scholarly journals Towards a Bibliometric Mapping of Network Public Opinion Studies

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 17
Author(s):  
Yujie Qiang ◽  
Xuewen Tao ◽  
Xiaoqing Gou ◽  
Zhihui Lang ◽  
Hui Liu
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Knowledge Base ◽  
Public Management ◽  
Current Status ◽  
Future Research ◽  
Bibliometric Mapping ◽  
User Influence ◽  
Depth Analysis ◽  
Network Public Opinion

To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and keyword burst analysis were performed using VOSviewer and CiteSpace software. The results show that the NPO is mainly distributed in the disciplinary fields associated with journalism and communication and public management. There are four main hotspots: analysis of public opinion, analysis of communication channels, technical means and challenges faced. The knowledge base in the field of NPO research includes social media, user influence, and user influence related to opinion dynamic modeling and sentiment analysis. With the advent of the era of big data, big data technology has been widely used in various fields and to some extent can be said to be the research frontier in the field. Transforming big data public opinion into early warning, realizing in-depth analysis and accurate prediction of public opinion as well as improving decision-making ability of public opinion are the future research directions of NPO.

scholarly journals A Hotspot Information Extraction Hybrid Solution of Online Posts’ Textual Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
HuiRu Cao ◽  
Xiaomin Li ◽  
Songyao Lian ◽  
Choujun Zhan
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Information Extraction ◽  
Clustering Algorithm ◽  
Computing Time ◽  
Extraction Methods ◽  
Traditional Methods ◽  
Textual Data ◽  
Hybrid Solution ◽  
Network Public Opinion

Online posts have gradually become a major carrier of network public opinion in social media, and the social network hotspots are the important basis for the study of network public opinion. Therefore, it is significant to extract hotspots for monitoring Internet public opinion from online posts textual big data. However, the current hotspot extraction methods are focused on the users’ features that are based on textual big data with spam and low-quality content. Meanwhile, these methods seldomly consider the time span of posts and the popularity of users. Accordingly, this article presents a hotspots information extraction hybrid solution of online posts’ textual data. Firstly, a filtering strategy to obtain more high-quality textual data is designed. Secondly, the topic hot degree is presented by considering the average number of replies and the popularity of the participant. Thirdly, an improved co-word analysis technology is used to search the same topic posts and Bisecting k-means clustering algorithm using repliers’ popularity and key posts are designed for studying and monitoring the hotspots of online posts in a valid big data environment. Finally, the proposed algorithms are verified in experiments by extracting the hotspots of online posts from the dataset. The results show that the data filtering strategy can help to obtain more valuable information and decrease the computing time. The results also demonstrate that the proposed solution can help to obtain hotspots comparing the traditional methods, and the hot degree can reflect the trend of the online post by comparing the traditional methods.

scholarly journals Research on Mining and Application of Group Events Based on Network Public Opinion Big Data

2021 ◽  
Author(s):  
Weimin Gao ◽  
Jiaming Zhong ◽  
Yuan Xiao
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Social Life ◽  
The Internet ◽  
Sir Epidemic Model ◽  
Control Effect ◽  
Public Events ◽  
Sir Epidemic ◽  
The Government ◽  
Network Public Opinion

Network Public Opinion is significant in maintaining social harmony and stability and promoting transparency in government affairs. However, with the development of economy and transformation of society, our country has entered a high-risk period, which is full of unexpected public events. Unexpected mass accidents also cause hot discussions among the Internet users once they are exposed on the network. Different ideas, opinions, emotions, and attitudes about unexpected public events will be collected and collide on the Internet. It makes Network Public Opinion play an increasingly important role in the evolution of unexpected public events. It could promote the spread and upgrade of unexpected public events and bring more and more profound influence on to our social life. We use the case study method to analyze and solve the problems by applying the dynamic principles of the SIR epidemic model, comprehensively considering the social environment and various influencing factors, and constructing a mathematical model for the spread of network group events. The study uses Matlab to simulate the change trajectory of the number of participants in the network group events. By adjusting the number of contacts φ in the model, the development of network group emergencies can be effectively controlled and managed. As long as the government takes timely intervention measures, the dissemination of network group events can be basically controlled. Combined with public opinion big data to discover the important factors affecting the spread of public opinion, the control effect is obvious.

scholarly journals Impact of Network Public Opinion Disposal Model on Public Value Factors under the Background of Big Data

CONVERTER ◽  
2021 ◽  
pp. 559-565
Author(s):  
Peng Bo, Xu Xiao-Long
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Quantitative Research ◽  
Prediction Models ◽  
Control Model ◽  
Government Control ◽  
Mechanism Model ◽  
The Government ◽  
And Control ◽  
Network Public Opinion

It is the key for the government to control the degree of information alienation to study the mechanism and control model of network public opinion information alienation for big data. This provides a theoretical basis for the government to deal with and manage the network public opinion. This paper uses qualitative analysis of the information alienation mechanism of network public opinion under the big data environment, and expands the evolution mechanism model of network public opinion to the information alienation control model. On this basis, the classification of government control information alienation is studied by numerical simulation. This paper takes the actual forum, blog, website with news comment function as the research object, and proposes a prediction platform construction scheme based on Java, which integrates a variety of prediction models. This provides useful exploration and ideas for quantitative research on the complex social phenomenon of network public opinion.

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