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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.

Over the past fifty years, public opinion polls have assumed an increasingly important role in daily life. Quite possibly every person living in America has at one time or another been affected by polls. In fact, few aspects of our lives remain untouched by polls—from the television programs we watch, to the choice of candidates for public office, to national political issues, to the products we buy. Polling has become widely known through frequent use by television and the mass market print media.

The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results.

This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion.

The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression.

To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.

Although Human Papilloma Virus (HPV) is the most commonly diagnosed sexually transmitted infection in the USA, much controversy exists with respect to HPV vaccination, especially among parents of adolescents. Previous research has shown that exemplars in the media influence public opinion estimates about controversial social issues. However, little is known about the underlying psychological processes of how exemplars influence public opinion formation. The purpose of this paper is to systematically explore such psychological processes based on the projection theory. To this end, the important yet controversial public health issue, the mandatory HPV vaccination, was chosen.

A two-factor (exemplar vs proportion), between-subject experiment was conducted using online newspaper articles as main stimuli. A total of 138 participants completed the study. The analytical framework comprised the Sobel test with the Bootstrap method and a series of Ordinary Least Square hierarchical regression analyses.

The higher the proportion of exemplars against the HPV vaccination in a news article was, the greater the number of individuals who became opposed to it was. And the high personal opposition translated into negative public opinion change estimation.

The findings indicate that news exemplars may influence individuals’ personal opinion formation, and, in turn, contribute to their estimations of future public opinion climate, as suggested by the projection theory. Theoretical, methodological and practical implications for journalists, health educators and policy makers are discussed.

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.”

The UK police service has a major challenge to introduce innovative ways of improving efficiency and productivity, whilst at the same time improving public opinion as to their effectiveness in the “fight against crime”. The purpose of this paper is to outline an exploratory study of the ability to cluster police forces based on their sanction detection levels over a number of different offence groups and whether these clusters have different associated public opinions towards them.

Using secondary data and the fuzzy c‐means clustering technique to exposit clusters of police forces based on sanction detection levels, relating them in a statistical analysis with public opinion on the police.

The clustering analysis shows how police forces can be considered relative to each other, based on their sanction detection levels of certain offence groups, including; burglary, fraud and forgery and criminal damage. Using the established clusters of police forces, in respect of independent variables relating to public opinion, including confidence in police; there does appear to be statistically significant differences amongst the clusters of police force.

The results demonstrate the connection between the police's attempt to fight crime and public opinion. With the public opinion measures considered post the establishing of police forces’ clusters, the results show the public does notice the level of sanction detections achieved. The identified disconnect of the public with the criminal justice system is something that can be improved on in the future.

Demonstrates that there is a significant link in the relationship between the levels of sanction detection levels of police forces and public opinion about their ability to fight crime.

This paper employs fuzzy c‐means, a modern clustering technique nascent in this area of research.

The paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant departments in formulating public opinion control measures for specific time and space contexts.

The spatiotemporal situational awareness framework comprises situational element extraction, situational understanding and situational projection. In situational element extraction, the data on the COVID-19 vaccine, including spatiotemporal tags and text contents, is extracted. In situational understanding, the bidirectional encoder representation from transformers – latent dirichlet allocation (BERT-LDA) and bidirectional encoder representation from transformers – bidirectional long short-term memory (BERT-BiLSTM) are used to discover the topics and emotional labels hidden in opinion texts. In situational projection, the situational evolution characteristics and patterns of online public opinion are uncovered from the perspective of time and space through multiple visualisation techniques.

From the temporal perspective, the evolution of online public opinion is closely related to the developmental dynamics of offline events. In comparison, public views and attitudes are more complex and diversified during the outbreak and diffusion periods. From the spatial perspective, the netizens in hotspot areas with higher discussion volume are more rational and prefer to track the whole process of event development, while the ones in coldspot areas with less discussion volume pay more attention to the expression of personal emotions. From the perspective of intertwined spatiotemporal, there are differences in the focus of attention and emotional state of netizens in different regions and time stages, caused by the specific situations they are in.

The situational awareness framework can shed light on the dynamic evolution of online public opinion from a multidimensional perspective, including temporal, spatial and spatiotemporal perspectives. It enables decision-makers to grasp the psychology and behavioural patterns of the public in different regions and time stages and provide targeted public opinion guidance measures and offline event governance strategies.

A novel asynchronous network-based model is proposed in this paper for the sentiment analysis of online public opinions. This new model provides a new approach to analyze the evolution characteristics of online public opinion sentiments in complex environment.

Firstly, a new sentiment analysis model is proposed based on the asynchronous network theory. Then the graphical evaluation and review technique is employed and extended to design the model-based sentiment analysis algorithms. Finally, simulations and real-world case studies are given to show the effectiveness of the proposed model.

The dynamics of online public opinion sentiments are determined by both personal preferences to certain topics and the complex interactive influences of environmental factors. The application of appropriate quantitative models can improve the prediction of public opinion sentiment.

The proposed model-based algorithms provide simple but effective ways to explore the complex dynamics of online public opinions. Case studies highlight the role of government agencies in shaping sentiments of public opinions on social topics.

This paper proposes a new asynchronous network model for the dynamic sentiment analysis of online public opinions. It extends the previous static models and provides a new way to extract opinion evolution patterns in complex environment. Applications of the proposed model provide some new insights into the online public opinion management.

In the new era of highly developed Internet information, the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.

Aiming at the complex and nonlinear characteristics of the network public opinion, considering the accuracy and stability of the applicable model, a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network (BES-RBF) is proposed. Empirical research is conducted with Baidu indexes such as “COVID-19”, “Winter Olympic Games”, “The 100th Anniversary of the Founding of the Party” and “Aerospace” as samples of network public opinion.

The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information, has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.

A method for optimizing the central value, weight, width and other parameters of the radial basis function neural network with the bald eagle algorithm is given, and it is applied to network public opinion trend prediction. The example verifies that the prediction algorithm has higher accuracy and better stability.