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The purpose of this research is to investigate the usage characteristics and the information propagation patterns of Chinese microblogs in different stages of an epidemic, given that the microblogging in China is different from other parts of the world. In addition, the authors aim to conceptualize the roles of different users and provide insights for using microblogging platforms to disseminate information in this context.

The authors conducted an analysis on Sina Weibo microblogs about the African Swine Fever epidemic from August to October 2018. The authors firstly applied a label propagation algorithm to classify users into government, media, verified users and nonverified users. The authors analyzed several user metrics, traced the information propagation patterns of their microblogs and calculated the average speed of information propagation using computational approaches.

The authors’ findings show that different types of users played different roles, such as supplying information, amplifying information, relaying information and engaging with other users. The microblogs posted by media dominated the propagation in most cases, but general users can propagate information faster. The direction of information propagation is one-way for the majority of microblogs, and few users repost earlier information. Additionally, microblogs attract more attention at the beginning and the middle phases of an epidemic. In the context of managing epidemics, the authors recommend governments and other verified users can work together to use microblogging platforms efficiently.

This research is one of the few studies to investigate information propagation patterns of different user categories on a Chinese microblogging platform during an epidemic. The authors’ work can be used by government agencies and public health authorities for disseminating information efficiently during epidemics or emergencies, especially in the early stages.

Under the new media environment, while enjoying the convenience brought by the propagation of public opinion information (referred to as public opinion), learning the evolution process of public opinion and strengthening the governance of the spreading of public opinion are of great significance to promoting economic development and maintaining social stability as well as effectively resisting the negative impact of its propagation.

Thinking about the results of empirical research and bibliometric analysis, this paper focused on introducing key factors such as information content, social strengthening effects, etc., from both internal and external levels, dynamically designed public opinion spreading rules and netizens' state transition probability. Subsequently, simulation experiments were conducted to discuss the spreading law of public opinion in two types of online social networks and to identify the key factors which influencing its evolution process. Based on the experimental results, the governance strategies for the propagation of negative public opinion were proposed finally.

The results show that compared with other factors, the propagation of public opinion depends more on the attributes of the information content itself. For the propagation of negative public opinion, on the one hand, the regulators should adopt flexible guidance strategy to establish a public opinion supervision mechanism and autonomous system with universal participation. On the other hand, they still need to adopt rigid governance strategy, focusing on the governance timing and netizens with higher network status to forestall the wide-diffusion of public opinion.

The research conclusions put forward the enlightenment for the governance of public opinion in management practice, and also provided decision-making reference for the regulators to reasonably respond to the propagation of public opinion.

Our research proposed a research framework for the discussion of public opinion propagation process and had important practical guiding significance for the governance of public opinion propagation.

In modern society, considering the multi-channel of public opinion information (public opinion) propagation and its strong influence on social development, it is necessary to study its propagation law and discuss the intervention strategy in online social networks (OSN).

First, a conceptual model of double-layer OSN was constructed according to their structural characteristics. Then, a cross-network propagation model of public opinion in double-layer OSN was proposed and discussed its spreading characteristics through numerical simulations. Finally, the control strategy of public opinion, especially the timing and intensity of intervention were discussed.

The results show that the double-layer OSN promotes the propagation of public opinion, and the propagation of public opinion in double-layer OSN has the characteristics of that in two single-layer OSN. Compared with the intervention intensity, the regulator should give the priority to the timing of intervention and try to intervene in the early stage of public opinion propagation.

This study may help the regulators to respond to the propagation of public opinion in OSN more actively and reasonably.

This research has a deep comprehension of the cross-network propagation rules of public opinion and manages the propagation of public opinion.

Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality.

This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis.

Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality.

This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks.

There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.

Social media, especially microblog, has become one of the most popular platforms for public opinion dissemination. However, so far few studies have been conducted to explore information dissemination under the mobile environment. This paper aims to introduce the approach to analyze the public opinion information dissemination in mobile social networks.

This paper chooses “network attack” as the research topic and extracts 23,567 relevant messages from Sina Microblogs to study the structure of nodes for public opinion dissemination and the characteristics of propagation paths on mobile internet. Public opinion dissemination is compared on both mobile and non-mobile terminals.

The results reveal the characteristics of public opinion dissemination in mobile environment and identify three patterns of information propagation path. This study concludes that public opinion on mobile internet propagates more widely and efficiently and generates more impact than that on the non-mobile internet.

The methods used in this study can be useful for the government and other organizations to analyze and identify problems in online information dissemination.

This paper explores the mechanism of public opinion dissemination on mobile internet in China and further investigates how to improve public opinion management through a case study related to “network attack.”

Understanding the computer-virus propagation is quite essential for the construction and development of anti-virus policy. While researches about the anti-virus policy have been extensively investigated, the viewpoint from sociological perspective is relatively ignored. Therefore, this paper aims to explore the dynamics of computer-virus propagation and evaluate the effectiveness of anti-virus policies through the sociological perspective.

This research constructs a virus-propagation model based on the susceptible-exposed-infective-recovered epidemic concept to simulate and explore the dynamic behavior of multipartite computer viruses through the tool of system dynamics. The effectiveness of various anti-virus policies is then evaluated via this model.

The frequency of media contact has a significant effect on the virus infection rate. The effectiveness of user self-prevention relies on the usefulness of the virus signatures. The reporting/alarm process can enhance the capability of anti-virus software company and the detected intensity of new threat. The quarantine policy can effectively reduce the spread of computer virus.

Individuals should strengthen the self-awareness of information security to reduce the negative impact. Managers should construct and implement the information security norm to regulate the behavior of staff. Anti-virus software companies should strengthen the capability of their automatic reporting/alarm mechanism to early detect the exceptional conditions and control new threats in time.

Information security management research is still in the growth phase, but it is critically important to establish the groundwork for understanding of computer viruses and the effectiveness of anti-virus policy from assorted perspectives. The major contribution of research is to explore the propagation of multipartite computer viruses and study how to prevent their destruction from the sociological and technical perspectives.

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.

In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.

Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.

This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

This study aims to investigate how Internet memes affect brand image.

The authors first used the Delphi method to refine Internet memes' constructs and dimensions and developed a scale for Internet memes. Second, the authors used a questionnaire to collect data from 348 valid Internet consumers.

The authors proposed four significant characteristics of Internet memes: humour, high positive emotional intensity, brand interactions and prestige, and high spreadability to prompt consumers to remake, share and spread memes. The study results indicate that Internet memes positively influence the brand image. However, not all meme characteristics were correlated with the brand image; only brand prestige, interaction and humour enhanced brand image.

For scholars in online marketing communication research, this study shifts the current paradigm of brand-generated and customer-passive to user-generated and customer-active. It also addresses the importance of pandemic effects of Internet memes on brand image. To be specific, this study presents the important symbolic values that Internet memes need to include to affect consumers' behaviour in response to perceived brand image by applying both mind infection and symbolic value perspectives. Finally, this study redefines the dimensions and measurements of Internet memes to address the lack of consensus and concrete scales regarding meme transmission characteristics and extending into online marketing communication strategies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2019-0364