Research Article

Reverse-engineering censorship in China: Randomized experimentation and participant observation

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Science  22 Aug 2014:
Vol. 345, Issue 6199, 1251722
DOI: 10.1126/science.1251722

Structured Abstract


Censorship has a long history in China, extending from the efforts of Emperor Qin to burn Confucian texts in the third century BCE to the control of traditional broadcast media under Communist Party rule. However, with the rise of the Internet and new media platforms, more than 1.3 billion people can now broadcast their individual views, making information far more diffuse and considerably harder to control. In response, the government has built a massive social media censorship organization, the result of which constitutes the largest selective suppression of human communication in the recorded history of any country. We show that this large system, designed to suppress information, paradoxically leaves large footprints and so reveals a great deal about itself and the intentions of the government.

Embedded Image

The Chinese censorship decision tree. The pictures shown are examples of real (and typical) websites, along with our translations.


Chinese censorship of individual social media posts occurs at two levels: (i) Many tens of thousands of censors, working inside Chinese social media firms and government at several levels, read individual social media posts, and decide which ones to take down. (ii) They also read social media submissions that are prevented from being posted by automated keyword filters, and decide which ones to publish.

To study the first level, we devised an observational study to download published Chinese social media posts before the government could censor them, and to revisit each from a worldwide network of computers to see which was censored. To study the second level, we conducted the first largescale experimental study of censorship by creating accounts on numerous social media sites throughout China, submitting texts with different randomly assigned content to each, and detecting from a worldwide network of computers which ones were censored.

To find out the details of how the system works, we supplemented the typical current approach (conducting uncertain and potentially unsafe confidential interviews with insiders) with a participant observation study, in which we set up our own social media site in China. While also attempting not to alter the system we were studying, we purchased a URL, rented server space, contracted with Chinese firms to acquire the same software as used by existing social media sites, and—with direct access to their software, documentation, and even customer service help desk support—reverseengineered how it all works.


Criticisms of the state, its leaders, and their policies are routinely published, whereas posts with collective action potential are much more likely to be censored—regardless of whether they are for or against the state (two concepts not previously distinguished in the literature). Chinese people can write the most vitriolic blog posts about even the top Chinese leaders without fear of censorship, but if they write in support of or opposition to an ongoing protest—or even about a rally in favor of a popular policy or leader—they will be censored.

We clarify the internal mechanisms of the Chinese censorship apparatus and show how changes in censorship behavior reveal government intent by presaging their action on the ground. That is, it appears that criticism on the web, which was thought to be censored, is used by Chinese leaders to determine which officials are not doing their job of mollifying the people and need to be replaced.


Censorship in China is used to muzzle those outside government who attempt to spur the creation of crowds for any reason—in opposition to, in support of, or unrelated to the government. The government allows the Chinese people to say whatever they like about the state, its leaders, or their policies, because talk about any subject unconnected to collective action is not censored. The value that Chinese leaders find in allowing and then measuring criticism by hundreds of millions of Chinese people creates actionable information for them and, as a result, also for academic scholars and public policy analysts.

Censorship of social media in China

Figuring out how many and which social media comments are censored by governments is difficult because those comments, by definition, cannot be read. King et al. have posted comments to social media sites in China and then waited to see which of these never appeared, which appeared and were then removed, and which appeared and survived. About 40% of their submissions were reviewed by an army of censors, and more than half of these never appeared. By varying the content of posts across topics, they conclude that any mention of collective action is selectively suppressed.

Science, this issue 10.1126/science.1251722


Existing research on the extensive Chinese censorship organization uses observational methods with well-known limitations. We conducted the first large-scale experimental study of censorship by creating accounts on numerous social media sites, randomly submitting different texts, and observing from a worldwide network of computers which texts were censored and which were not. We also supplemented interviews with confidential sources by creating our own social media site, contracting with Chinese firms to install the same censoring technologies as existing sites, and—with their software, documentation, and even customer support—reverse-engineering how it all works. Our results offer rigorous support for the recent hypothesis that criticisms of the state, its leaders, and their policies are published, whereas posts about real-world events with collective action potential are censored.

The Chinese government has implemented “the most elaborate system for Internet content control in the world” (1), marshaling hundreds of thousands of people to strategically slow the flow of certain types of information among the Chinese people. Yet the sheer size and influence of this organization make it possible to infer via passive observation a great deal about its purpose and procedures, as well as the intentions of the Chinese government. To get around the well-known inferential limitations inherent in observational work, our experiment depended on large-scale random experimentation and participant observation.

We begin with the theoretical context. The largest previous study of the purpose of Chinese censorship (2) distinguished between the “state critique” and “collective action potential” theories of censorship and found that, with few exceptions, the first was wrong and the second was right: Criticisms of the government in social media (even vitriolic ones) are not censored, whereas any attempt to physically move people in ways not sanctioned by the government is censored. Even posts that praise the government are censored if they pertain to real-world collective action events (2).

In both theories, regime stability is the assumed ultimate goal (36). Scholars had previously thought that the censors pruned the Internet of government criticism and biased the remaining news in favor of the government, thinking that others would be less moved to action on the ground as a result (79). However, even if biasing news positively would in fact reduce the potential for collective action, this state critique theory of censorship misses the value to the central Party organization of the information content provided by open criticism in social media (1013). After all, much of the job of leaders in an autocratic system is to keep the people sufficiently mollified that they will not take action that may affect their hold on power. In line with the literature on responsive authoritarianism, the knowledge that a local leader or government bureaucrat is engendering severe criticism—perhaps because of corruption or incompetence—is valuable information (14, 15). That leader can then be replaced with someone more effective at maintaining stability, and the system can then be seen as responsive. This responsiveness would seem likely to have a considerably larger effect on reducing the probability of collective action than merely biasing the news in predictable ways.

The collective action potential hypothesis holds that the Chinese censorship organization first detects a volume burst of social media posts within a specific topic area, and then identifies the real-world event that gives rise to the volume burst (2). If the event is classified as having collective action potential, then all posts within the burst are censored, regardless of whether they are critical or supportive of the state and its leaders. Unlike the uncertain process involved in coherently classifying individual posts as to their collective action potential, this procedure is easily implemented with extremely high levels of intercoder reliability. No evidence exists as to whether any such rules were invented and directed by a person or committee in the Chinese government, or whether they merely represent an emergent pattern of this large-scale activity.

Although the largest existing study analyzed more than 11 million social media posts from almost 1400 websites across China (2), it and other quantitative studies of censorship (16, 17) were solely observational, with some conclusions necessarily depending on untestable assumptions. For example, the data for these studies were controlled by an earlier stage in which many social media websites use automated review (based on techniques such as keyword matching) to immediately move large numbers of prospective posts into a temporary limbo to receive extra scrutiny before possible publishing (for a guide, see Fig. 1). Whereas the ex post content-filtering decision is conducted largely by hand and takes as long as 24 hours, the ex ante decision of whether posts are slotted for review is automated, instantaneous, and thus cannot be detected by observational methods. This also means that the automated review process could induce selection bias in existing studies of censorship, which could only observe those submissions that were not stopped from publication by automated review. And, of course, observational research generally also risks endogeneity bias, confounding bias, and other problems.

Fig. 1 The Chinese censorship decision tree.

The pictures shown are examples of real (and typical) websites, along with our translations. Observational studies are based only on the first three paths through this decision tree; our experimental study includes all five. Full screen shots are in (19).

To avoid these potential biases and to study how automated review works, we conducted a large-scale experimental study in which random assignment, controlled by the investigators, substituted for statistical assumptions. We created accounts on numerous social media sites across China; wrote a large number of unique social media posts; randomized the assignment of different types of posts to accounts; and, to evade detection, observed from a network of computers all over the world which types were published or censored. Throughout, we attempted to avoid disturbing the flow of normal discourse by producing social media content on topics similar to those in real social media posts (including the content of those censored, which our methods could access). Although very-small-scale nonrandomized efforts to post on Chinese websites and observe censorship have been informative (18), randomized experiments have not before been used in the study of Chinese censorship.

In addition to our randomized experiment, from which we drew causal inferences, we also sought to produce more reliable descriptive knowledge of how the censorship process works. This is important information in its own right; the process is intensely studied and contested in the academic and policy communities. Until now, such information has mostly come from highly confidential interviews with censors or their agents at social media sites or in government. This information is necessarily partial, incomplete, potentially unsafe for research subjects, and otherwise difficult to gather. Participant observation provided us with a new source of information absent from previous studies of censorship. From inside China, we created our own social media website, purchased a URL, rented server space, contracted with one of the most popular software platforms in China used to create these sites, submitted, automatically reviewed, posted, and censored our own submissions. The website we created was available only to our research team, so as to avoid affecting the object of our study or otherwise interfering with existing Chinese social media discourse. However, we had complete access to the software, documentation, help forums, and extensive consultation with support staff; we were even able to get their recommendations on how to conduct censorship on our own site in compliance with government standards. The “interviews” we conducted in this way were unusually informative because the job of our sources was in fact to answer the questions we posed.

Overall, this work offers three intended contributions. First, by analyzing large numbers of posts at numerous social media sites, we are able to resolve some disagreements in the policy and academic literatures on the subject, such as explanations for the presence of conflicting keyword lists and the absence of a coherent or unified interpretation for the operation of these lists at individual sites. Consistent with this disagreement, we show that the large number of local social media sites in China have considerable flexibility, and choose diverse technical and software options, in implementing censorship. Second, we show that the automated review process affects large numbers of posts on fully two-thirds of Chinese social media sites, but is a largely ineffective step in implementing the government’s censorship goals. This is surprising but consistent with the known poor performance of most keyword-based approaches to text classification. Finally, despite automated review’s large presence, high potential for generating selection bias in observational studies, and overall ineffectiveness due to keyword matching, we find that the government is still able accomplish its objectives—as summarized by the collective action potential hypothesis—by using very large numbers of human coders to produce post hoc corrections to automated review and to censorship in general.

Our research offers clear support for the collective action potential hypothesis and then offers some important extensions. We find—consistent with the implications of this theory, but untested in prior research—that there is no censorship of posts about collective action events outside mainland China, collective action events occurring solely online, social media posts containing critiques of top leaders, and posts about highly sensitive topics (such as Tibet and Xinjiang) that do not occur during collective action events.

Research designs

We now describe the challenges involved in large-scale experimentation, participant observation, and data collection in a system designed to prevent the free flow of information, especially about the censors [see (19) for additional details]. These include avoiding detection, implementing the experiment in many geographically distant places, keeping a large research team safe, and ensuring that we did not disturb or alter the system we were studying. The human subjects aspects of our experimental protocol were preapproved by our university’s institutional review board (IRB). For obvious reasons, we are unable to reveal certain details of how we implemented this design, but we do give complete information on the statistical and scientific logic behind our choices (20).

We begin with the outcome variable we are studying and then describe our experimental protocols.

Participant observation

Aspects of the process by which censors in the Chinese government and social media companies implement censorship directives have been gleaned over the years in interviews with sources who have first-hand knowledge, including the censors themselves. We have also conducted many such interviews, and each one produces some information, but it is necessarily a partial picture, highly uncertain, and potentially unsafe for the sources and researchers.

Thus, we looked for a way to learn more by changing the incentives of our sources. We did this by creating our own Chinese social media site from inside China, using all the infrastructure, procedures, and rules that existing sites must follow. We purchased a URL, contracted with a company that provides hosting services, and arranged with another company to acquire the software necessary to establish a community discussion forum [a bulletin board system (BBS)]. We downloaded the software and installed it ourselves. This infrastructure gave us complete access to the software and its documentation so that we could fully understand and make use of its functionality. Support employees at these firms were happy to help show us how to censor in such a way as to maintain our website in accordance with their view of government requirements. Thus, instead of trying to convince people to spare some of their time for researchers, we were able to have conversations with employees whose job was to answer questions like those we posed; fortunately, they seemed quite good at this. We then studied and customized the software, submitted posts ourselves, and used the software’s mechanisms to censor some of them. We took every step we could (short of letting individuals in China post on the site) to avoid causing any interference to actual social media discourse.

The biggest surprise we found relative to the literature was the huge variety of technical methods by which automated review and human censorship can be conducted. Table 1 summarizes some of these options.

Table 1 Options for content filtering on forum platform.
View this table:

When we installed the software, we found that, by default, it included no automated review or blocking. But webmasters can easily change the option of automatically reviewing specific types of users (those who are moderators, super users, users who have been banned from posting, or those who have been banned from visiting the site), Internet protocol (IP) addresses, new threads, or every response—all of which can be tailored for each of as many forums as desired on each website. Functionality also exists to bulk-delete posts, which can be implemented by date range, user name, user IP, content containing certain keywords, or length of post. On the back end, the webmaster also has flexible search tools to examine content, search by user name, post titles, or post content. What the user sees can also be limited: The search function can be disabled for users, and webmasters have the option of allowing users to see whether posts of theirs are being automatically reviewed (and, if so, which ones).

We found employees of the software application company to be forthcoming when we asked for recommendations as to which technologies have been most useful to their other clients in following government information management guidelines. On the basis of their recommendations as well as user guides, detailed analyses from probing the system, and additional personal interviews (with sources granted anonymity), we deduce that most social media websites that conduct automatic review do so via a version of keyword matching, probably using hand-curated sets of keywords (we reverse-engineer the specific keywords below) (21).

We summarize our understanding of the censorship process in Fig. 1. The process begins when one writes and submits a blog or microblog post at a social media website (left). This post is either published immediately (top left node) or held for review before publication (middle left node in red). If the post is published immediately, it may be manually read by a censor within about 24 hours and, depending on the decision, either remains online indefinitely (top box) or is removed from the Internet (second box). As can be seen from the screen shots of actual websites in Fig. 1 [full examples in (19)], the decisions of the censors, and the fact that they are making these decisions, are unambiguous.

The censors then read each post in review (usually within a day or two) and either publish the post (third box of Fig. 1) or delete it before publication (fourth box). We are able to identify review when it occurs because typically the user receives a message after post submission that the text has been slotted for review. In the absence of a warning message, the user can tell when a post is put into review because no public URL is associated with the post, and the user’s account page will show the status of the post as “under review.” Finally, on the basis of the current and previous posts, a submitted post can be censored and the account blocked so that no additional posts may be made (last box of Fig. 1). In this last case, when a user submits a text for posting, an error message notifying the user of account blocking is encountered. A key point is that the massive data set in (2) corresponds only to the first three boxes, whereas in our experiment we are able to study all five paths down the decision tree.

Experimental protocol

We designed our experimental protocol to make causal inferences without certain modeling assumptions. We first selected 100 social media sites, including 97 of the top blogging sites in the country, representing 87% of blog posts now on the web. We included the top three microblogging (i.e., Twitter-like) sites: Sina Weibo (, Tencent Weibo (, and Sohu Weibo ( The first two of these microblogging sites each have more than 500 million registered users and 50 to 100 million daily active users (22). Together, the 100 sites are geographically spread all over China; 20 are run by the government, 25 are state-owned enterprises, and 55 are private firms. Some cater to national audiences, whereas some only allow posting within a local area. Creating accounts on some of these sites requires the user to be in the country at a specific geographic locale, to have a local e-mail address, or to provide another method of communication for identification. We devised procedures to create two accounts at each of these 100 social media sites.

We kept our design close to aspects of (2). The theory in that paper was not that every social media post with the potential to generate collective action is censored; after all, almost any issue could in principle be used as a hook to generate protest activity. Instead, the theory is that pro- or anti-government posts concerning a collective action event are censored. Collective action events are those “which (a) involve protest or organized crowd formation outside the Internet; (b) relate to individuals who have organized or incited collective action on the ground in the past; or (c) relate to nationalism or nationalist sentiment that have incited protest or collective action in the past” [(2), p. 6].

We conducted three rounds of experiments (18 to 28 April, 24 to 29 June, and 30 June to 4 July 2013) during which social media posts would need to be written in real time about current issues. This presented a logistical challenge. At the beginning of each round, we scoured the news and selected ongoing collective action events and non–collective action events about which there was a volume burst in social media discussion. We chose a ratio of one collective action event to two non–collective action events, because collective action events are more scarce and so that we could average over different non–collective action events. We included non–collective action events only if they were widely discussed topics pertaining to actions taken by the Chinese government, officials, or the Chinese Communist Party (CCP) that were unrelated to events with collective action potential. We also attempted where possible to select events that mentioned specific officials’ names and addressed what has been described as especially “sensitive” topics. (We also included several edge cases, described below.) Details of all events appear in (19), but here are the four collective action events we found when our study was conducted, all of which meet the definition but some of which are more incendiary than others:

1. Qui Cuo, a 20-year-old mother, self-immolated to protest China’s repressive policies over Tibet. Her funeral drew protesters.

2. Protesters in Panxu, a village in Xiamen Fujian, took to the streets because they claimed officials did not adequately compensate them for requisitioning their collectively owned farmland to build a golf course. Village representatives went to local authorities to demand compensation but were instead detained. Thousands of villagers went to the town hall to demand the release of the village representatives, police moved in to arrest the villagers, and the villagers retaliated by smashing police cars and taking the local Party secretary into custody.

3. On the second anniversary of the 2011 arrest of artist-dissident Ai Weiwei, he released a music album that talked about his imprisonment. Ai Weiwei was arrested in 2011 on charges of tax evasion, but more likely the true reason was either that he called upon his followers to mimic the Arab Spring or that he organized volunteers to collect the names of children who died in the Sichuan earthquake. The release of the album by Ai Weiwei is chosen as an example of collective action under part (b) of the definition, where posts about individuals who have organized or incited collective action on the ground in the past are censored.

4. An altercation between protesting Uyghurs (a minority ethnic group) and local police in Lekeqin township of Shanshan county in Turpan, Xinjiang, led to the deaths of 24 people, including 16 Uyghurs. Police and many official news reports of the event termed it an act of Uyghur terrorism, but rumors circulated in social media that the protest was precipitated by forced housing demolition.

For each event, we had a group of native Chinese speakers write some posts supportive and others critical of the government. These posts were based on social media posts that had already appeared online, including posts that were censored as well as those that remained online. [We used the technology of (2) to obtain access to the censored posts.] In other words, we obtained posts that were immediately published after submission, including those that remained online and those that were removed (top two boxes of Fig. 1). We provided our writers with background on the event, the definition of what we meant by pro- and anti-government for each topic (19), and examples of real posts from Chinese social media similar to those we needed written. So that we could minimize any experimenter effect, we checked each text ourselves by hand and attempted throughout to ensure that the posts we submitted were similar in language, sentiment, and content to those already found in (or written and censored in) Chinese social media.

From a statistical point of view, we ensured balance by blocking (23) on (that is, randomly sampling only within each cell of the cross-classification of) three variables: First, our posts included the same keywords in both the treatment and control conditions. Second, we controlled for individual writing style by blocking on author in our experimental design. That is, posts in each set of four experimental conditions (defined by our two variables: pro- or anti-government, and with or without collective action potential) were written by the same set of research assistants. Finally, we constrained all posts to be between 100 and 200 characters in length. In addition, we also ensured that no two posts submitted were exactly identical to each other or to any we found in social media. All posts were submitted between 8 a.m. and 8 p.m. China time, either from the United States or from the appropriate place within China, depending on what was feasible because of the technology used at each social media site (24).

We were interested in testing the causal effect of both pro- versus anti-government content and collective action versus non–collective action content, leading by cross-classification to four logical treatment categories. To make the most efficient use of each individual account, we submitted two posts to each. But it makes little sense for one account (representing a single person) to write both pro- and anti-government posts regarding the same event. Thus, we submitted posts about two different events to each account; some of these posts were pro-government collective action and anti-government non–collective action, and others were anti-government collective action and pro-government non–collective action. In this way, every account contributed to the causal effect estimate of each hypothesis. We also ensured our ability to make causal inferences without extra modeling assumptions by randomizing (i) the choice between these two pairs, (ii) the order within each pair, and (iii) the specific collective action and policy events we wrote about in each submission. Missingness could occur when websites were down, if an account we created expired, or if an account had been blocked because of prior posts. Largely because of the design, any missingness is almost exactly independent of our two treatment variables; empirically that proved to be the case.

Each of the 100 different social media websites in our study offers a different way of expressing oneself online. When possible, we submitted posts on the home page we created for each account. For discussion forums, we started a new thread with the content of the post in the most popular subforum. On sites where users are not permitted to create new threads, we submitted posts as a reply to an existing thread relevant to the topic. In all cases, we wrote our posts so as not to stand out from the stream of existing information; we followed all social media, website, and cultural norms. In total, we wrote 1200 posts by hand, every one unique, and none referring to each other (25).

After submitting a post, we observed whether it was caught by the process of automated review; if in automated review, whether and when it was eventually published; and if not caught by the automated review process, whether it was eventually censored after the fact or remained on the web. When a post appeared on the web, we recorded the URL and verified censorship from computers inside and outside of China. We recorded the outcome in terms of censorship, which corresponds to the branches of the decision tree in Fig. 1.

Throughout, our goal was that anyone looking at our submissions would have no idea that this was part of an academic research project, and that the posts were typical of what anyone might otherwise find online and would not in any way disrupt or change the social media ecosystem we were studying. We also needed to ensure that our checking of published posts for censorship was not obtrusive. So far as we are aware, no one outside of our research team and confidants were aware of this experiment before now, and no one on the web indicated any suspicion about or undue attention toward any of our posts.


We find that in aggregate, automated review affects a remarkably large portion of the social media landscape in China. In total, 66 of the 100 sites in our sample (automatically) review at least some social media submissions, and 40% of all of our individual social media submissions from our 100 sites (and 52% of submissions from sites that review at least sometimes) are put into review. Of those submissions that go into review, 63% never appear on the web.

These figures indicate that automated review affects a large component of intended speech in China and so deserves systematic attention from researchers. This is especially so because of conflicting conclusions and lack of a unified interpretation in the academic and policy literatures about which keywords provoke action by the government, how automated review works, and what impact this process ultimately has on the content of speech that is blocked and that which can be consumed by the Chinese people (26, 27). We offer a possible resolution to these issues here.


Using our broader sample, unaffected by selection during the automated review process, and with our experimental randomization, we began by testing the collective action potential hypothesis. On the basis of a difference in means between the treatment and control groups, the black dots in the left panel of Fig. 2 summarize the point estimate for the causal effects on censorship of submitting posts about four separate collective action events. The vertical lines are 95% confidence intervals (as with all our figures). The effects are substantial, ranging from about 20 to 40 percentage point differences (denoted on the vertical axis) solely due to writing about an ongoing collective action event as compared to an ongoing non–collective action event.

Fig. 2 The causal effect on censorship of posts with collective action potential (left panel) and the mediation effect of review (right panel).

Collective action events are more highly censored than non–collective action events within the same time period. However, censorship of collective action events is not mediated through automated review.

We also examined some of the other decision paths in Fig. 1. To do this, we estimated the “causal mediation effect” (28, 29) of submitting posts about collective action events (versus non–collective action events) on censorship, and found that almost none of this effect is mediated through automated review: The overall effect is a trivial 0.003 probability, with a 95% confidence interval of (–0.007, 0.016) (19). The (non)effect for each of the four collective action events we studied is displayed in the right panel of Fig. 2, and each is similarly approximately zero, with a small confidence interval. Review, which appears to be fully automated, is thus applied in a manner independent of other relevant variables, and, like most keyword-only methods of automated text analysis, it does not work well when applied to large numbers of documents. From this result, it even appears that the censors largely ignore it, or at least do not get much information from it (see below).

In parallel to the large causal effect for collective action, Fig. 3 reports tests of the state critique hypothesis for each of our four collective action events and eight (non–collective action) policy events. The black dots summarize point estimates of the causal effect of submitting posts in favor of the government versus opposed to the government about each event. As can be seen, the dots are all very close to the horizontal dashed line, drawn at zero effect, with six dots above and six below, and all but one of the confidence intervals crossing the zero line. Note especially that there is no hint of more censorship of anti-government posts when they involve topics that might be viewed as more sensitive or which specifically mention the names of Chinese leaders [see (19) for contextual details]. This finding runs counter to anecdotal evidence that rumors and names of leaders unrelated to collective action lead to censorship.

Fig. 3 The causal effect on censorship of posts for or against the government.

Posts that support the government are not more or less likely to be censored than posts that oppose the government, within the same topic.

Automated review

The overall results in favor of the collective action potential hypothesis and against the state critique hypothesis thus appear unambiguous. The automated review process has a nearly undetectable effect on evidence about that hypothesis, because the human censors correct errors after the keyword-matching techniques are applied in automated review (although even incorrect keyword filtering slows down communications on many subjects). We now go back up the decision tree of Fig. 1 to study the automated review process more directly.

We first noticed that not all websites have automated review turned on, and that the method of censorship varies enormously by website [this is also true for account blocking (19)]. This is consistent with what we learned from creating our own social media site, where the software platform not only allows the option of whether to review, but also offers a large variety of choices of the criteria by which to review. Indeed, there exists considerable diversity in the technologies used by different social media sites for automated review (17). It is this diversity in technology across sites, then, that appears to account for why different researchers typically find different patterns when looking at different sites or at specific issues. This also accounts for why researchers have been unable to offer unified interpretations of their observations that are consistent with reasonable assumptions about the goals of the Chinese leadership. Only by looking at the whole process does the simplicity of the Chinese government’s goals become clear.

Why would the government, in the course of providing top-down authoritarian control, allow for a free choice from a large number of censorship methods? To answer this question, we collected detailed information about all software platforms and plug-ins available for purchase or license by social media sites to control information. From this study, we conclude that the government is (perhaps intentionally) promoting innovation and competition in the technologies of censorship. Such decentralization of policy implementation as a technique to promote innovation is common in China (3033).

On the basis of interviews with those involved in the process, we also found a great deal of uncertainty over the exact censorship requirements and the precise rules under which the government would interfere with the operation of social media sites, especially for smaller sites with limited government connections. This uncertainty is in part a result of encouraging innovation, but it may also in some situations be a means of control as well; it is easier to keep people away from a fuzzy line than from a clearly drawn one.

Our systematic empirical study began by investigating which social media websites use any automated review process. Figure 4 presents a density estimate (a continuous version of a histogram) of the distribution of the proportion of posts reviewed for three types of sites, depending on ownership. As can be seen, it is government sites that have the highest probability of a post being put into review, followed by the state-owned enterprises, followed last by privately owned sites (which tend to have the largest user bases).

Fig. 4 Histogram (density estimate) of the proportion of posts reviewed by site.

The graph shows that government-controlled social media sites catch many more posts by automated review than do privately owned sites; social media sites controlled by state-owned enterprises (SOEs) are in the middle.

Why would government sites be more likely to delay publication until after automated review, whereas private sites publish first and make censorship decisions later? So far as we can tell from qualitative evidence, the reason is that the penalty for letting offending posts through differs between government and private sites. A government worker who fails to stem collective action could lose his or her job immediately; in contrast, a worker in a private site who makes the same mistake cannot usually be directly fired by the government. Indeed, government workers have a historical legacy of giving priority to following orders and not making mistakes, even if it is considerably more inefficient to do so (34). Private social media sites, on the other hand, have incentives to publish as much as they can, so as to attract more users. A private site can, of course, be taken down entirely, but that kind of “nuclear option” is used less often than more generalized pressure on the leadership of these private sites.

What are these largely government sites reviewing? In a manner directly parallel to Figs. 2 and 3 for the ultimate variable of censorship, we analyzed the effects on automated review of collective action and pro- and anti-government posts. Figure 5 gives results for the effect of collective action on review; they include four positive estimated effects, but two are small and three have zero inside their confidence intervals. If the goal of the censors is to capture collective action events, the automated algorithm is performing marginally at best, although this is quite common for keyword algorithms, which tend to work well for specific examples for which they can be designed but often have low rates of sensitivity and specificity when used for large numbers of documents.

Fig. 5 Causal effect on review of collective action potential events.

Collective action events are overall slightly more likely to be reviewed than non–collective action events.

Also interesting is the causal effect of pro- versus anti-government posts in Fig. 6. These are all small, and most of the confidence intervals cross zero. If there exists a nonzero relationship here, it is that submissions in favor of the government are reviewed more often than those against the government. Indeed, 9 of 12 point estimates are above zero, and two even have their entire confidence interval above zero. This presents a mystery: Government social media sites are slightly more likely to delay publication of submissions that favor the government, its leaders, or their policies. Private sites do not use automated review much at all. Why is this? We found that the answer again is the highly inexact keyword algorithms used to conduct the automated review.

Fig. 6 Causal effect on review of posts for or against the government.

Automated review picks up slightly more posts that are for the government as compared to posts that are against the government.

To understand this better, we reverse-engineered the Chinese keyword algorithms in order to discover the keywords that distinguish submissions reviewed from those not reviewed. Because the number of unique words written overwhelms the number of published posts, we could not find these keywords uniquely. However, we could identify words highly associated with review using a “term frequency, inverse document frequency” algorithm (35, 36). That is, we took the frequency of each word within the review posts and divided this number by the number of nonreviewed documents in which that same word appeared. Thus, for every word we obtained a measure of its frequency in review posts, relative to posts that were not reviewed. Words with high values on these measures are likely to be used within the automated review process.

Table 2 gives the top keywords (and key phrases) that we estimate were used to select posts we wrote into automated review. We can see that the words associated with review could plausibly detect collective action and relate to the government and its actions, but are also just as likely to appear in pro-government posts as in anti-government posts. For example, more pro- than anti-government posts are reviewed in the Corruption Policy topic in Fig. 6. This appears to be because the reviewed pro-government posts used the word corruption

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more frequently than did anti-government posts. However, corruption was used in the context of praising how the new policy would strengthen anti-corruption

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efforts. Not only is automated review conducted by only a subset of websites and largely ineffective at detecting posts related to collective action events, it also can backfire by delaying the publication of pro-government material.

Table 2 Top keywords distinguishing posts held versus not held for review.

Words within this list match keyword lists provided by the software provider.

View this table:

It turned out that we could provide an independent test of the veracity of these keywords. In the context of setting up our own website, we unearthed a list of keywords for review that a software provider offered to its clients running social media websites. The list is dated April 2013, and all of the keywords we found related to events taking place prior to April 2013 were on this list; the exceptions were from events that occurred after April 2013.

It thus appears that the workers in government-controlled websites are so risk-averse that they have marshaled a highly error-prone methodology to try to protect themselves. They apparently know not to take this automated review methodology very seriously; whether it is used or not, the manual process of reading individual posts must still be used widely, as our results show that automated review does not affect the causal effect of collective action events on censorship decisions.

Edge cases

We now attempt to define the outer boundaries of the theory of collective action potential by examining cases close to but outside the theory (where no effect is anticipated), as well as one extreme case inside the theory: criticism of the top leaders.

Internet-only and external-only collective action

The first case is an event in which collective action took place, but only on the Internet. At the end of May 2013, the principal of Hainan Wanning City No. 2 Elementary School was being investigated for taking six elementary school girls to a hotel. Ye Haiyan, a women’s rights advocate, went to the elementary school and protested with a sign in her hand that read “Principal: Get a hotel room with me, let the elementary students go! Contact telephone: 12338 (Ye Haiyan).” Ye’s protest went viral and her sign became an online meme, where individuals would take and share photos of themselves, holding a sign saying the same thing with their own phone numbers or often with China’s 911 equivalent (110) as the contact phone number (37).

The second event occurred on 1 July 2013, which was the 16th anniversary of the handover of sovereignty of Hong Kong from Britain to China. Every year on this day, thousands take to the streets of Hong Kong in protest, but typically with little or no such protest on the mainland. In 2013, between 30,000 people (according to the police) and 430,000 people (according to the organizers) took to the streets to call for true democracy and Chief Executive C. Y. Leung’s resignation (38).

Neither of these “edge case” examples meet the definition of collective action events given above, but they are obviously close. We ran our experimental design for these events too (Fig. 7, left panel). In both cases, the overall causal effect is near zero, with confidence intervals that overlap zero. There is a hint of a possibly positive effect only for posts reviewed about Hong Kong protests, but in the context of the natural variability of Figs. 2 and 3, this effect is not obviously different from zero.

Fig. 7 Testing edge cases for the causal effect of collective action potential (left panel) and of posts about corruption (right panel).

Corruption and wrongdoing among senior leaders

Next, we consider the effects of writing about corruption and wrongdoing among senior leaders in the government, Party, and military on censorship. Nothing in the theory of collective action potential supports this effect, but because corruption so directly implicates leaders who could control censoring, considerable suspicion exists in the literature that posts about corruption are censored (16, 18, 26). We can even point to the odd result that posts supporting the government’s efforts to deal with corruption are more censored than posts opposed to the government’s efforts to deal with corruption (see Fig. 6).

We selected three corruption-related topics for the analysis. The first relates to a new corruption policy that imposes criminal charges against bribes exceeding 10,000 Chinese yuan. The second topic relates to the investigation of Guo Yongxiang, a member of the Sichuan Province Central Committee and a Vice Governor of Sichuan, for serious breaches in discipline. The final topic relates to the naming of Li Tianyi, the son of the well-known People’s Liberation Army performer Li Shuangjiang, for participating in a gang rape. The Li Tianyi case led to speculations of corruption that Li’s father’s ties to the People’s Liberation Army would allow Li to avoid punishment commensurate with his crimes. The results for an analysis of the three corruption events (Fig. 7, right panel) clearly show no effect, thus again supporting the theory of collective action potential. Similarly supportive is the fact that posts in these topics name specific Chinese government and CCP leaders, both at central and local levels of government (19).

Top leaders and highly sensitive issues

Finally, we used observational methods to study the question of the censorship of discussions about top Chinese leaders, arguments for deep political reform, and discussion of highly sensitive or salient issues.

To study more directly whether Chinese censors allow direct criticism of top leaders, we began by finding a social media volume burst about Chinese President Xi Jinping that (i) by our specific definitions does not have collective action potential, (ii) includes posts that cover meaningful and important topics, and (iii) is about a topic that could generate highly critical posts about the leader. We found the following volume burst that met these conditions.

On 28 December 2013, President Xi Jinping visited a Feng Qing Steamed Bun Shop in Beijing (Feng Qing is a chain restaurant) and ate steamed buns “just like the rest of us.” He waited in line, he paid 21 CNY for steamed pork and onion buns along with a side of stir-fried liver, and he brought his own tray to a table. Xi’s visit unleashed a storm of traditional media coverage and a large volume burst on social media. Although Xi’s visit to the bun shop sounds like an innocuous event, online discussions related the visit to important and high-profile issues such as Xi’s China Dream, corruption of government officials, rising real estate prices, and the plight of China’s elderly and impoverished, as well as propaganda, censorship, the absence of elections, and multiparty competition. However, this event is not connected to any ongoing collective action events.

During this volume burst, we collected 82,280 social media posts related to this event before any posts were censored, and then checked each one from a network of computers around the world that were eventually censored. Finally, we applied the Hopkins-King algorithm (39) (using a training set of 592 hand-coded posts) to determine the proportion of censored posts that were critical versus supportive, and applied the Bayesian algorithm derived in (2) to invert this. We found, consistent with the collective action potential hypothesis, that posts critical of President Xi were censored just about as much as those that were supportive. Among posts that were critical of Xi and his actions, 18% were censored (95% confidence interval, 13 to 22%). Among the posts supportive of Xi, 14% were censored (95% confidence interval, 8 to 22%). [The proportion of posts censored among posts that simply described the event was 21% (95% confidence interval, 18 to 24%).]

The supplementary materials (19) include the text of examples of uncensored posts that are highly critical of President Xi and that use this event to discuss important issues. These posts involve many vivid personal attacks on Xi and his policies. In our experience, these posts are not surprising or unusual.

Next, we looked for uncensored discussion of deep political reform. In August 2013, three commentaries were published in People’s Daily condemning constitutionalism, describing constitutionalism as incompatible with socialism and doomed to fail in China. These commentaries sparked a social media volume burst with intensive online discussions about whether China should adopt American-style constitutionalism and multiparty competition. In the days after these commentaries, we collected a random sample of 9850 blog posts related to political reform. Although this sample includes posts that toe the party line and criticize constitutionalism, there are also many uncensored posts advocating for the adoption of multiparty competition, describing reform as the only way to empower the Chinese people and to rein in corruption. We include several examples in (19).

Finally, we sought and identified social media volume bursts related to three highly salient and politically sensitive issues about real-world events that did not have collective action potential. These are discussions related to Tibet, Uyghurs, and Ai Weiwei.

First is the case of a volume burst in Tibet: In early August 2013, a post by a woman who claimed to have spurned her true love in order to marry a man who lived within view of Lhasa’s Potala Palace went viral. As expected, censorship of posts in this burst was low at 12%.

Second is a volume burst related to Xinjiang and Uyghurs, which occurred in March 2013 when a post poking fun at a government entity with an exceptionally long, 54-character name

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went viral. The governmententity is located in Xinjiang, and the name can be roughly translated as the “Public Security and Management Office of the Working Small Group of the Holistic Social Management Committee’s School and Surrounding Areas of Xinjiang Uyghur Autonomous Region Urumqi’s Chinese High Tech Development Zone.” This post was the butt of jokes and satire related to Chinese government bureaucracy but was completely unrelated to any ongoing collective action event. As expected, censorship of this volume burst was low, only 10%.

Finally, we identified a volume burst related to artists—including Ai Weiwei along with Matisse, Picasso, Andy Warhol, and others—and their cats. Censorship of this burst was also low, at 6%. Our definition of collective action potential includes real-world events related to those who have catalyzed or organized collective action in the past. This volume burst relates to Ai Weiwei but falls outside the definition because the burst is not related to a real-world event, nor is it solely related to Ai Weiwei.

Concluding Remarks

We offer the first large-scale randomized experimental analysis of censorship in China, along with participant observation of how censorship is conducted. We use these designs to conduct a rigorous test of the theory of collective action potential, and to further uncover and resolve academic conflicts about crucial aspects of the Chinese censorship program. With them, we are able to subject to empirical estimation what had previously been left to statistical assumption. We are also able to study the large program whereby enormous numbers of social media submissions are put into limbo before being considered for possible publication or censorship. Whereas censorship is a “publish first, censor later” process, automated review involves a “review first, maybe publish later” process.

Our flexible research designs also enabled us to study edge cases, just beyond the reigning theory of collective action potential, so that we can define the boundaries of where it applies. This includes the effects of highly salient and sensitive topics about events without collective action potential; posts about corruption; posts that name Chinese leaders specifically; and collective action events that are solely on the Internet or about collective action on the ground outside the Chinese mainland—none of which are predicted by the theory of collective action potential to be censored more than others, and which our data clearly show are not censored more than other non–collective action topics. We also show that academic controversies over confusing interpretations of which keywords are being censored in automated review are resolved once we realize that the Chinese government is surprisingly flexible concerning what methods and technology each social media site can use, even while imposing uniformity of results by requiring post hoc censoring by human coders.

Future researchers should consider comparing these results on censorship in social media with censorship in traditional media and other ways the Chinese government impedes the free flow of information.

Supplementary Materials

Materials and Methods

Figs. S1 to S7

References (50, 51)

References and Notes

  1. See supplementary materials on Science Online.
  2. We also added our own ethics rules, not required by the IRB, which dictate that we avoid, wherever possible, influencing or disturbing the system we are studying (19). The similarity to the Prime Directive in Star Trek notwithstanding, this seems like the appropriate stance for scientists attempting to understand the world, as distinct from advocates trying to change it, and in any event is more likely to yield reliable inferences.
  3. In the process of setting up the site, they recommended that we hire two or three censors for every 50,000 users. That enables us to back out an estimate of the total number of censors hired within firms at between 50,000 and 75,000, not counting censors within government, 50 Cent Party members, or the Internet police.
  4. See (40, 41) for numbers of registered users, which are substantial even if we account for automated sites created by marketing firms (42).
  5. All posts were made to mainland China accounts. Some were submitted from outside China, when feasible, and many from within China. Recent work has noted that overseas accounts are subject to less stringent censorship regulations than mainland accounts (43). This issue does not affect our work because all accounts created and used were mainland China accounts. Users could control account location when creating the account by specifying a location in China, by entering a local mobile number, or by creating the account from a local IP address. We used all of these methods.
  6. For each of our three rounds, we wrote 200 posts on non–collective action events (split equally between pro- and anti-government) and 200 posts on collective action events or edge cases (again split equally between pro- and anti-government). Thus, 600 posts submitted relate to non–collective action events, and 600 relate to collective action events or edge cases. We have in total four collective events and two edge cases, and so 400 posts focused on collective action events and 200 on edge cases.
  7. For examples, see (44).
  8. For news coverage of the protests, see (45–48).
  9. Acknowledgments: For helpful advice, we thank P. Bol, S. Chestnut, P. Gries, Y. Herrera, H. Huang, I. Johnston, S. Shirk, D. Tingley, and participants in a panel at the American Political Science Association meeting, 31 August 2013, and at the Midwest Political Science Association meeting, 3 April 2014. For expert research assistance over many months, we are tremendously appreciative of the efforts and insights of F. Chen, W. Cheng, A. Jiang, A. Jin, F. Meng, C. Li, H. Liu, J. Sun, H. Waight, A. Xiang, L.-S. Xu, M. Yu, and a large number of others whom we shall leave anonymous. We thank Crimson Hexagon Inc. for help with data. See (49) for replication data and information.

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