Why Does China Allow Freer Social Media? Protests Versus Surveillance And Propaganda

In this paper, we document basic facts regarding public debates about controversial political issues on Chinese social media. Our documentation is based on a dataset of 13.2 billion blog posts published on Sina Weibo--the most prominent Chinese microblogging platform--during the 2009-2013 period. Our primary finding is that a shockingly large number of posts on highly sensitive topics were published and circulated on social media. For instance, we find millions of posts discussing protests, and these posts are informative in predicting the occurrence of specific events. We find an even larger number of posts with explicit corruption allegations, and that these posts predict future corruption charges of specific individuals. Our findings challenge a popular view that an authoritarian regime would relentlessly censor or even ban social media. Instead, the interaction of an authoritarian government with social media seems more complex.published_or_final_versio

Herding Behavior in Social Media Networks in China

We conducted an interpretive, qualitative research study to investigate herding behavior around trending posts about disasters on Sina Weibo, one of the most popular social media websites in China. Our preliminary results show that in response to uncertain situations, users engage in sensemaking (Seidel, 2013) and become emotionally engaged (Taylor, 2015) as they converge around trending posts about disasters. Also, state, effect and response uncertainty (Milliken, 1987) may influence how users converge around these posts. Future research will examine the cross-cultural differences of herding behavior across Twitter (U.S.A) and Sina Weibo (China), as well as the differences across financial, scandalous, product based, and political trending posts

Using social media for air pollution detection-the case of Eastern China Smog

Air pollution has become an urgent issue that affecting public health and people’s daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as ‘sensors’ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence, our research (1) determine the correlation between the volume of air quality-related messages and observed Air quality index (AQI) with the help of time series analysis model; (2) investigate further the impact of a salient change of air quality on the relationship between the people’s subjective perceptions regarding to air pollution released on the Weibo and the extent of air pollution through a co-word network analysis model. Our study illustrates that the discussions on social media about air quality reflect the level of air pollution when the air quality changes saliently

Delegated Dictatorship: Examining the State and Market Forces behind Information Control in China

A large body of literature devoted to analyzing information control in China concludes that we find imperfect censorship because the state has adopted a minimalist strategy for information control. In other words, the state is deliberately selective about the content that it censors. While some claim that the government limits its attention to the most categorically harmful content—content that may lead to mobilization—others suggest that the state limits the scope of censorship to allow space for criticism which enables the state to gather information about popular grievances or badly performing local cadres. In contrast, I argue that imperfect censorship in China results from a precise and covert implementation of the government's maximalist strategy for information control. The state is intolerant of government criticisms, discussions of collective action, non-official coverage of crime, and a host of other types of information that may challenge state authority and legitimacy. This strategy produces imperfect censorship because the state prefers to implement it covertly, and thus, delegates to private companies, targets repression, and engages in astroturfing to reduce the visibility and disruptiveness of information control tactics. This both insulates the state from popular backlash and increases the effectiveness of its informational interventions. I test the hypotheses generated from this theory by analyzing a custom dataset of censorship logs from a popular social media company, Sina Weibo. These logs measure the government's intent about what content should and should not be censored. A systematic analysis of content targeted for censorship demonstrates the broadness of the government's censorship agenda. These data also show that delegation to private companies softens and refines the state's informational interventions so that the government's broad agenda is maximally implemented while minimizing popular backlash that would otherwise threaten the effectiveness of its informational interventions.PHDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147514/1/blakeapm_1.pd

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

Toward automatic censorship detection in microblogs

Social media is an area where users often experience censorship through a variety of means such as the restriction of search terms or active and retroactive deletion of messages. In this paper we examine the feasibility of automatically detecting censorship of microblogs. We use a network growing model to simulate discussion over a microblog follow network and compare two censorship strategies to simulate varying levels of message deletion. Using topological features extracted from the resulting graphs, a classifier is trained to detect whether or not a given communication graph has been censored. The results show that censorship detection is feasible under empirically measured levels of message deletion. The proposed framework can enable automated censorship measurement and tracking, which, when combined with aggregated citizen reports of censorship, can allow users to make informed decisions about online communication habits.Comment: 13 pages. Updated with example cascades figure and typo fixes. To appear at the International Workshop on Data Mining in Social Networks (PAKDD-SocNet) 201

Generations Apart: Cultural Revolution Memory and China\u27s Post-80\u27s Generation on the Chinese Internet

This thesis examines how the memory of the Cultural Revolution is used on the Chinese internet by China\u27s post-80\u27s generation and the Chinese Communist Party to describe and highlight examples of social instability. These comparisons are representative of the broad historical narrative written by the Party which forms the basis of how China\u27s younger generations learn about and internalize the Cultural Revolution. This study analyzes how the memory of the Cultural Revolution is held by China\u27s post-80\u27s generation as viewed through the lens of the Chinese Internet. Specifically, this research engages with the intended purposes of the post-80\u27s generation for invoking memories of the Cultural Revolution on the Chinese Internet. This revival and re-characterization of the Cultural Revolution\u27s social memory holds complex meanings for how China\u27s post-1980\u27s generation defines the Cultural Revolution