (5.24) (4.78) (4.44) (4.17) (3.95) year=2012 -0.810** -0.810** -0.810** -0.810** -0.810** -0.810** -0.810** (-15.40) (-12.99) (-11.52) (-10.51) (-9.77) (-9.19) (-8.72) year=2013 -0.310** -0.310** -0.310** -0.310** -0.310** -0.310** -0.310** (-5.25) (-4.51) (-4.06) (-3.73) (-3.48) (-3.28) (-3.12) year=2014 -0.148** -0.148* -0.148* -0.148 -0.148 -0.148 -0.148 (-2.12) (-1.84) (-1.67) (-1.54) (-1.44) (-1.36) (-1.29) year=2015 0.535** 0.535** 0.535** 0.535** 0.535** 0.535** 0.535** (7.48) (6.47) (5.86) (5.41) (5.07) (4.79) (4.56) Constant 0.289** 0.289** 0.289** 0.289** 0.289** 0.289** 0.289** (5.44) (4.60) (4.09) (3.74) (3.48) (3.27) (3.10) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey-West regression with lag from 1 to 7. Baseline of year is 2009. Baseline of season is winter. Xi_term is binary starting from 03/15/2013. * p<0.1 ** p<0.05 *** p<0.01 Regression Results (±1 Weeks, DV=percentage) # of Max. Lag (1) (2) (3) (4) (5) (6) (7) June4 -0.0247 -0.0247 -0.0247 -0.0247 -0.0247 -0.0247 -0.0247 (-0.70) (-0.63) (-0.60) (-0.59) (-0.58) (-0.57) (-0.57) Two Conf. -0.144** -0.144** -0.144** -0.144** -0.144** -0.144** -0.144** (-4.18) (-3.64) (-3.32) (-3.09) (-2.92) (-2.79) (-2.67) PRC60 -0.424** -0.424** -0.424** -0.424** -0.424** -0.424** -0.424** (-7.96) (-6.95) (-6.31) (-5.84) (-5.51) (-5.24) (-5.00) CCP18 -0.388** -0.388** -0.388** -0.388** -0.388** -0.388** -0.388** (-6.40) (-6.01) (-5.74) (-5.63) (-5.60) (-5.49) (-5.33) CCP_Plen -0.117** -0.117* -0.117 -0.117 -0.117 -0.117 -0.117 (-2.04) (-1.73) (-1.55) (-1.42) (-1.32) (-1.25) (-1.19) Ben_Ali 0.195** 0.195** 0.195** 0.195** 0.195** 0.195** 0.195** (3.61) (3.43) (3.18) (3.03) (2.96) (2.88) (2.81) Jasmine 0.371** 0.371** 0.371** 0.371** 0.371** 0.371** 0.371** (5.65) (5.23) (4.90) (4.64) (4.44) (4.32) (4.25) Umbrella 0.637** 0.637** 0.637** 0.637** 0.637** 0.637** 0.637** (12.83) (11.20) (10.15) (9.41) (8.85) (8.40) (8.02) Qian 0.412** 0.412** 0.412** 0.412** 0.412** 0.412** 0.412** (2.13) (2.28) (2.65) (3.12) (3.60) (4.01) (4.07) Dalian -0.309* -0.309 -0.309 -0.309 -0.309 -0.309 -0.309 (-1.76) (-1.51) (-1.40) (-1.34) (-1.32) (-1.32) (-1.33) Qidong 0.131 0.131* 0.131* 0.131** 0.131* 0.131** 0.131** (1.60) (1.85) (1.94) (1.97) (1.94) (2.00) (2.04) Maoming -0.0472 -0.0472 -0.0472 -0.0472 -0.0472 -0.0472 -0.0472 (-0.82) (-0.76) (-0.73) (-0.73) (-0.74) (-0.74) (-0.74) Wukan -0.321** -0.321** -0.321** -0.321** -0.321** -0.321** -0.321** (-2.82) (-2.47) (-2.29) (-2.21) (-2.17) (-2.15) (-2.14) Yushu 0.0352 0.0352 0.0352 0.0352 0.0352 0.0352 0.0352 (0.56) (0.54) (0.56) (0.55) (0.55) (0.53) (0.52) Train 0.128* 0.128* 0.128* 0.128* 0.128* 0.128* 0.128* (1.78) (1.71) (1.74) (1.80) (1.90) (1.92) (1.87) Stampede 0.0135 0.0135 0.0135 0.0135 0.0135 0.0135 0.0135 (0.21) (0.18) (0.17) (0.16) (0.15) (0.14) (0.14) Wang 0.341** 0.341** 0.341** 0.341** 0.341** 0.341** 0.341** (7.25) (6.49) (5.93) (5.48) (5.13) (4.89) (4.69) Bo_Purge -0.408** -0.408** -0.408** -0.408** -0.408** -0.408** -0.408** (-9.63) (-8.51) (-7.86) (-7.44) (-7.10) (-6.82) (-6.59) Bo_Verdict 0.0642 0.0642 0.0642 0.0642 0.0642 0.0642 0.0642 (0.55) (0.48) (0.43) (0.41) (0.39) (0.39) (0.39) Zhou_Verdict 0.0852 0.0852 0.0852 0.0852 0.0852 0.0852 0.0852 (1.20) (1.23) (1.26) (1.24) (1.22) (1.24) (1.26) IPAB_Crack -0.368** -0.368** -0.368** -0.368** -0.368** -0.368** -0.368** (-7.98) (-7.39) (-6.74) (-6.41) (-6.05) (-5.74) (-5.53) Anti-rumor -0.243** -0.243* -0.243 -0.243 -0.243 -0.243 -0.243 (-1.96) (-1.74) (-1.63) (-1.57) (-1.53) (-1.52) (-1.52) Xi_Term -0.734** -0.734** -0.734** -0.734** -0.734** -0.734** -0.734** (-16.87) (-14.86) (-13.66) (-12.74) (-12.01) (-11.39) (-10.87) Weekend 0.0944** 0.0944** 0.0944** 0.0944** 0.0944** 0.0944** 0.0944** (5.35) (5.40) (5.57) (5.81) (6.08) (6.13) (5.82) Holiday 0.0157 0.0157 0.0157 0.0157 0.0157 0.0157 0.0157 (0.32) (0.28) (0.26) (0.24) (0.23) (0.23) (0.22) Spring 0.000296 0.000296 0.000296 0.000296 0.000296 0.000296 0.000296 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Summer 0.00595 0.00595 0.00595 0.00595 0.00595 0.00595 0.00595 (0.18) (0.15) (0.14) (0.13) (0.12) (0.11) (0.11) Autumn -0.223** -0.223** -0.223** -0.223** -0.223** -0.223** -0.223** (-5.54) (-4.73) (-4.23) (-3.90) (-3.65) (-3.45) (-3.29) year=2010 0.959** 0.959** 0.959** 0.959** 0.959** 0.959** 0.959** (17.37) (14.69) (13.05) (11.91) (11.04) (10.35) (9.78) year=2011 0.404** 0.404** 0.404** 0.404** 0.404** 0.404** 0.404** (7.63) (6.45) (5.73) (5.24) (4.86) (4.56) (4.32) year=2012 -0.800** -0.800** -0.800** -0.800** -0.800** -0.800** -0.800** (-15.28) (-12.85) (-11.38) (-10.36) (-9.61) (-9.02) (-8.54) year=2013 -0.331** -0.331** -0.331** -0.331** -0.331** -0.331** -0.331** (-5.65) (-4.83) (-4.33) (-3.97) (-3.70) (-3.48) (-3.30) year=2014 -0.169** -0.169** -0.169* -0.169* -0.169 -0.169 -0.169 (-2.41) (-2.08) (-1.87) (-1.73) (-1.62) (-1.52) (-1.45) year=2015 0.508** 0.508** 0.508** 0.508** 0.508** 0.508** 0.508** (7.14) (6.15) (5.55) (5.12) (4.79) (4.53) (4.31) Constant 0.258** 0.258** 0.258** 0.258** 0.258** 0.258** 0.258** (4.85) (4.09) (3.63) (3.31) (3.08) (2.89) (2.73) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey-West regression with lag from 1 to 7. Baseline of year is 2009. Baseline of season is winter. Xi_term is binary starting from 03/15/2013. * p<0.1 ** p<0.05 *** p<0.01 Regression Results (±2 Days, DV=percentage) # of Max. Lag (1) (2) (3) (4) (5) (6) (7) June4 -0.0338 -0.0338 -0.0338 -0.0338 -0.0338 -0.0338 -0.0338 (-0.64) (-0.63) (-0.64) (-0.67) (-0.68) (-0.69) (-0.70) Two Conf. -0.139** -0.139** -0.139** -0.139** -0.139** -0.139** -0.139** (-3.42) (-2.99) (-2.74) (-2.56) (-2.43) (-2.31) (-2.21) PRC60 -0.334** -0.334** -0.334** -0.334** -0.334** -0.334** -0.334** (-5.84) (-5.33) (-4.86) (-4.48) (-4.20) (-3.98) (-3.80) CCP18 -0.315** -0.315** -0.315** -0.315** -0.315** -0.315** -0.315** (-5.29) (-5.15) (-5.17) (-5.06) (-4.85) (-4.63) (-4.42) CCP_Plen -0.0478 -0.0478 -0.0478 -0.0478 -0.0478 -0.0478 -0.0478 (-0.57) (-0.49) (-0.44) (-0.41) (-0.39) (-0.38) (-0.37) Ben_Ali 0.198** 0.198** 0.198** 0.198** 0.198** 0.198** 0.198** (3.64) (3.46) (3.20) (3.05) (2.98) (2.90) (2.83) Jasmine 0.325** 0.325** 0.325** 0.325** 0.325** 0.325** 0.325** (5.15) (4.80) (4.56) (4.38) (4.22) (4.12) (4.07) Umbrella 0.644** 0.644** 0.644** 0.644** 0.644** 0.644** 0.644** (12.81) (11.16) (10.09) (9.31) (8.74) (8.28) (7.89) Qian 0.415** 0.415** 0.415** 0.415** 0.415** 0.415** 0.415** (2.14) (2.29) (2.67) (3.13) (3.62) (4.02) (4.08) Dalian -0.313* -0.313 -0.313 -0.313 -0.313 -0.313 -0.313 (-1.78) (-1.53) (-1.41) (-1.36) (-1.34) (-1.33) (-1.35) Qidong 0.137* 0.137* 0.137** 0.137** 0.137** 0.137** 0.137** (1.67) (1.94) (2.04) (2.07) (2.04) (2.09) (2.14) Maoming -0.0377 -0.0377 -0.0377 -0.0377 -0.0377 -0.0377 -0.0377 (-0.65) (-0.60) (-0.58) (-0.58) (-0.59) (-0.59) (-0.59) Wukan -0.317** -0.317** -0.317** -0.317** -0.317** -0.317** -0.317** (-2.76) (-2.41) (-2.24) (-2.16) (-2.12) (-2.10) (-2.09) Yushu 0.0432 0.0432 0.0432 0.0432 0.0432 0.0432 0.0432 (0.68) (0.66) (0.69) (0.67) (0.67) (0.64) (0.64) Train 0.124* 0.124* 0.124* 0.124* 0.124* 0.124* 0.124* (1.74) (1.66) (1.69) (1.76) (1.87) (1.88) (1.84) Stampede 0.0226 0.0226 0.0226 0.0226 0.0226 0.0226 0.0226 (0.35) (0.31) (0.28) (0.26) (0.25) (0.24) (0.23) Wang 0.353** 0.353** 0.353** 0.353** 0.353** 0.353** 0.353** (7.46) (6.67) (6.08) (5.62) (5.26) (5.01) (4.81) Bo_Purge -0.409** -0.409** -0.409** -0.409** -0.409** -0.409** -0.409** (-9.55) (-8.47) (-7.87) (-7.49) (-7.19) (-6.93) (-6.70) Bo_Verdict 0.0945 0.0945 0.0945 0.0945 0.0945 0.0945 0.0945 (0.81) (0.70) (0.64) (0.60) (0.58) (0.57) (0.57) Zhou_Verdict 0.0863 0.0863 0.0863 0.0863 0.0863 0.0863 0.0863 (1.21) (1.24) (1.26) (1.25) (1.22) (1.24) (1.26) IPAB_Crack -0.346** -0.346** -0.346** -0.346** -0.346** -0.346** -0.346** (-7.25) (-6.71) (-6.14) (-5.86) (-5.55) (-5.29) (-5.10) Anti-rumor -0.225* -0.225 -0.225 -0.225 -0.225 -0.225 -0.225 (-1.75) (-1.55) (-1.45) (-1.39) (-1.36) (-1.35) (-1.35) Xi_Term -0.706** -0.706** -0.706** -0.706** -0.706** -0.706** -0.706** (-16.44) (-14.51) (-13.37) (-12.48) (-11.76) (-11.15) (-10.62) Weekend 0.0946** 0.0946** 0.0946** 0.0946** 0.0946** 0.0946** 0.0946** (5.30) (5.35) (5.52) (5.76) (6.05) (6.10) (5.78) Holiday 0.0163 0.0163 0.0163 0.0163 0.0163 0.0163 0.0163 (0.34) (0.29) (0.27) (0.25) (0.24) (0.23) (0.23) Spring -0.00364 -0.00364 -0.00364 -0.00364 -0.00364 -0.00364 -0.00364 (-0.11) (-0.09) (-0.08) (-0.08) (-0.07) (-0.07) (-0.07) Summer 0.0125 0.0125 0.0125 0.0125 0.0125 0.0125 0.0125 (0.37) (0.32) (0.29) (0.27) (0.25) (0.23) (0.22) Autumn -0.249** -0.249** -0.249** -0.249** -0.249** -0.249** -0.249** (-6.22) (-5.29) (-4.73) (-4.35) (-4.06) (-3.84) (-3.65) year=2010 0.981** 0.981** 0.981** 0.981** 0.981** 0.981** 0.981** (17.68) (14.92) (13.24) (12.07) (11.18) (10.48) (9.89) year=2011 0.427** 0.427** 0.427** 0.427** 0.427** 0.427** 0.427** (8.10) (6.84) (6.07) (5.54) (5.13) (4.81) (4.55) year=2012 -0.787** -0.787** -0.787** -0.787** -0.787** -0.787** -0.787** (-15.07) (-12.65) (-11.17) (-10.16) (-9.41) (-8.82) (-8.34) year=2013 -0.338** -0.338** -0.338** -0.338** -0.338** -0.338** -0.338** (-5.80) (-4.94) (-4.41) (-4.04) (-3.75) (-3.52) (-3.34) year=2014 -0.177** -0.177** -0.177* -0.177* -0.177* -0.177 -0.177 (-2.52) (-2.17) (-1.96) (-1.80) (-1.68) (-1.59) (-1.50) year=2015 0.496** 0.496** 0.496** 0.496** 0.496** 0.496** 0.496** (7.02) (6.04) (5.45) (5.02) (4.69) (4.43) (4.20) Constant 0.233** 0.233** 0.233** 0.233** 0.233** 0.233** 0.233** (4.32) (3.63) (3.22) (2.94) (2.72) (2.55) (2.42) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey-West regression with lag from 1 to 7. Baseline of year is 2009. Baseline of season is winter. Xi_term is binary starting from 03/15/2013. * p<0.1 ** p<0.05 *** p<0.01 Regression Results (±2 Weeks, DV=Standardized) # of Max. Lag (1) (2) (3) (4) (5) (6) (7) June4 -0.0956* -0.0956 -0.0956 -0.0956 -0.0956 -0.0956 -0.0956 (-1.74) (-1.55) (-1.44) (-1.36) (-1.30) (-1.25) (-1.20) Two Conf. -0.261** -0.261** -0.261** -0.261** -0.261** -0.261** -0.261** (-4.01) (-3.44) (-3.10) (-2.87) (-2.69) (-2.55) (-2.44) PRC60 -0.910** -0.910** -0.910** -0.910** -0.910** -0.910** -0.910** (-7.77) (-6.64) (-5.97) (-5.52) (-5.19) (-4.93) (-4.72) CCP18 -0.977** -0.977** -0.977** -0.977** -0.977** -0.977** -0.977** (-9.38) (-8.57) (-8.02) (-7.62) (-7.36) (-7.13) (-6.90) CCP_Plen -0.342** -0.342** -0.342** -0.342** -0.342** -0.342** -0.342** (-4.16) (-3.56) (-3.18) (-2.91) (-2.71) (-2.55) (-2.42) Ben_Ali 0.383** 0.383** 0.383** 0.383** 0.383** 0.383** 0.383** (3.63) (3.45) (3.18) (3.04) (2.96) (2.88) (2.81) Jasmine 0.772** 0.772** 0.772** 0.772** 0.772** 0.772** 0.772** (6.36) (5.99) (5.68) (5.38) (5.10) (4.89) (4.74) Umbrella 1.239** 1.239** 1.239** 1.239** 1.239** 1.239** 1.239** (13.50) (11.86) (10.79) (10.01) (9.42) (8.95) (8.52) Qian 0.830** 0.830** 0.830** 0.830** 0.830** 0.830** 0.830** (2.18) (2.34) (2.73) (3.21) (3.71) (4.15) (4.22) Dalian -0.615* -0.615 -0.615 -0.615 -0.615 -0.615 -0.615 (-1.75) (-1.50) (-1.39) (-1.34) (-1.32) (-1.31) (-1.32) Qidong 0.224 0.224* 0.224* 0.224* 0.224* 0.224* 0.224* (1.41) (1.65) (1.75) (1.79) (1.77) (1.83) (1.87) Maoming -0.135 -0.135 -0.135 -0.135 -0.135 -0.135 -0.135 (-1.21) (-1.12) (-1.09) (-1.08) (-1.09) (-1.09) (-1.08) Wukan -0.652** -0.652** -0.652** -0.652** -0.652** -0.652** -0.652** (-3.01) (-2.64) (-2.45) (-2.36) (-2.32) (-2.29) (-2.28) Yushu 0.0343 0.0343 0.0343 0.0343 0.0343 0.0343 0.0343 (0.27) (0.26) (0.27) (0.27) (0.27) (0.26) (0.26) Train 0.270* 0.270* 0.270* 0.270* 0.270** 0.270** 0.270** (1.93) (1.84) (1.87) (1.94) (2.05) (2.07) (2.01) Stampede 0.0480 0.0480 0.0480 0.0480 0.0480 0.0480 0.0480 (0.37) (0.33) (0.30) (0.28) (0.27) (0.26) (0.25) Wang 0.626** 0.626** 0.626** 0.626** 0.626** 0.626** 0.626** (6.95) (6.23) (5.66) (5.20) (4.86) (4.62) (4.43) Bo_Purge -0.822** -0.822** -0.822** -0.822** -0.822** -0.822** -0.822** (-10.17) (-9.02) (-8.36) (-7.92) (-7.55) (-7.23) (-6.96) Bo_Verdict 0.00273 0.00273 0.00273 0.00273 0.00273 0.00273 0.00273 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Zhou_Verdict 0.194 0.194 0.194 0.194 0.194 0.194 0.194 (1.44) (1.48) (1.52) (1.50) (1.47) (1.50) (1.53) IPAB_Crack -0.821** -0.821** -0.821** -0.821** -0.821** -0.821** -0.821** (-9.14) (-8.46) (-7.66) (-7.25) (-6.81) (-6.45) (-6.18) Anti-rumor -0.543** -0.543** -0.543** -0.543** -0.543** -0.543** -0.543** (-2.49) (-2.22) (-2.10) (-2.02) (-1.98) (-1.96) (-1.96) Xi_Term -1.515** -1.515** -1.515** -1.515** -1.515** -1.515** -1.515** (-18.04) (-15.97) (-14.72) (-13.73) (-12.92) (-12.23) (-11.64) Weekend 0.158** 0.158** 0.158** 0.158** 0.158** 0.158** 0.158** (4.71) (4.75) (4.90) (5.09) (5.32) (5.35) (5.09) Holiday -0.00221 -0.00221 -0.00221 -0.00221 -0.00221 -0.00221 -0.00221 (-0.03) (-0.02) (-0.02) (-0.02) (-0.02) (-0.02) (-0.02) Spring 0.0373 0.0373 0.0373 0.0373 0.0373 0.0373 0.0373 (0.60) (0.52) (0.47) (0.43) (0.40) (0.38) (0.36) Summer 0.0244 0.0244 0.0244 0.0244 0.0244 0.0244 0.0244 (0.38) (0.33) (0.30) (0.27) (0.26) (0.24) (0.23) Autumn -0.323** -0.323** -0.323** -0.323** -0.323** -0.323** -0.323** (-4.05) (-3.47) (-3.12) (-2.87) (-2.69) (-2.55) (-2.44) year=2010 1.844** 1.844** 1.844** 1.844** 1.844** 1.844** 1.844** (16.92) (14.32) (12.73) (11.62) (10.79) (10.12) (9.57) year=2011 0.738** 0.738** 0.738** 0.738** 0.738** 0.738** 0.738** (6.91) (5.85) (5.20) (4.74) (4.41) (4.14) (3.92) year=2012 -1.529** -1.529** -1.529** -1.529** -1.529** -1.529** -1.529** (-14.82) (-12.49) (-11.08) (-10.11) (-9.39) (-8.83) (-8.38) year=2013 -0.516** -0.516** -0.516** -0.516** -0.516** -0.516** -0.516** (-4.43) (-3.80) (-3.42) (-3.14) (-2.92) (-2.76) (-2.62) year=2014 -0.241* -0.241 -0.241 -0.241 -0.241 -0.241 -0.241 (-1.76) (-1.52) (-1.37) (-1.27) (-1.19) (-1.12) (-1.06) year=2015 1.070** 1.070** 1.070** 1.070** 1.070** 1.070** 1.070** (7.64) (6.60) (5.96) (5.50) (5.15) (4.87) (4.63) Constant 0.520** 0.520** 0.520** 0.520** 0.520** 0.520** 0.520** (4.95) (4.18) (3.72) (3.40) (3.16) (2.96) (2.81) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey-West regression with lag from 1 to 7. Baseline of year is 2009. Baseline of season is winter. Xi_term is binary starting from 03/15/2013. * p<0.1 ** p<0.05 *** p<0.01 Regression Results (±1 Weeks, DV=Standardized) # of Max. Lag (1) (2) (3) (4) (5) (6) (7) June4 -0.0445 -0.0445 -0.0445 -0.0445 -0.0445 -0.0445 -0.0445 (-0.65) (-0.59) (-0.56) (-0.55) (-0.54) (-0.53) (-0.52) Two Conf. -0.279** -0.279** -0.279** -0.279** -0.279** -0.279** -0.279** (-4.15) (-3.62) (-3.29) (-3.06) (-2.89) (-2.76) (-2.65) PRC60 -0.847** -0.847** -0.847** -0.847** -0.847** -0.847** -0.847** (-8.01) (-6.97) (-6.31) (-5.85) (-5.51) (-5.24) (-5.02) CCP18 -0.744** -0.744** -0.744** -0.744** -0.744** -0.744** -0.744** (-6.42) (-6.03) (-5.76) (-5.64) (-5.61) (-5.50) (-5.34) CCP_Plen -0.240** -0.240* -0.240 -0.240 -0.240 -0.240 -0.240 (-2.15) (-1.82) (-1.63) (-1.49) (-1.39) (-1.31) (-1.25) Ben_Ali 0.387** 0.387** 0.387** 0.387** 0.387** 0.387** 0.387** (3.67) (3.48) (3.22) (3.07) (2.99) (2.91) (2.84) Jasmine 0.727** 0.727** 0.727** 0.727** 0.727** 0.727** 0.727** (5.71) (5.28) (4.94) (4.67) (4.47) (4.34) (4.28) Umbrella 1.253** 1.253** 1.253** 1.253** 1.253** 1.253** 1.253** (12.91) (11.24) (10.16) (9.40) (8.83) (8.38) (7.99) Qian 0.836** 0.836** 0.836** 0.836** 0.836** 0.836** 0.836** (2.19) (2.35) (2.74) (3.22) (3.71) (4.13) (4.19) Dalian -0.612* -0.612 -0.612 -0.612 -0.612 -0.612 -0.612 (-1.75) (-1.50) (-1.39) (-1.33) (-1.31) (-1.31) (-1.32) Qidong 0.259 0.259* 0.259** 0.259** 0.259** 0.259** 0.259** (1.63) (1.91) (2.02) (2.06) (2.04) (2.10) (2.14) Maoming -0.110 -0.110 -0.110 -0.110 -0.110 -0.110 -0.110 (-0.99) (-0.91) (-0.89) (-0.88) (-0.90) (-0.90) (-0.89) Wukan -0.641** -0.641** -0.641** -0.641** -0.641** -0.641** -0.641** (-2.91) (-2.55) (-2.37) (-2.28) (-2.24) (-2.22) (-2.21) Yushu 0.0542 0.0542 0.0542 0.0542 0.0542 0.0542 0.0542 (0.43) (0.42) (0.43) (0.42) (0.42) (0.40) (0.40) Train 0.272* 0.272* 0.272* 0.272** 0.272** 0.272** 0.272** (1.96) (1.87) (1.91) (1.98) (2.10) (2.12) (2.07) Stampede 0.0653 0.0653 0.0653 0.0653 0.0653 0.0653 0.0653 (0.51) (0.45) (0.41) (0.38) (0.36) (0.35) (0.34) Wang 0.611** 0.611** 0.611** 0.611** 0.611** 0.611** 0.611** (6.84) (6.12) (5.58) (5.15) (4.82) (4.60) (4.41) Bo_Purge -0.808** -0.808** -0.808** -0.808** -0.808** -0.808** -0.808** (-10.04) (-8.86) (-8.18) (-7.75) (-7.39) (-7.10) (-6.85) Bo_Verdict 0.122 0.122 0.122 0.122 0.122 0.122 0.122 (0.55) (0.48) (0.44) (0.41) (0.40) (0.39) (0.39) Zhou_Verdict 0.163 0.163 0.163 0.163 0.163 0.163 0.163 (1.18) (1.21) (1.23) (1.21) (1.19) (1.21) (1.23) IPAB_Crack -0.729** -0.729** -0.729** -0.729** -0.729** -0.729** -0.729** (-7.79) (-7.20) (-6.57) (-6.25) (-5.90) (-5.61) (-5.40) Anti-rumor -0.473** -0.473* -0.473* -0.473 -0.473 -0.473 -0.473 (-2.03) (-1.79) (-1.69) (-1.62) (-1.59) (-1.58) (-1.58) Xi_Term -1.418** -1.418** -1.418** -1.418** -1.418** -1.418** -1.418** (-16.75) (-14.73) (-13.51) (-12.57) (-11.84) (-11.23) (-10.71) Weekend 0.158** 0.158** 0.158** 0.158** 0.158** 0.158** 0.158** (4.62) (4.67) (4.83) (5.05) (5.31) (5.36) (5.09) Holiday -0.0151 -0.0151 -0.0151 -0.0151 -0.0151 -0.0151 -0.0151 (-0.16) (-0.14) (-0.13) (-0.12) (-0.12) (-0.11) (-0.11) Spring 0.0196 0.0196 0.0196 0.0196 0.0196 0.0196 0.0196 (0.32) (0.27) (0.25) (0.23) (0.21) (0.20) (0.19) Summer 0.0262 0.0262 0.0262 0.0262 0.0262 0.0262 0.0262 (0.41) (0.35) (0.31) (0.29) (0.27) (0.26) (0.24) Autumn -0.430** -0.430** -0.430** -0.430** -0.430** -0.430** -0.430** (-5.49) (-4.67) (-4.18) (-3.85) (-3.60) (-3.41) (-3.25) year=2010 1.904** 1.904** 1.904** 1.904** 1.904** 1.904** 1.904** (17.49) (14.77) (13.11) (11.96) (11.08) (10.39) (9.81) year=2011 0.796** 0.796** 0.796** 0.796** 0.796** 0.796** 0.796** (7.60) (6.42) (5.70) (5.20) (4.83) (4.53) (4.29) year=2012 -1.504** -1.504** -1.504** -1.504** -1.504** -1.504** -1.504** (-14.66) (-12.32) (-10.90) (-9.93) (-9.20) (-8.63) (-8.17) year=2013 -0.558** -0.558** -0.558** -0.558** -0.558** -0.558** -0.558** (-4.82) (-4.11) (-3.68) (-3.37) (-3.14) (-2.95) (-2.80) year=2014 -0.281** -0.281* -0.281 -0.281 -0.281 -0.281 -0.281 (-2.04) (-1.75) (-1.58) (-1.45) (-1.36) (-1.28) (-1.22) year=2015 1.019** 1.019** 1.019** 1.019** 1.019** 1.019** 1.019** (7.30) (6.27) (5.65) (5.21) (4.87) (4.60) (4.38) Constant 0.457** 0.457** 0.457** 0.457** 0.457** 0.457** 0.457** (4.35) (3.66) (3.25) (2.96) (2.75) (2.58) (2.44) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey-West regression with lag from 1 to 7. Baseline of year is 2009. Baseline of season is winter. Xi_term is binary starting from 03/15/2013. * p<0.1 ** p<0.05 *** p<0.01 Regression Results (±2 Days, DV=Standardized) # of Max. Lag (1) (2) (3) (4) (5) (6) (7) June4 -0.0668 -0.0668 -0.0668 -0.0668 -0.0668 -0.0668 -0.0668 (-0.66) (-0.64) (-0.66) (-0.68) (-0.69) (-0.70) (-0.71) Two Conf. -0.275** -0.275** -0.275** -0.275** -0.275** -0.275** -0.275** (-3.48) (-3.03) (-2.78) (-2.60) (-2.46) (-2.34) (-2.24) PRC60 -0.669** -0.669** -0.669** -0.669** -0.669** -0.669** -0.669** (-5.78) (-5.29) (-4.85) (-4.50) (-4.22) (-4.00) (-3.82) CCP18 -0.605** -0.605** -0.605** -0.605** -0.605** -0.605** -0.605** (-5.32) (-5.17) (-5.19) (-5.06) (-4.85) (-4.63) (-4.42) CCP_Plen -0.106 -0.106 -0.106 -0.106 -0.106 -0.106 -0.106 (-0.64) (-0.55) (-0.50) (-0.46) (-0.44) (-0.42) (-0.41) Ben_Ali 0.393** 0.393** 0.393** 0.393** 0.393** 0.393** 0.393** (3.70) (3.51) (3.24) (3.09) (3.01) (2.93) (2.86) Jasmine 0.640** 0.640** 0.640** 0.640** 0.640** 0.640** 0.640** (5.26) (4.90) (4.65) (4.46) (4.29) (4.18) (4.13) Umbrella 1.267** 1.267** 1.267** 1.267** 1.267** 1.267** 1.267** (12.87) (11.18) (10.08) (9.29) (8.71) (8.24) (7.85) Qian 0.842** 0.842** 0.842** 0.842** 0.842** 0.842** 0.842** (2.21) (2.36) (2.76) (3.24) (3.73) (4.14) (4.20) Dalian -0.620* -0.620 -0.620 -0.620 -0.620 -0.620 -0.620 (-1.77) (-1.52) (-1.41) (-1.35) (-1.33) (-1.33) (-1.34) Qidong 0.269* 0.269** 0.269** 0.269** 0.269** 0.269** 0.269** (1.70) (1.98) (2.10) (2.15) (2.12) (2.19) (2.23) Maoming -0.0929 -0.0929 -0.0929 -0.0929 -0.0929 -0.0929 -0.0929 (-0.84) (-0.77) (-0.75) (-0.75) (-0.75) (-0.75) (-0.75) Wukan -0.633** -0.633** -0.633** -0.633** -0.633** -0.633** -0.633** (-2.85) (-2.50) (-2.32) (-2.24) (-2.19) (-2.17) (-2.16) Yushu 0.0685 0.0685 0.0685 0.0685 0.0685 0.0685 0.0685 (0.54) (0.53) (0.54) (0.53) (0.53) (0.51) (0.50) Train 0.264* 0.264* 0.264* 0.264* 0.264** 0.264** 0.264** (1.91) (1.83) (1.86) (1.94) (2.06) (2.09) (2.03) Stampede 0.0829 0.0829 0.0829 0.0829 0.0829 0.0829 0.0829 (0.65) (0.57) (0.52) (0.48) (0.46) (0.44) (0.43) Wang 0.635** 0.635** 0.635** 0.635** 0.635** 0.635** 0.635** (7.05) (6.29) (5.73) (5.29) (4.95) (4.72) (4.52) Bo_Purge -0.813** -0.813** -0.813** -0.813** -0.813** -0.813** -0.813** (-9.98) (-8.86) (-8.23) (-7.84) (-7.52) (-7.25) (-7.00) Bo_Verdict 0.182 0.182 0.182 0.182 0.182 0.182 0.182 (0.83) (0.73) (0.66) (0.62) (0.60) (0.59) (0.59) Zhou_Verdict 0.164 0.164 0.164 0.164 0.164 0.164 0.164 (1.19) (1.21) (1.23) (1.21) (1.18) (1.21) (1.23) IPAB_Crack -0.685** -0.685** -0.685** -0.685** -0.685** -0.685** -0.685** (-7.07) (-6.53) (-5.99) (-5.70) (-5.40) (-5.15) (-4.97) Anti-rumor -0.438* -0.438 -0.438 -0.438 -0.438 -0.438 -0.438 (-1.81) (-1.59) (-1.50) (-1.43) (-1.41) (-1.40) (-1.40) Xi_Term -1.362** -1.362** -1.362** -1.362** -1.362** -1.362** -1.362** (-16.38) (-14.44) (-13.28) (-12.37) (-11.65) (-11.04) (-10.51) Weekend 0.158** 0.158** 0.158** 0.158** 0.158** 0.158** 0.158** (4.57) (4.63) (4.79) (5.01) (5.28) (5.34) (5.07) Holiday -0.0142 -0.0142 -0.0142 -0.0142 -0.0142 -0.0142 -0.0142 (-0.15) (-0.13) (-0.12) (-0.11) (-0.11) (-0.11) (-0.10) Spring 0.0132 0.0132 0.0132 0.0132 0.0132 0.0132 0.0132 (0.20) (0.17) (0.16) (0.15) (0.14) (0.13) (0.12) Summer 0.0395 0.0395 0.0395 0.0395 0.0395 0.0395 0.0395 (0.61) (0.52) (0.47) (0.43) (0.40) (0.38) (0.36) Autumn -0.481** -0.481** -0.481** -0.481** -0.481** -0.481** -0.481** (-6.18) (-5.25) (-4.69) (-4.30) (-4.02) (-3.80) (-3.62) year=2010 1.947** 1.947** 1.947** 1.947** 1.947** 1.947** 1.947** (17.80) (15.01) (13.31) (12.12) (11.23) (10.51) (9.92) year=2011 0.842** 0.842** 0.842** 0.842** 0.842** 0.842** 0.842** (8.07) (6.81) (6.04) (5.50) (5.10) (4.78) (4.52) year=2012 -1.476** -1.476** -1.476** -1.476** -1.476** -1.476** -1.476** (-14.41) (-12.09) (-10.67) (-9.70) (-8.98) (-8.42) (-7.96) year=2013 -0.571** -0.571** -0.571** -0.571** -0.571** -0.571** -0.571** (-4.96) (-4.22) (-3.77) (-3.44) (-3.20) (-3.00) (-2.84) year=2014 -0.296** -0.296* -0.296* -0.296 -0.296 -0.296 -0.296 (-2.14) (-1.84) (-1.66) (-1.52) (-1.42) (-1.34) (-1.27) year=2015 0.997** 0.997** 0.997** 0.997** 0.997** 0.997** 0.997** (7.20) (6.18) (5.56) (5.12) (4.78) (4.51) (4.28) Constant 0.406** 0.406** 0.406** 0.406** 0.406** 0.406** 0.406** (3.81) (3.20) (2.84) (2.59) (2.40) (2.25) (2.12) Observations 2208 2208 2208 2208 2208 2208 2208 Note: Observation is days. Newey-West regression with lag from 1 to 7. Baseline of year is 2009. Baseline of season is winter. Xi_term is binary starting from 03/15/2013. * p<0.1 ** p<0.05 *** p<0.01 Appendix 11: LDA Topic Modeling of Two Thematic Discussion Boards

COMPLAINTS (Grassroots Voices) with English Translation Topic

(Probability) Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7 Topic 8 Topic 9 Topic 10 (0.138976) (0.124988) (0.12188) (0.102281) (0.109248) (0.086584) (0.095417) (0.074157) (0.08243) (0.064038) 转载中国转载农民转载一个转载政府转载拆迁问题贴图领导举报法院法律事件百姓公司强拆求助社会没有村民医院老百姓河南违法骗子非法维权农民工关注书记人民何在山东腐败公安局暴力诈骗开发商孩子土地北京国家农村官员派出所老人严重黑心看看实名法官警察学生上访有限公司真相业主最牛干部村官上海天理何在发生地方工作生活集团老板救救征地警察公道河南省农民湖南殴打江苏工资希望山东省求助断电书记黑社会局长执法不能现在真的贪污社会山东公安局书记河南省黑社会国家到底不要河南省山东派出所领导维权江苏部门村民看看不能违法官员地方局长发生上海派出所到底天理何在到底殴打上访农民维权国家山东河南省河南真相老百姓江苏救救问题国家山东省不要看看山东事件现在警察公安局不能上访领导领导没有局长举报派出所公道领导河南省没有救救工资山东省黑社会老人公道上访农民书记上海老人执法不要希望求助湖南强拆干部法官救救开发商山东省上访关注百姓问题真相孩子工资派出所派出所生活湖南公司河南山东省老板工作看看法官拆迁农村农民 Topic (Probability) Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7 Topic 8 Topic 9 Topic 10 (0.138976) (0.124988) (0.12188) (0.102281) (0.109248) (0.086584) (0.095417) (0.074157) (0.08243) (0.064038) repost China repost farmers repost a repost government repost demolition forced problem map leadership report court law event people company demolition Ordinary seek help social no villagers Hospital Henan illegal A liar illegal people Migrant Party public security activist Focus on people What is the Shandong corruption violence workers secretary bureau fraud developers child land Beijing countries rural officials police station old man serious evil mind look at real-name judge police students petition Co., LTD. truth owner The best cadres Village cadre Shanghai injustice happen place work life Land group The boss save police justice Henan farmers Hunan beating requisition Power Law Jiangsu wage hope Shandong seek help Party secretary underworld Director outages enforcement public security Party Can't now really corruption social Shandong Henan underworld bureau secretary police countries what on earth Don't Henan Shandong leadership activist Jiangsu department station police villagers take a look Can't illegal officials place Director happen Shanghai station what on earth injustice what on earth beating petition farmers activist countries Shandong Henan Henan truth ordinary people Jiangsu save problem countries Shandong Don't look at public security Shandong event now police Can't petition leadership leadership no bureau Director To report police station justice leadership Henan no save wage Shandong Party Law underworld old man justice petition farmers Shanghai old man Don't secretary enforcement forced hope seek help Hunan cadres judge save developers Shandong petition demolition police watching people problem truth child wage police station life Hunan station company Henan Shandong The boss work look at judge demolition rural farmers GENERAL (Free) with English Translation Topic (Probability) Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7 Topic 8 Topic 9 Topic 10 (0.119914) (0.117303) (0.113201) (0.098341) (0.100429) (0.099551) (0.091083) (0.093706) (0.085076) (0.081397) 转载转载转载中国没有问题一个转载转载公司北京政府铁路世界孩子天涯转载事件工作骗子医院领导专区美国社会求助女人学生生活不要上海举报发展日本真的朋友男人专区到底真相人民官员中国知道教育看看人生中国公务员网络官员看看今天转载现在一下生活今天国家今天到底淘宝公司女人不能知道事件不要应该淘宝看看应该真相举报到底社会真的社会现在知道一个学生国家没有一下领导不能朋友不能转载没有一下世界淘宝铁路北京今天一下知道看看人生网络学生北京政府工作世界国家中国孩子工作医院真的学生上海孩子看看北京真相一个不能工作孩子到底骗子事件孩子应该朋友女人现在事件应该孩子真相今天朋友现在政府事件事件专区北京天涯学生教育铁路铁路孩子到底发展求助不能不能一个世界官员骗子北京日本女人发展不要发展不要网络一下女人社会现在应该公司到底男人求助政府上海医院世界教育不要不要工作不要发展中国天涯真的事件北京公司教育公务员工作公务员转载不要不能公司不能 Topic (Probability) Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7 Topic 8 Topic 9 Topic 10 (0.119914) (0.117303) (0.113201) (0.098341) (0.100429) (0.099551) (0.091083) (0.093706) (0.085076) (0.081397) repost repost repost China no problem a repost repost company Beijing government railway world child Tianya repost event work liar The United Hospital leadership special zone social seek help woman students life Don't States special what on Shanghai To report development Japan really man man truth zone earth Civil people officials China know education look at life China network servants officials look at today repost now the life today countries today what on earth Taobao company woman Can't know event Don't should Taobao look at should truth report what on earth social really social now know a students countries no the leadership Can't friend Can't repost no the world Taobao railway Beijing today the know look at life network students Beijing government work world countries China child work Hospital really students Shanghai child look at Beijing truth a Can't work child what on earth liar event child should friend woman now event should child truth today friend now government event what on event special zone Beijing Tianya students education railway railway child earth development seek help Can't Can't a world officials liar Beijing Japan woman development Don't development Don't network the woman social now should company what on earth man seek help government Shanghai Hospital world education Don't Don't work Don't development China Tianya really event Beijing company education Civil servants work Civil servants repost Don't Can't company Can't

View publication stats