Rumormongering of genetically modified (GM) food on Chinese social network

Telematics and Informatics 37 (2019) 1–12

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Rumormongering of genetically modified (GM) food on Chinese social network

T

⁎

Jiaojiao Jia, , Naipeng Chaoa, Jieyu Dingb a b

Computational Communication Collaboratory, School of Journalism and Communication, Nanjing University, China Department of Communication, University of California, Davis, United States

A R T IC LE I N F O

ABS TRA CT

Keywords: Scientific rumormongering Genetically modified food Peer influence Attitude Social media extravert Social reputation

This study employed digital footprints to explore why people engage in genetically modified (GM) food rumormongering on Chinese social media, Sina Weibo at both the group level and individual level. 9070 posts about GM food were obtained from 1 million users. Social network analysis was used to analyze the effect of peer influence on GM food rumormongering at the group level, and we did not find any effect that users would depend on their friendship network on social media to spread GM food rumors. To determine the effect of social media extravert, reputation and attitude on scientific rumormongering at the individual level, we used logistic regression. Results revealed that people who hold negative attitudes towards GM food and who are social media extraverts are more likely to spread rumors, while social reputation did not influence the spread of rumors.

1. Introduction GM food has received huge controversies since it was first introduced in 1994 (Dona and Arvanitoyannis, 2009) even though a majority of scientists believe that GM food is safe for human consumption (Brodwin, 2017; National Academies of Sciences, Engineering, and Medicine, 2016). Most controversies are over the health risk of GM food, and studies suggest that negative media contents, mistrust in government regulation and a lack of scientific knowledge have exacerbated negative perceptions of the public (Gaskell et al., 1999). Government regulations in both US and Europe have been established to ensure the safety of biotechnology development and biosafety, and international trade, as western countries have been important actors in the GMOs trade network (Xanat et al., 2018). Detailed procedures for risk assessment and biosafety management vary between countries, and the regulations were set very early in developed countries. The overarching policy for the US federal government regulation of GM foods was set in 1986 called the Coordinated Framework for Regulation of Biotechnology, and the European Commission, executive cabinet of the EU, established its general policy for GM food regulation in 2002 which is stricter than that of the US (Same Science, Different Policies, 2015). Despite strict government regulations, Europeans still have low acceptance of GM food because of trust and ethical concerns (Gaskell et al., 1999). Chinese Government has invested aggressively in GM food biotechnology development, however, government regulations in GM food technology development and food safety have not yet been updated to cope with the fast growth of the GM food industry (Newell, 2008). In 2003, China has implemented regulations on labelling GM food on soybean products and subsequent survey study ⁎ Corresponding author at: School of Journalism and Communication, Nanjing University, 163 Xianlin Road, Qixia District, Nanjing, Jiangsu 210023, China. E-mail addresses: [email protected] (J. Ji), [email protected] (N. Chao), [email protected] (J. Ding).

https://doi.org/10.1016/j.tele.2019.01.005 Received 16 July 2018; Received in revised form 6 November 2018; Accepted 7 January 2019 Available online 09 January 2019 0736-5853/ © 2019 Elsevier Ltd. All rights reserved.

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showed that the majority of Chinese consumers have little knowledge of GM food (Ho et al., 2006). Scholars predict that given restricted access to GM food information, the acceptance or rejection of GM food among Chinese consumers can be easily swayed in either direction (Ho et al., 2006). Yet, there have been limited studies examining public discourse about GM food in China. As for the public engagement on GM food in the US and EU, a lot of attempts have been made to address the issues relevant to public acceptance or rejection of GM food, scientific facts about GM food, cultural values and political dispositions (Frewer et al., 2004). However, there are not many activities regarding science communication on GM food in China despite China’s shifting attention from food supply issues to food safety concerns (Lam et al., 2013). Lack of scientific understanding of GM food yields public susceptibility to risks and heated discussion on Chinese social media, Sina Weibo, which is China’s equivalent of Twitter. Different online sources may frame differently about GMO, as Jiang et al. (2018) found Federal webpages, top Google search pages and online news titles present the term “GMO” differently. The debate on social media is almost one-sided in recent years stating GM food will do harm to human health as well as have the environmental impact of GM crops on agricultural eco-systems (Cui and Shoemaker, 2018). Rumors regarding GM food is a typical type on Chinese social media, and rumors about food safety accounted for 45% of all online rumors in China (Cui and Shoemaker, 2018). Rumor is a type of unverified statements (DiFonzo and Bordia, 2007), and it may turn out to be true, or partly or entirely false; alternatively, it may also remain unresolved (Zubiaga et al., 2018). In this study, we define it as information that has already been falsified by credible sources, such as government, news media, and journal articles (Tan et al., 2015), and rumormongering is the act of spreading rumors. Rumors negatively influence people’s attitudes and behaviors from political misperceptions, anti-vaccination to climate change (Lewandowsky et al., 2012), distort people’s beliefs and may have lasting effects even after it is debunked (Nyhan and Reifler, 2015; De keersmaecker and Roets, 2017), therefore it is very important to understand the mechanism why people engage in rumormongering on social media. This study is designed to understand why people spread scientific rumors on social media. Much of scholarly attention has been focused on a range of topics (i.e., political issues, natural disasters, financial issues, crimes, etc.) and issues of considerable importance to the public (i.e., personal safety or livelihood) (Bordia et al., 2006). However, limited attention are allocated to scientific rumors which are related to scientific issues and little is known about what kind of social media traits of people that will influence their rumoring behaviors. Rumors regarding GM food are harmful and will mislead people’s beliefs about science. So in this study, we will explore factors that influence people’s engagement in scientific rumormongering on Chinese social media platform—Weibo. This study will have important implications on public opinions of GM food and future GM food regulation policy in China. 2. Theoretical framework There are several explanations for the probability of diffusion of rumors that could be classified into three levels: the information level, the individual level and the group level. At the information level, characteristic of information matters. The ambiguity of information is the most significant feature that contributes to the strength of rumor (Allport and Postman, 1947), which usually encompasses source ambiguity and content ambiguity. Ambiguity is mainly caused by incapable communication infrastructures in the time of crisis or catastrophes (Kendra and Wachtendorf, 2003), or by hiding of critical information by certain spreaders (Rosnow, 1991). While for scientific rumors, they mainly lack scientificity. At the individual level, emotion (usually anxiety) or sentiment, personal involvement (topical importance), personality and motivations are important factors of rumor spread (Anthony, 1973; Rosnow, 1980; Rosnow, 1991; Chen, 2016; Chen et al., 2015; Bordia and DiFonzo, 2005). First, anxiety and anger are the most common emotions that propagate a rumor. When people holding a negative attitude, the information may even disseminate at a faster speed (Hornik et al., 2015). Second, if a rumor is crucial to one person, the involvement of the individual will be very high (Oh et al., 2013). Third, as the personality has been considered a salient predictor in internet use, some researchers tried to find the relationship between rumormongering and personality on social media. Neuroticism and openness demonstrated a significant effect on rumor sharing (Chen, 2016). Finally, rumors are purposive and fulfill three aims including fact-finding, relationship-building and self-enhancing (Bordia and DiFonzo, 2005). At the group level, based on social network theory, people’s behavior and attitude are easily influenced by social ties (peer influencers) (Aral and Walker, 2011), and their engagement in rumormongering are influenced by their networks (DiFonzo et al., 2013). When confronting a confusing situation, people will turn to reliable formal channels such as authorities and figure out the uncertainty with information collected. If they fail to obtain timely and relevant trustworthy information at the beginning, they will turn to their social networks, and members of their social network will influence their attitudes and behaviors (Oh et al., 2013). Do above-mentioned factors influence the spread of rumors on scientific issues on social media? We are interested in what kind of factors on social media that will promote people’s behavior of scientific rumormongering at the group level and the individual level. As for the factors at the individual level, only those characteristics that exhibited on social media are taken into consideration including online attitudes towards GM food, being extraverted on social media and social reputation. At the group level, whether people’s online friendship network will exert influence on their engagement in scientific rumormongering will be examined. 2.1. Peer influence Rumor is a collective social behavior, which is a rich conversation of involving the exchange of opinions and ideas and circulation is its basic characteristic (DiFonzo and Bordia, 2007). Rumors often include collective talking, information sharing and social support (Festinger, 1962) among groups of people. Commonly, people are more likely to share information with acquaintances with similar backgrounds and are biased to believe rumors from their acquaintances (Garrett, 2011). Based on the dynamic social impact theory, people’s focus and belief polarization will be strengthened if they belong to the same social network clusters (DiFonzo et al., 2013). 2

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Therefore, people’s behavior of sharing information will be probably influenced by their peers within the same community. In social network studies, people’s attitudes and behaviors are easily influenced by social ties (peer influencers). For example, a peer influencer’s uncertainty or ignorance about using a technology can make the target more susceptible to influence (Contractor and DeChurch, 2014). There are two types of relationship on Sina Weibo stated as before, symmetrical and asymmetrical. The symmetrical relationship is more likely to be friendship and two people who know each other, while the asymmetrical relationship usually indicates endorsement and are often perceived as supporters or “fan base”. The symmetrical relationship may exert greater influence than the asymmetrical relationship. According to McPherson et al. (2001), people generally tend to form connections with individuals having similar (i.e., homogeneous) interests and characteristics, such as political attitude. Vice versa, with the established social ties, the user may act similarly as their friends, due to some form of influence that acts along the social network ties (Shalizi and Thomas, 2011). In times of uncertainty and crisis, individuals often seek peers’ opinions and may discuss with them about the evaluation of rumors (Chen et al., 2016). Rumors are characterized as dealing with anxieties and uncertainties (Rosnow, 1980), and can also be considered as an expression of negativity. Negative information might draw more attention and is more influential through the social network (Hornik et al., 2015). Generally, health and environmental risks have long been the concern of the public and people are uncertain about the safety of GM food (Bawa and Anilakumar, 2013). If Weibo users are confused and uncertain about the safety, they may refer to the opinions of their peers and be deeply influenced by them. From this, we propose the first hypothesis: H1. If a friend is spreading rumors on GM food, the user will also be very likely to spread rumors on GM food.

2.2. Social media extravert People who are very sociable are usually extraverts, prone to attend leisure activities and self-expression and welcome information of new perspectives. Golkar Amnieh and Kaedi (2015) suggested that extraverts would face new message eagerly and be more likely to forward it on Twitter, so they would have a higher chance of posting rumors, and they post rumor as a way to gain social influence and maintain social relationship with others. Building social relationships and boosting self-esteem are the additional psychological motivations to be engaged in rumormongering, besides the dominant highlight of the uncertainty reduction function of rumor (Bordia and DiFonzo, 2005). Extraverts who want to build relationship with others would grab the attention of the listener, appear “in the know”, maintain the status difference (Bordia and DiFonzo, 2005), so they would probably post rumors to build the relationship. Negative information is more easily transmitted between friends as a way of reminding harmful consequence (Weenig et al., 2001). Negative posts on Sina Weibo about GM food would easily be transmitted by extraverts. As we propose in the later part that rumors are more often associated with negative attitude, thus extraverts would be more likely to be engaged in rumormongering. Friendship on Sina Weibo is bidirectional, similar to that of Facebook. Friendship is the symmetric follower–followee relationship and both of users have to be the audience of each other. Users with many friends are social media extraverts and seek to establish friendship with others. We thus propose social media extraverts would be more likely to spread rumors. H2. Social media extraverts will be more likely to be engaged in scientific rumormongering.

2.3. Social reputation Rumors are said to be one of the strategies that people gain influence over friends, as information, of which rumor is a form, can be considered as the currency of power and influence (Stevens and Fiske, 1995). However, highly influential individuals and organizations would not put their reputation at risk and would be less likely to be engaged in rumormongering (Castillo et al., 2011). According to relationship maintenance motivation and reputation management, maintaining reputation and credibility requires considerable amount of efforts and time, so people and organizations who want to be known as a source of accurate information and remain a valued and trustworthy member of the communication would be very cautious in information spreading (Stevens and Fiske, 1995). Similar to Twitter, Sina Weibo has a verification mechanism. There are two types of user accounts on Sina Weibo, verified accounts and unverified accounts. Verified user accounts are usually celebrities and organizations. Verified users often have a large number of followers and of high popularity. Follower–followee relationships on Sina Weibo are similar as those of Twitter, asymmetrical and one user can follow another without their permission. Cha et al. (2010) have defined indegree influence as the number of followers of a user, which indicates the size of the audience of that user. The number of followers of a Twitter user is often used as a proxy for their influence. For verified accounts, especially news accounts on Twitter, they would be furnished with more credible information compared to the unverified ones. Accounts of high social influence will pay more attention to their tweet’s veracity, and they will verify the information before posting it (Jain et al., 2016). A justification of high quality information from verified accounts can be explained by the role of these accounts as “gatekeeper”. Studies have shown that people tend to listen to verified accounts more than an unverified one and verified accounts can be considered trustworthy; tweets from verified accounts can help to slow the spread of false information or spread important facts by posting a denial (Andrews et al., 2016). So we propose the following hypotheses: H3a. Sina Weibo user accounts that are verified will be less likely to be engaged in scientific rumormongering. 3

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Fig. 1. The proposed model for the mechanism of scientific rumormongering. Factors on social media at the group level (friendship network) and at the individual level (attitude, social reputation and social media extravert).

H3b. Sina Weibo user accounts with a large number of followers will be less likely to be engaged in scientific rumormongering. 2.4. Negative attitude Rumor research has agreed on sentiment as a key variable of rumoring. Allport and Postman (1947)’s seminal work entitles The Psychology of Rumor pointed rumor strength varies with two important factors: most notably information's importance and ambiguity. Information's importance is strongly related to anxiety that the more anxious a person feel, the more likely the content is important for the rumor recipient (Anthony, 1973). Rosnow (1980) proposed that rumors result from combinations of uncertainty and anxiety that are related to rumor strength differently as state and trait factors. Besides anxiety, other types of sentiments also dominate spread of rumors like uncertainty, credulity, and outcome-relevant (Kwon et al., 2013). Rumors are more likely to prevail and spread on social media in the event characterized by negative emotions, like crisis, conflicts, risks and ethnic riots (Oh et al., 2010). Health and environmental risks of GM food have long been the main concern of the public. A major controversy associated with GM food appeared even when they were not in the market in the late 1980s (Bawa and Anilakumar, 2013). There are a lot of rumors about GM food. As for topical importance, food is an essential part for everyone’s lives. People who are not uncertain about the safety of GM food and worrying about GM food may be linked to allergies, antibiotic resistance, or cancer, are usually negative about GM food and show anxiety and depression. This negativity may incite rumor spread, and transmitting rumor helps vent anxiety. We would like to make predictions: H4. People with negative attitude towards GM food are more likely to be engaged in rumoring. In aggregate, our research model about factors that motivate people to be engaged in rumormongering at the individual and the group level is represented as Fig. 1. 3. Methodology 3.1. Data collection Digital footprint is used to describe one’s unique set of digital activities, actions, contributions and communications that people leave online, including passive forms (website visits and actions, searches and online purchases without consent) and active forms (blog posts, image and video uploads, email, phone calls and chats) (Digital footprint, 2018). Subjectivity and sample bias could not be avoided in the survey, while digital footprints are the real and true reactions of users. Attitude of a person obtained from digital footprints on social media could be more objective than self-report (Youyou et al., 2015). Digital footprints of the first registered one million users on Sina Weibo were provided by a data company. The 9070 collected posts were obtained by filtering the posts of the first registered one million users on Sina Weibo with the keyword “genetically modified” (‘转基因’ in Chinese character). At the same time, the followers counts (the number of user’s followers), verification (whether the user is verified by Sina Weibo), friends counts (the number of user’s friends) and friends lists of the users who posted the GM food were also collected. These data go far beyond what could be gathered from survey data and self-reports. The validity of the study derives from the fact that it analyses digital footprints of the spontaneous reactions of authentic users (rather than of a sample of a certain group of people who may even not care about GM food). 3.2. Coding scheme Among 9070 posts, 3592 posts with gender or education were manually coded by four coders majoring in communication and two of them with science communication background. Before human coding, we selected posts that are relevant to GM food (commercials 4

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Table 1 Credible websites for coding rumors. Website

Hyperlink

Guokr (the largest science communication and knowledge sharing website in China) Official website of the Chinese government (Ministry of Agriculture) about genetically modified organism The report by the World Health Organization

https://www.guokr.com/ http://www.moa.gov.cn/ztzl/zjyqwgz/

Science communication report about GMO by South China Agricultural University

http://www.who.int/foodsafety/areas_work/food-technology/faqgenetically-modified-food/zh/ http://2016.igem.org/wiki/images/a/a1/Handbook.pdf

about GM food or jokes just containing GM food were excluded). All the coders were trained to understand the context of GM food and the definition of each coding category. First, we human coded people’s attitudes (positive, neutral or negative) towards GM food in their posts. The attitudes towards GM food in the manuscript include positive (support GM food), neutral (neither support nor antiGM food), and negative (anti-GM food). Posts that describe “supporting GM food or benefits of GM food” were coded positive towards GM food; Posts that describe “opposition to GM food, dislike GM food, or adverse effects and risk of GM food” were coded as negative towards GM food; Other posts were coded neutral. The attitudes of online expressed statements were coded first and then attitudes of the users were coded based on their online posts. Second, we used credible websites to verify whether a post was true or false, and if it was false we define it as a rumor for later study. The websites are listed in Table 1. The websites are deemed to be credible as the articles in the above-mentioned websites are either written by experts in the field or issued by the government. Finally, on the ground of previous studies (Durant et al., 1998; Nisbet and Lewenstein, 2002) about biotechnological frame and Sina Weibo context, the topics were coded and were classified into 9 categories, including progress, risk and ethics, globalization, regulation, public engagement, nature, interest group, right to know and others. The Cohen kappa scores (Cohen’s kappa, 2018), K value, for the intercoder reliability are shown in Table 2. The K value can be interpreted as follows (Altman, 1990): < 0.2 as indicating poor agreement, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as good, and 0.81–1.00 as very good (Altman, 1990). Accepting 0.40 to 0.60 as “moderate” may imply the lowest value (0.40) is adequate agreement (McHugh, 2012). Usually, to obtain high Cohen kappa score, careful training of coders and several rounds of intercoder reliability tests are performed prior to, and sometimes after, the analysis (Krippendorff, 2004). In this study, we went first round of coding after training and found there were some ambiguous posts that could not be easily agreed by two group of coders, which were not good for future machine learning. So the ambiguous posts and a certain degree of discrepancy between coders were tolerated. 2096 posts were coded the same for relevance of GM food topic, sentiments, and rumor. And the 2096 posts were posted by 1153 users. The process of data processing is shown in Fig. 2. 3.3. Measures As we want to measure the social media factors at the individual level and the group level, so in this study if a user posted a rumor regardless of the number of rumors, the user is defined a rumormonger. Social reputation is measured by followers count and verification of the account, and social media extravert is measured by user’s friends count. The probability of rumormongering at the individual level is tested as follows:

logit (pi ) = β0 + β1 A + β2 Fo + β3 V + β4 Fr Where:

• p is the probability of rumormongering. • β is the intercept. • β , β … β are the coefficients (effects) of each of the variables. • A is the attitude towards genetically modified technology; F is the followers count; V is the verification of the count; F i

0 1

2

4

o

r

is the

friends count.

At the group level, friend relationship network was extracted between users who posted about GM food and Quadratic Assignment Procedure (QAP) analysis was employed to test whether peer influence affects people’s engagement in rumormongering. Table 2 Cohen kappa score for the intercoder reliability.

Attitude Rumor Topic

Rater1&rater2

Rater3&rater4

0.560 0.525 0.521

0.557 0.506 0.429

5

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Fig. 2. Process of data collection and coding.

4. Results 4.1. Description of rumor Posts that contain genetically modified were obtained from the posts of 1 million users. As is shown in Fig. 3 (Left), among the manually coded 2096 posts, which were agreed between the coders, 338 (16.13%) were rumors and 1758 (83.87%) were not rumors. According to CREDBANK established by Mitra and Gilbert (2015), an alarming 23.46% of the global tweet stream is not credible. The percentage of rumors about GM food on Sina Weibo were consistent with that of CREDBANK. 2096 posts were posted by 1153 users. Fig. 3 (Right) shows 213 of them have posted rumors and 940 of them have not posted any rumors. It is really a big problem that rumor related scientific issue like GM food is also of such a high proportion. Fig. 4 shows the distribution of GM food rumors and non-rumors on Sina Weibo according to posting timestamps ranging from March 2010 to September 2014. The spark of rumor pattern goes the same as non-rumor pattern. 5 peaks of non-rumors, which were also the peak of non-rumors were annotated. In September 2012, a furious row has erupted over a French study claiming to have found tumors and other problems in rats fed on genetically modified maize and exposed to a common, associated herbicide. In June 2013, China’s agricultural ministry has announced that it recently approved the import of three types of genetically modified soybeans and it attracted the attention of the public. Cui filmed a documentary on GM food in America at the end of 2013, which he posted online on March 1, 2014, and it sparked heated discussion. In July 2014, CCTV reported over the weekend that three out of five bags of rice bought at supermarkets in Wuhan were found to contain Bt63, an ingredient from a genetically modified rice string named Bt Shanyou 63. Peaks appeared to be triggered by safety issues and regulations issues, indicating that rumor may be highly related to health and safety risks. Generally, the discussion about GM food started in China at a late stage. People began to discuss the

Fig. 3. (Left) The number of rumor and not rumors about genetically modified posts; (Right) The number of rumormongers and not rumormongers who posted about genetically modified organism. 6

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Fig. 4. The distribution of GMO rumors and non-rumors on Sina Weibo according to posting timestamps.

issue of GM food with the emergence of social media. So it is very interesting and important to understand scientific rumormongering on social media. 4.2. Group level: peer influence Friendship network for users who discussed genetically modified organism was extracted as is shown in Fig. 5. There are 230 nodes who formed the relationship ties and 258 edges between the nodes. The density of friendship network is 0.007, which is consistent with Huberman et al. (2008) that friendship network of Twitter is sparse. The green nodes are rumormongers while the red nodes are not rumormongers. Generally, the red nodes connected with red nodes and there was a small part of red nodes connected with green nodes. Only two pairs of green nodes connected with each other. There was one big green node in the network, but all of its friends were immune to its rumors. So the preliminary result of the sparse network showed that rumormongering of GM food is not influenced by their online friendship network, even the engagement in posting GM food is not influenced. According to the results of social network and QAP analysis in Table 3, H1 is rejected as the significance is 0.399. Usually, a peer influencer’s uncertainty or ignorance about using a technology can make the target more susceptible to influence (Contractor and DeChurch, 2014). For the topic of GM food, people would be less dependent on their friendship networks on social media to spread rumors, and their rumormongering behaviors were not influenced. The finding for the scientific rumor is different from that of other types of rumors which will be discussed further in the discussion. 4.3. Individual level: social media extraverts and attitude matter Table 4 shows the probability of scientific rumormongering for factors at the individual level including social media reputation (verification and followers count) and social media extravert (friends count) and attitude. Logistic regression results revealed social media extraverts, who have a large number of friends, are more likely to spread rumors, which is consistent with psychological motivations of building social relationships and boosting self-esteem (Bordia and DiFonzo, 2005). However, we didn’t find effects of reputation on rumormongering, as according to the p-value, the H3a and H3b hypothesis are rejected. Users with high reputation (verified account and more followers) were equally likely to spread rumors about GM food as users without high reputation. People who have high social reputation but with less professional knowledge were likely to engage in scientific rumormongering. Hence, high reputation didn’t necessarily lead to less engagement in scientific rumormongering. Table 4 shows a significant effect of negative

Fig. 5. Friendship network for users who discussed genetically modified organism. Nodes are user accounts, and edges are bidirectional friendship. The sizes of nodes are proportional to their degree. 7

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Table 3 QAP result between friendship network and rumor similarity network. Data Matrices

Friendship relationship network Rumor similarity network

QAP # of Permutations: 5000 Obs Value 131.000

Hubert Gamma

Significance 0.399

Average 1.528

Std Dev 0.782

Table 4 Results for the independent effects on rumormongering at the individual level.

Negative attitude Neutral attitude Positive attitude Verification Followers count Friends count Intercept

Coef

Std err

z

P > |z|

[0.025

0.975]

2.7587 0.8108 −0.2777 −0.2269 −2.127e-06 0.0004 −3.7553

0.392 0.569 0.810 0.313 3.09e-06 0.000 0.388

7.029 1.425 −0.343 −0.726 −0.689 2.067 −9.673

0.000*** 0.154 0.732 0.468 0.491 0.039** 0.000

1.989 −0.304 −1.866 −0.840 −8.18e-06 1.91e-05 −4.516

3.528 1.926 1.311 0.386 3.93e-06 0.001 2.994

** Significant at the 0.05 level. *** Significant at the 0.01 level.

attitude on rumormongering with p < 0.001 and coefficient 2.6082, leading to strong support of H4. Negative attitude had a significant effect on the spread of rumors, which means people who didn’t support GM food were more likely to spread rumors. While positive attitude and neutral attitude didn’t result in rumormongering. Since negative attitude is a significant factor for scientific rumormongering, we also demonstrate how rumors and non-rumors of GM food are distributed among users who hold positive, neutral, and negative attitudes, as is shown in Fig. 6(A). For rumor posts, all of them are negative, while for non-rumor posts, the majority of them are negative. For both rumor and non-rumor posts, they are negative towards GM food. In order to understand why people are negative towards GM food, the topics of the posts are studied. Fig. 6(B) demonstrates the topic distribution of all the posts. The topic of progress is 0.894%, risk and ethics 52.6181%, globalization 3.6398%, regulation 3.9591%, public engagement 4.5338%, nature 0.0639%, interest group 3.8953%, right to know 3.8953% and others 22.2861%. Fig. 6(C) shows that negative posts are mainly about risk and ethics, indicating that online users are negative towards GM food as

Fig. 6. (A) Number of rumors and non-rumors for people holding positive, neutral, and negative attitude; (B) Topic proportion of GMO posts; (C) Topic proportion of GMO posts among the different attitudes; (D) Topic distribution of GMO posts between rumors and non-rumors. 8

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Fig. 7. Summarization of the research results.

they are worried about the risk and uncertainty of GM food. Fig. 6(D) demonstrates that the majority of the rumor posts are about risk and ethics, which implies that these online users lack scientific knowledge and uncertain about GMO, so they are engaged in rumormongering. 5. Discussion 5.1. The judgement of a scientific rumor is often evidence-based As for spreading rumors on scientific issues, there is no effect of peer influence on online scientific rumormongering (Fig. 7). Rumors related to GM food are scientific rumors, which is different from other types of rumors. In this study, users on Sina Weibo would not depend on their online friendship network to be engaged in rumormongering. Rumors are characterized as dealing with anxieties and uncertainties (Rosnow, 1980). Usually, for rumors that are not about science, in the absence of institutional channels at the initial stage, individuals turn to their social networks like friends and make sense of uncertain situations (Bordia and DiFonzo, 2005), and they would seek peers’ opinions and may discuss with them about the evaluation of rumors (Chen et al., 2016). The judgement about scientific posts is evidence-based and science-based. When people encounter an unverified post about science, their stored knowledge will be recalled immediately and make a judgement without turning to their social contacts for further information. Rumors that GM food cause cancer, autism, allergies, gluten intolerance, or other illnesses and conditions in humans and animals can be rejected if a person is armed with genetic knowledge. If not equipped with corresponding knowledge, the person will probably search online or turn to a friend with relevant professional knowledge, but not a friend without expertise. For example, according to Contractor and DeChurch (2014), it is important to identify who has the most potential to be influential opinion leaders in communicating scientific information within a community, however, it cannot be assumed that opinion leaders are already primed with the scientific opinion that one would like to convey to the community; only highly central individuals equipped with relevant professional knowledge could be influential. While for rumors that are not about science, people could not make judgement immediately since the facts could not be known through stored knowledge, which is obtained through education. Recalling stored knowledge, searching online and turning to a friend with relevant knowledge are defined as “search”, as is shown in Fig. 1. Hence, when confronting a scientific issue, people are less likely to be influenced by their online friendship network and less likely to spread rumors. 5.2. Negative attitude promotes scientific rumormongering Scientific rumors are often about unverified statements of certain technology. Attitude towards a rumor has been studied in rumor diffusion, however, attitude towards a rumor of a technology has not been examined extensively in the previous studies. It is interesting to know whether the attitude towards the technology influences the behavior of rumoring. Based on digital footprints on Sina Weibo, the dominant attitude towards GM food is negative, and people who are negative about GM food are more disposed to be engaged in rumormongering (Fig. 7). To explain why negative attitude is dominant and why people holding negative attitude are prone to be engaged in rumoring, the topics of posts will justify. Previous studies about biotechnological frame and Sina Weibo context classified GM food into 9 categories: The topic of progress is 0.894%, risk and ethics 52.6181%, globalization 3.6398%, regulation 3.9591%, public engagement 4.5338%, nature 0.0639%, interest group 3.8953%, right to know 3.8953% and others 22.2861%. Risk and ethics are the most dominant and the focus of public attention. People are holding negative attitudes because they are uncertain and worried about the safety of GM food. When talking about GM food, people talk a great deal about ‘Frankenfood’ and risks, believing GM foods are very likely to bring problems for the environment along with health problems for the population as a whole. Rumors often emerge during times of uncertainty (Starbird et al., 2016). When people are uncertain about what they talk about, they are more likely to be engaged in rumoring. Table 4 shows a significant effect of negative attitude on GM food rumor indicating online users were uncertain and worried about the safety of GM food. They posted rumors as a way to reduce uncertainty and anxiety which is consistent with the idea that the dominant psychological approach to rumor has been highlight the uncertainty reduction function of rumor (Bordia and DiFonzo, 2005). The majority of negative GM food posts focused on risks and uncertainty, which 9

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explains negative attitude dominates and negative attitude promotes scientific rumormongering. 5.3. Social media extraverts and rumormongering Social media extraverts are more likely to engage in rumormongering (Fig. 7), which also hold true for scientific issues according to the results of logistic regression. Extraverts usually are more eager to face new messages (Golkar Amnieh and Kaedi, 2015) and may post rumors as a way to gain social influence and maintain social relationship with others, which is consistent with psychological motivations including building social relationships and boosting self-esteem (Bordia and DiFonzo, 2005). Most of the posts about GM food are negative, and negative information is more easily diffused between friends (Weenig et al., 2001), accordingly extraverts would be more likely to post rumors about GM food to build and maintain relationship. What’s more, as food is important to many people, this topic would be used as a good option to catch the eye of friends. However, the effect of social media extravert is much lower than that of negative attitude according to their coefficients in Table 4, which may have something to do with the attention and interests of the public. As rumormongering is a way to gain social influence and maintain social relationship, social media extraverts would prefer to choose a more appealing issue than the topic of GM food to attract more audience. The key topics that trend on Sina Weibo are mainly related to entertaining contents like jokes, images and videos (Yu et al., 2011). The issue of GM food is less compelling and thus social media extraverts would not dedicate much to spreading rumors about GM food. 5.4. Users with high reputation engage in scientific rumormongering Spreading rumors will do harm to the reputation of influential individuals and organizations, and maintaining reputation and credibility requires huge amount time and efforts, so usually for those who want to be a valued, credible and trustworthy member of the communication would be very circumspect about their posts on social media. However, in this work we found social media reputation does not have effects on scientific rumormongering (Fig. 7). Even with many followers and being verified, users are not less likely to spread scientific rumors, which is different from other types of rumors. There are several justifications that may explain why people with high reputation also spread scientific rumors. First, users who enjoy high social reputation but not familiar with relevant knowledge may engage in rumormongering as they are not capable of detecting rumors, while users with professional knowledge are less likely to spread rumors. For example, former CCTV Host Cui Yongyuan with many followers posted a lot of claims about GM food, but some of his claims are not scientific as he lacks professional expertise (Xia et al., 2015). Second, there is a lack of trust in the scientific community, while 23 percent were prepared to “believe in biologist’s opinion,” compared to 45.5 percent who chose “do not trust biologist’s opinion” (Cui and Shoemaker, 2018). Users with high reputation do not believe comments from the scientists, and they would like to express their own unprofessional comments about GM food. Third, as the topic of GM food is heated discussed, some users whose accounts are verified and have lots of followers just paid attention to the self-expression, but not the fact as a way of gaining attention from the public, which is consistent with goals of spreading rumors, building social relationships and boosting self-esteem. Many users are not GM food experts, and they just want to gain more followers and be famous, so they would like to post contents about GM food regardless of their scientificity. 6. Conclusion 6.1. Limitations The present study has two limitations. First, this study did not use a random sampling method. We selected the earliest one million users who registered on Sina Weibo and analyzed their digital footprints. Therefore, the study results do not represent all Sina Weibo rumormongering behaviors. However, we believe our sample is large enough to explain the phenomenon. Second, the measurement of social media extravert is not comprehensive. We only used total friends count to decide whether this user is extravert or not. Future studies should use multiple indicators to define social media personality. Last but not least, our study only tested the topic of GM food. Future studies should replicate our method and examine whether these results still hold in other scientific topics. 6.2. Implications This study explores factors that influence people’s engagement in scientific rumormongering on Chinese social media platform—Sina Weibo at both the group level and individual level, bringing new insights for both theoretical implications in rumor research and practical implications for rumor control. This study emphasizes rumors on the topic of GM food and factors gathered on social media, which is different from other rumor studies. The most interesting finding is that for scientific rumormongering, people’s scientific rumoring behaviors are not influenced by their friendship network on social media. Besides, reputation is not applied in the scientific topic as well and celebrities also spread GM food rumors. The novel findings should be explored in other scientific issues in the future for enriching rumor studies. Generally, online users are negative towards GM food and it is highly related to their lack of scientific knowledge about GMO. People are uncertain and worried about safety issues, however, they are not influenced by their online friendship network and they want to learn about relevant professional knowledge. Therefore, it is paramount to provide Chinese citizens with credible and easily accessible sources of scientific information. Sometimes, some celebrities of good social 10

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