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Article

Information Alienation and Circle Fracture: Policy Communication and Opinion-Generating Networks on Social Media in China from the Perspective of COVID-19 Policy

by
Yuanchu Dai
,
Xinyu Cheng
* and
Yichuan Liu
School of Journalism and Communication, Shandong University, Jinan 250100, China
*
Author to whom correspondence should be addressed.
Systems 2023, 11(7), 340; https://doi.org/10.3390/systems11070340
Submission received: 25 May 2023 / Revised: 10 June 2023 / Accepted: 29 June 2023 / Published: 2 July 2023
(This article belongs to the Section Systems Practice in Social Science)
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Abstract

:
The emergence of the Internet and social media provides a new platform for information diffusion, promoting the interaction among relatively independent participants in the opinion market and changing the balance of the intrinsic mechanism and external dynamics based on political communication. In this way, it is necessary to investigate the new interactive landscape of political communication and political discourse regarding digital media. In this study, we conduct a social and semantic network analysis of the dissemination and public opinion generation landscape of the COVID-19 “New Ten Articles” policy communication by the Chinese government, exploring the network relationships and emotional value interactions behind the contact of a new public policy in China. The results show that, in the political communication system, the influence of information’s position in the communication field has surpassed the information source impact, and the power of network opinion leaders is significant; the policy communication network presents a situation of “identity status circle” division, and the information circle connection presents a trend of fracture and barrier thickening, which may cause policy information alienation and social opinion polarization risk; the imbalance between policy information supply and public demand is further enhanced, and the negative emotion “cloud” is distributed on a scale and condenses into grassroots social governance pressure; and the content released by some key opinion leaders, experts, online media, and local mainstream media accounts is significantly correlated with network emotions. These emotions continue to spread in subsequent discussions, and to some extent, influence the formation process of political public opinion.

1. Introduction

Since the 19th century, the connection between digital technology and media has appeared continuously. In the 1930s and 1940s, scientists focused on studying information metrics and feedback mechanisms. Information technology and the Internet have facilitated the emergence of digital media that record, process, and disseminate information in binary numbers [1]. Digital media has overturned the traditional media’s one-way linear communication mode and has a more substantial intrinsic value of power interaction and a broader commercial value of content production [2]. The proliferation of information channels brought about by digital media has caused a disruptive transformation in the field of culture, politics, and social development, which has led to deepening connections between citizens in society and changing in the previous information structures, political communication experiences, or even the boundaries of nationalism [3]. In this context, several attempts have been made regarding the relationship between the structural changes of information on the Internet and political communication, with research mainly coming from communication studies, political science, and sociology. Manuel Castells proposed the concept of the “network society” and conducted an in-depth analysis of the impact of global networks on the institutions of Western welfare states and social movements and splits [4]. Karpf introduced the concept of “Politics 2.0”, which refers to the utilization of the lower transaction costs of the Internet and its information-rich conditions to establish more participatory and interactive political institutions [5]. The development of social media has already changed the physical laws of information diffusion, and more and more citizens—and political institutions—are using it in the political environment.
In China, the uniqueness of journalism and communication gives political communication a meaning different from that in Western countries. It is often regarded as part of national governance [6], dramatically impacting public opinion, personal beliefs, audience attitudes, and government credibility. On December 7, 2022, the Chinese government changed its original “Dynamic Zero COVID” (Dynamic ZERO COVID policy: From 11 December 2021 to 7 December 2022, the dynamic zero policy serves as the general policy for COVID-19 prevention and control in China. Specifically, it refers to integrating comprehensive prevention and control measures the Chinese government takes when there are indigenous cases. The “dynamic zero” is not zero infection but includes the ideal level of no patients on the social side and the whole level to cut off the transmission chain so that society gradually moves towards the dynamic zero to prevent the formation of large-scale rebound. Available online: https://baijiahao.baidu.com/s?id=1718835957758032013&wfr=spider&for=pc (accessed on 5 June 2023)) policy and issued a “Notice on Further Optimizing the Implementation of COVID-19 Prevention and Control Measures” (Abbreviation: “New Ten Articles” Policy (“New Ten Articles” Policy: On 7 December 2022 China’s State Council Joint Prevention and Control Mechanism Integrated Group published the Notice on Further Optimizing the Implementation of COVID-19 Prevention and Control Measures based on the current situation in China, including re-demarcation of risk areas, adjustment of isolation measures and frequency of nucleic acid testing, and protection of basic drug purchase for the public) [7]. The policy, once released, had a considerable impact on society and became the focus of online public opinion. The large volume of public opinion has allowed Sina Weibo to expand its digital space towards politics and the public health realm. As health information began to be closely related to social stability or even national image, health communication’s public and political attributes were stimulated, resulting in a “communication convergence” with political communication [8]. This paper is based on a focus on this particular policy-changing period and explores the new political communication pattern created by the aggregated interaction of online users on Weibo after the release of a new health public policy, observing the interaction relationships and narrative features in the subjectivity to provide more targeted suggestions for the enhancement of social efficacy and risk avoidance of political communication in the digital era.
This research constructs a social network and semantic network for the communication of new policy information from social media posts shared by Weibo users, analyzes the degree of integration of the communication network and the distribution of rights, compares the main semantic differences, and identifies the most significant concepts expressing positive and negative emotions in the network, as well as emotional interaction relationship. It provides new insights for understanding the crucial role of social media in disseminating further policy information.

2. Literature Review

2.1. Digital Public Sphere and Political Communication

The “public sphere” concept originated and developed mainly based on Habermas’ views on the evolvement from personal opinion to public opinion and social structural change. With the emergence of digital technology as a new social driving force, Habermas has made a further explanation for the deformation of the theory of digital public sphere interaction, pointing out that one of the most significant changes brought by digital media is the blurring of the distinction between the private and public spheres [9]. However, some scholars argued that the most significant change in the digital public sphere is not simply a change in inter-subject relations but a concession of inter-subject dialogue ethics to the interface ethics of the digital space, which reflects that the interaction in the digital public sphere is a kind of virtual interpersonal relationship, which is formed based on the “interface” [10].
In this context, the process of people finding and organizing groups of similar interests and views and their ability to access a wide range of issues and share opinions have undergone significant changes. Scholars’ research on social media and political communication has focused on the impact of emotional interaction on information diffusion and the social deformation of political movements and political participation.
Digital media have shifted the realm of citizen engagement to a more private realm, which means that more personal emotions will be associated with the proliferation of information. Extensive research has emerged regarding emotional performances in social media by populist actors [11], the relationship between information diffusion and user sentiment and behavior in social media [12], as well as digital technology and affective computing [13]. All these studies highlight the significance of “emotional entanglement” as a critical factor in information communication, cultural consumption, and power interactions in the digital public sphere. The most notable of these is the concept of “affective publics” proposed by Zizi Papacharissi in 2015, in which she argues that political campaigns on social media should not be measured in terms of political effectiveness but rather by the emotional intensity of a specific issue that drives the audience [14]. Social media platforms have “affordances” [15] that keep emotional engagement and circulation and enable or constrain potential behavioral outcomes within specific contexts. It is on this basis that our research draws on the empirical research on the labelling of sentimental concepts [16] and the sustainability impact of “re-post” [17] to mark and distinguish the specific sentimental concepts in Chinese social media. In addition, this study further draws on the findings on the mediating effect of sentimental responses on political and online publics [18] to track the “empathic world and emotional diffusion” [19] relationship on social media.
In addition to research on affective interactions and influences, scholars’ studies of political campaigns and participation have mainly included voting, campaigning, and collective activities [20]. With the emergence of political polarization and populist issues brought about by social media, scholars have, on the one hand, criticized the unrealistic nature of idealized democracy and the dangers posed by disinformation and online heterogeneity [21] and, on the other hand, emphasized that the use of the Internet and social media has prompted changes in the shape of social movements. The “connective action”, coordinated by personalized and individualized paths, has become a typical feature of political movements on social media [22]. Digital technology development has enabled network convergence at the individual level, so “sharing” has become the core of connective action [22]. Vromen, Xenos, and Loader measured how young people use social media to organize political campaigns [23], and Papacharissi noted that a critical attribute of social media is that it allows for multiple connections to a variety of different social domains [20], so that the form and content of communication become an essential factor in organizing political campaigns on social media [24]. The changing role of individual identity has led scholars to turn their attention to the microscopic sphere. Robert Dahl first proposed the concept of “mini-publics” in 1989 [25], which is defined as smaller informed groups that participate in and promote the decentralization of (political) consultation networks, with the aim of using social science methods to assemble a “micro-world” composed of “mini-publics” [26]. With the development of the network society, this kind of “mini-public” has become increasingly common in the network environment, which means that every citizen, not only political elites, is a “mini-public.” Their political discourse and concerns constantly promote the emergence of new political communication content such as “micro public opinion [8]” and “micro-political discourse [8]”. The “interface” has shaped a brand new digital “micro public sphere” [27]. The new political demands and emotional needs it presents have brought about tremendous changes in human social interaction, the order of modern political operation, and the process of “mediation” and “re-mediation” of politics has become a significant focus of scholars.
Political mediation refers to the symbolic content and structure of social–political activities and governance being influenced by media logic and carried out through interaction with the media [28]. It is concerned not only with political communication itself but also with political systems, stakeholders, communication processes, and social backgrounds [29]. In the digital age, the concept of a “network society [4]” based on microelectronics and digital computer networks has laid the foundation for understanding political mediation in the digital public sphere and new forms of political communication [30]. Optimists regard social media as a vital issue in the process of political mediation, focusing on the role of digital technology in promoting political communication [31] and the vision of digital democracy [32]. Van Dijck and Poel point out that social media’s own four primary logics—programmability, connectivity, popularity, and datafication—give it the ability to spontaneously aggregate powerful attention [33], which also endows political communication with significant features such as emotional, precise, and visualization [34]. The enormous effects of these new features not only give citizens the identity of creators and main subjects, but also promote the prosperity of participatory culture [35], which fosters pro-social behavior in citizens and leads to higher levels of citizen political participation. However, a key issue now is that existing research has focused on the impact of social media from the perspective of cultural and political exchanges in the digital public sphere, but how is user-generated content linked to social network construction and transformation from the materiality and political economy perspective of the public sphere? How is the spatiotemporal chain of political communication on the digital “interface” constructed? How is this different from traditional political communication?
Compared with optimists, pessimists consider the potential negative impacts of political communication on the Internet. Some scholars are concerned that the limited information flow caused by the power of algorithms in filter bubbles will lead to social division and the digital divide [36]. Moreover, “mediation” and “re-mediation” are also core issues that scholars are concerned about. “Re-mediation” refers to the new challenges and constraints brought by social media to political communication have also become new opportunities for political communication, which gives political actors new opportunities to regain control of the political communication process [34]. The power relations in social media networks are not as clearly defined as hierarchical structures in formal organizations, but such power is still ubiquitous. “Re-mediation” focuses on how these more hidden rights are used to promote their social claims through social media. Although scholars have many disputes about the deformation of political communication brought by social media, the digital public sphere has indeed changed the original political communication structure and may also be changing politics and political systems themselves.
The digital public sphere is both a metaphor for the digital society in the new era and a reflection of a new form of political communication, public opinion, and information interaction. Based on this, this study aims to better understand the system of the digital public sphere and “mini-public,” as well as their impact on the structure of political communication. This paper focuses on the role of group formation and group consistency changes and proposes the following three research questions:
Q1: Who are the prominent participants in the dissemination of new policies on social media?
Q2: What social roles do they assume?
Q3: What is the relationship and interaction between them?

2.2. Social Network and Semantic Network Analysis for Policy Communication

From the second half of the 19th century, sociologists such as Durkheim and Simmel began to analyze social behavior from a structural relationship perspective, and policy network analysis is a typical representative of this. Policy network analysis assumes that policy networks are clusters based on resource exchange and dependence [37]. Relevant research is committed to studying the formation, maintenance, and transformation of social networks regarding national interests, as well as the impact of these networks on policy-making and dissemination [38].
In existing research, scholars have formed three policy network research paths: resource sharing, value sharing, and discourse sharing. The resource-sharing paradigm focuses on the exchange of interests among actors [39], while the research paradigm in terms of common values and discourse sharing regarding shared terminology concepts and emotional value systems as the core of policy network formation [12]. Particularly in the era of post-truth and post-pandemic, the awakening of “humans” has made emotional and value sharing an essential element of political information circulation [40], which has led to political communication in the digital public domain paying more attention to the emotional dynamics and post-emotional power deep in the human spirit [41]. Therefore, analyzing policy communication networks requires attention to both the exchange of resources among actors and the dissemination of conceptual and emotional values. This leads to the two commonly used research methods in policy network analysis: social network analysis and semantic network analysis.
Social networks refer to a series of social connections and relationships that connect actors [42]. A Spanish study reveals the identity of influencers with digital authority in political conversations on Twitter by detecting centrality on social networks [43]. On this basis, Casero-Ripollés further examined the influencing factors of political actors’ authority in the public debate on Twitter through the measurement of positional relationships in social network analysis [44]. Each social media account is a network node, and their relations constitute the entire policy communication network. Bonifazi et al. provides a new centrality measure method to detect the backbone of different community information disseminators on social platforms [45], which helps to reveal the communication bridges and those at the center of communication. Network density and centrality are vital indicators to measure the overall network size. Network density reflects the degree of connectivity among nodes in the web; the higher the density, the closer the connections. Centrality measures the position of actors in the network. On this basis, modularity algorithm and Lambda analysis examine the cohesive subgroup relationships at position relations level and their internal and external connections [46]. The larger the Lambda value (K), the more stable the relationships between nodes, which reflects the network’s hierarchical clustering relationship.
Semantic network analysis represents knowledge graphs based on meaningful text-based relationships [16]. It reveals the structural relationships among words in policy discourse and extracts core associated concepts and their shared value emotions from the context. Semantic network analysis provides an explanatory way to analyze the hidden meanings of lexical clusters [47] and identifies the emotional contagion among policy participants. Research related to COVID-19 and political issues has focused on “global threats” and “government responses” frameworks [48]. Semantic networks and textual analysis help us detect the structure of the construction of populist discourse stories on social media. For example, a study of U.S. and Brazilian national leaders on Twitter about the construction of COVID-19 political stories was conducted to reveal the enormous influence of populist discourse on the construction of fantastical social reality [49]. Moreover, with the resurgence of populism, appealing to emotional, political identification has become a central strategy of political communication. Emotion is regarded as part of the discourse and a mediating factor closely related to the “re-post” information dissemination mechanism [19]. However, most existing studies have focused on constructing political fantasy discourses by “political elites” in Western countries, represented by right-wing populist nationalism and Christian worldviews [50]. Building upon these insights, this study employs semantic network analysis to identify conceptual clusters and extract public sentiments to reveal the current state of political communication in digital media and social discourse related to social identity issues in non-Western countries.
Based on these foundations, this study focuses on the rapid expansion of the digital space after the Chinese government issued the “New Ten Articles” for COVID-19, aiming to explore the landscape of policy communication and public opinion generation in the digital public domain through social network analysis and semantic network analysis. In summary, our study addresses the following research questions:
Q4: What information do political elites convey on social media?
Q5: What are the primary information needs of the public regarding the new policy on social media?
Q6: How do these needs differ from the information the political elites convey?
Q7: What are the central concepts associated with positive and negative sentiments in policy communication on social media?
Q8: What is the relationship between sentiment diffusion among different types of elites and public sentiment feedback?

3. Methodology

3.1. Sample Selection and Research Method

This study chooses Sina Weibo as the digital public space to research the overall structure of policy communication in China, taking the “New Ten Articles” policy communication process as a case study. Sina Weibo was chosen as our research platform due to its popularity and political function in China (Sina Weibo, which started its internal testing on 14th August 2009, was the first website in China to provide microblogging services, with 584 million monthly active users as of September 2022, a net increase of about 11 million users year-on-year. According to the 51st Statistical Report on the Development of the Internet in China, Sina Weibo now posts an average of over 3 million tweets daily, making it the most popular social media platform in China. Meanwhile, as of December 2022, 145,000 government agencies have set up microblog accounts, making Sina Weibo a cohesive place for policy releases and public opinion. Available online: https://cnnic.cn/n4/2023/0302/c199-10755.html (accessed on 5 June 2023)). Specifically, the study builds on the idea of the three-step approach on extracting information about COVID-19 posts on Reddit [51]. We collected 190,641 “New Ten Articles” policy-related posts by Sina Weibo accounts from 14:00 on December 7, 2022 to 14:00 on 14 December 2022, and processed them with data cleaning. Then, the study regards the accounts of individuals, media institutions, and other social organizations on Weibo as research subjects, employing a combination of social network and semantic network analysis to examine and present the overall policy communication structure on Chinese social media. The study also reveals the relationship between sentiment diffusion among different types of core communicators and public sentiment feedback, providing insight into the social capital relationships, information interaction landscape, and emotional interaction features regarding a new policy communication in the post-emotional digital era.

3.2. Research Design and Variable Definitions

3.2.1. Core Communicators and Definitions

This study regards the original post accounts as the research target and the re-post volume as the measure to count the total number of re-posts related to COVID-19 and the “New Ten Articles” policy posted by each account at the selected time. The top 100 users with the most re-posts were selected as the core users. Then, we adopted “following-follower” as the network relationship index and used Ucinet and Gephi to construct the communication network. Then, our study further classifies the top 100 users with the highest re-posts number into six categories based on the type of account verification and the institution to which they belong:
  • China central mainstream media;
  • Local mainstream media;
  • Governmental new media;
  • Online media;
  • Experts;
  • Online key opinion leaders.
China central mainstream media refers to the official media accounts belonging to the 18 authorized major news agencies recognized by the Chinese government, which mainly include: @Xinhua News Agency, @China Daily, @CCTV news, etc. Local mainstream media refers to the official news newspaper group under the leadership of the provincial/municipal government, such as: @The Paper News, @Sichuan Daily, @Shanxi Daily, etc. Governmental new media refers to the government affairs accounts opened by administrative organs on Sina Weibo, such as: @Chengdu Fabu (Chengdu Release), @Shanghai Fabu (Shanghai Release), etc. Online media mainly refers to media agencies with Internet news information service qualifications, such as @Sohu News, @Toutiao News, etc. Experts refer to professionals who have been officially certified by Sina Weibo and belong to the topic (health care) category. Out of consideration for ethic and user privacy, this study anonymizes the experts according to the rank of re-post volume as: Expert 1, Expert 2, Expert 3. Finally, online key opinion leaders refer to accounts that have been formally approved by Sina Weibo and have caused more discussions on Sina Weibo, but not professionals in the topic (health care) field. Since it was found in the research that there are two types of accounts with relatively clear positive and negative views under the “online key opinion leaders” in the actual research process, this paper further analyzes the relationship between these two types of accounts and public emotional feedback in more detail. Similarly, considering information privacy and ethics, this research anonymizes them according to the rank of re-post volume as: KOL1, KOL2, KOL3…

3.2.2. Semantic Network

Based on the co-occurrence of high-frequency words, this research uses Rost CM to construct a critical semantic network analysis on the content of core communicators groups and public discussions, as well as the representative concepts of negative emotions and positive emotions on Sina Weibo, and conducts a comparative study, respectively.

3.2.3. Sentiment Extremum

The calculation formula for sentiment extremum is: (positive re-posts − negative re-posts)/(positive re-posts + negative re-posts). It can be seen from the formula that the more considerable extreme value of the sentiment extremum, the more positive (or negative) the public emotional inclination caused by the related post on the network.

4. Finding and Discussion

4.1. Overall Structure of the “New Ten Articles” Communication Network on Sina Weibo

4.1.1. Structure: Flattening and Partial Interaction of Opinion Leaders

This study constructs a network relationship among the top 100 accounts with original information forwarding volume on Sina Weibo through Ucinet and Gephi. The results show that the network density is 0.2243, indicating that the actors in this network all have a reasonable correlation (as shown in Figure 1). The study further divided and analyzed the target users into 371 cliques. Among them, @Xinhua News Agency, @People’s Daily, and @The Paper ranked as the top three in all cliques, appearing in 245, 182, and 153 cliques, respectively. This indicates that these three parties have carried out the broadest range of communication in the entire information communication network, covering 88.4%, and are the core “hubs” of information circulation.
Our study further examines the role of the three accounts in information dissemination. @Xinhua News Agency and @People’s Daily cover 32.3% of cliques together, but the probability of @Xinhua News Agency, @People’s Daily, and @The Paper co-occurring is only 12.4%. This shows that in the policy communication process, @Xinhua News Agency, @People’s Daily, and @The Paper show a complementary distribution. @Xinhua News Agency and @People’s Daily have a relatively strong interaction. The first level of their information transmission is mainly towards local mainstream media and online self-media accounts, which mainly undertake further spreading information to the public. This is also consistent with the co-occurrence of opinion leaders and local mainstream media in some cliques we observed from the network. For example, @Shenyang Fabu, @Expert2, @KOL7, etc., only appear in one to two cliques, indicating their relatively narrow interaction range. However, they have formed a brush-fire information circulation network and are essential members of network information deep mining [52]. In contrast, the content released by @The Paper is often directly re-posted by the general public, which quickly achieves two-way communication between the official media and the general public, and forms complementary information dissemination with the central mainstream media.
Although official media are still at the core of the communication network, about 50% of the top 20 re-posted accounts on Sina Weibo are online opinion leaders (As shown in Table 1). For example, the posts regarding infection experiences or treatment methods shared by online opinion leaders have gained the attention and discussion of the majority of users. Digital technology, on the one hand, is constantly helping political elites to actively build their social networks actively [21], accelerating and sublimating the time-space effect of political communication; on the other hand, it also revolutionarily gives the general public the power of information communication and interpretation, which breaks the original top-down vertical policy transmission path, the “initiative” of users and the “looseness” of information have become the power source of the political communication in the digital public sphere. On this basis, these online opinion leaders gain the attention and trust of a large number of users with their characteristics of closeness and equivalence [53], which help them promote the realization of the legitimacy of political actions at the individual level through their in-depth discussions and exchanges within a small wide range. Political communication patterns are increasingly showing a new trend of diversification and flattening [54].

4.1.2. Low Network Density and Thick Barriers in the Information Circulation Circle

To answer Q3, the study further conducted Lambda and Modularity algorithm analysis for the top 100 core communicators to reveal the hierarchical and positional relationship between nodes and subgroups. The results show (as shown in Table 2) the Lambda value (K) in the sample ranges from 0 to 36. The study further divided it into strong interaction (25–36), medium interaction (13–24), and weak interaction (0–12) levels, to observe the subject characteristics at different levels. Among them, there are only 11 subsets in the substantial interaction subgroup, mainly composed of China’s central mainstream media, and there are only two accounts in the five-level interaction, showing a slight hierarchical break. Comparatively speaking, the medium interaction and weak interaction levels are the dense areas of accounts (there are 35 and 54 accounts, respectively). The medium interaction level mainly includes online media and local mainstream media accounts. It is not until the secondary interaction in the weak interaction that the interaction between the media and the individual appears, and it is mainly the formation of an interactive chain between some online commercial press and political opinion leaders. Some opinion leaders in the health care field only exist in subgroups where Lambda value (K) is 6 or even 0, which indicates that the interaction relationship of opinion leaders in this field is very weak or even non-existent. The minimum value of Lambda value (K) is 0, which means that the correlation in this whole network is weak, the mainstream media as core members lack cohesion, and the network compactness and information circulation are also relatively weak.
Regarding the positional relationship among the subgroups, the research divides 100 core communicators into eight subgroups based on the level of connections. The overall network density critical value is 0.256, presenting a situation of “identity status circle” division, subdivided explicitly into four categories: “current affairs/comprehensive content” accounts, “healthcare/medical” accounts, central mainstream media, and online media. However, local mainstream media and new government media have not formed a significant internal interaction circle, which reflects that the local mainstream media and the government still face substantial shortcomings in disseminating new public policies—a vast time–space asymmetry between the governance reality and policy guidance in cyberspace.
This study regarded the overall network density of 0.256 as the threshold, replaced the subgroup relationship with a binary network, and re-drew the correlation matrix, as shown in Figure 2. The results show that subgroups 1, 2, 3, 4, and 6 are all isolated nodes, revealing that the online opinion leaders within each circle are still independent. Only subgroups 5, 7, and 8 are connected, which means that only the 3 subgroups of online media, central mainstream media, and local mainstream media are related, and the relationships between media and individuals and between individuals and individuals are all independent. This further confirms the conclusion mentioned above that this network relationship is relatively fragile. It also further proves that, although the content from different “circles” has a positive effect on the depth of content and the outward expansion of information, the “information cocoon room” caused by it has also led to the thickening of the barrier, which may further lead to the alienation of policy information and the public’s one-sided understanding of relevant policies.

4.2. Semantic Network: Information Asymmetry between Core Communication Content and Public Demand

Discourse plays a constructive and reshaping role in social–cultural production and dissemination, and the emergence of social media makes the discourse of political communication more complex [31]. The study constructs and compares the semantic networks (as shown in Figure 3) between the content posted by the top 100 re-posted volume users and the rest ordinary public on Sina Weibo, respectively, and measures their keyword centrality.
The network distribution shows that the core communicators focus on “national policy measures” and “nucleic acid testing,” with keyword degree centrality reaching 5.469 and 4.68, respectively, occupying a key connection position in the entire high-frequency words network. However, the topic of “nucleic acid detection” has shown a low degree of attention in the general public circle (centrality: 0.012), and it does not even appear in the statistical high-frequency semantic network. Similarly, some keywords in the core communication network, such as “State Council” and “policy,” related to national measures, have not appeared in the keyword network of public discussion content. Also, the topics related to the “joint prevention and control” mechanism and policy are only in the more marginal place of public concern (centrality: 2.308). Compared with the semantic network formed by the core communicators, “infection consequences and health” is a crucial topic in the semantic network of the general public that is different from the content of the core communicators (centrality: 10.769), which is connected with the livelihood of social media people and the ability of health topics such as “medical” and “vaccine” to radiate and spread outward. All these data indicate that in Sina Weibo, a so-called flat, multi-channel, and network structural social media field, the information released by users in the core position of policy communication is somewhat asymmetrical with the information that the public seeks and needs on the network. This has also, to some extent, led to the alienation of the public’s understanding of policy information; topics such as “epidemic politics” and “infection discrimination” have become hot spots of public concern.

4.3. Sentiment Significant Concepts and Diffusion Association

4.3.1. Positive and Negative Semantic Emotion Networks

Using “COVID-19” and “New Ten Articles” as the basis, this study produced two positive and negative semantic networks. As shown in Figure 4, the positive emotional semantic network presents stronger topic linkages and integration than the negative emotional semantic network. In contrast, the negative semantic network offers weak relationship chains among keywords, which indicates that the topics in the negative semantic network are more diverse and discrete. Specifically, the lexical cluster in terms of a positive emotional network mainly focuses on “policy optimization” and “health,” revealing the potential association between policy changes and individual health issues. The public adopted the “optimization” to express their approval of policy changes and called for “being the first person responsible for health” and sharing their experience in COVID-19 prevention and post-infection recovery. In contrast, in the negative semantic network, the mention of “policy” related topics is more marginal, and the lexical cluster concentrates on “medical measures” and “vaccine efficacy” issues. The public has heated discussions about distrust of policy changes and irresponsibility for causing public infection. In addition, it is worth noting that because the role of “experts” in the communication chain is somewhat ambiguous, some posts of “online celebrities” have become a serious source of negative public sentiment and skepticism.
In summary, many core concepts in the positive network also appear in the negative network, but the opposite is not the case. The negative semantic network involves a broader range of topics, and these topics have become potential obstacles in the “New Ten Articles” policy communication.

4.3.2. Semantic Emotion Diffusion and Public Emotion Reflection

In order to study the relationship between the semantic emotion diffusion of different types of core communicators and the feedback sentiment of the public, a seven-group correlation analysis was performed on these two variables of the sentiment score of the core communicator’s original post and the extreme sentiment value of the corresponding re-posts, as shown in Table 3. It can be found that the content published by the four types of accounts, online opinion leaders (negative content), experts, online media, and local mainstream media, is significantly correlated with network emotions (p < 0.05); in other words, the content published by these four types of accounts is an essential source of emotion and can significantly influence the feelings of public. In addition, the content posted by central mainstream media, online opinion leaders (positive content), and governmental new media are not significantly correlated with public sentiment (p = 0.786, 0.767, and 0.849), indicating that there is no significant relationship between the content they publish and changes in public emotion, which also proves the conclusion we found above that the mainstream discourse is facing unprecedented challenges of the times, and the structure of policy communication in the digital public sphere shows the trend of “flattening” development.
Specifically, the content posted by online opinion leaders (negative content) and online media is significantly correlated with negative emotions on Sina Weibo, which indicates that these two types of accounts highly attract negative emotions (as shown in Table 4). For example, some online opinion leaders in the fields of politics or society have linked issues such as “policy direction shifts” or “lack of medical resources” to grassroots livelihood issues, which allows negative emotions to radiate in a short time and prompts feelings to guide audience attention instead of the policy itself. Moreover, the content of online media dissemination has caused intense controversy and low acceptance among the audience. Even with positive content, the positive emotional extremes are also low. For example, “@Toutiao News” launched the topic of “Travel Plan Sharing” in the early period of the policy issued, which sparked strong criticism from the public about the issue of “ignoring public health.” Platform capitalism is increasingly taking on a more prominent presence in online media, referring to the purposeful enhancement of content designed to stimulate habitual consumption and enjoyment in specific contexts [55]. This reflects a new form of digital economic circulation [56], which combines with the “sharing economy” and promotes the transformation of Web 2.0, dominated by the commercial logic of “promoting user participation”, to Web 3.0, dominated by “user cooperation” [57].
Conversely, the main content published by experts and local mainstream media shows a significant positive correlation with positive emotions on Sina Weibo. For example, “@Sichuan Daily” published the local province adjustment measures after the “New Ten Articles” policy was released, allowing the public to have a clear and comprehensive understanding of the local implementation of the policy. Health care field experts have alleviated unstable public sentiment by sharing professional knowledge, drug preparation plans, self-diagnosis, treatment methods, etc. The positive or neutral emotional content published by both types of communicators has been further disseminated in subsequent discussions and plays a vital role in guiding a favorable public opinion environment.

5. Discussion and Conclusions

With the increasing complexity of the media system, re-examining the policy communication ecology under the digital public sphere requires the reconstruction of the interactive relationship between media, the public, and government institutions, as well as a re-grasp of the “transformation” of core concepts such as Agenda Setting and Spiral of Silence in the new media field [58]. The political capacity in the network depends on a stable core of official organizations and the redundant and dense behavior of self-organized links [22]. In our network, although official mainstream media still occupy a central position in the communication network, some online opinion leaders have become strong competitors through their inner interactions. The emergence of the digital public sphere has made interpersonal networks and mass media more intertwined than ever, and people who have less in common have easily become “digital neighbors” [59] who constitute connections. Their shared constituent roles [22] occupy a central place in the landscape that facilitates the formation of a public opinion climate, reflecting in part the political efficacy of social media in China. In contrast to previous studies on the forms of political communication and role dependence in digital media [60], this study focuses on the distinctive features of such collective participation in China. The communication pattern of the “New Ten Articles” policy presents a situation of “identity status circle” division. In social media, the influence of information position in the communication network sometimes surpasses the source of information impact, and the agenda-setting ability of mainstream media has been forced to give more contextualization and contingency. The influence of the mainstream communication paradigm on communication effects, even if it has yet to be replaced, is constantly being downgraded, weakening the core cohesion of mainstream media to some extent. At the same time, the disconnection of circle links and the continuous thickening of barriers lead to the public’s alienation and one-sided cognition of policy information.
From a semantic point of view, there is still a long way to go in building the capacity of Chinese official media organizations to respond to online information. With the increasing selectivity and fragmentation of media content and media use, public debate and public opinion relativism is increasing, and the information asymmetry between official policy communication and general needs is further enhanced. Especially when linked to topics of public relevance such as health, research shows that proximity (including geographical, gender, and ideological and emotional associations) and high relevance are more likely to stimulate users’ cognitive activities [61]. This is because “information efficacy” [62] is increasingly becoming a significant influence on information orientation, which in turn directly affects user engagement [63]. Thus, the public will give priority to physiological and safety requirements which closely related to themselves (such as health security issues, medical resource allocation issues, etc.), followed by needs arising from social order stability (such as local policy adjustment and implement). This, on the one hand, reflects the existing contradiction between reality and ideals in political communication; on the other hand, our research points out that the political discourse topics related to COVID-19 on Chinese social media differ from Western research regarding global threats, leadership image building and the political economy of welfare [64]. China presents governance practice-oriented political communication, which is regarded as part of the state’s and society’s governance. Also, our research notes that the government and mainstream media are increasingly unable to cover and meet the diversity of general information needs [65]. This limited communication leads to a vicious circle of limited public cognition and the risk of alienation of policy information.
Regarding social sentiment, negative sentiment has always been a significant problem in policy communication. After the “New Ten Articles” policy was released, although the lexical clusters of positive sentiment around critical concepts such as policy optimization are closer, negative sentiment shows a broader diversity of topics and has a more considerable connotation difference from the positive semantic network. Our study builds on Schreiner et al.’s study of the relationship between the sentiment of Twitter messages and retweeting behavior [18], and further analyzes the interaction links between different actors at the sentimental level. The study measured the sentimental connection between the content published by six types of core communication accounts and public re-posting. The study found a significant positive correlation between the negative remarks of some “pseudo-opinion leaders” who ignore reality and the negative sentiment regarding social split and lower political trust. The supply and diversity of quality information have decreased, making the existing “discordant” public voices more difficult to overcome differences. In this case, the communication function of social media is out of balance, and these negative voices may be the most significant potential obstacles in the communication of the “New Ten Articles” policy.
In contrast, the main content published by corresponding field experts and local mainstream media significantly correlates with positive emotions on Sina Weibo. In this way, these positive and negative sentiments spread in subsequent discussions. The deep integration of mass media and interpersonal networks in the online environment facilitates the influence of “emotions” on disseminating public opinion and the changing spiral of silence. Emotional factors, as part of political discourse, not only transmit information but also, to a certain extent, regulate the formation process of political opinion.
Basically, to better combine policy communication with the ever-changing reality, this study takes the communication process of the “New Ten Articles” policy as a case, revealing the picture of social media policy communication and information interaction after the release of a new public policy, supplementing the policy communication and development in the digital public sphere with empirical cases and providing new insights into the key role of social media in the dissemination of further policy information. However, the current study is still limited to a relatively superficial stage of phenomenon analysis. The situational conditions of event impact, the convergence and divergence of positions among actors, and the fusion and complementarity of boundaries between new and old media are crucial for understanding the effects of political communication in the digital public sphere. Our research has noted the deconstruction and reconstruction of the shift from Web 2.0 media logic to Web 3.0 logic in the new media era [57]. Numerous studies on platform capitalism and the digital economy are rethinking the unavoidable platform capitalism and media logic’s impact on the political logic of policy communication. Future research can build on this foundation to further explore the process of negotiated interactions between the public and the political economy on social media in China. In addition, our research depends on the selection of cases and data and is only limited to sentimental measurement under political communication, and the expansion of digital neighborhood boundaries has created new facilitators (or impediments) to the spiral of silence. To some extent, expanding communication networks on social media has stimulated a broader spiral of silence. The differences in regionalization of the spiral of silence [59] and anti-silent spiral effect [66] have been noted by a large number of scholars. The measure of sentimental feedback under a wider range of topics and the modern transformation of the spiral of silence on new media also need to be analyzed in the future.

Author Contributions

Conceptualization, Y.D. and X.C.; methodology, X.C.; software, X.C.; validation, Y.D. and X.C.; formal analysis, Y.D., X.C. and Y.L.; data curation, X.C.; writing—original draft preparation, X.C. and Y.L.; writing—review and editing, Y.D.; visualization, X.C.; supervision, Y.D.; project administration, Y.D.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The National Social Science Fund of China grant number 21&ZD313 and 21BXW003.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Core communication Network of the “New Ten Articles” on Sina Weibo.
Figure 1. Core communication Network of the “New Ten Articles” on Sina Weibo.
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Figure 2. Binary Matrix of Core Subgroup Network.
Figure 2. Binary Matrix of Core Subgroup Network.
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Figure 3. Elite–Public Political Communication Semantic Network Comparison Map.
Figure 3. Elite–Public Political Communication Semantic Network Comparison Map.
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Figure 4. Positive –Negative Sentiment Semantic Network Comparison.
Figure 4. Positive –Negative Sentiment Semantic Network Comparison.
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Table
Table 1. Comparison of the Centrality and Original Re-post Volume of the Top 20 Core Communicators of the “New Ten Articles” policy on Sina Weibo.
Table 1. Comparison of the Centrality and Original Re-post Volume of the Top 20 Core Communicators of the “New Ten Articles” policy on Sina Weibo.
CentralityRe-Post Volume
Account (@)Degree CentralityAccount (@)Volume
People’s Daily86.96KOL16414
CCTV news82.61China News Weekly4308
People’s Daily Online76.09People’s Daily3804
Toutiao News75.00KOL23023
The Paper72.83The Paper2695
Global Times70.65Guanchanet2587
China News Service68.48CCTV news2223
Xinhuanet68.48Bailu Video1543
Guanchanet61.96Expert11471
China News Weekly61.96Guowangjiangsu Dianli1394
Beijing Daily60.87KOL31025
Sina Finance57.61KOL4984
Beijing News57.61KOL5981
CCTV.com56.52CCTV news910
Caijing.com56.52Souhu News904
Beijing Toutiao55.44KOL6760
Guangming Daily51.09China Daily748
CCTV Finance51.09Expert2738
Beijing evening news50.00KOL7733
China Daily50.00Expert3731
Table
Table 2. Lambda analysis Result for Top 100 Core communicators.
Table 2. Lambda analysis Result for Top 100 Core communicators.
Interaction TypeInteraction LevelAccounts
Weak Interaction (0–12)Level1 (0–6)
Level2 (7–12)		38
16
Medium Interaction (12–24)Level3 (13–18)
Level4 (19–24)		20
15
Strong Interaction (25–36)Level5 (25–30)
Level6 (31–36)		2
9
Table
Table 3. Correlation between original text sentimental score and corresponding re-post sentimental Extremum.
Table 3. Correlation between original text sentimental score and corresponding re-post sentimental Extremum.
TypePearson Correlationp
Online Opinion Leaders (Positive content)−0.1080.767
Online Opinion Leaders (Negative content)0.722 10.043
Experts0.712 10.031
Central Mainstream Media0.0880.786
Online Media0.819 20.003
Local Mainstream Media0.539 10.047
Governmental New Media0.0810.849
1 p < 0.05, significant correlation. 2 p < 0.01, significant correlation.
Table
Table 4. Sentimental Collocations Examples. (Online Opinion Leaders (Positive content), Experts, Online media and Local mainstream media.
Table 4. Sentimental Collocations Examples. (Online Opinion Leaders (Positive content), Experts, Online media and Local mainstream media.
TypeSentiment Score (Posts) 1Sentiment Extremum (Re-Posts) 2
Online Opinion Leaders (Positive content)−40.33
−8−0.93
−2−0.11
−6−0.87
−8−0.64
Experts−8−0.64
60.74
00.19
20.9
101
Online Media30.02
6.3−0.13
3−0.54
0−0.12
−11.8−0.92
Local Mainstream Media130.73
50.44
00
101
00.33
1 Display the top five accounts according to the re-posts volume ranking. 2 Corresponding to the re-post sentiment extremum, keep two decimal places.
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Dai, Y.; Cheng, X.; Liu, Y. Information Alienation and Circle Fracture: Policy Communication and Opinion-Generating Networks on Social Media in China from the Perspective of COVID-19 Policy. Systems 2023, 11, 340. https://doi.org/10.3390/systems11070340

AMA Style

Dai Y, Cheng X, Liu Y. Information Alienation and Circle Fracture: Policy Communication and Opinion-Generating Networks on Social Media in China from the Perspective of COVID-19 Policy. Systems. 2023; 11(7):340. https://doi.org/10.3390/systems11070340

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Dai, Yuanchu, Xinyu Cheng, and Yichuan Liu. 2023. "Information Alienation and Circle Fracture: Policy Communication and Opinion-Generating Networks on Social Media in China from the Perspective of COVID-19 Policy" Systems 11, no. 7: 340. https://doi.org/10.3390/systems11070340

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