Conspiracy and debunking narratives about COVID-19 origins on Chinese social media: How it started and who is to blame

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Implications

COVID-19 has garnered a massive number of conspiracy narratives on social media since January 2020. Conspiracy refers to “an effort to explain some event or practice by reference to the machinations of powerful people, who attempt to conceal their role” (Sunstein & Vermeule, 2009, p. 205). In the COVID-19 context, conspiracy centers around virus origination (i.e., who created and spread it). Such misbelief can erode institutional trust, dampen international relations, generate xenophobia, or decrease preventive health behaviors (Swire-Thompson & Lazer, 2020). Conspiracy narratives have been examined in the United States (Mitchell et al., 2020; Silver et al., 2020) and in Europe (Georgiou et al., 2020). For example, prominent narratives promote conspiracy ideation that the U.S. government created the virus, the virus is a Chinese bioweapon (Jamieson & Albarracin, 2020), 5G spreads COVID-19 (Ahmed et al., 2020), or Bill Gates was behind the virus for vaccination programs (Georgiou et al., 2020). So far, no study has examined the evolution of conspiracy narratives in China. Understanding variations of conspiracy narratives across different sociopolitical contexts is imperative in correcting such misinformation and is pivotal in building effective transnational cooperation to mitigate the pandemic. 

This study focuses on the Chinese social media context, which, against a backdrop of escalating Sino-U.S. conflicts, has fostered various COVID-19 conspiracies that present a different picture from that of the United States and the globe. We examined social media posts that propagate and debunk COVID-19 conspiracies. This paper defines conspiracy posts as those that spread conspiracies about the origins of COVID-19. This paper defines debunking posts to broadly include any posts that disprove, disagree with, or refute such conspiracies, either with or without providing evidence (see Appendix G for examples of conspiracy posts and debunking posts). The debunking posts were classified by their content and not restricted to any particular type of user or source. Overall, our findings suggest three important real-world implications.

The first key implication is that political parties, media, and public agencies should avoid purposefully or inadvertently propagating conspiracy narratives, as they not only misdirect the public’s attention during a public health crisis but can also breed long term harm such as declining trust towards governments and authorities (Freeman et al., 2020). Our findings suggest conspiracy narratives were a direct response to the deteriorating Sino-U.S. relationship, and in turn, debilitated the relationship even further, creating a precarious downward spiral. Conspiracies either covertly or overtly endorsed by the two countries’ political figures have exacerbated the problem and devastated international collaborations for global pandemic responses. 

Further, pandemic and conspiracy narratives carry highly contextualized cultural and political assumptions and nuances (Ding & Zhang, 2010; Jovančević & Milićević, 2020). As we show, prominent conspiracies about the origins of COVID-19 center on either human synthesis or biological weapons on Weibo. By contrast, popular conspiracies concerning 5G, Dr. Fauci, and Bill Gates in the United States and elsewhere are seldom mentioned on Weibo. Underlying the differences in conspiratorial arguments are different cultural and political orientations toward technology and government. For example, Chinese nationalism in the posts in portraying the United States as a political and economic threat fuels the bioweapon conspiracy. Correcting such conspiracies thus requires further addressing constructed nationalism. A practical implication is that efforts to mitigate conspiracy narratives need to work on increasing intercultural and international dialogues to identify common interests and values, and to dispel unfounded claims and misunderstandings. In this regard, we suggest government agencies, media, and educators work on developing more constructive and unbiased narratives of the pandemic and its global responses.

The second implication informs governmental policy on fighting against the susceptibility to conspiracy beliefs. Just as most conspiracy posts on Weibo faulted the United States for originating COVID-19, most debunking posts sought to absolve China from responsibility. The finding suggests that people may selectively endorse and share debunking messages that support their own group, resulting in an ideologically narrow flow of debunking messages to their followers (Shin & Thorson, 2017). Against the backdrop of increasing Sino-U.S. tension, it is challenging to engage the public when debunking certain conspiracy narratives consistent with one’s political or national identity. Communication strategies thus need to facilitate dissolving echo chambers around certain conspiracy narratives that politicize health issues (Del Vicario et al., 2016). For example, inoculation could be an effective strategy to reduce the public’s susceptibility to conspiracy beliefs (Roozenbeek & van der Linden, 2019). By giving small doses of conspiracy narratives and explicitly warning the public about how specific political motives (e.g., partisanship, international conflict) fuel each conspiracy narrative, we could help the public become more sophisticated at processing various information on social media.

 A final implication of this study concerns platform design around creating effective debunking strategies to counteract conspiracy posts. We showed that users were less engaged (i.e., less likely to retweet, like, comment) in debunking posts than conspiracy posts, which echoes previous work that false information is distributed significantly faster, farther, and more broadly than true information on social media (Vosoughi et al., 2018). Fighting conspiracy is a difficult battle, but our study highlights that influencers and verified organizational users with a larger following could help draw more user participation to debunking posts. Influencers and organizational users can be considered as critical seeds for disseminating debunking information through online social networks (Rubin, 2017). Social media platforms and public agencies may consider actively enlisting their help in the debunking process.

In sum, we propose the following practical recommendations: 

• Political parties, media, and public agencies should avoid citing nationalistic and politically motivated conspiracy narratives and make an effort to dispel conspiracy thinking through increased international dialogues.
• Public communication efforts can consider employing inoculation and media literacy education to decrease susceptibility to conspiratorial thinking.
• Social media platforms need to encourage trusted influencers, organizations, and scientists to disseminate debunking information.

Findings

Finding 1: Popular conspiracies about the origins of COVID-19 on Chinese social media differ remarkably from those in the United States. Conspiracies about COVID-19 as human-synthesized or bioweapon are prevalent on Weibo, and these posts attribute more responsibility to the United States than to China.

Figure 1 shows the number of posts that attribute responsibility to the United States, China, and other entities for each origin type and responsibility attribution comparing conspiracy posts vs debunking posts. We found that conspiracy origin types that dominate the Chinese social media differ from those in the United States and around the globe. In the United States or around the globe, conspiracy about 5G, Dr. Fauci, and Bill Gates are prevalent (Goodman & Carmichael, 2020; Mitchell et al., 2020). These conspiracies, however, constitute a small proportion of conspiracies on Weibo (4.95%). Prevalent conspiracies on Weibo focus on whether COVID-19 was deliberately made by country actors in labs or as bioweapons.

Comparing the attribution of responsibility between debunking posts and conspiracy posts, we found that people were more likely to debunk conspiracies that blame China while propagating conspiracies that blame the United States more frequently (χ²= 564.29, p < 0.01). Responsibility attribution to the United States and China also substantially differed between conspiracies about the natural/unknown origin of COVID-19 versus those about the deliberate formation of COVID-19. For conspiracy posts expressing belief that the origin of COVID-19 is natural/unknown, responsibility was attributed more frequently to China (31.07%) than those stating that COVID-19 was deliberately synthesized by humans (15.36%) or used as bioweapons (4.20%).

Finding 2: Conspiracies that blamed the United States for COVID-19 surged following Sino-U.S. conflicts

Conspiracy and debunking narratives, as well as responsibility attribution, evolved over time with an interesting pattern. Conspiracy posts surged when President Trump first referred to the coronavirus as the China Virus and announced sanctions on China such as the green card ban and the 5G cleaning plan on Huawei. While conspiracy posts surged during times of Sino-U.S. conflict, debunking posts surged when China’s cases surged around mid-February due to changes in diagnosis testing and when Trump said he would stop using the term China Virus on March 24, 2020 (Figure 2, top panel). This pattern of how Weibo posts evolved with Sino-U.S. conflict also persisted in terms of responsibility attribution of COVID-19. We found that posts that attribute responsibility to the United States for creating COVID-19 virus surged during times of Sino-U.S. conflict (Figure 2, bottom panel).

These findings on how conspiracies and responsibility attribution evolved with Sino-U.S. conflict underscore the pandemic as a catalyst for geopolitical conflicts, nationalism, and misinformation. Our findings echo the recent literature stressing that nationalism might harm the equal distribution of COVID-19 vaccines between the Global North and the Global South (Rutschman, 2020). As scholars in psychology explained, the mechanism of “identity-protective cognition”¹Identity-protective cognition refers to the phenomenon that individuals tend to adopt the beliefs that are held by members of their in-groups in order to protect the self-esteem and well-being of their identities. might facilitate the spread of science misperception (Kahan, 2017), as demonstrated by our empirical evidence that conspiracy theories and blame went hand in hand with Sino-U.S. conflicts. Moreover, the pandemic is reshaping power structures and international systems between China and the United States, intensifying the Sino-U.S. competition and rivalry (Basu, 2020; Fiona et al., 2020). Narratives focusing on the politics of blame between China and the United States have escalated from political speeches to media coverage (Jaworsky & Qiaona, 2020). Science communication has become politicized (Hart et al., 2020) and ideological (Wolfe, 2018). As science communication intertwines with political communication (Scheufele, 2014), it is vital to develop mutual understanding and meaningful dialogues between world powers to share responsibility for coping with pandemics and fighting misinformation. 

Finding 3: Men are more likely to propagate and debunk conspiracy posts than women; compared to the overall Weibo population, influencers and organizational users are overrepresented in debunking posts.

We found that the users who propagate conspiracy and who debunk conspiracy are similar in profile. Ordinary users, men, and users with followers between 100-1000 constitute the majority who post conspiracy as well as debunking posts. However, compared with the user profile of the overall Weibo population, a few notable trends emerge (Figure 3). 

Among the users propagating conspiracy posts, organizational users are overrepresented (5.19%) compared to the overall Weibo population (1.46%), while influencers are slightly underrepresented (7.96%) compared to the overall Weibo population (8.59%). Among the users debunking conspiracy posts, organizational users are again overrepresented (8.51%) in comparison with the overall Weibo population, while influencers are overrepresented (10.48%) when compared to the overall Weibo population (8.59%). Men are disproportionately more likely than women to post conspiracy (χ² = 108.52, p < 0.01) and debunking posts (χ² = 52.57, p < 0.01), compared to the gender composition in the overall Weibo population. 

Finding 4: Debunking posts have less user engagement than conspiracy posts; however, debunking can be more engaging when it comes from women and influencers and cites scientists.

In the baseline models with only user attributes and post type (conspiracy vs debunking posts), we found that, compared to conspiracy posts, debunking posts are associated with 10.06% (p = 0.07) decrease in user participation (i.e., retweets, likes, comments), but 11.96% percent (p < 0.01) increase in user mobilization (i.e., number of @ and hashtags to mobilize others). Although debunking posts are associated with lower participation, we found that the association is moderated by several factors. Within debunking posts, those posted by men received 36.87% less participation than those posted by women (Figure 4, panel A, right bars), while the same engagement gap for conspiracy posts between men and women was 13.93% (Figure 4, panel A, left bars). Within debunking posts, a 10% increase in the number of followers is associated with 4.08% increase in participation (Figure 4, panel B, blue line), while for conspiracy posts, a 10% increase in the number of followers is associated with 3.05% increase in participation (Figure 4, panel B, red line). For debunking posts, citing scientists as sources is associated with a higher level of mobilization (20.93%, p = 0.02) than those without citing sources (Figure 4, panel C, right bars). However, for conspiracy posts, citing scientists is associated with 3.92 percent lower mobilization than those without sources (Figure 4, panel C, left bars). 

Our finding that debunking posts by women received more participation than those posted by men responds to a growing body of literature that examines gender differences in public engagement with social media content. For instance, Jia et al. (2018) showed that female online video uploaders were more popular than most male uploaders. The gender differences in how debunking messages were engaged with might be due to the language differences women and men use in persuasion (Carli, 1990; Falk & Mills, 1996). Taking a close reading of the post content by women users that received many reposts, we found that these women used storytelling such as sharing about how COVID-19 has influenced their lives as oversea students. They also used more soft and tentative languages to discuss the origins of COVID-19 such as asking for people’s mutual understanding about COVID-19 issues, suggesting that people not eat wild animals, rather than using hard propaganda language to attribute responsibility with assertion, which could backfire on audience acceptance of the message senders (Huang, 2018). In revealing the nuance of these moderators (such as gender), our study provides fruitful future research directions such as investigating how debunking strategies could be matched with specific senders to increase public engagement with science.

Methods

We performed content analyses and regression analyses to examine conspiracy narratives and user engagement. COVID-19 related social media posts were retrieved from Weibo (the Chinese Twitter with 560 million monthly active users at the end of 2019) (Sina Weibo, 2020). However, Weibo does not provide application programming interface (API) access to independent researchers and limits keyword search output to 50 pages (around 1000 posts). To bypass these limitations, we utilized a large Weibo user pool of 250 million users (with bots filtered out) (Hu et al., 2020; Shen et al., 2020), which accounts for 48.1% of all monthly active Weibo users in 2019 (Sina Weibo, 2020). This user pool was originally built in 2018 and started from a list of 5 million active Weibo users collected in our previous studies unrelated to COVID-19 (Li et al., 2020; Zhang et al., 2020), along with a snowball sampling process.²Using a snowball sampling method, we then retrieved the initial 5 million users’ followers and followees (second degree users), the followers and followees of the second-degree users (third degree users), and so on until no new users appeared. This snowball process resulted in a pool of 250 million users (with bots filtered out) (Shen et al., 2020).

From the user pool, we retrieved COVID-19 related posts using a comprehensive list of 179 keywords (for a complete list, see Hu et al., 2020). After removing duplicates, we obtained a main corpus of 40,893,953 COVID-19 related Weibo posts between December 1, 2019 (the date of the first known COVID-19 case) and April 30, 2020. Drawing from academic, government, and news resources,³We drew from 1) earlier research on COVID-19 conspiracies (Imhoff & Lamberty, 2020; Leng et al., 2020), 2) Chinese fact-checking websites (e.g., Tencent Jiaozhen), 3) news websites (e.g., BBC), and 4) government websites (e.g., Embassy of People’s Republic of China in Germany). we found 35 COVID-19 conspiracy theories (e.g., “5G spreads virus”, “China utilizes COVID-19 to paralyze the Western economy”). We summarized keyword combinations for each specific conspiracy narrative (see Appendix A) via close observation of their relevant posts on Weibo, along with several rounds of back-and-forth discussions. These keyword combinations were adopted to filter the COVID-19 corpus, yielding 153,472 posts from Jan 1, 2020 to April 30, 2020. We removed duplicate posts and reposts as practiced by other studies (González-Ibánez et al., 2011; Shen et al., 2020), because (a) we focus on the narrative of the person who initiated the conspiracy, and (b) we study the number of retweets of a post as a dependent variable. The final dataset contained 6,735 unique original Weibo posts⁴Original posts are posts that start threads, not reposts or comments. about COVID-19 conspiracies dated from Jan 1, 2020 to April 30, 2020. These 6,735 original posts reached a large audience, generating 31,421 reposts, 260,355 likes, and 38,075 comments.

We developed a comprehensive coding scheme to manually annotate each post based on four dimensions: post types, origin types, responsibility attribution, and sources cited (see Appendix B for coding scheme). Post types focus on distinguishing posts that propagate conspiracies vs disapprove/refute conspiracies. Origin types concern the various theories on the origins of COVID-19 such as whether the source of COVID-19 is unknown or made by human actors. Responsibility attribution concerns the countries or entities who are pointed out in a post as responsible for causing the COVID-19 pandemic. Sources cited concerns the type of sources cited in a post such as from government, scientists, media, and so on. Six native Chinese speakers were trained and coded the posts independently, and satisfactory inter-coder reliability was achieved (see Appendix C). 

To examine the relationship between post type, origin types, responsibility attribution, and sources cited, we first calculated the frequency of each category (see Appendix C). To examine the association between post type and user engagement, two regression models were conducted. In the models, our two dependent variables are post participation including like number, repost number, and comment number and post mobilization including number of @s and hashtags in a post. In our baseline model, our independent variables include post type (conspiracy or debunking), user gender, user type, geolocation (Hubei or outside Hubei), emotional factors (i.e., emotion score, emotion polarity, and emotion types which were calculated drawing upon Zhang et al., 2017 and Zhao et al., 2016), and length of a post (see Appendix D). In the full model, we added hand-coded variables such as origin types, responsibility attribution, and source cited in addition to the baseline variables. To examine what debunking strategies might be associated with variation in user engagement, our full model also included the interaction of debunking and source cited, debunking and origin types, and debunking and user attributes (see Appendix E). 

Limitations and robustness

This study has several limitations. First, Weibo posts were collected retrospectively on May 16, 2020 and thus our dataset does not contain deleted or censored posts. However, this potential exclusion should not interfere with our conclusions as a previous study found that only 0.17% of all Weibo posts on COVID-19 were censored, and these censored posts were generally about the government’s missteps in COVID-19 response, not about COVID-19 origination (Fu & Zhu, 2020). Second, our study is exploratory in nature. Findings on associations between debunking strategies and user engagement and patterns of conspiracy and responsibility attribution evolution with Sino-U.S. conflicts should not be interpreted as causal. Finally, as our findings demonstrate, conspiracies prevalent on Chinese social media might differ significantly from those of other countries or other media systems. It will be fruitful for future research to examine major conspiracy theories emerged during COVID-19 in other countries to compare how conspiracy narratives might differ among various media systems. It will also be interesting to examine responsibility attribution by U.S. users on Twitter. In fact, some research has shown that over 78% of Americans blamed China for its role in spreading COVID-19 (Silver et al., 2020).