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When COVID-19 first emerged in China, there was speculation that the outbreak would trigger public anger and weaken the Chinese regime. By analyzing millions of social media posts from Sina Weibo made between December 2019 and February 2020, we describe the contours of public, online discussions pertaining to COVID-19 in China. We find that discussions of COVID-19 became widespread on January 20, 2020, consisting primarily of personal reflections, opinions, updates, and appeals. We find that the largest bursts of discussion, which contain simultaneous spikes of criticism and support targeting the Chinese government, coincide with the January 23 lockdown of Wuhan and the February 7 death of Dr. Li Wenliang. Criticisms are directed at the government for perceived lack of action, incompetence, and wrongdoing—in particular, censoring information relevant to public welfare. Support is directed at the government for aggressive action and positive outcomes. As the crisis unfolds, the same events are interpreted differently by different people, with those who criticize focusing on the government’s shortcomings and those who praise focusing on the government’s actions.

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As fact-checking videos increasingly circulate on video-sharing platforms, more research is needed to understand the prevalent features of such videos and how they are associated with audience engagement. Drawing from the literature on fact-checking, communication, marketing, and computer science, we identified eight audiovisual features as well as seven persuasive strategies that are most relevant to fact-checking videos. Using a hybrid video analysis framework combining both automated and manual content analysis, we examined 4,309 fact-checking videos on Douyin, the Chinese version of TikTok. We found that fact-checking videos on Douyin tended to have higher brightness, less cool color dominance, and faster tempo than non-fact-checking videos from the same accounts and Douyin Trending videos, and frequently used persuasive strategies like clickbait and humor. Through feature clustering, we established three types of fact-checking videos on Douyin—long storytelling cartoons, short stimulating videos, and short authoritative videos. We found that several audiovisual features and persuasive strategies were associated with audience engagement, such as likes, comments, and reshares. This study sheds light on the common practices of fact-checking videos in Chinese cyberspace, extends the current image-as-data paradigm to fact-checking videos, and helps fact-checkers make evidence-based decisions on content creation.

The COVID-19 pandemic unleashed a torrent of conspiracy theories across different social media platforms. Parallel to this conspiracy wave was a heightened sense of nationalism, which manifested through both in-group solidarity and perceived out-group threats. In this study, we examine how individuals’ use of government social media to gather political information correlated with nation-related conspiracy beliefs during the pandemic. Data were collected from 745 subjects in China and analyzed through path analyses, which allowed us to examine the direct association with political information consumption from government social media and the indirect association with nationalism on conspiracy beliefs. The results indicated that the use of government social media to gather political information was associated with greater beliefs in nation-variant COVID-19 conspiracies, both directly and through different mediations of nationalism. Our findings highlight the importance of examining government social media use and how nationalism can have differentiated mediation effects on beliefs in conspiracy theories.

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Government censorship—internet shutdowns, blockages, firewalls—impose significant barriers to the transnational flow of information despite the connective power of digital technologies. In this paper, we examine whether and how information flows across borders despite government censorship. We develop a semi-automated system that combines deep learning and human annotation to find co-occurring content across different social media platforms and languages. We use this system to detect co-occurring content between Twitter and Sina Weibo as Covid-19 spread globally, and we conduct in-depth investigations of co-occurring content to identify those that constitute an inflow of information from the global information ecosystem into China. We find that approximately one-fourth of content with relevance for China that gains widespread public attention on Twitter makes its way to Weibo. Unsurprisingly, Chinese state-controlled media and commercialized domestic media play a dominant role in facilitating these inflows of information. However, we find that Weibo users without traditional media or government affiliations are also an important mechanism for transmitting information into China. These results imply that while censorship combined with media control provide substantial leeway for the government to set the agenda, social media provides opportunities for non-institutional actors to influence the information environment. Methodologically, the system we develop offers a new approach for the quantitative analysis of cross-platform and cross-lingual communication.