# Unveiling the Dynamics of Censorship, COVID-19 Regulations, and Protest:
An Empirical Study of Chinese Subreddit r$/$china$\\_$irl

Siyi Zhou, Luca Luceri, Emilio Ferrara

###### Abstract

The COVID-19 pandemic has intensified numerous social issues that warrant
academic investigation. Although information dissemination has been
extensively studied, the silenced voices and censored content also merit
attention due to their role in mobilizing social movements. In this paper, we
provide empirical evidence to explore the relationships among COVID-19
regulations, censorship, and protest through a series of social incidents
occurred in China during 2022. We analyze the similarities and differences
between censored articles and discussions on r/china_irl, the most popular
Chinese-speaking subreddit, and scrutinize the temporal dynamics of government
censorship activities and their impact on user engagement within the
subreddit. Furthermore, we examine users’ linguistic patterns under the
influence of a censorship-driven environment. Our findings reveal patterns in
topic recurrence, the complex interplay between censorship activities, user
subscription, and collective commenting behavior, as well as potential
linguistic adaptation strategies to circumvent censorship. These insights hold
significant implications for researchers interested in understanding the
survival mechanisms of marginalized groups within censored information
ecosystems.

## Introduction

#### The Path to the White Paper Movement.

Since 2019, China has implemented strict COVID-19-related regulations in
pursuit of a “zero-COVID-19” public health objective. However, in March 2022,
an abrupt and rashly-planned lockdown in Shanghai led to shortages of
essential supplies and widespread unemployment (PRC Government of Shanghai
2022). Consequently, citizens turned to Chinese social media platforms, such
as Weibo and WeChat, to express their dissatisfaction and frustration. Despite
stringent censorship measures aimed at curtailing the spread of sensitive
critiques, social media users persisted in voicing their opposition to the
“zero-COVID-19” regulations. To circumvent censorship, users employed
homonyms, shared screenshots of censored content, and edited film clips like
Les Miserables to portray their experiences. These collective actions,
referred to as the Shanghai lockdown protest or the 414 cyberprotest, set the
stage for a series of protests that unfolded from October to December 2022.

On October 13, 2022, an image of a banner on Beijing’s Sitong Bridge bearing a
slogan opposing COVID-19 testing, lockdowns, censorship, and dictatorship went
viral online but was quickly censored. Later that month, images of Foxconn
workers striking and protesting against the lockdown in a bid to return home
were also censored. In early November, videos of crowds assisting a man
shouting “Bu zi you, wu ning si” (Give me liberty, or give me death) while
escaping the police were similarly censored. Despite censorship efforts,
protesters continued to voice their dissent. For example, an article featuring
mosaic text, designed to mimic censorship, was circulated by protesters and
subsequently censored. In late November, citizens spontaneously gathered for a
public memorial service for the Urumqi fire victims, brandishing white papers
to mock censorship and lockdowns. These white papers, which were banned from
sale during the movement, symbolized the year 2022—a year characterized by
COVID-19-19 regulations, censorship, and protests in China (cf. timeline of
events in Table 1).

Table 1: Detailed timeline of the Shanghai lockdown protest. Date | Incident |  Description
---|---|---
April 2022 | Shanghai Lockdown |  A sudden and lengthy lockdown in Shanghai displeased citizens with economic effects and generated outrage on social media sites. The complaints were difficult to suppress despite of the censorship.
October 13, 2022 | Beijing Sitong Bridge Banner |  A banner with anti-lockdown and pro-democracy banner was hung on the parapet of Sitong bridge in Beijing on the eve of the 20th National Congress of the Chinese Communist Party.
November 22, 2022 | Foxconn Strike |  Workers of the Foxconn were locked in the factory in response to the zero-COVID-19 policy while ensuring to keep the facetory running. Videos of the workers scaled through barriers and fled home with bare foot went viral online.
November 24, 2022 | Chongqing Superman |  A man from Chongqing was filmed giving an anti-lockdown speech, shouting “Give me freedom, or give me death.” The crowd helped him escaped from police even though he was arrested eventually.
November 24, 2022 | Urumqi fire |  A fire in a Urumqi building caused 10 deaths. The victims were locked at home during the disaster because of the ZERO-COVID-19 policy.
November 26, 2022 and onwards | White Paper Movement |  Chinese citizens located different cities and Universities, individuals located all over the world, voluntarily attended memorial meetings for the Urumqi fire victim. During the public gathering, they shouted the slogans from the previous incidents and held up blank white paper to protest against COVID-19 regulations and censorship.

#### Theoretical Background.

For decades, scholars have delved into the mechanisms that drive the
mobilization and organization of social movements. The concept of
organization, which underlies the unity of social movements, has been a
subject of continuous inquiry due to its significant influence on the ability
of social movements to challenge established power structures (Castells 2013).
While some theorists consider formal organization essential for preventing
disintegration and primitivism (Hobsbawm 1959), others argue that excessive
organizational structure can undermine the disruptive potential and hinder
social activism (Michels, Paul, and Paul 1915; Piven and Cloward 1977). Tarrow
posits that an optimal degree of organization is necessary to sustain momentum
and achieve a social movement’s goals while maintaining a structure flexible
enough to capitalize on informal networks (Tarrow 1998).

The emergence of the digital communication era has facilitated new forms of
collective action through information sharing and user connectivity on online
social media platforms. These features have amplified the frequency and impact
of social movements (Myers 1994; Garrett 2006; Bennett 2003). Online social
media introduced a novel domain of organizational structure and information
dissemination for collective action, consequently drawing significant
scholarly interest. Most contemporary studies on social movements, such as the
Occupy Wall Street and Gezi Park movements, have centered on the dissemination
of information during and after protests (Conover et al. 2013a, b; Varol et
al. 2014).

The convergence of digital and physical spaces has generated new opportunities
for collective action. This hybrid space functions as a platform for
individuals to forge symbolic communities and foster transformative practices
(Castells 2013; Bennett and Segerberg 2012). While physical protests rely on
public demonstrations of solidarity, such as chanting slogans and displaying
signs, cyberspace enables more personalized expressions of emotions and
individual motivations (Allen, Chen, and Ferrara 2021; Chang, Richardson, and
Ferrara 2022).

In 2022, a series of protests against COVID-19 regulations and censorship
emerged in both physical and digital spaces in China. The enforcement of
censorship has rendered overt protests and social movements seemingly
unattainable, particularly under the “zero-COVID-19” regulation and lockdown
policy. Online content censorship has obstructed people’s ability to share
information about social incidents, while stringent lockdown policies have
impeded offline connection. The 2022 protests in China present a fertile area
for researchers to examine the formation of unity and community amidst
disintegration and surveillance, as well as to investigate the actions of
affected, vulnerable populations in such constrained environments.

### Contributions of this Paper

In this paper, we present an empirical study on censored articles, government
censorship activity, and user online behavior within a Chinese-speaking Reddit
community during the 2022 COVID-19 protests in China. This paper aims to glean
actionable insights into the dynamics of COVID-19 regulation, censorship, and
protest through an exploratory analysis that addresses the following research
questions:

  * RQ1

_How do the topics of discussion in r/china_irl interact with the articles
censored by the Chinese government?_

  * RQ2

_How do subscription and commenting behavior in r/china_irl relate to
government censorship behavior? Can the temporal dynamics predict one behavior
based on another?_

  * RQ3

_Do individuals adapt and modify their language or linguistic style to evade
censorship?_

To address these questions, we compile a heterogeneous dataset comprising
censored articles, whose access is no longer guaranteed to the public via
their original URLs, and one year of user activity in the most popular
Chinese-speaking subreddit, r/china_irl. By employing tokenization and topic
modeling in the Chinese language (ensuring that newly invented Chinese phrases
can be accurately detected and tokenized), we offer a quantitative and
qualitative analysis of the unfolding protest and related online discussions.
Our exploratory analysis uncovers novel social phenomena and poses several
potential questions to initiate and stimulate further research.

## Related Work

When studying social protest, multiple dimensions are considered, from
mobilization to participant demographics. Technological advancement allows
scholars to gain more insights into social protests and movements. Scholars
first study protests through news articles (Uba and Uggla 2011; Sönmez, Ozgur,
and Yörük 2016; Lankina and Skovoroda 2017). In studying the Tweeting
behavior, Steinert-Threlkeld found that increased protests generate increased
message coordination of specific hashtags (Steinert-Threlkeld et al. 2015).
Independent studies pointed out the important role of image in online protest
mobilization in the case of Black Lives Matter (Casas and Williams 2019;
Chang, Richardson, and Ferrara 2022). MMCHIVED collects images from Twitter
and identifies demographic information from the protesting images during the
Venezuela and Chile protest (Steinert-Threlkeld and Joo 2022). Most of the
protest data covers images, hashtags, or news articles. Existing tools and
studies help us understand the demographics and mobilizations of protests, yet
limited information for us to investigate detailed topics and participant
agendas of the protests.

Existing studies on Chinese social protest mainly focus on the non-sensitive
ones. The CASM database uses convolution neural networks and LSTM to identify
possible offline protests on social media (Zhang and Pan 2019). However, the
collection of protest information from Chinese social media platforms is
heavily filtered by non-sensitive events if collected afterward. In order to
gain more insights into the highly sensitive social protests in China,
researchers mostly rely on in-depth interviews, court files, and newsletters
overseas (Liu 2021). Massive first-hand data is extremely limited. In search
for related content about the white paper movement or protests against
COVID-19 regulations in Chinese social media and newspapers, almost nothing
can be found about such information. The Wayback Machine, however, confirms
the existence of those incidents as some of the web pages were stored.

Scholars investigating censorship have focused on how governments use
censorship tactics to suppress protests and how individuals collectively
circumvent censorship. The 2019 Hong Kong protests provide an illuminating
case study where censorship was identified as having the ability to both
demobilize and rally the masses by selectively reporting threats and
downplaying events in newspapers (Ma and Weiss 2023). Web-blocking and DoS
attacks were identified as the primary censorship tactics used to repress
protests in several countries, including Iran, Malaysia, Ukraine, and
Venezuela (Kawerau, Weidmann, and Dainotti 2023). When faced with censorship,
individuals often seek to circumvent it to obtain additional information, with
crises such as COVID-19 acting as a catalyst for such behavior (Chang et al.
2022). The 2022 protests in China saw censorship initially used to repress
protests against zero-COVID-19 regulation, but ultimately backfired and
sparked the white paper movement that protested against censorship.

The case of the 2022 protests in China is not monocausal. A series of protests
are caused by the lockdowns in Shanghai, the zero-COVID-19 regulation in
Beijing, the secondary crisis in Urumqi, and the censorship of freedom of
speech nationwide. Collective attention is thought to exhaust fast if the
production and consumption gradient is high (Lorenz-Spreen et al. 2019).
However, public attention on COVID-19 regulation and censorship was high and
lasting. Limited resources help us understand how people are able to gather
together when being locked at home under censorship, and what are the
connections between a series of protests, and post-protest reactions. Thus we
collected data from censored articles and Chinese Reddit submissions and
comments to help study the extraordinary year of events in China.

Figure 1: Event plot to show frequency distribution of when articles of a
topic are censored.  Figure 2: $r/china\\_irl$ submission activity that
describes the number of comments of each submission on each day. Submission
with over 200 comments in one day is labeled with their topic.

## Methodology

This section elucidates the process of data collection, the reasoning behind
it, and the methods of data processing.

### Data Collection

The dataset presented in this paper is composed of two parts: (i) 1,249
censored articles published since 2019, (ii) Metadata and 1.6 million comments
from the Reddit community r$/$china$\\_$irl shared from December 2021 to
January 2022.

The censored articles are collected from China Digital Times (CDT) and contain
the title, content, original link, and archival dates retrieved from the
_Internet Archive_ , also known as the _Wayback Machine_
(https://archive.org/web/). We further manually verified the original content
and recorded all articles’ actual creation date by leveraging the Wayback
Machine.

We downloaded subscription statistics from _Subreddit Stats_
(https://subredditstats.com/) and all the comments of the most popular
Chinese-speaking subreddit, r$/$china$\\_$irl, from the Pushshift Reddit
database (PRD) (Baumgartner et al. 2020). The community-based style of the
platform, in general, and the r$/$china$\\_$irl subreddit, in particular,
enables us to comprehend how people and ideas congregate in the online space.
The subreddit r$/$china$\\_$irl is created for conversations related to
Chinese political and social issues after several Chinese forums, like _Hupu
kfq_ , were shut down in 2019, and internet users’ posts on Weibo were
massively censored. While there exist other Chinese-speaking subreddits,
r$/$china$\\_$irl represents the most prominent and widely used Reddit
community with 176K active users and a rather neutral political stance. With
the aid of both the CDT and Reddit datasets, we anticipate observing some
degree of correspondence between the censored articles and the topics
discussed in the subreddit.

### Data processing

Unlike many phonetic languages, Chinese is an ideogram language with a unique
grammar system that renders the automated identification of phrases and
stopwords a complex task. For example, one character can have multiple layers
of meaning. This characteristic challenges computational analysis of Chinese
textual content in many ways, including topic modeling, sentiment analysis,
and sarcasm detection.

To overcome these hurdles in our analysis, we preprocess the Reddit data using
both Jieba (https://github.com/fxsjy/jieba) and NLTK (Loper and Bird 2002). We
utilize a Hidden Markov Model (HMM) and the Viterbi algorithm in our
tokenization process to detect newly invented Chinese words and phrases,
especially homonyms, for different social incidents. This allows us to
evaluate and compare the linguistic patterns, length of comments, and volume
of submissions and comments at different times. To understand the content and
topics of the censored articles, we employ LDA to calculate word and topic
frequency and BERTopic (Grootendorst 2022) to classify the topics in the
dataset. While there are many pre-trained topic models, most are trained on
English or phonetic languages.

To obtain the most accurate topic classifications, we label and train our own
dataset. CDT provides the topic of the censored articles in the form of tags,
which we further refine to better generalize the topics. For example, articles
discussing the secondary crisis caused by the lockdown are tagged by CDT as
either “Shanghai Lockdown”, “secondary crisis”, or “COVID-19 regulation”. We
generalize such a group of articles with tangible social issues and policies,
such as “Shanghai Lockdown/COVID-19 regulation” to avoid bias. This helps us
determine the main topics of discussion and evaluate their relevance in our
analysis.

To investigate the relationship between Reddit user behavior and government
censorship behavior, we employ Granger causality and Pearson correlation
analysis. However, the relationship between censorship, commenting,
subscribing, and linguistic patterns are not straightforward. We use Granger
causality tests to estimate the time lag for variable X to respond to variable
Y or the number of time steps one action precedes another. Then, we test the
correlations of each pair of the two time series with the suggested time shift
provided by the Granger test. It’s worth noting that causality cannot be
always ascertained, as this multivariate relationship forms a complex system
where the variables could be inter-affecting each other. Consequently, for
this study we limit ourselves to determining the correlations of the variables
rather than their causal relationships.

## Results

### RQ1: Topics Interplay Between Censored Articles and Reddit Discussions

From the CDT dataset, the summary of when articles regarding a certain topic
is published and censored shows us the most sensitive topics of different time
periods in 2022. Previous assumption is that people will stop mentioning or
discussing a sensitive topic after being censored for a period. However,
Figure 1 suggests that while topics that touched upon one social incident were
censored for a concentrated period in 2022, the frequency of censoring
activities against topics related to social issues, especially COVID-19
regulations, is more dispersive. Under the discussion of COVID-19 regulation
topic, subtopics are generated in regards to multiple different social
incidents, such as the Shanghai lockdown, Beijing Sitong Bridge banner, Urumqi
fire, and White Paper protest, as shown in Figure 2.

Articles integrating multiple social incidents are labeled with the social
issues they attempt to discuss, such as Zero-COVID-19 and the
COVID-19-pandemic as a whole. Those COVID-19-related articles, especially when
discussing a social issue, tend to recur when a COVID-19-related incident
happens regardless of the risk of censorship. Articles on such topics keep
being published even though the author might already know the article has a
high risk of being censored. This behavior contributes to the outrage of the
White Paper movement later on.

The recurrence phenomenon of discussing high-risk censor-prone topics in China
also appears in r$/$china$\\_$irl. Figure 2 graphs the number of comments each
submission receives each day in 2022. The graph excludes 3 outlier submissions
(moderated submissions peaking over 1000 comments in one day) in order to get
a more general picture of the conversations. Submissions that received over
200 comments in one day are labeled with their titles. Titles related to the
same topic are highlighted with the same color. Preliminary observation
discovers that most of the submissions that receive over 200 comments in one
day are not talking about the same topic. However, topics about the Ukraine
war and protests in China are repeatedly mentioned in submissions and
popularly discussed in comments. The topic of protest in China was repeated
significantly more times than the Ukraine war topic.

While the censored articles collected from Chinese social media and the
subreddit forum share characteristics of topic recurrence, their emphasis are
slightly different. The censored articles are more oriented towards examining
one or more specific consequences of the COVID-19 regulation and explaining
why the situation needs to be changed. However, the popular recurring Reddit
submissions, although under the big picture of COVID-19 regulation, tend to
repetitively bring up topics in regard to protest behavior. Instead of
discussing why people are suffering, Reddit users compare COVID-19-related
protests to other major protests in China, such as Tiananmen Square movement
and Hong Kong Umbrella movement, as well as sharing overseas protests against
China’s policies on COVID-19 and censorship abroad.

Although paying attention to different aspects of a social issue, the public
agenda in r$/$china$\\_$irl is influenced and partially set by the censored
articles inside of China. In the next section, we look for other potential
correlations between censorship activity and Reddit user behaviors in
r$/$china$\\_$irl.

(a) Number of articles getting censored

(b) Subscriber increase rate

(c) Number of comments

Figure 3: Monthly activities of censorship and Reddit: (a) Number of censored
articles collected each month. (b) new subscribers to $r/china\\_irl$ each
month. (c) Stacked bar graph to summarize the number of comments generated
each month. Each color represents the number of comments in the range of
comment length specified in the legend.

### RQ2: Temporal Dynamics and Interplay of Censoring Activity, User
Subscriptions, and Collective Commenting

Figure 3 presents the monthly activities of the number of articles getting
censored, new subscriptions, and the number of comments on r$/$china$\\_$irl.
Although the graphs indicate a similar trend among the three activities, there
are slight variations in the timing of their respective peaks. To investigate
the relationship between these variables, we first conducted a Granger
causality test, as reported in Table 2.

Table 2: Granger causality test Variable X | Variable Y | Lag (w) |  F-test | P-value
---|---|---|---|---
no. censored articles | no. comments | 0 | 0 | 0
no. comments | no. censored articles | 4 | 2.87 | 0.022
no. censored articles | no. new subscribers | 6 | 2.64 | 0.034
no. new subscribers | no. censored articles | 1 | 8.80 | 0.0046
no. new subscribers | no. comments | 1 | 6.80 | 0.012
no. comments | no. new subscriber | 6 | 3.50 | 0.006
Table 3: Censoring activity v.s. Commenting Time Shift | Pearson corr. | p-value
---|---|---
No shift | 0.17 | 0.58
Censorship 1 month after comments | 0.64 | 0.026
Censorship 2 months after comments | 0.50 | 0.12

(a) No shift

(b) Shift 1 month

(c) Shift 2 month

Figure 4: Correlation regressions of censorship activity v.s comments in the r$/$china$\\_$irl Table 4: Censoring activity v.s. Subscription Time Shift | Pearson corr. | p-value
---|---|---
No shift | 0.38 | 0.22
Censorship 1 month prior to subscription | 0.84 | 0.009
Censorship 2 months prior to subscription | 0.16 | 0.64

(a) No shift

(b) Shift 1 month

(c) Shift 2 month

Figure 5: Correlation regressions of censorship activity v.s increase in
subscribers in the r$/$china$\\_$irl

Our results indicate that only the pair of censorship and comments pass the
test, revealing that an increase in comments anticipates an increase in
censorship activities with a lag of 4 weeks. The reverse relationship between
comments and censorship is not statistically significant. The bi-directional
relationships of the other pairs, subscriber and censorship, and comment and
subscribers, fail the test, prompting a question about their interdependence.
To further validate these relationships, we performed a Pearson correlation
test using the suggested time lags.

To account for the impact of localized noise and focus on a broader, global
scale analysis, we reduced the temporal resolution of our correlation analysis
by measuring the correlations between variables at a coarser granularity. The
regression models of censorship and comments, and censorship and subscribers
are depicted in Figures 4 and 5, respectively. The coefficient in Table 3
aligns with the censorship/comment Granger test (coeff. = 0.64), indicating
that an increase in censorship activity follows one month of the increase in
comments. Table 4 demonstrates a robust correlation (coeff. = 0.84) between
censorship and new subscriptions, with the increase in subscriptions
succeeding one month of the censorship activity. Although the
subscriber/comment pair passes the Granger causality test in two directions,
their p-values in the correlation test are too high to reject the null
hypothesis, suggesting that the number of new subscribers and the number of
comments are independent of each other.

The observed pattern wherein commenting behavior precedes censorship
activities is unsurprising, as sensitive topics that attract high collective
attention tend to raise government awareness and prompt censorship. Chang et
al. (2022) highlights the spillover effect of the COVID-19 crisis, with
increased downloads of Facebook and Twitter, as well as increased views of
Wikipedia from users, geolocated in China, serving as a means of censorship
circumvention. The increase in subscriptions following an increase in censored
articles aligns with the common tendency of individuals to seek new outlets
for expression when their original media platforms no longer allow them to
speak freely.

Figure 6: Proportion of different comment lengths of all comments each day.

### RQ3: Linguistic Patterns

To gain further insights into commenting behavior and linguistic patterns, we
employed a grouping strategy that split comments by length. Figure 6 revealed
a significant proportion of short comments (ranging from 0-20 characters) and
long comments (60+ characters), with moderate-length comments (20-60
characters) being less frequent.

We further sought to perform content analysis to understand the conversations
in the comments by tokenizing and removing stop words. However, we observed
that 140,459 of the comments were transformed into empty strings after
tokenization and the removal of stop words. Consequently, we retained all of
the stop words and analyzed the entire textual content in Reddit comments. On
average, 44.2$\%$ of all characters in comments were identified as stop words,
with 10.2$\%$ of comments consisting solely of stop words. The proportion is
substantial compared to the medium stop word frequency in text in English,
which is typically around 1$\%$ as reported by a stop word distribution
analysis in English with 19,449 articles using Gigaword corpus
(https://faculty.georgetown.edu/wilsong/IR/WD3.html).

Interestingly, the frequency of stop-words usage bears a resemblance to the
pattern of censorship activities in Figure 7, which led us to investigate
whether censorship behavior had an impact on stop-word usage. Our Granger
causality test showed that the increase in stop word usage follows the
increase in censorship activities with a 3-week lag, whereas the converse
failed the test. The correlation test further revealed a moderate correlation
(coeff. = 0.45) between censorship and stop word usage with a 3-week lag.

Figure 7: Stopwords usage frequency (line graph) and the censorship activity
(bar graph) by week.

One plausible explanation for the correlation between censorship and stop word
usage is a survival mechanism adopted by users to escape censorship. Users may
alter their linguistic style by using less machine-detectable words to avoid
censorship. However, this explanation does not seem to align with Reddit since
it is not a Chinese social media platform. Interestingly, one of the labeled
submissions in Figure 2 shows a subscriber’s complaint about the usage of
abbreviations in the subreddit community (see Figure 8 for translation). The
complaint provides examples of sarcasm on Twitter regarding the same issue.
Such collective attention to abbreviations suggests that this habit could be a
migration from typical practices on Chinese social media. We are unsure if the
behavior of using a lot of stop words/abbreviations is a retention of
linguistic habits from Chinese social media or an imitation of the linguistic
style from a censorship-heavy environment. Therefore, further research is
encouraged to investigate the correlations between censorship and linguistic
adaptation.

Figure 8: Translation: Please stop using abbreviations here whether or not
this is your first time using Reddit! You joined Reddit with VPN because you
can’t stand the inexplicable censorship and deletion of posts in China. I hope
to have a normal speaking environment. I really don’t want to see the Chinese
language become like this. What is gj, jc, zf, yyds, u1s1, qs, zzzq, zzp,
yygq? can’t you speak like a human? Don’t you know how much confusion those
abbreviations bring to others? Original link: $shorturl.at/eUV79$

## Discussion and Potential

This study opens up avenues for further research in multiple dimensions,
ranging from linguistics and behavioral science to politics and natural
language processing.

The results from the CDT dataset demonstrate how a topic can recur or
resurface when a new, related topic emerges. Cheng et al. (2016) summarized
characteristics of recurring behavior of memes on Facebook, such as a moderate
number of shares during the first occurrence, moderate demographic diversity,
and the existence of multiple copies. While they emphasized that multiple
copies act as a catalyst, the copies of the censored articles can only be
considered as the ideas and topics conveyed by those articles. Our results
suggest that new relevant topics stimulate the recurrence of old topics. We
encourage further research to explore whether the recurrence of a series of
relevant topics acts as a catalyst for social protest.

The examination of the relationships among censorship activity, comments, and
subscriptions reveals correlations between censorship and comments, and
between censorship and subscriptions. Our next step is to collect comments
from other Chinese-speaking Subreddits to identify similarities and
differences in relation to r/china_irl. In our future work, we aim to expand
our datasets to better assist researchers in studying communities and
examining collective action in protests.

A limitation of this study is that the CDT dataset does not capture a complete
record of all the articles censored in China. The correlations among the
investigated variables largely depend on the collective archival behavior on
the Wayback Machine, the database CDT collects from, which in turn is also a
reflection of collective attention. Our results have shown that the increase
in comments acts as a stable predictor of a coming increase in censorship. Our
next step is to identify more predictors and detect and save articles with a
high risk of being censored to build a more comprehensive database than CDT.

In most sentiment analysis tools, sentences are tokenized and stop words are
removed to optimize the accuracy of the polarity score. In a censored
environment, the extensive use of short phrases and stop words urges us to
understand and explore the meanings and functions of stop words. Are these
Chinese characters truly stop words? Could an NLP model for phonetic languages
help us better understand the content and construction of these Reddit
comments in Chinese? Assuming that the correlation between censorship and stop
words usage is true, we encourage further research to investigate whether the
same pattern applies to other forms of communication within autocratic
environments: How does one change their linguistic habits to adapt to
censorship?

Ultimately, this paper offers starting points for reflection about freedom of
information, protest, and the pandemic. In China, the zero-COVID-19 policy
reduced the death rate during the pandemic but triggered a series of protests.
Online communities provided a virtual environment for propagating people’s
ideas or concerns and documenting interactions corresponding to individual
reactions and behaviors in response to censorship during the pandemic.
Censorship has affected the free flow of these ideas. Collective actions also
exacerbated the dissemination of misinformation, such as ”COVID-19 is just a
big flu,” across multiple scales (Nogara et al. 2022; Pierri et al. 2023).

## Conclusions

In the past, numerous studies have focused on the relationship between
protests and COVID-19 regulations (Plümper, Neumayer, and Pfaff 2021), or
protests and censorship. Our paper presents empirical evidence to demonstrate
the intricate connections between COVID-19 regulations, censorship, and
protest.

The analysis of censored topic occurrences in subreddit discourses reveals
several intriguing findings. First, highly sensitive topics concerning
government and popular topics in r/china_irl generate interest in similar
social incidents. Second, similar topics from both datasets recur throughout
2022. Third, the censored articles and the content driving discussion in
r/china_irl slightly differ despite addressing the same issues. Censored
articles tend to focus on specific social issues and emphasize consequences,
while Reddit users concentrate more on seeking solutions or engaging in
protest behavior to respond to these social issues.

By examining censorship activities, user subscriptions, and commenting
behaviors, we identified correlations and interplays of collective attention
and actions. The part of speech analysis further uncovers potential linguistic
adaptations to avoid censorship.

By revealing the silenced voices through censored articles and investigating
conversations in an environment with a higher degree of freedom, a series of
discoveries establish possible correlations between censored articles and
public agenda-setting. Further analyses are encouraged to uncover universal
patterns, as such phenomena can help us understand potential survival
mechanisms of marginalized groups within censored information ecosystems.

#### Acknowledgements.

Work supported in part by DARPA (contract #HR001121C0169).

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