Title: Hazy messaging: Framing on Chinese social media during air

Title: Hazy messaging: Framing on Chinese social media during air pollution crises Authors and affiliations: Chris Cairns (Cornell University) & Elizabeth Plantan (Cornell University) Abstract: Environmental activists in China have heralded the advent of social media as a turning point in the environmental movement, praising microblogs like Sina Weibo for their ability to broadcast individual, localized complaints to a nationwide audience. However, what are the key frames that are originating on social media and who is responsible for spreading these messages? Do spikes in Weibo activity during environmental crises match with real-world surges in pollution or do they occur when key posts from environmental leaders go viral? To what extent do Chinese government censors permit these frames to spread? Are they more likely to allow environmental leaders’ messages to spread, or do they prefer to use Weibo to solicit “input from the masses”? Using recent Weibo data, we identify crisis moments in 2012 that acted as exogenous shocks for the formation of activist frames intended to shape public opinion on air pollution. The paper combines time-series statistics with narrative detail about the actors creating such frames within the Chinese environmental community. This allows us to draw conclusions not only about the role of environmental activists in raising netizen awareness during crises, but also Chinese leaders’ strategy in managing this information.

Paper prepared for the Midwest Political Science Association Annual Meeting, Chicago, IL, April 16-19, 2015

Cairns and Plantan 2

Introduction

The case of air pollution in China

Air pollution has become one of the most visible (quite literally) environmental problems

in China over the last decade. From the off the charts “Airpocalypse” in 2013 to the March 2015

documentary “Under the Dome,” discussion of air pollution in China has dominated the media,

both foreign and domestic. Besides obvious environmental concerns, high levels of air pollution

have been connected to human health issues. In 2005, an estimated $112 billion was lost in

economic productivity due to the health impacts of air pollution, including medical expenses,

wage loss, and leisure loss (Matus et al. 2011). Given the enormous environmental, human health,

and economic costs, the Chinese government has tried to make air pollution a priority, including

adding air emissions targets (sulfur dioxide reductions) to cadre performance incentives at the

local level (Zhou et al. 2013).

Air pollution has been a problem in China for quite some time, but the issue became a

political flashpoint in 2012. Although daily Air Quality Index (AQI) data has been available in

many Chinese cities since the early 2000s, there was a more recent controversy over including

measurements of PM2.5 (particulate matter of 2.5 micrometers in diameter or less) in this data,

making it more fine-grained than the official data that only included the larger PM10 (Chan and

Yao 2008). The U.S. Embassy in Beijing has been recording and reporting its hourly PM2.5

readings since 2008, and this practice slowly spread to U.S. Embassies and Consulates across

China.1 The Chinese government asked the U.S. Embassy to stop reporting the data, but the

United States continued to release its information on Twitter. These reports – and accompanying

1 See www.stateair.net for up-to-date readings and archival data for Beijing, Chengdu, Guangzhou, Shanghai, and Shenyang.


commentary – also spread on Sina Weibo (the Chinese version of Twitter) creating more public

awareness of China’s air pollution problems.

In early January 2012, in response to mounting public pressure, the Chinese government

announced that it would be releasing more detailed air quality data in Beijing using the PM2.5

standard by the end of January (Barboza 2012). Unfortunately for the government, air pollution

surged on January 10, 2012, going “beyond index” according to U.S. embassy PM2.5 data, but

air quality was still “good” on the official scale that only measured PM10 (AFP 2012). To make

matters worse, the Chinese government’s release of PM2.5 readings at the end of January

coincided with the Chinese Lunar New Year celebrations, when fireworks caused a spike in air

pollution (Bodeen 2012). Although the Chinese government was releasing data on PM2.5, these

measurements were suspiciously lower than the U.S. Embassy readings, which cast doubt over

whether official air quality measurements could be trusted. Throughout the spring, these sorts of

incidents continued to be covered in the foreign press and discussed online, but they culminated

in an official statement in June 2012. On World Environment Day (June 5), Wu Xiaoqing, the

Vice Minister of Environmental Protection, demanded that the U.S. Embassy stop releasing its

air pollution data (Ford 2012). He argued that it was unfair to judge China’s air pollution using

American standards, since China was at a different level of development. This comment set off a

firestorm on Weibo, as netizens both mocked the Vice Minister’s statement and generated much

debate about the Chinese government’s responsibility to solve the problem of air pollution.

From the January 2012 spike in air pollution to the June 2012 official statement, the

events of 2012 highlight a key feature of state-society relations in China. Through its reform and

opening policies over the past several decades, China has opened its NGO sector and allowed for

some civil society development to help identify problems and improve governance. However, the


Chinese government, like many other authoritarian systems, also wants to carefully control and

manage its growing civil society. Along with this central tension between state control and

improving governance through social pressure, the rise of the Internet and social media has

presented both new opportunities and new challenges for state and societal forces alike. Spaces

like Weibo allow the general Chinese public to air grievances, such as complaints about

environmental degradation. Through this sharing of information, the central government can use

NGOs and social pressure from public opinion to improve environmental governance; however,

in pursuing this strategy, the government also risks allowing critics to use social media to

mobilize, and to spread issue frames that may damage leaders’ reputation.

Using the case of air pollution in 2012, this paper aims to better understand the

interaction of state and society through new media by asking four broad questions. What are the

key frames related to this issue that prevailed on social media in 2012, and who was responsible

for spreading these messages? Did spikes in Weibo activity corresponding to incidents that year

match with real-world surges in pollution or did they occur in response to viral posts from key

environmental leaders? To what extent did Chinese government censors permit these frames to

spread? Were they more likely to allow environmental leaders’ messages to spread, or did they

prefer to use Weibo to solicit “input from the masses”? We gain leverage on these questions by

using a unique dataset consisting of posts from some of Sina Weibo’s most influential users. This

dataset gives insight into the key air pollution-related issue frames that proliferated on social

media at the time, how messages from NGOs and other “policy entrepreneurs” (Mertha 2009)

might have affected this spread of information, and also to what degree the government chose to

censor (or not) this spread of information.


By focusing on this particular case, we contribute to the literature on civil society,

authoritarian regimes (and the problem of authoritarian control), environmental politics, and

science and technology studies. The relevant literature pertaining to each of these sub-disciplines

is discussed in detail in the next section. After that, we develop expectations and hypotheses for

the answers to our key research questions enumerated above. Then, we explain our methods and

data in detail before moving on to the analysis of our case and evaluation of our hypotheses in

light of the data. The conclusion of the paper reiterates our findings for key frames, messengers,

and government censorship and enumerates the plans for our future research as we continue to

analyze and build our unique dataset.

Background

From environmental crisis to environmental consciousness

Environmental degradation in China has no doubt existed as long as humans have lived

on the Asian continent. However, the speed and scale of environmental degradation increased

dramatically at the beginning of the communist era. Following in the tradition of the Soviet

model, Chinese policies under Mao Zedong reflected the dominant ideology that humans “could

change nature and force it to serve them” (Bao 2010, p. 329). This ideology spurred huge Mao-

era industrial projects and massive campaigns that left devastating effects on the natural

environment (Shapiro 2001). Even in the post-Mao era, economic priorities in China continue to

cause widespread environmental degradation. Elizabeth Economy (2004) notes that Chinese

economic reform “is leaving as large a footprint on the natural environment as did centuries of

imperial, republican, and early Communist rule” (p. 64). From land to water to air, the Chinese

environment has been exploited and degraded while the Chinese government subordinated

environmental protection to goals for rapid economic development.


Despite the fact that the Chinese environment had endured centuries of degradation and

exploitation, civil society concern for the environment did not emerge until after economic

reform and opening. The increase in Chinese environmental NGOs over the past two decades can

be attributed to “the interaction between an increasing environmental crisis and the practice of

reforms and the open door policy” (Bao 2009, p. 7). More specifically, the emergence of

environmental organizations in China can be connected to the 1992 Rio Declaration on

Environment and Development, which allowed for the growth of environmental NGOs across

China as a way to meet national Agenda 21 goals for environmental protection (Xie 2011; Bao

2009). Many scholars have chronicled the rise of Chinese ENGOs during this period, from the

establishment of Friends of Nature and Global Village Beijing in 1993 to Green Earth Volunteers

in 1997 (Ho 2001; Xie 2011). These flagship NGOs learned mostly from the Western experience

and modeled their organizations on counterparts abroad. Chinese ENGOs responded to

environmental degradation both out of civic duty and the fact that the government was not

responding; meanwhile, the government responded to these ENGOs with a mix of policies “from

stringent control to tolerance and encouragement” (Ho 2001, p. 901). The government’s dual

strategy for managing environmental organizations echoes broader trends in the state-society

relationship.

State responses to a growing civil society and new technology

Assuming a link between civic associations and democracy (Putnam 1993), the

expectation is for an authoritarian regime to always choose to repress civil society to avoid its

own demise. However, the relationship between the Chinese government and its domestic civil

society is more complicated than a simple story of blanket repression. Some civil society groups

can be useful, including those that provide crucial social service provision and those groups that


align with state goals (Dickson 2011). Even with some allowance for civil society autonomy, the

state still carefully oversees the growing sector. This phenomenon – where a fairly autonomous

civil society exists concurrently with the rise of sophisticated state control – has been described

as “consultative authoritarianism” (Teets 2013). Input from civil society could result in better

governance as the state learns about and responds to key issues of public concern. Therefore, as

long as China continues its sophisticated measures of control, civil society will actually reinforce

and improve the current Chinese regime.

This background on the relationship of civil society and the state helps to explain why

expectations of the Internet as “liberation technology” (Diamond 2010) in China might be

premature. Since the introduction of the Internet into China in 1994, some have argued that the

technology has the potential to be a catalyst for social and political transformation in China

(Shirk 2010; Yang 2009; Qiang 2011). However, most China scholars have a measured view of

what the Internet means for Chinese society, being careful to note that the Internet is a tool, but

not a cause of political change (MacKinnon 2008; 2012). Similarly, others have noted that the

Internet is a “platform for state-society interactions” rather than a force for democratic change

(Noesselt 2014, p. 451). This is far from the idea that the Internet and new social media will

necessarily lead to rapid social and political change, as in the oft-cited examples of the Color

Revolutions and the Arab Spring. Instead, there are more nuanced views of how the Internet will

affect state-society relations in China much more in line with Teets’ (2013) “consultative

authoritarianism” or Jiang’s (2010) “authoritarian deliberation.” These alternative views of the

role of the Internet in China include the idea of online platforms as a “safety valve” for public

opinion (Hassid 2012), as an information gathering tool for the regime (Noesselt 2014), or as a

platform for that state’s own “strategic censorship” that allows corruption and other mis-


governance to be punished without overtly revealing the regime’s overall weakness (Lorentzen

2014).

While these explanations may be relevant for understanding state-society online

interactions generally, they are of limited use for understanding how the Chinese government

manages mobilization on social media during incidents like the 2012 U.S. Embassy controversy.

Rather, to explain what happened in this and similar “breaking incidents”, where discontent over

issues like pollution suddenly erupts into netizen consciousness, it is useful to take a more

abstract theoretical look at the relationship between authoritarian regime durability and media

freedom (for example, see Egorov, Guriev and Sonin 2009; Gehlbach and Sonin 2014; He and

Warren 2011). Much of this work focuses on autocrats’ problem with ascertaining their level of

popular support, since citizens and bureaucrats often withhold information out of fear. To

address this, savvy autocrats have established quasi-democratic institutions (Gandhi and

Przeworski 2007) that may serve as feedback mechanisms about popular discontent (Stockmann

2013; Lorentzen 2014).

The emergence of the Internet since the 1990s has injected a new technology into the

equation. However, while much descriptive work (Yang 2009; Zheng 2007; MacKinnon 2008;

Morozov 2011) has thoroughly illustrated the high degree of control successful autocrats

exercise over the Internet, it has not adequately addressed the nuanced control such regimes

exercise with respect to different online platforms and social groups. Additionally such work, in

our view, has focused too exclusively on the state’s rationale for using the Internet to gather

citizen opinions, neglecting other possible incentives that might affect its decision to allow or

suppress social mobilization online. While many reasons – fear of collective action, desire to

preserve reputation, or an ignorant public – exist for why the state censors, the non-censorship


(e.g. tolerance for civil society speech) that we occasionally observe is puzzling. One leading

explanation is that relaxing censorship increases media’s credibility with audiences, increasing

their “trust” in the central government (Stockmann 2013). While this explanation is well-

elaborated, it has thus far been applied only to explain the level of traditional (print) media

censorship, and has not been adapted to the unique context of social media.

Therefore, in addition to understanding which civil society actors used Weibo in 2012 to

speak out on air pollution and what frames they promoted, a second goal of this paper is to

develop and test theoretical expectations regarding what incentives the Chinese state had to not

censor social media at key points during the year, a phenomenon that we observe repeatedly in

our data despite the presence at times of topics theorized to bring about censorship (Ng 2014;

Zhu et al. 2012; Bamman, O’Connor and Smith 2011). Such variation, then, serves as a window

into how the government seeks to manage this emergent combination of ENGOs and

environmentally concerned netizens, vis-à-vis its own goals of survival and legitimation. In the

next section, we further develop our theoretical expectations in this area.

Research questions, theory, & hypotheses

Understanding the background of China’s environmental consciousness, the growth of

NGOs in China, and the state’s strategic response to civil society and management of social

media helps to give context to the research questions and theoretical expectations of this paper.

These, then, can be grouped into three main areas of focus: key frames and messengers, real-

world connections, and government censorship. We take each in turn below.

Key frames and messengers

First, what were the key frames that originated on social media in 2012 and who was

responsible for spreading these messages? Using air pollution as our case suggests multiple


possible issue frames. One of the most intriguing ones is the juxtaposition of air pollution as an

issue of environmental protection, versus a threat to human health. Over the last few years,

Chinese citizens have become more and more concerned with the issue of air pollution.

According to a recent public opinion report, 31% of respondents considered air pollution as

among the biggest problems in China in 2008, while in 2013 47% considered it a big problem

(Pew Research Center 2013). This 16-point increase in concern about air pollution could be

attributed to growing public understanding of its impact on human health. Reporting on smog as

a threat to human health has steadily increased over the last few years, often reporting on high-

profile scientific studies linking air pollution to increased rates of lung cancer (World Cancer

Report 2014) and low birth weight (Dadvand et al. 2013). Furthermore, studies of the effects of

framing have shown that interest in and willingness to take action on climate change increases

when it is framed as an issue that impacts human health – instead of being framed as simply an

environmental issue (Maibach et al. 2010; Cardwell and Elliott 2013). Based on this, although

we consider both the environmental and human health frames of air pollution in our analysis, we

expect that the health frame will occur with more frequency than purely environmental frames in

discussions of this topic on Sina Weibo.

In addition to focusing on the environment versus health frames, this project also

considers a range of frames that fall under two broad categories: oppositional frames or state

frames. Oppositional frames are those that contain some element of government criticism –

including direct claims of government blame or responsibility and indirect comparisons of

China’s domestic situation to (better) standards abroad. State frames are those that are

perpetuated by the regime, mostly through official news channels or government propaganda.

Our expectation for when we will see state frames versus oppositional frames can be articulated


as “punctuated partial equilibria” (Mertha 2008; Baumgartner and Jones 1993). We expect that

the state will control the message and information surrounding air pollution most of the time, but

that this will be punctuated by brief moments of the opposition breaking through (which the state

reactively allows from time to time). Oppositional frames on air pollution can break through at

two distinct times: 1) during an external shock (such as severe pollution or a natural disaster),

and 2) when elite action opens the political opportunity structure and invokes a response from

civil society. The first situation is self-explanatory: at times of extreme pollution, we would

expect oppositional frames to surge. The second scenario describes a chain reaction. When a

member of the Chinese political elite takes action – often an embarrassing public statement or

position --this action is generally covered in the press, spreads on Weibo, and gives users the

opportunity to comment. This prime opening in the political opportunity structure gives

oppositional frames to chance to emerge and spread in response to and critique of elite action.2

Thus, within our dataset, we will look for surges in oppositional frames at these two key times.

Now that we know the key frames and expectations for when they will arise, we can

address who is responsible for spreading them. From studies on environmentalism in China, we

know that “highly resourced individuals” play a significant role in taking the lead on

environmental issues (Xie 2011, p. 220). These are people with access to networks, elites, and

information. Besides these influential individuals, Chinese ENGOs have an increasing presence

in China and regularly use the media “to expand their influence and to win support from the

public” (Bao 2009, p. 8). In addition to domestic Chinese ENGOs, there are several branches of

international ENGOs that operate in China, and scholars have shown that localized international

organizations (those with local offices) are more likely to be connected to the broader Chinese

2 While not central to our theoretical account here, such critique on the Chinese Internet often takes the form of humor, mockery or other subversive speech. Such speech, of course, has a long tradition of analysis in political science (see Scott 1985, 1990; Yang 2009; Herold and Marolt 2011; Cairns and Carlson 2014b)


environmental community (Xie 2011). This leads to three potential categories of non-state actors

for the purposes of this paper: domestic Chinese environmental NGO leaders, public intellectuals,

and branches of international environmental NGOs. These categories include both domestic and

international NGO influence and unaffiliated “highly resourced” Chinese celebrities or public

intellectuals who enjoy a broad following on platforms like Sina Weibo. In a future iteration of

this study, we will consider how these actors (specifically Pan Shiyi, Ma Jun, and Greenpeace

Beijing) frame the issue of air pollution, their relationship to the wider conversation on Sina

Weibo, and when the government chooses to censor (or not) posts from these actors.

Real-world connections

Our second research question goes beyond the issue of frames and messengers to

consider the relationship of commentary on Weibo with real-world conditions. Do spikes

in Weibo activity during environmental crises match with real-world surges in pollution or do

they occur when key posts from environmental leaders go viral? Under normal political

conditions, we expect that air pollution drives commentary on Weibo, particularly when air

pollution is worse than usual. This is because air pollution is a visible problem that ordinary

users on Weibo can easily see and experience the severity of the issue. It is not unexpected that

users will comment on issues that touch on their everyday experience. However, during other

times of relatively low air pollution, it is possible that the issue will be given greater attention.

For example, conversations about air pollution may increase during high-profile commentary or

a political scandal involving the issue of air pollution. Therefore, given a political shock, we

expect that users on Weibo will be more responsive to human-caused controversy than the actual

pollution conditions outdoors might suggest.

Government censorship


Finally, this paper considers several questions pertaining to the critical issue of

government censorship in China. To what extent did Chinese government censors allow air

pollution-related discussion to spread a) during 2012 overall and b) in response to shifting

external events throughout the year? Which specific frames were allowed and which suppressed?

Which non-state actors had the greatest latitude in not being censored? These questions are

answered in turn with a discussion of a broader theory of Internet censorship in China more

generally.

Over the course of the year, we expect that censorship during 2012 would be relatively

low, for multiple overlapping reasons. First, air pollution is an issue that as of 2012 had not

generated any significant street protests or other visible collective actions in China, in contrast to

more site-specific issues such as water pollution or the construction of chemical plants.

Additionally, the government does not view discussion of air pollution as inherently hostile to

the Communist Party’s legitimacy or continued rule – rather, certain bureaucratic groups, such as

the Ministry of Environmental Protection, may see an opportunity to increase trust among online

citizens by allowing discussion and then being seen to meet the public’s demands in this area.

Indeed, our theoretical claim is that that Chinese leaders selectively relaxed control over air

pollution discussion led by Weibo’s so-called “Big V” – high-profile bloggers – at key moments

throughout the year in order to increase the government’s perceived credibility to address the

issue in the eyes of China’s ascendant urban middle class. Weibo users are unrepresentative of

Chinese society and even Internet users, being much better educated than average; according to

recent survey data, they are also among the least trusting of the central government, and place

increasing priority on a healthy environment. Thus, the government cannot use traditional media

propaganda as effectively with this group. The bloggers we consider, however, did enjoy


widespread credibility with such an educated demographic. By occasionally allowing such Big V

to speak even during politically sensitive moments, and on sensitive topics, we argue that this

glaring absence of censorship was itself intended to signal to Weibo users that Beijing took their

concerns seriously.3

We also have specific censorship expectations for January versus June, for specific issue

frames, and for different authors that vary according to the potential of each to a) benefit the

government according to the mechanism above, b) make the government look bad, or c) further

collective action. We expect censorship to be low in January as the issue had not been politicized,

high in June around World Environment Day due to the government’s potential embarrassment

factor, but low later in June as the risk of acute embarrassment (in response to the Vice

Minister’s comments) ebbs but Chinese leaders see greater potential in appearing accountable to

the public.

Regarding specific frames, we expect opposition frames to be censored at higher rates

than state frames, particularly when these frames carry a risk of mobilizing society. When the

risk is lessened, however, and provided such opposition frames do not directly challenge state

legitimacy, they may be briefly allowed, a phenomenon that future versions of this paper will

investigate more deeply. Finally, we expect ENGOs and environmental campaigners to be

censored less, in general than other Big V. However, even censorship within Big V like Pan

Shiyi is likely to vary widely, depending on the overall level of political sensitivity at the time a

comment is made, and into which category (oppositional or loyal to the state) a given Big V’s

comment falls, with oppositional-framed tweets likely to be censored at a higher rate than loyal

tweets.

3 The full logic of this argument is beyond the present article’s scope, but is the core of one of the author’s dissertation work.


Methods

To test these hypotheses and expectations, we relied on a trailblazing dataset collected by

researchers at the University of Hong Kong that consisted of over 38,000 Weibo Big V (Fu, Chan

and Chau 2013), which the researchers defined as all users with a Verified user account status

and more than 10,000 followers as of January 2012.4 To our knowledge it is the most

comprehensive dataset of Weibo posts currently available and the methodology used to collect it

is described in detail in Fu, Chan and Chau (2013). Each row in the dataset consisted of one

social media post (including reposted or “retweeted” content) plus associated meta-data. The

data files did not include multimedia such as images or videos. We relied only on the post text,

and counted embedded reposts as part of the text.

As a first pass to only analyze data relevant to air pollution, we filtered out all posts that

contained one or more of the following keywords: “air pollution” (空气污染 or 大气污染), “air

quality” (空气质量 or 大气质量), “smog” (雾霾), “haze” (灰霾 or 灰雾), and “PM 2.5”.5 This

left 71,088 posts for all of 2012. The authors went through several stages of pre-coding exercises

to determine the key categories before moving on the full coded sample.6 Next, we assembled a

team of three coders, including the two co-authors and a third undergraduate, native Mandarin-

speaking assistant. We drew a fresh random sample of 500 posts for analysis, taken from all of

2012, and worked independently to assign them into categories.7 We then met to reconcile

divergent scores according to strict rules. Through this process, we were able to agree on a

consensus score for 473/500, or 94.6% of posts. As a backup procedure if consensus could not be

4 As part of the verification process, Sina confirms the identity of verified user applicants to be sure that they are the high profile individual applicants that they claim to be. 5 As a robustness check, a future version of this paper will search key dates during 2012 when discussion about pollution was known to spike, and holistically read for posts relevant to air pollution but not containing one of the above keywords, to ensure that keyword sampling does not induce a selection issue. 6 For a full description of this process, please see Appendix A. 7 Appendix A reports inter-coder reliability scores for this exercise.


reached but a majority was present, we broke impasses by voting in 16/500 or 3.2% of cases.

Finally, in a handful of cases (11/500, or 2.2%) there were two coders who gave divergent scores,

and a third who had given one or the other score (or picked a completely different category), but

after discussion was “on the fence” between the other two positions; we resolved this by flipping

a coin.8 This process resulted in a set of 500 random posts from Weibo, coded according to the

categories described below.

Category descriptions Table 1: Sentiment Categories

1. Domestic vis-à-vis foreign 4. News (or news plus comment) 2. Government blame/responsibility 5. AQI Monitoring 3. Environment vs. Health 6. Valence (positive, negative, or neutral)

For reference, Table 1 summarizes our sentiment categories, which we describe here in

greater detail. First, we wished to code whether a post contained the notion of Chinese

comparing the air quality situation in their own country to other countries or to the international

community: we termed this category domestic vis-à-vis foreign. Recent work by one of the

authors (Cairns and Carlson 2014a) has highlighted the prevalence of nationalist discourse on

Weibo and the pervasiveness of China’s and Chinese citizens’ view of themselves vis-à-vis the

situation in other countries, including as regards environment and quality of life. Such a

discourse, moreover, from top leaders’ view is invariably among the most sensitive and difficult

to manage of all political themes, invoking as it does questions of the state’s own legitimating

narrative. While codings of domestic vis-à-vis foreign encompassed both pro- and anti-state

commentary, we found that a large majority of such comments viewed the health and quality of

8 In a handful of cases, coders gave three different scores. We followed the same procedure as above, except with simultaneous persuasion attempts in three directions. Without exception, such discussion reduced the options on the table to two codings (no cases occurred where no coder agreed to switch positions after discussion). We then followed regular rules to resolve the two-way impasse.


life crisis in a negative light, and some of these posts could be read as reflecting poorly on

Beijing’s inability to deal with the problem.

A second and related category, then, was whether posts assigned any responsibility (or

even blame) to the Chinese government either for having allowed air pollution to worsen, or for

not doing enough to clean it up. We labeled this category simply government

responsibility/blame. To what extent Weibo users and the Big V were likely to hold the

government responsible for the problem of pollution is central to our project.

Next, we wished to determine whether air pollution-related comments frame the issue as

one of protecting the environment (for the environment’s sake) or as an issue of human health.

As mentioned in the theory section, given air pollution’s dire consequences for human health,

such as an increased risk of lung cancer, we expected to find more of the latter, which would

allow us to measure the extent of genuine netizen fear about pollution’s health consequences.

That said, we did not want to neglect the possibility that comments addressing air pollution for

its own sake – as integral to a clean and balanced environment and as a traditional environmental

NGO issue frame – might also co-exist on Weibo.

Fourth, we coded posts based on whether they consisted primarily of news, or news plus

comment (e.g. an embedded news story or news link, plus original netizen commentary). This

was easy to do and we achieved very high inter-coder agreement. We found that 268/500 or

53.6% posts contained news or news and comment. We counted mere reports (from newspapers,

monitoring centers, or government agencies) of daily pollution statistics in various cities as

“news”, but also coded these under a separate category, AQI monitoring. The remainder of news

contained a large amount of mostly official papers reporting on various local government efforts


to start publishing PM 2.5 data, as well as to implement various air quality standards. However,

other news also more directly addressed PM 2.5 and its health consequences.

Our final category, which we termed post valence, consisted of our general appraisal of

the “positive,” “negative,” or “neutral” tone of each post – we assigned this score in addition to

coding our other categories. The significance that valence took on depended on what other

codings we simultaneously applied to a given post. For example, if a post was coded government

responsibility/blame, and negative, this dual marking allowed us to signify posts that tended to

be critical of the Chinese government.

While each category, analyzed individually, provided some insight into our topic, we

found that forming composites was more useful as it enabled isolating certain sentiments, and

making more precise predictions regarding our research questions, particularly expectations

about “oppositional” and “state” frames. Specifically, we generated composites for the following

five combinations and their categorization as oppositional or state frames: domestic vis-à-vis

foreign plus negative (oppositional); government, news, and positive (state); government plus

negative (oppositional); and finally, monitoring and positive (state). We considered these

combinations alongside health and news as single categories (see Table 2).

Table 2: Composite Frames Using Original Categories

Oppositional State

Domestic vis-à-vis foreign + negative Government + news + positive

Government + negative AQI monitoring + positive

Additionally, and although our main empirical focus was on measuring whole sentiment

categories, we included a few simple measures of daily counts, taken across 2012, of select

keywords of interest. First, we included the daily mentions count of “embassy” (使馆) in an


attempt to capture yearlong discussion of the US embassy controversy. Second, we measured the

daily count of “PM 2.5” mentions, as a key scientific term that educated the general public about

air pollution’s threat to human health.

Our end goal for the sentiment categories and category combinations was to generate

yearlong time series of shifting category proportions, as a percentage of topic-relevant posts; this

allowed us to corroborate what categories dominated discussion at particular times with the

censorship rate on those days, and to test our theory at different levels of variation. However,

since drawing and coding a post sample from each of the year’s 364 days was infeasible given

available time and resources,9 we used a computer assisted text analysis (CATA) algorithm

called ReadMe (Hopkins and King 2010) to estimate the proportions.10 We present time series

graphs based on these proportion estimates along with our select keyword count graphs in the

next section.

Analysis of Overall Trends in 2012

To begin exploring our specific hypotheses, we first graph general trends throughout

2012 to show natural and human-made factors behind the ebb and flow of Weibo attention to air

pollution. Figure 1 below reveals the total daily counts of posts that contained one or more of our

selected keywords:11

9 The Hong Kong University dataset began collection on January 2 and ended on December 30 in order to collect exactly 52 even weeks, or 364 days of data (since 2012 was a leap year). 10 In brief, ReadMe functions by using the category information from a human-coded ‘training’ sample (in our case the 2012 whole-year sample), and the ‘features’ (word occurrences) from that sample to estimate about how many posts in an uncoded sample belong in each category, given these new posts’ word occurrence patterns. The algorithm requires optimizing across two parameters, although Hopkins and King (2010) find that it typically yields a root mean squared error within 2-3% of the true proportion. Cross-validating ReadMe results by drawing additional hand-coded samples for select days is necessary to test robustness, and we will do so in a future iteration. 11 To ease readability, we take the natural logarithm of all post counts (except for the Beijing air quality data, which is displayed normally, and the post censorship rate, displayed as a proportion).


Figure 1: Daily Post Counts

At first glance, five major “spikes” or post surges are immediately evident. The first two

correspond to pollution surges in January (a sixth, small spike occurs in March). Below, Figure 2

displays daily volatility in the 2012 Beijing Air Quality Index (AQI) measured by the US

Embassy:

Figure 2: Beijing Daily Air Quality Index (AQI) Averages


From Figure 2, it is clear that pollution spikes considerably at the beginning of the year

in January 2012. This is not uncommon, as air pollution generally surges near the beginning and

the end of the calendar year, during the winter season. For the rest of the year, pollution ebbs and

flows, but it does not reach the same severe level that it did in January 2012. If we combine this

graph on AQI data with a graph of relevant posts, we can consider our hypothesis about real-

world drivers of air pollution discussions. Do spikes in air pollution cause matching spikes in

Weibo commentary on the topic? We can compare the actual AQI data in Figure 2 with

information about relevant posts about air pollution during the year (Figure 1).

Above, Figure 1 shows the relevant posts about air pollution on Weibo during 2012. On

this graph, there is a small spike during January 2012, but an even larger spike that corresponds

to June 2012. Breaking this down into the two time periods, January 2012 and June 2012, the

relationship (or lack thereof) between air pollution and relevant posts is more apparent (see

Figures 3 and 4).

Figure 3: January Relevant Posts & AQI Figure 4: June Relevant Posts & AQI

While the correlation between pollution and posts in January 2012 is not perfect, Figure

3 shows that both posts and pollution do spike together on January 10, and again on January 19.

Particularly on the latter date, pollution was severe, reaching well into the U.S. Environmental


Protection Agency’s definition of “Hazardous.”12 Pollution was lower, on average, in June –

usually a relatively good time of the year for Beijing air quality. In contrast, Weibo post counts

spiked three times that month, all in response to human-caused events that will be discussed in

detail in the next section.

Using our coded categories and scores from ReadMe, we can further support the

argument that air pollution is related to posts in January, but not to posts in June. From the above

graphs and suggested relationships, we would expect our key coded variables on social media

posts to vary considerably between January and June. Table 3 reports the results of a simple

binary regression with key variables of interest and our exogenous shock (air pollution).

Table 3: Binary Regressions with Key Variables and AQI data

January 2012 AQI data

June 2012 AQI data

Government +

negative -0.364** (0.124)

-0.272 (0.329)

Government + news + positive

-1.049** (0.273)

0.101 (0.493)

US embassy

mentions 0.139

(0.233) -0.444 (0.324)

Government

censorship rate 0.073

(0.112) 0.003

(0.078)

N (N=days) 31 30 Standard errors in parentheses. † =p<0.10; *= p<0.05; **= p<0.01

For January, air pollution values strongly predict both a decrease in negative posts about the

government and a decrease in positive news articles about the government. However, there is no

significant relationship between AQI and mentions of the US Embassy or the true rate of

censorship. This suggests that the conversation on Sina Weibo in January was mostly driven by a

response to air quality conditions outside. Users may have flooded social media with information

12 Readings over 300 are “Hazardous.” Over 500 is “Beyond Index.”


on air quality during those times, with little response from the government in terms of positive

propaganda and very little critique of the government’s handling of the situation (given that the

government had not yet stepped up to address the issue). For June, however, air quality does not

reliably predict any of the key types of posts.

To better understand what is driving social media posts in January versus in June, it is

helpful to unpack this discrepancy by looking at key dates. Four key dates, one in January and

three in June, illustrate the overall trends and tone of posts on Weibo after an some sort of shock,

either exogenous (pollution itself) or due to government actions, as per our earlier hypothesis.

For June, there are three main human-caused shocks. The first was the Vice Minster’s World

Environment Day announcement, the second centered around Pan Shiyi’s tweet, and the third

was a retweet of a comment Han Han made on television, which Pan’s wife (and SOHO China

CEO) Zhang Xin posted and which Wang Lifen, a former CCTV host and Weibo “Big V,” re-

commented. These four mini-case studies – the January 2012 pollution spike, World

Environment Day, Pan Shiyi’s tweet, and the Han Han tweet – will be discussed in more detail in

the next section. First, however, we consider both trends in our main sentiment categories, and

the general year-long censorship trend. Figure 5 gives these trends and Figure 6 gives the

censorship rate:13

13 We calculate the censorship rate because the log count of censored posts is not very informative – normally, more posts equals more posts censored (deleted). The rate in Table Three is calculated directly from the WeiboScope data. For reasons explained in the Appendix, this calculation considerably underestimates the true rate. A future version of this paper will attempt to calculate the true rate. However, other work (Cairns and Carlson, 2014a, Appendix B) has shown that the true rate is a monotonic transformation of this ‘raw’ rate, so we can usefully use the latter to analyze broad trends (and sudden shifts) over time.


Figure 5: 2012 Main Sentiment Category Trends

Figure 6: 2012 Weibo Censorship Rate

Beginning with the sentiment trends (Figure 5), several series exhibit strong trend-like

behavior. For example, “AQI monitoring plus positive” trends gradually upward throughout the

year, reflecting the increasing volume of government-supplied AQI monitoring data from

throughout the country. In another notable trend, “health” decreases as the year goes on. This in

part may reflect lower air pollution in spring, summer and fall, but its lack of an uptick in


December suggests that pro-state sentiments may be taking over, displacing more negative (and

oppositional) discussions about pollution as health threat.14

Overall, censorship in 2012 (Figure 6) exhibited a U-shaped pattern – relatively low in

January and December, increasing in spring, and decreasing in late fall. Major exceptions to this

were the sudden drops in June, which are of major theoretical interest as they may indicate

intentional and strategically timed reductions in censorship by the state. In general, the U-shaped

trend is consistent with the notion that the Chinese government is more permissive of online

discussion when actual pollution is bad, and when post surges (which we later show) are

responding to real-world conditions, than when Weibo users react to human-caused incidents or

scandals. Through four case studies in the next section, we consider both natural, and human-

caused spikes and what evidence they provide to test our censorship hypotheses.

Analysis of key dates in 2012

Key Date 1: January 19 (Worst pollution)

On January 19, 2012, the air quality index in Beijing climbed over 400 – into the range

that the U.S. Environmental Protection Agency (used by the U.S. Embassy in Beijing for their

AQI readings) would deem extremely hazardous to human health. Although not the highest value

of AQI that Beijing has ever witnessed (during the “Airpocalypse” in 2013, pollution surpassed

the index’s limit of 500), this was the highest value for all of 2012 and drew attention to the issue

of pollution. To further unpack the kinds of comments that appear on social media during such

air pollution crises, we can look at data on that day’s relevant posts (see Figure 7).

14 While we coded both “Health” and “Environment” during the coding exercise, mentions of “Environment” in our 500 coded posts were too few for ReadMe to generate a reliable proportion for the whole year. In future versions of the paper, we will hand-code more “Environment” posts to feed ReadMe so that it can estimate the year-long trend. But, for now, knowing that the “Health” frame dominated the “Environment” frame is in line with our earlier hypotheses.


Figure 7: Relevant posts by category on January 19, 2012

During the worst pollution of 2012, social media users unsurprisingly shared posts

mentioning or re-tweeting news, air quality monitoring data, and the word “PM2.5.” However, it

is interesting to note that mentions of health dominate the conversation. Mentions of “blue sky”

are added here to show some comparison between health-related concerns and concerns for a

clean or beautiful environment (many users lament the lack of a blue sky in their environmental-

related posts).15 Mentions of “blue sky” are low throughout the year, and, surprisingly, even on

the day with the worst air pollution out of the year. This gives support to our hypothesis that the

health frame of air pollution will dominate the conversation, given that it attracts more attention

and awareness to the issue than the environment frame.

Mentions of the U.S. Embassy, negative posts about the government, and domestic vis-à-

vis foreign critiques are present, but not overwhelming. Meanwhile, noticeably absent is any

news painting the Chinese government in a positive light and the rate of government censorship

is quite low. This data illustrates how both society and the state react to the “exogenous shock”

15 We used “blue sky” counts here since the “environment” proportions from ReadMe were not reliable (see Footnote 14).


of severe air pollution. The public shares information and connects the pollution outside with

concerns about health. Meanwhile, unable to hide a natural phenomenon, the government

remains quiet and relatively inactive in terms of censorship. This fits with our earlier hypotheses

that spikes in air pollution will drive comments on Weibo (in absence of a political scandal), and

that government censors will allow these conversations to continue as long as they do not

become politicized or have a the potential for mobilization.

Key Date 2: June 5-7 (World Environment Day)

On June 5, 2012, relations between the U.S. Embassy and China’s Ministry of

Environmental Protection (and the Chinese government more broadly) reached a low point when

Vice Minister Wu Xiaoqing demanded during a World Environment Day press conference that

the US Embassy stop releasing its air monitoring data. Wu said that the Embassy’s data release

did not abide by the spirit of the Vienna Convention on Diplomatic Relations, which Wu said

requires diplomats “to respect and follow local laws.”16 Wu’s remarks, after an overnight lag, led

to a large post surge on Weibo, with a large amount of content consisting of mere news reposts.

However, netizens also expressed a variety of sentiments in response to Wu’s comment, as

Figure 8 shows for the day after (June 6):

16 The Vancouver Sun. 2012. “In China, pollution is not up for debate; Government orders embassies to stop issuing readings to the ‘outside world.’” 6 June.


Figure 8: Relevant Posts by Category on June 6, 2012

Unsurprisingly, news dominated Weibo on June 6 at about 70% of posts, with many news

stories containing discussion of PM 2.5. Another finding of note is the issue was framed mostly

in terms of health, even though the pollution outside was actually fairly low (142 or “unhealthy

for sensitive groups”). By comparison, mentions of “blue sky” are almost nonexistent. This may

be another indication that the health frame causes people to become more responsive and more

active about the issue of air pollution, as per our earlier hypothesis. The proportion of posts

coded as “domestic vis-à-vis foreign plus negative” was quite high that day, and the proportion

of negative posts about the government and mentions of the U.S. embassy were also not

insignificant. Furthermore, it is intriguing to note that the rate of censorship is significantly

higher than it was with severe air pollution in January. This supports the argument that

oppositional frames are more likely to spread after a government actor creates an opening in the

political opportunity structure for negative commentary. It also supports the idea that the

government would censor more during these more politically sensitive moments. As we explain


in the next two sections, these trends for negative commentary and censorship are magnified in

the responses to the two events later in June.

Key Date 3: June 13 (Pan Shiyi Tweet)

On June 13, 2012, one week after the World Environment Day incident, Chinese real

estate mogul and noted pro-environmental protection commenter Pan Shiyi claimed on his

microblog that he had “misspoken” regarding earlier comments that had been interpreted as anti-

government. Pan’s exact words were “说拧了。谁（任何人）都不会指望使馆改善空气质量。

首先要知道空气污染多严重，对人身体带来多大伤害。治理要依靠每个人”，which

translates as “I misspoke. Nobody can expect the embassy to [actually] improve air quality. First,

[we] need to know how serious the pollution is, and how damaging it is to human health.

Managing it depends on everyone.” While Pan, known until then as somewhat of a provocateur

on criticizing Beijing’s air pollution situation, may have genuinely intended the post as one of

de-escalating confrontation with the government and emphasizing the need for shared social

responsibility to deal with the problem, the post triggered a major reaction on Weibo as many

netizens mocked Pan’s apparent retreat, while others interpreted his comment that “managing

[air pollution] depends on everyone” as a backhanded swipe: Pan had in fact meant the exact

opposite and was implicitly holding the government responsible. Regardless, frames that we had

classified as “oppositional” surged alongside reposts of his comment, as Figure 9 shows:


Figure 9: Relevant posts by category on June 13, 2012

First, news (a state frame) though still common, is less dominant on June 13 than other days –

this shows an infrequent instance where netizen commentary constituted the bulk of posts.

Meanwhile, opposition frames are relatively high. The prevalence of negativity toward the

government alongside U.S. Embassy mentions shows how the Embassy dispute a week earlier

was the root incident that galvanized Weibo users to speak out, with Pan’s comment serving as

catalyst. Additionally, nontrivial levels of “health”, and “domestic vis-à-vis foreign plus negative”

posts showed continuing netizen concern over air pollution’s harmful effects and their longing

comparison with better air conditions outside China.

For its part, censorship was relatively high compared with January, though lower than on

June 6. The difference in censorship suggests that the government was not as concerned about

the negative impact of netizen comments in response to Pan’s tweet as they were immediately

following the Vice Minister’s June 5 remarks; this makes sense because in that prior incident, by

making a controversial statement, the Minister increased the government’s vulnerability to

“losing face.” This lack of potential further reputational harm a week later, however, was


countered by what may have been authorities’ concern over potential collective action spurred by

the tweet, since Pan was such a famous and polarizing figure,17 leading them to delete many re-

posts of the original tweet.18

Key Date 4: June 28 (Han Han Tweet)

Our last incident, occurring three weeks after the initial June post surge, focuses on a

single tweet that while not directly related to the government or any tangible breaking incident,

nevertheless captivated Weibo attention to the problem of air pollution. On June 28, Wang Lifen,

a former China Central Television (CCTV) host and one of Weibo’s “Top 100 Big V” in 2012,19

re-tweeted a post by Zhang Xin (CEO of SOHO China and Pan Shiyi’s wife). Zhang’s original

tweet read “韩寒和我们一样也渴望空气干净”, or “just like us, Han Han also yearns to breathe

clean air.” Han Han, of course, is a noted author, intellectual, racecar driver and all-around

celebrity in China known for his non-conformity and willingness to speak out on politics. As

Pan’s wife and a noted Weibo commentor, Zhang’s reference to Han Han immediately captured

Wang’s attention: Wang was connected to Han Han in that she had hosted him on CCTV’s

Dialogue years before, when Han Han was a relatively unknown teenage sensation. The tweet

also references Huang Silu, a virtuoso pianist and talented student also invited to appear on the

same Dialogue episode. During the show, Wang contrasted Han Han to Huang, casting Han Han

as a “rebel” and pursuing a non-traditional path to success and fame by staying in China while

Huang, like many talented students in China’s “post-80s generation”, desired and pursued

17 According to our dataset, Pan’s original tweet was eventually deleted, but not until June 19, six days after it was originally posted. This suggests, given that we know the Chinese censorship regime is capable of removing unwanted post threads within minutes (Zhu et al 2012), that deleting it was not a high priority for censors, and raises the possibility that it was allowed to circulate for a defined time. 18 A future version of this paper will more closely analyze which reposts (plus netizens’ own comments) were deleted, which not, and when. 19 Source: http://data.weibo.com/summary/2012year/influence


overseas study. In the show, Huang had compared overseas life favorably with conditions within

China, while Han Han refused to express this rather conventional aspiration.

By saying that Han Han “also wished to breathe clean air,” Zhang Xin was emphasizing

that all Chinese desire clean air and a healthy environment without having to leave the country, a

sentiment that even a “rebel” like Han Han would agree with. Wang’s comment in her repost of

Zhang further supports this interpretation, talking about how she “envies” foreign students

studying overseas who are able to breathe low PM 2.5 air.20 Since many Weibo users were

intimately familiar with Han Han (as some were with Dialogue) and also with Wang Lifen and

Zhang Xin, Zhang’s retweet of Wang’s post attracted enormous attention and led to a massive

surge in air pollution-related comments, most of which contained the retweet. Figure 10 breaks

down such commentary by sentiment category:

Figure 10: Relevant posts by category on June 28, 2012

20 The relevant Dialogue episode is posted on Sohu (China’s version of YouTube), and has received over 500 million views. Its popularity may be due in part to it being Han Han’s public debut. Available at http://tv.sohu.com/20120515/n343221460.shtml


News is relatively high, possibly due to some unrelated occurrence.21 However, “health”

and “domestic vis-à-vis foreign plus negative” are higher, with nearly 100% of posts containing

these sentiments.22 The combination of these two categories reveals that Zhang’s and Wang’s

comments struck a chord with netizens, with unfavorable comparison’s of China’s domestic

pollution vis-à-vis better foreign air unfolding in tandem with negativity toward the Chinese

government.

Additionally, and possibly spurred by the events of a few weeks earlier, government,

news and positive is relatively high, indicating that municipal and provincial governments were

busy pushing out positive propaganda on recent achievements to release air monitoring data, and

other positive pollution-related initiatives. Also notable is the fact that despite high negativity

toward the government, censorship is very low – by far the lowest of our three June cases. Taken

together, these two observations suggest that the government had moved from its initial stance of

suppressing negative commentary on pollution, to deliberately tolerating negative speech while

attempting to boost positive propaganda, a strategy associated with “public opinion guidance”

(yulun daoxiang, 舆论导向) rather than outright suppression of opposing views. The reason for

this may be that by late June, leaders were responding to the depth of public anger over pollution,

and the embarrassment of the US Embassy data by acknowledging public concerns.

Concluding remarks

Our analysis of the overall yearly trends, monthly cases, and the four key date mini-cases

all give context to the conversations on Sina Weibo about air pollution during 2012. From this

fine-grained analysis, we can support our many of our earlier hypotheses and arguments. First,

21 Future iterations will investigate this further. Currently, we have surveyed major international news coverage of events (we also intend to do so for Chinese newspapers) and are unaware of any major “breaking event” that might have triggered a surge in news reposts. 22 The number may be slightly inflated due to ReadMe classification error, an issue we will address in future versions.


the frame of air pollution as a threat to human health is much more widespread than as an

environmental protection issue. This frame is highest at the beginning of the year during the

highest levels of air pollution in January, and then it tapers off steadily throughout the year. It

also spikes at key moments, such as the June 6 World Environment Day statement and the Han

Han tweet on June 28. Meanwhile, mentions of the environment remain low throughout the year

(so low that we were unable to generate ReadMe proportions for all of the days in our dataset).23

This supports our hypothesis that the “health” frame dominates the “environment” frame for the

discussion of air pollution in China. Furthermore, our expectations for “oppositional” and “state”

frames are also supported by our data. “Oppositional” frames are able to break through at key

points during the year where there is either an “exogenous shock” of high pollution or

government action that opens the political opportunity structure to invoke negative commentary.

We see this during our key dates in June. The comparative rate of censorship in January 2012

versus June 2012 also supports our argument about government censorship. Censorship is low in

January, although air pollution is severe, because the issue has not yet been politicized. After the

events in June, however, rates of censorship increase as the government tries to control the

message and ensure social stability.

The final part of our paper, to identify and examine those who are responsible for

spreading these frames and messages on Sina Weibo, will be included in a future iteration of our

paper. As discussed in our hypotheses, we expect that three types of individuals are likely to

influence the conversation about environmental issues in China: environmental activists,

international environmental NGOs, and public intellectuals. For the next version of this paper,

we have chosen Ma Jun (a well-known Chinese environmentalist), Greenpeace Beijing (the local

office of the international environmental NGO), and Pan Shiyi (Chairman of SOHO China with a 23 See Footnote 14 for an explanation of this issue.


large following on Weibo). We will take a sample of 500 topic-relevant posts from these three

individuals in 2012 and code them according to the same categories that we used to code the 500

post random sample from all users in our dataset. Then, we can compare the frames that these

individuals chose to use to the overall conversation about air pollution. In addition, we may be

able to do a network analysis of viral posts from these uses to get an idea of how, when, and

where their messages spread. We can also gather information on when and how much these

individuals were censored by the government. This part of the project will allow us to identify

the influence of these key individuals in the overall conversation on Weibo about air pollution

and their relationship with government censors.

When completed, there are several potential contributions of this paper and implications

of its findings for future research on China and other similar authoritarian regimes. In terms of its

contributions to scholarly debates, this paper has three key contributions. First, the study

contributes to literature in comparative environmental politics on ENGOs as a bridge between

state and society to improve environmental governance. Second, this case gives insight into the

key frames that allow information to spread on social media and the potential impact of this

media in authoritarian regimes. Finally, the paper speaks to the broader topic of adaptive

authoritarianism and the logic of authoritarian control through its focus on censorship.

Censorship is an important indicator of state intentions regarding the permissible boundaries and

forms of citizen participation in governance or the limits of civil society. As such, this paper

ultimately provides insight into the types of citizen participation and civil society growth that are

permitted – and perhaps even useful – under authoritarian control in an era of increasing access

to information.


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Appendix A Description of coding process: Once we had narrowed down our dataset to the 71,088 relevant posts on air pollution for all of 2012, the authors went through several stages of pre-coding exercises to determine the key categories before moving on the full coded sample. Our next step, undertaken by both co-authors, was to draw a random sample of 100 posts which each author independently read through, thinking holistically about how to divide up poster comments into distinct sentiment categories. Our concept-building for this project was therefore grounded in the data, rather than attempting to impose prior theoretical notions about air pollution Internet discourse in China onto the data. After independent reading and annotating, we met for a lengthy discussion about possible categories, and debated their existence and prevalence in the data. Next, we assembled a team of three coders, including the two co-authors and a third undergraduate, native Mandarin-speaking assistant. Having agreed on a set of candidate categories, we then drew a second random sample of 50 posts and separately attempted to classify them according to our draft category scheme. We then met again and informally attempted to reconcile disparate scores, and to refine our coding scheme. During a series of additional meetings over several weeks, we agreed on a final collection of what we judged to be replicable and salient categories, and then weighed these by their theoretical potential to illuminate our research questions. The final set of categories we chose are detailed in the methods section of the paper. We drew a fresh random sample of 500 posts for analysis, taken from all of 2012, and worked independently to assign them into categories. We then met to reconcile divergent scores according to strict rules. To militate against potential undue influence during discussion due to differences in age, gender and race, interruptions were banned and only one coder spoke at a time, with our undergraduate assistant always speaking first. When scores diverged, each coder briefly stated reasons why she/he had assigned a particular coding. After all had spoken, if a majority existed (with two coders favoring one score and the third dissenting), the dissenter was first allowed to justify her/his divergent score and persuade others to switch. Then, if switching did not occur, the majority coders took turns explaining their point of view and encouraging the dissenter to switch scores – all made effort throughout to maintain a spirit of respect and cordiality. Our inter-coder reliability scores for the full set of 500 randomly selected posts is included below in Appendix Table 1. Most of the scores here are all considered to be good according to standards established in the discipline. The average pairwise agreement for domestic vis-à-vis foreign, government blame, news, and AQI monitoring data are all well above 80%. The Fleiss’ Kappa and Krippendorff’s alpha scores for these categories are all above 0.70, which indicates substantial agreement. Environment vs. health has a slightly lower score for average pairwise agreement at 79.8%. Its Fleiss’ Kappa and Krippendorff’s alpha scores are lower as well. This may be due to the fact that as we went through the coding exercise, we agreed in our consensus meetings about how to further refine these categories. The consensus score reported in the paper thus much higher. We


also noted that there were few environment posts, which is something that we will have to address in the future iterations of this paper. As for valence, our average pairwise agreement was 68.3% and the Fleiss’ Kappa and Krippendorff’s alpha were lower as well. This is unsurprising with such a complex category like valence. The authors’ native language is English, and although both know Mandarin Chinese quite well, this category was influenced greatly by having a native Mandarin speaker as our third coder. There were some posts where sarcasm, commonly used emoticons (and their meanings), or references to other cultural phenomena evaded the authors, but were caught be the third native-speaking Mandarin coder. These were discussed on a case-by-case basis during the consensus-building meetings and agreed upon by all three coders as per our system for agreement. Appendix Table 1: Inter-coder Reliability Scores

Domestic vis-à-vis foreign

Government blame

Environment vs. Health News AQI

data Valence (+,-,0)

Average pairwise

agreement 96% 88.8% 79.8% 86.9%

94.9%

68.3%

Fleiss’ kappa

0.869

0.709 0.553 0.758

0.861

0.519

Krippendorff’s alpha

0.869

0.710

0.554 0.759

0.861

0.520

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Title: Hazy messaging: Framing on Chinese social media during air