A Study of Public Opinion Reversal Recognition of Emergency Based on Hypernetwork.

With the development of social media and online platforms, the speed of dissemination and influence of emergencies in cyberspace have increased significantly. The rapid change of public opinion, especially the reversal of public opinion, may have a significant impact on social stability and government credibility. The hypernetwork structure has complex multilevel and multidimensional characteristics, and it is of great significance to analyze the multiple participating subjects of public opinion evolution and their complex relationships based on the hypernetwork theory, and to further identify the public opinion reversal for the public opinion response and guidance of emergencies. According to the complex interaction between the participants of emergencies and internal and external factors, this paper constructs a hypernetwork model including four subnets of users, time series, opinions, and emotions, and analyzes the network structure in detail. On this basis, the method steps of emergency public opinion inversion recognition are proposed. Taking the public opinion event caused by Hongxing Erke donation during the rainstorm in Henan Province of China as an example, the empirical analysis is carried out. The research shows that the proposed emergency hypernetwork model provides effective support for the identification of public opinion inversion, and the identification method of public opinion inversion based on the hypernetwork is helpful to find the trend of public opinion evolution, so as to infer the tendency of public opinion inversion, which provides new ideas for the related research of public opinion monitoring and emergency response.

PurposeThe outbreak and continuation of COVID-19 have spawned the transformation of traditional teaching models to a certain extent. The Chinese Ministry of Education’s guidance on “keep learning and teaching during class suspension” has made OTC and learning (OTC) become routinized, and the public’s emotional attitudes toward OTC have also evolved over time. The purpose of this study is to segment the emotional text data and introduce it into the topic model to reveal the evolution process and stage characteristics of public emotional polarity and public opinion of OTC topics during public health emergencies in the context of social media participation. The research has important guiding significance for the development of OTC and can influence and improve the efficiency and effect of OTC to a certain extent. The analysis of online public opinion can provide suggestions for the government and media to guide the trend of public opinion and optimize the OTC model.Design/methodology/approachThis paper takes the topic of “OTC” on Zhihu during the COVID-19 epidemic as an example, combined with the characteristics of public opinion changes, chooses Boson emotional dictionary and time series analysis method to build an OTC network public opinion theme evolution analysis framework that integrates emotional analysis and topic mining. Finally, an empirical analysis of the dynamic evolution of the communication network for each stage of the life cycle of a specific topic is realized.FindingsThis paper draws the following conclusions: (1) Through the emotional value table and the change trend chart of the number of comments, the analysis found that the number of positive comments is greater than the number of negative comments, which can be inferred that the public gradually accepts “OTC” and presents a positive emotional state. (2) By observing the changing trend of the average daily emotional value of the public, it is found that the overall emotional value shows a stable development trend after a large fluctuation. From the actual emotional value and the fitted emotional value curve, it can be seen that the overall curve fit is good, so ARIMA (12, 1, 6) can accurately predict the dynamic trend of the daily average emotional value in this paper. Therefore, based on the above-mentioned public opinion, emotional analysis research, relevant countermeasures and suggestions are put forward, which is conducive to guiding the development direction of public opinion in a positive way.Originality/valueTaking the topic of “OTC” in Zhihu as an example, this paper combines Boson emotional dictionary and time series to conduct a series of research analyses. Boson emotional dictionary can analyze the public’s emotional tendency, and time series can well analyze the intrinsic structure and complex features of the data to predict the future values. The combination of the two research methods allows for an adequate and unique study of public emotional polarization and the evolution of public opinion.

In the current network environment, the sources of information are gradually diversified and online public opinion events are often “enlarged” by the network, which will have an impact on network security, people's lives and social stability. So the prediction and monitoring of online public opinion has become an inevitable work. However, the majority of simulation studies on the evolution of public opinion, most scholars use social network analysis to study complex social networks. Such studies mainly consider discontinuous changes when selecting corresponding variables. However, the representation of variables in the evolution process is often overly idealistic, since the dynamic and continuous changes of the corresponding thresholds are not considered. The Hegselmann-Krause (H-K) bounded trust model offers a more comprehensive representation of the dynamic evolution of online public opinion. Still, the existing H-K bounded trust model has shortcomings in text content analysis. This paper presents the related research on the internet public opinion and its evolution, describes the H-K model and proposes an improved dynamic H-K model. Finally, a simulation experiment utilizing real-world data demonstrates the effectiveness of the improved dynamic model.

Online social speech contains rich emotional expressions, which promote the evolution of public opinion. To explore this influencing mechanism, this paper constructs a research model of users’ group emotions affecting the evolution of public opinion based on emotional event theory and social telepresence theory. Firstly, we calculate group emotional value based on text analysis and users’ social presence value through principal component analysis. Then, we construct a regression model to test the research hypothesis. Finally, the explanatory variables and regulatory variables are lag processed to test the robustness of the model. The results show that users’ group emotional intensity and extreme emotion significantly positively impact public opinion evolution. The stronger the group emotion and the more extreme emotion, the higher the popularity of public opinion network and the richer the text information. The stronger the social presence of group users, the more significant the impact of emotional intensity on the evolution of public opinion. Social presence inhibits the promotion of extreme emotion in the evolution of public opinion. This study reveals the influencing mechanism of the effects of user emotion on public opinion evolution, expands the theories related to public opinion evolution, and provides new ideas and theoretical references for the in-depth study of “user emotion-online public opinion”. In addition, it provides a corresponding theoretical basis, decision support, and management countermeasures for social media and public opinion supervision departments.

Using data from the General Social Survey and the Pew Research Center, this chapter analyzes the extent to which the change in American public opinion about gay marriage between 1988 and 2014 is due to age, cohort, and period effects. It also examines the extent to which people’s moral judgments, attitudes, and beliefs about homosexuality account for the change in public opinion over time. The analyses show that cohort and period have effects on support for gay marriage, independent of ideology, worldview, and other demographic variables, but they leave unanswered questions about how and why cohort and period affect public opinion as they do.

Public persons are nodes with high attention to public events, and their opinions can directly affect the development on events. However, because of rationality, the followers' acceptance to the public persons' opinions will depend on the information trait on public persons' opinions and own comprehension. To study how different opinions of the public persons guide different followers, we build an opinion dynamics model, which would provide a theoretical method for public opinion management. Based on the classical bounded confidence model, we extract the information quality variables and individual trust threshold and introduce them to construct our two-stage opinion evolution model. And then in the simulation experiments, we analyze the different effects of opinion information quality, opinion release time, and frequency on public opinion by adjusting the different parameters. Finally, we added a case to compare real data, the data from classical model simulation and the data from improved model simulation to verify the effectiveness on our model. The research found that the more sufficient the argument and the more moderate the attitude, the more likely to guide the public opinion. If public person holds different opinions and different information quality, he should choose different time to present his opinion to achieve ideal guide effect. When public person holds neutral opinion and the information quality is relatively general, he/she can intervene in public opinion as soon as possible to control final public opinion; when public person holds extreme opinion and the information quality is relatively high, he/she can choose to express opinion after a certain period on public opinion evolution, which is conducive to improve the guidance effect on public opinion. The frequency of releasing opinions of public person consistently has a positive impact on the final public opinion.

Comparative Analysis and Strategy Research of Enterprises Dealing with Network Public Opinion Based on Text Mining

In the new media era, there are more ways of information dissemination, and the speed of information dissemination becomes faster. Along with it, various public opinions and rumors flood the cyberspace. As a mainstream social media information publishing platform, microblog has become the main way for netizens to obtain, disseminate and publish information. Because microblog can freely make speeches, and has a fast transmission speed and a wide range, it is easy for public opinion information to be widely disseminated in a short time. In particular, information such as rumors in public opinion can affect the network environment and social stability. Therefore, it is necessary to analyze and predict public opinion changes and to provide early warning. The literature uses the classic BP-NN (BP-NN) as the base prediction model, and uses the information published on the Sina microblog platform as a sample to analyze and predict the public opinion of influenza diseases. Due to the BP-NN’ slow convergence speed, this paper introduces an improved genetic algorithm to select the optimal parameters in the BP-NN (IGA-BP-NN), shorten the calculation time, and improve the analysis and prediction efficiency. The experiments verify that the work in this paper can provide more accurate early-warning information for the public opinion management of related departments.

Age and Change in Public Opinion: The Case of California, 1960-1970

Over the past few decades, the situation of lesbian, gay, bisexual and transgender (LGBT) people has improved significantly and attitudes towards these vulnerable social groups have become more favorable, but discrimination, hate speech and hate crimes based on sexual orientation and gender identity remain widespread. Based on the corpus of data obtained during 1991–2023 from series of representative international and national studies, changes in public opinion about LGBT people in two post-Soviet countries were analyzed. It is shown that despite the numerous differences between Estonian and Ukrainian societies, different legislative fields and other factors, the dynamics of attitudes towards LGBT issues in both countries have many common features (namely, the initial period of stable low public support for LGBT is replaced by its rapid growth, which, in in turn, passes into a period of stable high support), and the process of changes in public opinion over time can be described by a logistic equation. The nature of changes in public opinion described in the article is applied to most of the considered data, and the duration of the initial period of stable-low support (on average, 11 years for Estonia and about 20 years for Ukraine) is comparable to the period separating two generations. The applied mathematical model gives grounds for predicting that the maximum of public support for LGBT may be reached in Estonia at the end of the 2020s, and in Ukraine at the beginning of the 2030s. Unfortunately, social stigmatization of LGBT issues significantly limits the availability of data that can become basis of the analysis. The article discusses some possible factors of changes in public support for LGBT issues (the number and openness of LGBT people, instability in the respective societies / states, etc.), and outlines the heuristic value of the proposed model and directions for further development.

With the rapid development of social media in recent years, the social network platform has become an important platform for the current public opinion communication. The gathering of the crowds and the great communication power of the network platform not only promoted the wide flow of information, but also easily formed irrational discourse power, which led to the uncontrollable network public opinion. Studying the evolution of public opinion in the new situation is crucial to building a good network environment. Based on the Weisbuch-Deffuant opinion evolution model, this paper considers the important parameters of common friends among individuals, and combines the actual interactions to construct a new public opinion evolution model and conduct simulation experiments. The experimental results show that the trust threshold is inversely proportional to the evolution of the viewpoint of the final public opinion. The network structure plays an important role in the evolution of public opinion. The shorter social network evolution time with large clustering coefficient under the same group size and network. Under the same structure, the increase in the number of groups and the time of the evolution of public opinion are not simply proportional. The results of this study provide a new basis for government management.

Monitoring people's perspectives on the COVID-19 vaccine is crucial for understanding public vaccination hesitancy and developing effective, targeted vaccine promotion strategies. Although this is widely recognized, studies on the evolution of public opinion over the course of an actual vaccination campaign are rare. We aimed to track the evolution of public opinion and sentiment toward COVID-19 vaccines in online discussions over an entire vaccination campaign. Moreover, we aimed to reveal the pattern of gender differences in attitudes and perceptions toward vaccination. We collected COVID-19 vaccine-related posts by the general public that appeared on Sina Weibo from January 1, 2021, to December 31, 2021; this period covered the entire vaccination process in China. We identified popular discussion topics using latent Dirichlet allocation. We further examined changes in public sentiment and topics during the 3 stages of the vaccination timeline. Gender differences in perceptions toward vaccination were also investigated. Of 495,229 crawled posts, 96,145 original posts from individual accounts were included. Most posts presented positive sentiments (positive: 65,981/96,145, 68.63%; negative: 23,184/96,145, 24.11%; neutral: 6980/96,145, 7.26%). The average sentiment scores were 0.75 (SD 0.35) for men and 0.67 (SD 0.37) for women. The overall trends in sentiment scores showed a mixed response to the number of new cases and significant events related to vaccine development and important holidays. The sentiment scores showed a weak correlation with new case numbers (R=0.296; P=.03). Significant sentiment score differences were observed between men and women (P<.001). Common and distinguishing characteristics were found among frequently discussed topics during the different stages, with significant differences in topic distribution between men and women (January 1, 2021, to March 31, 2021: χ23=3030.9; April 1, 2021, to September 30, 2021: χ24=8893.8; October 1, 2021, to December 31, 2021: χ25=3019.5; P<.001). Women were more concerned with side effects and vaccine effectiveness. In contrast, men reported broader concerns around the global pandemic, the progress of vaccine development, and economics affected by the pandemic. Understanding public concerns regarding vaccination is essential for reaching vaccine-induced herd immunity. This study tracked the year-long evolution of attitudes and opinions on COVID-19 vaccines according to the different stages of vaccination in China. These findings provide timely information that will enable the government to understand the reasons for low vaccine uptake and promote COVID-19 vaccination nationwide.

An agent-based model of opinion dynamics with attitude-hiding behaviors

Research on the dynamic mechanism of group emotional expression in the crisis

China has entered the network of public expression of mobile Internet era,network public opinion will experience a latent public opinion,networking crowd,arguing aggregation and influence faded four stages of the process of change in the agglomeration effects are obvious.From quantitative to qualitative change,the network of public opinion through thekeywordcalculation to determine the critical value of the various stages of network public opinion.Meanwhile,according to the changes of network public opinion,can draw an irregular zigzag,pyramid-shaped,glacier-type three categories curve.Therefore,the changes of public opinion research network,reducing network for the probability of occurrence of mass incidents,maintain social stability and the government's image,has a very important practical significance.