Changing Social Representations and Agenda Interactions of Gene Editing After Crises: A Network Agenda-Setting Study on Chinese Social Media

Abstract

By incorporating social representation theory with science communication and by using a critical milestone scientific crisis (i.e., the scandal of Chinese gene-edited human babies in 2018) as a dividing point, this study adopted a network agenda-setting approach to explore how various actors (i.e., scientists, the media, laypeople, and the government) engaged in the construction of social representations of the controversial issue of gene editing on Chinese social media (i.e., Sina Weibo). Based on large-scale social media data, supervised machine learning was employed to identify attribute categories, and semantic network analysis was used to construct attribute networks. Results reveal that after the 2018 crisis, gene editing received increasing social attention on Chinese social media. Further, two trends emerged in social representations of gene editing on social media: de-scientization and medialization. The following dynamic agenda interactions among various actors were found: On the one hand, the media and laypeople’s attribute network agendas converged while scientists and the media’s diverged after the scandal. This indicates a scientific crisis can serve as a trigger for agenda convergence and divergence among different actors online. On the other hand, there were constant agenda interactions, such as between the Chinese government and the media. This reveals a feature of Chinese science communication—the media not only mediates between scientists and the public, it also observes the government’s agenda closely when representing controversial scientific issues such as gene editing.

Keywords

social representation gene editing gene-edited human babies crisis network agenda-setting Sina Weibo

Emerging science and technology (S&T) usually arouse extensive media attention and public discussion, especially when a breakthrough or application causes public controversy (Nisbet et al., 2003). For instance, cloning is one of the most controversial genetic technologies, which is used to produce “a precise genetic replica of a biological object” (Kass et al., 1998, p. 10). Further, this technology has started to gain prominence in Western media and public agendas since the 1997 announcement of the birth of Dolly the sheep, the world’s first cloned adult mammal, which was born in Scotland. Many scholars have examined the media representations (Holliman, 2004; Petersen, 2002) and public perceptions (Nisbet, 2004; Wellcome Trust, 1998) of cloning within the Western context. Gene editing (also referred to as genome editing or genetic engineering) is a group of genetic technologies used to insert, delete, modify, or replace DNA in the genome of a living organism. This first became an issue for nationwide discussion after the announcement in 2018 of the births of Lulu and Nana, who were born in China and were the world’s first gene-edited human babies (GHB). Accordingly, this provides a good opportunity to understand how the controversial issue of gene editing is perceived and discussed in non-Western contexts (such as China), where genetic technologies have been advancing rapidly in recent years.

Since the 1980s, the field of science communication has witnessed a transformation from the idea of a public understanding of science (referred to as PUS hereafter), in which scientists and laypeople are differentiated by their levels of scientific expertise, into the concept of public engagement with S&T (referred to as PEST hereafter). Here, various actors (including scientists, the media, laypeople, and policy makers) articulate their opinions and values and negotiate policy for S&T (Jones, 2014; Schäfer, 2009). In this regard, social media are potential spaces for science communication because of their ability to involve various users in open discussions about scientific issues (Kouper, 2010; Wen & Wei, 2018). Science communication has emerged on social media and deserves special attention in China, where scientific advances have raced ahead (Zhou & Leydesdorff, 2006) and a unique social media landscape has developed over the last decade. Most Western-based social media are blocked in China, while indigenous social media have developed under some degree of state control and intervention. However, the extent to which various actors are engaged in discussing controversial S&T (such as gene editing) on Chinese social media remains unclear.

In this study, using the scandal of Chinese GHB as a dividing point, we adopted a network agenda-setting (NAS) approach to explore how various actors represented gene editing on Sina Weibo (referred to as Weibo hereafter). Although relatively unknown in the West, Weibo is one of the most popular social media sites in China. The NAS approach was developed from agenda-setting theory, which suggests a transfer of salience of objects and/or attributes describing an object from the media to public agenda (McCombs, 2004). The NAS approach (also known as the third level of agenda setting) asserts that the salience of network relationships between objects and/or attributes can also be transferred from news media to the public (Guo, 2012). Therefore, NAS can be adopted to explore the attributes of the gene-editing issue and the network relationships transferred between different actors.

Specifically, based on large-scale data, we conducted supervised machine learning, semantic network analyses, and a series of multiple regression quadratic assignment procedures (MRQAPs) to examine the attribute agenda networks of various actors (such as scientists, the media, laypeople, and the government). These actors actively engaged with gene editing on Chinese social media (i.e., Weibo). The outcomes from this study were as follows: (1) We explored how various actors engaged in the online discussions of controversial S&T (such as gene editing), extending the social representation theory to include both media and public representations in online settings; (2) we highlighted various actors’ attribute network agendas and their interactions in such online discussions, with NAS being first adopted in science communication to understand the process of PEST more fully; (3) we offered empirical and theoretical insights on science communication under a scientific crisis context; and (4) we provided a more nuanced understanding of science communication within the Chinese social media environment.

Literature Review

Pluralistic Actors Engaged With S&T

Over the past decades, science communication has seen a shift of focus from PUS toward PEST, exhibiting more open, egalitarian, and participatory communications regarding S&T (Kouper, 2010; Schäfer, 2009). Accordingly, science communication is no longer regarded as merely a vehicle to increase PUS by transferring scientific knowledge from scientists to the public, as it was from the perspective of PUS (Wynne, 1995). Instead, PEST highlights the need to engage scientists, laypeople, and policy makers in open discussions about S&T. Therefore, PEST is pluralistic in terms of participants, including various actors or groups within society, each having their own needs, interests, and levels of knowledge in science communication. Previous research from the perspective of actor-network theory (ANT; Latour, 2005) also holds that science is a collective process involving multiple actors including scientists, politicians, lawyers, journalists, academic colleagues, and users of knowledge (Fioravanti & Velho, 2010).

Scholars have identified the major actors who are engaged in science communication. Further, Burns et al. (2003) argued that “the public” engaged in science communication is a very heterogeneous group consisting of many overlapping actors. These actors include scientists, mediators (i.e., science communicators, the media, and other members of the media), decision makers (i.e., policy makers in government and scientific and learned institutions), general public, attentive public (i.e., the sector of the public who are interested in and well-informed about science), and interested public (i.e., the sector of the public who are interested in but not necessarily well-informed about science). Moreover, two key actors commonly contrasted in science communication of a particular field are the science community (scientists who are experts and are directly involved in some aspect of scientific practice and research) and lay public (or laypeople, including other scientists who are nonexpert in a particular field), who are also known as expert and nonexpert (Burns et al., 2003; Liang et al., 2019).

Various actors are increasingly becoming engaged in science communication, especially with the rise of social media, which can be considered a range of Internet-based applications designed for the creation and exchange of user-generated content (A. M. Kaplan & Haenlein, 2010). On social media, actors with different views and levels of scientific expertise are enabled to share their perceptions of and opinions on controversial scientific issues, forming multiple agendas online. However, research on such online agendas and their interrelationships remain lacking.

NAS as an Analytical Approach

NAS is an emerging analytical approach used to investigate different agenda networks. Traditional agenda-setting research focuses on transferring the salience of objects (such as issues and political figures; first level of agenda setting) and/or attributes that describe an object (second level of agenda setting) from the media agenda to the public agenda (McCombs, 2004). By contrast, NAS introduces network analysis into agenda-setting research and asserts that the salience of network relationships among objects and/or attributes can also be transferred from the news media to the public (Guo, 2012). Drawing insights from cognitive psychology, the idea of a network presents one’s cognitive understanding as a picture in which numerous nodes are connected to each other (S. Kaplan, 1973). In NAS, these nodes (or elements) can be either objects (or their attributes) or higher level constructs such as frames (Guo et al., 2015). Further, their interrelationships can be enacted through concurrent mentions of two or more elements (Carroll, 2015). Accordingly, NAS is considered the third level of agenda setting, with an emphasis shifted from individual, discrete elements to the overall picture of agendas (Guo et al., 2012).

Initially, NAS was mainly adopted in political communication research, where it was used to examine news media’s capacity to transfer the salience of network relationships among political issues and/or political candidates’ attributes to the public conscience (Guo & McCombs, 2011a, 2011b; Wu & Guo, 2017). For example, based on an analysis of data collected in the 2002 Texas gubernatorial election, the first NAS study found that the interrelationships among the political candidates’ attributes emphasized by news media were significantly correlated with the public’s perception of the candidates (Guo & McCombs, 2011a). With the rapid development of the Internet and social media, subsequent studies on NAS have examined the NAS effect of online partisan media (Vargo & Guo, 2017) and social media (such as Twitter; see Vargo et al., 2014). In addition, recent NAS studies have focused on the interrelationships of agenda networks among different media types (intermedia NAS; see Vargo et al., 2018) and different social media accounts (Z. Chen et al., 2019).

With regard to science communication, S&T was often examined as a general issue within national agendas to examine the traditional agenda-setting effect (Tan & Weaver, 2013). Drawing upon previous NAS studies, we adopted NAS to delineate the salient attributes of the gene-editing issue and their networks in the online agendas of gene editing and to investigate the interactions between various actors’ agendas.

Social Representations of Controversial S&T

With regard to the social representations of S&T, extant research can be divided into two approaches: media representations and public representations. The first approach was prevalent in the presocial media era, where the media (especially traditional mass media) played the central role of representing S&T in society (Plesner, 2012). This phenomenon has been characterized in theory as “medialization of science,” suggesting that media coverage of S&T is a key channel that the public rely on to gain scientific knowledge on and form their perceptions of S&T (Schäfer, 2009; Weingart, 1998). Thus, the media produces a unique medialized representation of S&T and provides an extensive framework for it to be understood by the public. Remarkably, the medialization of science includes both “science logic” (research-based claims) and “media logic” (news criteria based on journalists’ angles and editorial choices) when representing S&T (Plesner, 2012). With an emphasis on representing the perspectives of scientists, the public, the government, and society, media logic also engenders “de-scientized” narratives of science (Plesner, 2012; van Dijck, 2006). For instance, the media representation of S&T usually includes both science-orientated frames (such as scientific progress and factual) and social-orientated frames (such as ethics, social impact, and the economy; Nisbet & Lewenstein, 2002; Ruan et al., 2019). Accordingly, science is interpreted in a more comprehensive and multifaceted way, resulting in a de-scientized social representation of S&T.

Media representations of genetic technologies and their applications in particular (Petersen, 2001; Ruan et al., 2019) and controversial S&T such as biotechnology (Listerman, 2010; Nisbet & Lewenstein, 2002), nuclear power (Gamson & Modigliani, 1989), geoengineering (Luokkanen et al., 2014), and nanotechnology (Anderson et al., 2005) in general provide a reference for our attribute categorization of gene editing. For instance, Nisbet and Lewenstein (2002) analyzed biotechnology-related coverage in New York Times and Newsweek between 1970 and 1999, from which they developed a typology of frames applicable to biotechnology. These were progress, economics, ethics, Pandora’s Box, runaway, nature/nurture, public accountability, and globalization. With regard to genetic technologies and their applications, genetically modified organisms (GMO) and genetically modified foods (GMF) are frequently examined issues. Scholars have examined various frames or themes used in the media coverage of GMO or GMF across different countries (Ruan et al., 2019), across different types of media (Vicsek, 2013), and over time. For example, a recent study comparing news coverage of GMO in the United Kingdom, the United States, and China identified seven frames: factual, conflict, human interest, regulation, morality, economic consequences, and leadership (Ruan et al., 2019). More recently, Du et al. (2020) conducted a qualitative content analysis of Chinese news articles regarding gene editing published between 2006 and 2017 and identified key themes such as ethical concerns, risks, and benefits.

The second approach (public representations of S&T) has recently attracted increasing scholarly attention with the prevalence of social media, as audiences increasingly consult social media sources for information about specific scientific issues (Brossard & Scheufele, 2013). In contrast to media representations, public representations emphasize the roles of multiple members of public in collectively representing S&T by elaborating and sharing their own ideas and values on scientific topics. Regarding public representations of controversial S&T, recent studies have provided some insight into the public’s views of gene editing. For instance, Calabrese et al. (2019) conducted a semantic network analysis of tweets regarding gene-edited babies and identified four major relevant themes: research/applications, the “CRISPR babies” event, agricultural regulations for biotechnology, and advancements in muscular dystrophy research.¹ Similarly, by conducting a survey on the Chinese public’s cognition of and attitudes toward gene-editing technology, L. Chen and Zhang (2018) outlined the key issues in public perceptions of gene editing: technology application, scientific progress, ethics, and law and regulation.

In summation, various actors other than the media have a say in representing scientific issues in the social media era. Therefore, we adopted a social representations theory suggesting that when society faces a new phenomenon or issue, widely shared ideas about it may emerge spontaneously (Washer, 2004). That is, social representations of gene editing on social media involve both media and public representations, which is consistent with the principle of PEST. Specifically, a preliminary attribute framework was developed by adopting the available categories in previous literature combined with a semiopen manual coding procedure to determine the final attribute categories used in our study.

Gene Editing Crisis in China: The GHB Scandal

Recently, a scientific crisis known as the scandal of Chinese gene-edited human babies (hereafter, the GHB scandal) first brought the controversial issue of gene editing into the national discourse in China. Jiankui He was an associate professor in the Department of Biology of the Southern University of S&T in Shenzhen. On November 26 (2018), he announced that the world’s first twin baby girls had been born in China who were naturally immune to the human immunodeficiency virus through CCR5 genes edited by using CRISPR-Cas9. This announcement immediately shocked the world and triggered widespread criticism due to unresolved technical and ethical concerns. Technically, using CRISPR-Cas9 might cause off-target mutations, causing potential health risks. Morally, there were ethical problems such as questionable scientific value, unreasonable risk-benefit ratio, an illegitimate ethics review, and invalid informed consent (J. Li, Walker, et al., 2019). Hundreds of Chinese and international scientists cosigned a letter to condemn He for conducting human embryo gene editing intended for reproduction (Cyranoski & Ledford, 2018). Chinese authorities immediately suspended He’s research activities and launched an investigation. On December 30 (2019), He was sentenced to three years in prison and fined 3 million yuan (Greely, 2019).

The GHB scandal has been widely regarded as a critical milestone scientific crisis in the world, receiving intensive and widespread criticism from scientists and the public (Kolata & Belluck, 2018). Some regarded GHB as extremely irresponsible behavior that violated the ethical consensus of scientists (Wang & Yang, 2019). Generally, the scandal was not just a crisis within the scientific community—it also damaged the public trust in gene editing and could cause a public opinion reversal regarding gene editing (Cyranoski & Ledford, 2018). Before the GHB scandal, public opinion polls in China, the United States, and Japan found widespread support for gene editing among the public (L. Chen & Zhang, 2018; Funk & Hefferon, 2018; Regalado, 2018; Uchiyama et al., 2018). Accordingly, scientific crises such as the GHB scandal can constitute a significant threat to the reputation of scientists and even the scientific community, which in this case can reshape the ways in which various actors perceive and represent gene editing.

Media coverage of biotechnology in the 1980s and early 1990s was overwhelmingly positive and was dominated by the frames of scientific progress and economic prospects in Western countries (Nisbet & Lewenstein, 2002). However, a departure from this trend occurred following the debate over cloning in the late 1990s. This was aroused by the first cloning case of the Dolly sheep, as news media increasingly framed biotechnology (or more specifically, genetic technology) as a safety risk and an ethical concern (Listerman, 2010). Moving to the Chinese context, we pondered whether the same trend occurred in Chinese social representations of gene editing after the GHB scandal. According to a previous study conducted by Du et al. (2020) before the GHB scandal, the press coverage of gene editing tended to be largely favorable toward human gene editing. In addition, L. Chen and Zhang (2018) found that the Chinese public were generally supportive of gene-editing research and applications, although they mostly reported as knowing little about gene editing. Accordingly, the question of how gene editing is socially represented by Chinese major actors following the GHB scandal remains unclear.

Since the GHB scandal, gene editing has been increasingly discussed online by four major types of actors: professionals, general journalists, laypeople, and the government (X. Zhang et al., 2021). However, few studies have examined the salient attributes of the gene-editing issue and the way these attributes are structured by various actors on their own agendas before and after this crisis. Therefore, using the GHB scandal as a dividing point, we split online discussions on gene editing into two phases: Phase 1 (before the GHB scandal, January 1, 2010²–November 25, 2018) and Phase 2 (after the GHB scandal, November 26, 2018–December 31, 2019). Further, by drawing on the work of X. Zhang and colleagues (2021), we categorized actors engaged in such online discussions into four types: scientists, the media, laypeople, and the government. The following research questions were proposed:

Research Question 1: Are there any changes in social attention regarding gene editing between the two phases on Weibo?

Research Question 2: What are the salient attributes in the four actors’ agendas when discussing the gene-editing issue on Weibo? Are there any changes between the two phases?

Research Question 3: How do the four actors’ attribute network agendas correlate with each other when discussing the gene-editing issue on Weibo? Are there any changes between the two phases?

The interactions between various actors’ agendas on social media remain unclear. Moreover, actors on social media with different standpoints and scientific expertise can influence and be influenced by each other’s agendas. Therefore, we examined the attribute network agendas and their interactions among four major actors (scientists, the media, laypeople, and the government) who have actively engaged in Chinese online discussions of gene editing (X. Zhang et al., 2021). In science communication, laypeople are usually assumed to be informed about scientific knowledge by scientists (Wynne, 1995), while the media usually mediates between scientists and laypeople to shape public perceptions of S&T (Leydesdorff & Hellsten, 2005). Within the Chinese context, previous studies have suggested that the government plays an important role in setting online agendas (Jiang, 2014; Luo, 2014). Therefore, we hypothesized that the way laypeople discuss gene editing on Weibo would be correlated to the way scientists, the media, and the government talk about it during each phase:

Hypothesis 1: The attribute network of laypeople is positively correlated to that of (Hypothesis 1a) scientists, (Hypothesis 1b) the media, and (Hypothesis 1c) the government when discussing the gene-editing issue on Weibo during Phase 1.

Hypothesis 2: The attribute network of laypeople is positively correlated to that of (Hypothesis 2a) scientists, (Hypothesis 2b) the media, and (Hypothesis 2c) the government when discussing the gene-editing issue on Weibo during Phase 2.

Given the media “play a central role in publicizing science and managing the interface between science and society” (Jensen, 2012, p. 42), it is important to determine who influences the media agenda on gene editing. First, as major sources of scientific news, scientists have made a concerted effort via the media to promote public science literacy while generally being critical of media coverage for its sensationalism and inaccuracies (Besley & Nisbet, 2013). Second, China’s news media has been subjected to state control and intervention, usually in the form of censorship (E. Zhang & Fleming, 2005). Considering Weibo is part of the Chinese media system, it is logical to assume the agendas of the Chinese government and scientists are correlated with the media agenda when discussing gene editing online. Therefore, we proposed the following two further hypotheses:

Hypothesis 3: The attribute network of media is positively associated with that of (Hypothesis 3a) scientists and (Hypothesis 3b) the government when discussing the gene-editing issue on Weibo at Phase 1.

Hypothesis 4: The attribute network of media is positively associated with that of (Hypothesis 4a) scientists and (Hypothesis 4b) the government when discussing the gene-editing issue on Weibo at Phase 2.

The increasing medialization of science documented in previous studies highlights an adaptation to media criteria among scientists as a response to the growing necessity of legitimating science through public communication (Peters, 2012; Peters et al., 2008). Scientists have become increasingly involved in PEST activities to gain public acceptance and, more importantly, to obtain government funding for their research (Rödder, 2009). As Schäfer (2009) suggests, science itself is “increasingly influenced by other societal realms, most important, by politics, economics, and the media” (p. 476). Especially regarding S&T in China, research indicates that the rapid biotechnology development predominantly originated within state-sponsored programs (F. Zhang et al., 2011). In this regard, the agendas of the media, laypeople, and the government might in turn have a degree of influence on scientists’ agenda of gene editing on Weibo, which is an emerging space for PEST in China. Thus, we also proposed the following two further hypotheses:

Hypothesis 5: The attribute network of scientists is positively associated with that of (Hypothesis 5a) the media, (Hypothesis 5b) laypeople, and (Hypothesis 5c) the government when discussing the gene-editing issue on Weibo during Phase 1.

Hypothesis 6: The attribute network of scientists is positively associated with that of (Hypothesis 6a) the media, (Hypothesis 6b) laypeople, and (Hypothesis 6c) the government when discussing the gene-editing issue on Weibo during Phase 2.

Method

Data Collection

To answer the above questions, an NAS analysis was performed on Weibo posts to determine the dynamics of changing social representation and agenda interactions regarding gene editing on Chinese social media. Posts related to gene editing were retrieved from Weibo, which is one of the largest social media platforms in China (similar to Twitter) with 560 million monthly active users at the end of 2019 (Sina Weibo, 2020). As independent researchers have limited access to Weibo API, we used the keyword-based query function provided by Weibo to retrieve posts related to gene editing. Following best practices for social media data collection (Y. Li, Luo, & Chen, 2019; Shen et al., 2020; X. Zhang et al., 2020), a Python-based program was designed to retrieve posts with the keyword combination of “gene” (“基因”) and “editing” (“编辑”) from January 1, 2010² to December 31, 2019. After removing duplicate posts and reposts, a final data set of 27,682 original posts (posts that started/initiated the thread, also known as thread post/original post) were retained for analysis.

Variables

Attributes of the gene-editing issue

The key coding category for the content analysis was the attributes mentioned in each post to represent gene editing. Pilot content analysis was performed using semiopen coding to define the attribute categories. Drawing upon a recent study on Chinese online discourses pertaining to gene editing (X. Zhang et al., 2021) and a preliminary analysis of 415 randomly sampled posts, we identified the following nine attribute categories: (1) science development, which is informing or celebrating new developments and important events of gene-editing technologies; (2) science risk, which is informing potential risks caused by gene-editing technologies; (3) law and regulation, which is discussing or calling for laws and regulation on gene-editing research and practice; (4) ethics, which is discussing moral or ethical issues related to gene-editing S&T; (5) economics, which is discussing economic or capital issues related to gene-editing research and practice; (6) social issues, which is discussing social issues caused by or associated with gene-editing technologies (such as inequality); (7) scandal, which is informing or condemning science scandals and their subsequent development (such as the GHB scandal); (8) scientist, which is introducing or evaluating controversial or outstanding scientists (such as He Jiankui); and (9) international issues, which is discussing international competition or conflicts on or reflected by gene-editing research and practice. Example posts of each attribute are listed in Table 1.

Table 1.

Attribute Categories With Examples.

Attributes	Example
Science development	Breaking news! Solving the global problem, #The world’s first genetically engineered pig with Huntington’s disease was born in China!
Science risk	CCR5 gene editing may risk shortening one’s life
Law and regulation	Professor Wang Yue interpreted gene-editing technologies from legal perspectives: If statutory procedures are violated or there are adverse consequences, the implementing agency and parents should bear corresponding responsibilities
Ethics	The Minister of S&T talks about gene editing: Restrict scientific research ethics and let more people in the scientific community know how to be in awe of science
Economics	It was really scary to think about “Gene-edited human babies”: Is the accelerating capitalization of medical treatment “Resident Evil” or “Beauty New Human”?
Social issues	Hawking’s final warning during his lifetime: The rich will modify their genes to produce “super humans”
Scandal	Guangdong province conducted an initial investigation into the “gene-edited human baby incident”
Scientist	Disclose He Jiankui, a genetic madman: He conducted a secret experiment for 20 months and pushed the risk to the patient, and his company raised 218 million yuan
International issues	The first CRISPR anti-cancer human trial in the United States is about to start, and the Sino-US competition starts

Actors

Drawing on the study by X. Zhang and colleagues (2021) about Chinese online discussions over gene editing and based on account information (i.e., screen name, account brief introduction, authentication information, user labels, occupational information, and education information) of each post (see the Online Supplementary Material), two coders manually distinguished actors into four categories. The categories were as follows: (1) the government, including different levels and departments of the Chinese government, such as the Commission of Politics and Law of Guangdong Province and the People’s Procuratorate of Wuzhou City; (2) scientists, who are broadly defined as scientific professionals in this study. This includes individuals and various organizations that claim to have expertise in science or more specifically in relevant fields of genetics, bioscience, and medical science. Some examples here include Doctor Hongtao Sun (known as a cardiovascular surgeon and health blogger), Guokr (an S&T information website), and Science and Technology Daily; (3) the media (or general news media), including non-science-focused news media and those who are working in non-science-focused news media. Examples include Witness Yanpeng (known as a news reporter of Shenzhen Media Group), ChinaNews Online (which is a news website launched by the China News Service), and People’s Daily; and (4) laypeople, including those who explicitly show their identities (other than for the above three categories) or do not share their identities. Among the collected posts, 15.19% (n = 4,205), 17.78% (n = 4,923), 21.84% (n = 6,047), and 45.18% (n = 12,507) were generated by the government, scientists, the media, and laypeople, respectively.

Content Analysis

For the formal manual content analysis, 20% of posts were randomly sampled from the data set, yielding a sample of 5,536 posts. Two Chinese native speakers were trained and then they coded the posts independently. The intercoder reliability was satisfactory, with the Krippendorff’s αs for science development, science risk, law and regulation, ethics, economics, social issues, scandal, scientist, and international issues being 0.83, 0.94, 0.91, 1.0, 0.79, 0.85, 0.92, 0.87, and 0.85, respectively.

Computer-assisted content analysis combined with supervised machine learning techniques was conducted to classify the remaining posts under the aforementioned attributes of the gene-editing issue. For training the predictive models, we employed various algorithms (i.e., AdaBoost and Gradient Boosting) and tuned parameters (see the Online Supplementary Material) to assess their performance scores (see Table 2). The optimal predictive model was selected for each attribute (based on the relative performance score) which was then applied to the entire dataset. Results of the computer-assisted content analysis were then compared with results of the manual content analysis, which were considered valid with an average agreement of 0.90 across attributes.

Table 2.

Performance of Each Classifier With Nine Attributes.

Classifier	Attributes of the Gene-Editing Issue
Classifier	1	2	3	4	5	6	7	8	9
AdaBoost
Accuracy	.925	.960	.958	.970	.953	.970	.867	.982	.934
Precision	.942	.971	.957	.967	.960	.964	.906	.981	.924
Recall	.942	.970	.957	.967	.960	.964	.906	.980	.922
F1	.942	.970	.957	.967	.959	.964	.906	.979	.923
Gradient boosting
Accuracy	.925	.971	.951	.973	.943	.974	.896	.985	.944
Precision	.946	.972	.956	.978	.951	.967	.907	.991	.953
Recall	.945	.970	.954	.978	.947	.967	.906	.990	.949
F1	.945	.970	.954	.978	.946	.967	.906	.978	.949

Note. 1 = science development; 2 = science risk; 3 = law and regulation; 4 = ethics; 5 = economics; 6 = social issues; 7 = scandal; 8 = scientist; 9 = international issues.

The boldface values indicate better performance scores for one classifier compared with another classifier for each issue; the classifier with boldface values was thus selected as the formal predictive model for each issue.

Network Analysis

Network analysis greatly enriches agenda-setting research by examining the relationship between different nodes, offering more meaningful insights compared to simply examining individual nodes (Guo, 2012). Accordingly, data from the content analysis were then transferred to matrices to conduct a network analysis. The matrices were constructed to reflect the associations among the nine attributes, and the weight between each pair of attributes was calculated based on the frequency of the two attributes’ co-occurrences in the same post. Each matrix contained nine rows and nine columns, with the entry in each cell representing the degree of association between the two corresponding attributes. The same approach was used to create matrices that represent the attribute networks of different Weibo accounts. The posts generated within the two studied time periods (January 1, 2010–November 25, 2018; November 26, 2018–December 31, 2019) were then respectively divided into four groups based on Weibo user-account categories. For each group, a matrix of attribute networks was created, generating a total of eight matrices (4 × 2). To determine the statistical correlations between agendas, the MRQAP combined with double semipartialing permutation was adopted to evaluate the unique effect of each independent network on the dependent network while partialing out the effects of other networks (Dekker et al., 2007).

Results

Regarding Research Question 1, we outlined the distribution of posts related to gene editing across time. While gene editing received relatively little attention on Weibo before the 2018 GHB scandal (n = 5,655, 20.43%), extensive attention was subsequently received (n = 22,027, 79.57%). Specifically, among such generally increasing social attention toward gene editing, we observed an obvious growth in media (from n = 1,034, 18.28% to n = 5,013, 22.76%) and public (from n = 2,056, 36.36% to n = 10,451, 47.45%) attention after the GHB scandal.

Research Question 2 examined the dynamics of the attributes regarding the gene-editing issue emphasized by the four major actors across two phases. According to Table 3, the three attributes most frequently mentioned by the government during Phase 1 were science development, science risk, and international issues, which changed to scandal, science development, and law and regulation during Phase 2. For scientists, the three most salient attributes during Phase 1 were science development, international issues, and science risk, which changed to scandal, science development, and scientist during Phase 2. The three most prominent attributes mentioned by the media during Phase 1 were science development, science risk, and international issues, changing to scandal, scientist, and ethics during Phase 2. For laypeople, the attribute salience of scientist, scandal, and social issues increased significantly, becoming the top three discussed attributes during Phase 2. In contrast, the top three attributes during Phase 1 were science development, international issues, and scandal.

Table 3.

Attribute Distribution by Actors in the Two Phases.

Issues	Government %(R)		Scientists %(R)		Media %(R)		Laypeople %(R)
Issues	Phase 1	Phase 2	Phase 1	Phase 2	Phase 1	Phase 2	Phase 1	Phase 2
1	82.30% (1)	30.12% (2)	80.35% (1)	44.16% (2)	73.50% (1)	29.80% (5)	69.70% (1)	5.98% (5)
2	35.94% (2)	12.76% (7)	15.15% (3)	16.04% (6)	27.18% (2)	16.14% (7)	10.89% (4)	5.69% (6)
3	4.56% (6)	29.54% (3)^↑	7.00% (5)	15.28% (7)	5.51% (5)	28.94% (6)	5.30% (7)	3.77% (7)
4	3.26% (7)	23.11% (6)^↑	4.26% (6)	18.36% (5)^↑	2.22% (8)	32.04% (3)^↑	3.65% (8)	9.77% (4)^↑
5	5.10% (5)	0.76% (9)	3.89% (7)	2.62% (9)	3.97% (6)	2.15% (9)	6.32% (6)	2.53% (9)
6	2.39% (8)	29.11% (4)^↑	3.89% (8)	18.42% (4)^↑	3.29% (7)	31.96% (4)^↑	7.54% (5)	17.29% (3)^↑
7	5.97% (4)	64.89% (1)^↑	7.60% (4)	47.76% (1)^↑	11.80% (4)	65.55% (1)^↑	11.43% (3)	51.66% (2)^↑
8	1.41% (9)	27.38% (5)^↑	3.22% (9)	32.39% (3)^↑	1.74% (9)	43.71% (2)^↑	3.06% (9)	68.86% (1)^↑
9	13.14% (3)	5.30% (8)	21.96% (2)	12.99% (8)	17.99% (3)	9.16% (8)	21.21% (2)	3.43% (8)
Total N	921	3,284	1,644	3,279	1,034	5,013	2,056	10,451

Note. 1= science development, 2 = science risk, 3 = law and regulation, 4 = ethics, 5 = economics, 6 = social issues, 7 = scandal, 8 = scientist, 9 = international issues.

% refers to the percentage of the attribute in the corresponding time period. R refers to the rank order.

^↑indicates that the attribute rank increased compared to the former phase.

Research Question 3 (and the six subhypotheses) was asked to investigate possible associations between the agendas of the four major actors in each phase. With regard to Hypothesis 1, while our results indicate that the attribute networks of laypeople were significantly positively associated with those of scientists, there was no significant correlation with those of the government and the media during Phase 1 (see Table 4, Model 1). Hence, Hypothesis 1 is partially supported, Hypothesis 1a is supported, and Hypotheses 1b and c are rejected. With regard to Hypothesis 2, our results show that the attribute networks of laypeople on Weibo were separately and significantly associated with those of the government, scientists, and the media during Phase 2 (see Table 4, Model 4). Further, the association appeared to be strongest between laypeople and the media (β = 1.466, p < .01) followed by those between laypeople and scientists (β = 0.721, p < .01) and between laypeople and the government (β = −1.443, p < .001). This supported Hypotheses 2a–c. With regard to Hypotheses 3 and 4, our results reveal that the attribute networks of the media were significantly positively associated with those of scientists and the government during Phase 1 (see Table 4, Model 2). Further, the association appeared to be stronger between the media and the government (β = 0.700, p < .001) than between the media and scientists (β = 0.353, p < .01). This supported Hypotheses 3a and b while rejecting Hypothesis 3c. With regard to Hypothesis 4, our results (see Table 4, Model 5) indicate that the attribute networks of the media were positively correlated to those of the government (β = 0.820, p < .001) and laypeople (β = 0.205, p < .05); however, they were not significantly associated with those of scientists (β = 0.025, p > .05). Hence, Hypothesis 4 was partially supported. For Hypothesis 5, the results show that the attribute networks of scientists were positively correlated to those of the media (β = 1.478, p < .001) and laypeople (β = 0.368, p < .001) and significantly negatively associated with those of the government (β = −0.912, p < .01). This demonstrated that Hypotheses 5a and b were supported while Hypothesis 5c was rejected (see Table 4, Model 3). With regard to Hypothesis 6, only Hypothesis 6a was supported with a significant correlation in the attribute networks between scientists and the media (β = 0.1.459, p < .001). However, Hypothesis 6c was rejected due to a significant negative association in the attribute networks between scientists and the government (β = −0.604, p < .001). Further, Hypothesis 6b was rejected due to the nonsignificant correlation in the attribute networks between scientists and laypeople (see Table 4, Model 6).

Table 4.

MRQAP Analysis of the Attribute Network Agendas of the Four Types of Actors.

Attribute Networks		B	β	Adjusted R²
Model 1
T1—Laypeople	T1—Government	−0.287	−0.231	.907***
	T1—Scientists	1.186**	1.162**
	T1—Media	0.086	0.064
Model 2
T1—Media	T1—Government	0.646***	0.700***	.992***
	T1—Scientists	0.292**	0.353**
	T1—Laypeople	0.003	0.005
Model 3
T1—Scientists	T1—Government	−1.016**	−0.912**	.968***
	T1—Media	1.785***	1.478***
	T1—Laypeople	0.329***	0.368***
Model 4
T2—Laypeople	T2—Government	−3.383***	−1.443***	.735***
	T2—Scientists	2.429**	0.721**
	T2—Media	1.678**	1.466**
Model 5
T2—Media	T2—Government	1.678***	0.820***	.963***
	T2—Scientists	0.074	0.025
	T2—Laypeople	0.179*	0.205*
Model 6
T2—Scientists	T2—Government	−0.252***	−0.604***	.965***
	T2—Media	0.346***	1.459***
	T2—Laypeople	0.077	0.161

Note. MRQSP = multiple regression quadratic assignment procedure.

*p < .5.**p < .01. ***p < .001.

Figure 1 illustrates how the four major types of actors on Weibo structured different attributes to characterize the gene-editing issue. Each node represents an attribute, while an edge connecting two nodes means that the two attributes appeared together in the corpus. The thickness of edges represents the co-occurrence frequency, with thicker edges representing a stronger connection between the two attributes. The size of each node corresponds to the salience of each attribute on the four actors’ agendas on Weibo.

Figure 1.

Attribute networks by actors on Weibo before and after the gene-edited human babies scandal.

Discussion

Multiple attribute network agendas of gene editing and their interactions among four major actors on Weibo were examined in addition to determining how they changed after the GHB scandal. Accordingly, this study represents one of the first attempts to investigate changing social representations of controversial scientific issues within a crisis context in science communication research. The findings suggest the agenda-setting process within a scientific crisis context might differ from the well-documented agenda-setting process in daily science communication. The results enhanced our understanding of changing social representations of gene editing, especially after such critical milestone scientific crises as the GHB scandal. Further, they provided insights into the dynamic agenda interactions among major actors engaged with S&T on Chinese social media.

Increasing Social Attention and Changing Social Representations: De-Scientization and Medialization

As Brader et al. (2008) argues, the self-initiated behavior of social media users (such as posting or forwarding a tweet on specific issues) could represent social attention more effectively and unobtrusively. Therefore, the dynamics in the number of relevant Weibo posts show a change in social attention toward gene editing. Specifically, while gene editing as an emerging biotechnology received sparse social attention before the GHB scandal, online discussions about gene editing gained momentum sharply afterward, unprecedentedly increasing the salience and visibility of the gene-editing issue on the social agenda.

After the GHB scandal, two trends emerged in social representations of gene editing on Chinese social media: de-scientization and medialization. De-scientization refers to a recontextualization of science, a de-scientized narrative of science facts, or a shift from “science logic” (with an emphasis on research-based claims) to “social logic” (that places science in a peripheral position when representing scientific issues; Plesner, 2012; van Dijck, 2006). According to the results of the rank-order analysis, the salience rank of attributes regarding the gene-editing issue altered significantly after the GHB scandal, indicating that social representations of gene editing were reshaped substantially. Specifically, compared to the precrisis period, attributes including scandal, scientists, social issues, and ethics increased their prominence on all the four actors’ agendas in the postcrisis period (see Table 3). Overall, the attributes’ ranking of the four major actors changed disruptively after the crisis. Social representations of gene editing have shifted from being science- to social-oriented, with the online discussion focus distracted from science development, international issues, and science risk to scandal, scientists, and law and regulation.

Second, it is widely assumed that science is medialized, which has manifested in two ways: increasing media attention toward science on the one hand and increasing orientation of science toward media on the other hand (Peters, 2012; Peters et al., 2008; Schäfer, 2009). When applied to controversial gene-editing technologies that have been rapidly developed in China, our empirical results reveal a medialization of gene editing on Chinese social media, especially after the GHB scandal. First, media attention on gene editing has substantially increased on Weibo since the GHB scandal. Second, according to the results of our MRQAP analysis, scientists’ attribute networks of gene editing have always been positively associated with the media’s attribute networks before and after the GHB scandal (see Table 4, Models 3 and 6). In other words, there has been a tight coupling between scientists and the media in terms of their agendas regarding gene editing in the context of Chinese social media. This is in accordance with Weingart’s (1998, 2012) observations of the medialization of science in the context of Western mass media.

Convergence and Divergence: Dynamic Gene-Editing Agenda Interactions Among Actors

In addition to the aforementioned agenda interactions between scientists and the media, our results reveal two constant agenda interactions: between laypeople and scientists and between the media and the government. Specifically, laypeople’s attribute networks of gene editing have always been positively associated with scientists’ attribute networks (see Table 4, Models 2 and 5). Similarly, the media’s attribute networks of gene editing have always been positively correlated to the government’s attribute networks (see Table 4, Models 1 and 4), regardless of whether the GHB scandal happened. These results render empirical support for the important role of scientists in providing laypeople with the resources they need to form rational perceptions of controversial S&T (Haran & O’Riordan, 2018). Further, these results reveal a feature of online science communication within the Chinese context: Chinese media not only mediates between science and the public (Leydesdorff & Hellsten, 2005), it also observes the government’s agenda on social media closely when representing controversial scientific issues such as gene editing.

It should be noted that significant changes can emerge in some agenda interactions between particular actors in the aftermath of a critical milestone scientific crisis. This can result in agenda convergence between some actors (emphasizing the same attributes in different actors’ agendas) while leading to agenda divergence between others (emphasizing different attributes in different actors’ agendas). For example, after the GHB scandal, media’s attribute networks of gene editing started to exhibit a positive correlation with laypeople’s attribute networks (see Table 4, Models 1 and 4), while scientists’ attribute networks of gene editing lost its once significant influence on the media’s attribute networks (see Table 4, Models 2 and 5). First, the change in agenda interaction between the media and laypeople can result from the tendency of individuals seeking reliable information to reduce uncertainty about what happened during and after a crisis (Boyle et al., 2004). As previous studies have demonstrated, information produced by journalists affiliated to broadcast, print, and online news media have been considered the most reliable sources for accurate crisis communication (Littlefield & Quenette, 2007). Therefore, in our case, laypeople can be motivated to seek information from media accounts on Weibo to learn about the scandal, resulting in the increasingly convergent attribute networks of laypeople and media when discussing the gene-editing issue online. Such agenda convergence suggests that these two types of actors share an increasingly similar agenda after the crisis.

Second, the change in the agenda interaction between scientists and the media indicates that critical milestone scientific crises such as the GHB scandal can result in agenda divergence among major actors who once shared identical network agendas. According to our results, increasingly dissimilar agendas have emerged between scientists and the media when discussing gene editing after the crisis. This agenda divergence can be explained partially by the different standpoints and interests of scientists and the media engaged in such online science communication, especially after the scientific scandal happened. In brief, scientists are interested in mitigating reputational harm to the science community (Coombs & Holladay, 2002) and present a positive view of relevant S&T to “secure public confidence which is necessary for the continuation of funding for research” (Petersen, 2002, p. 76). In contrast, journalists in their gatekeeper functions attempt to provide accurate and timely information regarding scientific scandals (van der Meer et al., 2017), which could result in different issue priorities on their respective agenda of gene editing.

Limitations and Conclusions

There were a few limitations of this study. First, we used the keyword “gene editing” to search and retrieve related posts from Weibo, which was reasonable for Phase 1 (before the GHB scandal). However, for Phase 2 (after the scandal), users might have discussed this issue with others relevant keywords such as Jiankui He, Renli Zhang, and Jinzhou Tan, who were the notorious scientists involved in the GHB scandal. This could result in incompleteness of data. Second, we collected posts subsequently (rather than in real time), which might cause the loss of quite a number of posts because of censorship or self-deletion. Finally, to provide more insights into the agenda interactions among actors across time, future research could conduct a time-series analysis to test the actual agenda-setting effects, or in other words, the casual relationships among different actors’ agendas.

In conclusion, the social representation of gene editing was examined in this study using large-scale data. This extends the current literature with a focus on media representations that emphasize science- or media-dominated influences on defining S&T and shaping public perceptions of scientific issues (McCombs & Shaw, 1972; Nisbet & Lewenstein, 2002). According to the ANT, scientific knowledge reproduction is a procedure of interactions involving scientists and different groups of actors (Latour, 2005). As a part of such knowledge reproduction, science communication is not only an exclusive activity between scientists and media but also a collective and interactive activity that can emerge, evolve, and advance as a result of negotiations among different actors (Fioravanti & Velho, 2010). This study helps to increase our understanding of the increasingly de-scientized and medialized social representations of controversial S&T in general and gene editing in particular. Moreover, our findings suggest that scientific crises such as the GHB scandal can reshape social representations and agenda interactions between different actors because crises usually have an adverse impact on public trust in the science community (Cyranoski & Ledford, 2018).

Supplemental Material

Supplemental Material, sj-pdf-1-ssc-10.1177_0894439321998066 - Changing Social Representations and Agenda Interactions of Gene Editing After Crises: A Network Agenda-Setting Study on Chinese Social Media

Supplemental Material, sj-pdf-1-ssc-10.1177_0894439321998066 for Changing Social Representations and Agenda Interactions of Gene Editing After Crises: A Network Agenda-Setting Study on Chinese Social Media by Anfan Chen and Xing Zhang in Social Science Computer Review

Footnotes

Authors’ Note

A. Chen and X. Zhang contributed equally to this study.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the 2019 New Humanities Funding of University of Science and Technology of China (USTC; grant number: YD2110002015), the Science Popularization and Risk Communication of Transgenic Biotechnologies project (grant number: 2016ZX08015002), and the 25th department funding of USTC (grant number: DA2110251001).

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Anfan Chen (PhD) is a postdoctoral researcher at the Department of Science Communication and Science Education, University of Science and Technology of China. His research focuses on computational communication, science communication, and new media. Email: caftsinghuaedu@gmail.com

Xing Zhang is a PhD candidate at the Department of Communications and New Media, National University of Singapore. Her research focuses on science communication, crisis communication, public opinion, and social media studies. Email: zhang.xing@u.nus.edu

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