Network Public Opinion Research Articles

A fog‐computing architecture for network public opinion monitoring based on intelligent semantic recognition

AbstractCurrently, the online data contains rich user emotional information and public opinion information. These data can provide massive support for network public opinion monitoring and analysis. However, there are two problems in the network public opinion analysis of the online data. On the one hand, a vast amount of online data with discursiveness and concealment are processed in the cloud platforms, which consumes a long time. On the other hand, the massive online public opinion data is disperse and hidden, resulting in the dependence on manual screening for the analysis of public opinion. Therefore, it is still an important challenge to study the efficient and low‐latency extraction of valuable information from network public opinion. In this paper, we proposed a fog computing based framework using the technologies of intelligent semantic recognition and data mining for the analysis of network public opinion. Firstly, we build a fog computing architecture to collect the text data of network public opinion. Then, an efficient network public opinion model is constructed by intelligence semantic recognition. Finally, we achieve the function of public opinion analysis and early warning. The experimental results show that the method proposed in this paper achieves better performance against some existing methods.

Public opinion communication mechanism of public health emergencies in Weibo: take the COVID-19 epidemic as an example.

As a major public health emergency, the COVID-19 epidemic not only has a real risk of infection, but also easily generates public opinion risks. Under the condition of social communication of microblog, how to effectively identify public opinion and the harm of public health emergencies, avoid the overlay of real risk of epidemic and network public opinion risk, and prevent and resolve major public opinion risks is an important public opinion research topic in the new era. Taking the most influential outbreak of COVID-19 pneumonia in China in 2020 as an example, this paper discusses the effect of sudden major public health cases on Chinese microblogs and the spreading mechanism of public opinion. This paper mainly explores the communication motivation of public opinion from the four communication elements of the microblog public opinion center, public opinion object, public opinion carrier and public opinion ontology. And combined with the life cycle theory, this study analyzes the interaction between the communication elements of public opinion in different stages. In the fluctuation period of public opinion, the amount of public opinion information decreases relatively, and the frequent occurrence of secondary public opinion in the outbreak period promotes the continuation of public opinion. Compared with the diversified demands of public materials and medical assistance during the pandemic, public opinion in the fluctuation period reacts on the epidemic situation, which to some extent alleviates the tension of the epidemic situation. Based on this, this study puts forward the guidance strategy of public opinion of public health emergencies.

Optimization Model Construction of Online Public Opinion Dissemination Based on Behavioral Data Mining

Abstract In this paper, we propose an optimization method based on behavioral data mining for the emergence of negative emotions in network public opinion in unexpected situations leading to problems that endanger social stability and use data mining to confirm the correspondence of the kNN algorithm. In the opinion propagation model, the radius of opinion radiation is assumed to use the network node density and distribution density as features of node exchange information. Then, the kNN algorithm is used to train the comment set for the analysis of user sentiment evolution of network opinion in the social network environment, and the web crawler technology is used to obtain the output interface data APIs of microblogs and WeChat, and the social media user comment data is used as the data for the empirical analysis of network opinion. snowNLP and kNN algorithms are used to analyze the sentiment score of network opinion and the sentiment score less than 0.5, i.e., the sentiment polarity. There were 15 days when the sentiment score tended to be negative and 65 days when the sentiment score was greater than 0.5, i.e., the sentiment polarity tended to be positive.

Cooperative Education of College Students' Ideological and Political Health Education from the Perspective of Network Public Opinion

Cooperative Education of College Students' Ideological and Political Health Education from the Perspective of Network Public Opinion

Research on Hybrid Teaching Based on Chaoxing Learning Platform—Take the Course of “Network Public Opinion Research and Judgment” as an Example

Research on Hybrid Teaching Based on Chaoxing Learning Platform—Take the Course of “Network Public Opinion Research and Judgment” as an Example

Exploring the Causes and Countermeasures of Cyber Violence in the Internet Era

In this current Internet era, the Internet is very popular and almost everyone is using it. However, some problems have arisen from this, and cyber violence is one of them. Today, cyber violence is happening more and more. Therefore, this article will study the causes of cyber violence and the countermeasures for cyber violence. This study provides an insightful and comprehensive study of cyber violence and broadens the research on cyber violence. Finally, it is found that the causes of cyber violence are: First of all, The virtuality and anonymity of the Internet. Secondly, some information media and netizens disseminate information irrationally. Thirdly, the relevant laws and regulations are not perfect. Fourthly, the supervision of Internet platforms is not in place. Fifthly, Internet users are getting younger. The countermeasures for cyber violence are: Firstly, improve the regulatory policies and laws for cyber violence. Secondly, the Internet platform should improve the regulatory strategy. Thirdly, improve the network public opinion supervision and platform reporting system. Fourthly, strengthen the early warning of public opinion and the guiding role of mainstream media. Fifthly, improve the rule of law and moral cultivation of Internet users.

Analysis of civil and commercial law protection based on decentralized Internet

Abstract To promote fairness and justice in law, it is important to explore the protective role of civil and commercial law. The relationship between civil and commercial law and public opinion supervision is examined through Internet public opinion. A decentralized Internet public opinion index system has been established, and a better random network public opinion dissemination model is proposed through parameter inversion of the SIR model and network simulation. The protection of transaction security and information rights by civil and commercial law is the focus of the evolution of Internet public opinion cases. The promotion of industry self-regulation is enhanced by 14.63% when civil and commercial law intervenes. On the flip side, it aids in protecting personal information, and the likelihood of personal information leakage is reduced by 25.48%. This study can effectively reference the implementation of civil and commercial law in the Internet era.

Qualitative Prediction and Quantitative Analysis of Events in Educational Network Public Opinion Crisis: a Deep Learning Approach

Qualitative Prediction and Quantitative Analysis of Events in Educational Network Public Opinion Crisis: a Deep Learning Approach

Build the "Firewall" of University Network Ideology Security under the 5G Trend

With the rapid development and wide application of 5G technology, the security position of network ideology in universities is facing unprecedented challenges and opportunities. Using the literature analysis method, taking the network ideology security of universities under the background of 5G technology as the research object, the new opportunities and challenges brought by 5G technology for the network ideology security of universities are analyzed, and the new ideas and new measures for the network ideology security in universities and universities are put forward. The research shows that the network ideology position of colleges and universities is faced with the hidden dangers of technology monopoly, media integration and the influence of the network public opinion, so the theoretical foundation should be consolidated, the cohesion of the field should be enhanced, and the management leadership should be strengthened to guarantee the security of the network ideology in colleges and universities. The influence of 5G on network ideology work in colleges and universities is systematically analyzed to provide some theoretical and practical references for building the "firewall" of ideological security in colleges and universities.

IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies.

When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.

The challenges of ideological and political education in universities based on the Internet environment and its optimization path

Abstract In order to explore the challenges and optimization paths faced by ideological and political education in colleges and universities under the Internet environment. This paper establishes a comprehensive evaluation model for the ranking of university online public opinion, conducts quantitative posture analysis based on rank affiliation function, starts qualitative posture analysis based on language grade evaluation, determines objective weights of quantitative and qualitative posture ranking indicators by using deviation method, uses binary, ordered ratio method to determine for expert weights, and integrates subjective and objective assignments based on empirical factors. After establishing the comprehensive evaluation of the posture level of college network public opinion, the influence of network public opinion on thinking and political education was analyzed: bad network information can easily distort college students’ ideology and value orientation, and the posture level evaluation of the influence of network public opinion on college students’ values reached −0.85. Meanwhile, the network public opinion hinders the formation of the moral sentiment of college students, and the posture level evaluation of the influence reached −0.67. This study analyzes the influence of college network public opinion on thinking and politics from both qualitative and quantitative aspects and provides an intentional reference for college thinking and politics education to respond effectively to the network environment.

The Impact of Online Public Opinion Dissemination on the “Wraparound Involvement” of College Students' Academic Qualifications: A Comparative Study of Shandong X University and Henan Y University as an Example

The 21st century is the information age. The rapid development of new media has brought about sweeping changes to society, and while helping to improve the quality of higher education, a phenomenon of “hostage-type involution” of college students' academic qualifications has also emerged. This paper adopts the research methods of literature analysis and case study analysis and selects Shandong X University and Henan Y University as examples for comparative analysis under the “college entrance examination province”. The evolution of the above phenomenon has reached a white-hot stage, invariably affecting the physical and mental health development of college students and, at the same time, arousing the great concern of the school, society and the state, which needs to deeply explore the reasons for the formation of hostage-type involution of the network public opinion dissemination to the college students' blind pursuit of academic qualifications. Finally, we propose effective strategies and suggestions for universities and social media to manage the construction of online public opinion and to help students establish a correct view of pursuing academic qualifications according to various reasons.

Retracted: Research on University Network Public Opinion Monitoring and Early Warning System Based on Big Data

Retracted: Research on University Network Public Opinion Monitoring and Early Warning System Based on Big Data

Retracted: Emergency Project Management Decision Support Algorithm for Network Public Opinion Emergencies Based on Time Series

Retracted: Emergency Project Management Decision Support Algorithm for Network Public Opinion Emergencies Based on Time Series

Retracted: Interoperability of Multimedia Network Public Opinion Knowledge Base Group Based on Multisource Text Mining

Retracted: Interoperability of Multimedia Network Public Opinion Knowledge Base Group Based on Multisource Text Mining

Tripartite Evolution Game Analysis among Public Opinion Speaking Subjects in the Network Public Opinion Field

The arrival of the omnimedia era has formed a new form of information exchange and public opinion environment. This study proposes the concept of network public opinion field, and constructs a tripartite evolutionary game model of "network media-opinion leaders-netizens" as the main body. The simulation analysis of the model results is carried out, and some suggestions are put forward for the evolution and governance of network public opinion in the omnimedia era from the perspective of field theory.

Opinion-aware information diffusion model based on multivariate marked Hawkes process

Currently, information diffusion on online platforms plays an important role in social governance. Opinions contained in information have a significant influence on information diffusion. However, both classic and extended diffusion models lack consideration of the influence of opinions. Therefore, in this study, we propose an opinion-aware information diffusion model based on the multivariate marked Hawkes process to simulate the information diffusion process with effect of opinions. In addition, to simulate this process more accurately, we consider various endogenous and exogenous stimuli apart from the opinion. Simulation results show that the diffusion trend prediction evaluation metrics of our model are 31.4% (on average) better than the contemporary models tested. Moreover, the effects of internal and external factors are found to be qualitatively similar to the real information diffusion process; hence, we gain useful inspiration for social network research and public opinion guidance.

Understanding service quality concerns from public discourse in Indonesia state electric company

Indonesia State Electricity Company (PLN) has transformed its business process into a more modern practice with the intention of boosting efficiency and enhancing service quality. In the digital economy space, customer expectation is more complex, influenced by the network effect and public opinion. Facing these facts, it is imperative for PLN to be able to listen to its customers' voices using publicly available social media channels. Understanding and acting upon large-scale opinions and conversations is a nontrivial task. To filter large-scale data, we employ Natural Language Processing (NLP) methodology called Latent Dirichlet Allocation (LDA) to find the dominant topics in public discourse. It is followed by UTAUT2 model implementation to further clarify the most critical issues found. Finally, this study modified the e-ServQual and e-RecServQual methodologies to propose the best and most personalized solution for each issue. By using the proposed approach, PLN could accelerate the implementation of data-driven decision-making and induce sustainability.

Research on Urban Monitoring Network Management Platform based on AI Data Stream Processing

In recent years, with the upsurge of the continuous development of high-tech information industry, the security industry has also made a new leap in technology, products, industry and so on. The traditional single small-scale monitoring and management software obviously cannot meet the actual needs of users in terms of system scale and functional requirements. This paper puts forward a network management platform for urban monitoring based on AI data stream processing. With the help of comprehensive processing and analysis of urban complex information, through network remote monitoring means, information supervision and public opinion trends are used to control social environmental security, timely warning and response to unexpected accidents, so as to ensure that governance departments can fully control and prevent possible problems such as road traffic, population distribution and disaster emergency.

Network public opinion monitoring and semantic event discovery strategy in mobile edge computing scenario

AbstractBuilding a public opinion monitoring system can detect public opinion crises in advance and deal with crisis public relations in a timely manner. Semantic communication is a new type of communication technology with development potential, which reduces the amount of data required for transmission by mining semantic information in information sources. In this paper, a multi‐user semantic communication system based on federated learning deployment is used to realize data transmission in edge computing scenarios. Use the data from the client side to train the deep learning model more effectively. This paper also evaluates the performance of the proposed semantic communication system. The model trained by federated learning can achieve an effect close to centralized training and protect user privacy.