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.

scholarly journals RESEARCH ON THE GOVERNANCE OF GOVERNMENT NETWORK PUBLIC OPINION IN THE AGE OF BIG DATA

2020 ◽  
Vol 11 (3) ◽  
pp. 4-12
Author(s):  
Wang Chunjuan ◽  
Zhu Xiao
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Rule Of Law ◽  
Case Analysis ◽  
Mobile Terminals ◽  
The Rule Of Law ◽  
Data Platform ◽  
The Government ◽  
Government Network ◽  
Network Public Opinion

With the popularization of mobile terminals, information is becoming more and more unimpeded, along with the advent of the era of big data. It brings both opportunities and challenges to the governance of government network public opinion. Using the literature research methodology and the case analysis, combing the research results of domestic and foreign scholars, this paper analyzes the current situation of the network public opinion governance, concludes that having initially built a big data platform for network public opinion and realized the transformation from managing to governing network public opinion, the government strengthens the awareness of the rule of law of network public opinion gradually. Also, it is believed that the government has not fully grasped the opportunity brought about by the big data, with idea, technology as well as ethical dilemma remaining. Finally, from the three aspects it provides development strategies for the government to create a healthy and green network public opinion ecology.

scholarly journals Novel Network Public Opinion Prediction and Guidance Model Based on “S-Curve”: Taking the Loss of Contact with “Malaysia Airlines”

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangdong Liu ◽  
Axiao Cao ◽  
Chuyang Li
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Information Diffusion ◽  
Prediction Models ◽  
Loss Of Contact ◽  
Curve Theory ◽  
S Curve ◽  
The Government ◽  
Network Public Opinion

It is of great significance for the government to control the network public opinion in time and maintain social stability to predict the network public opinion in emergency. This paper proposes a novel improvement method to “S-curve” theory in the context of big data and establishes three novel network public opinion prediction models. These models take into account the proliferation trend of initial and follow-up network public opinion over a long period of time when emergencies are formed and the objective environment suddenly changes, based on the information diffusion model conforming to the traditional “S-curve” theory. The novel improvement and establishment allow our model to have more accurate predictions than other scholars’ models that mainly study the first network public opinion in a shorter period of time. And it is more applicable to real social conditions, in line with the public’s cognition of reality, and provides more reference for the government to take preventive and corresponding positive guiding measures in advance. To better establish the model, we obtained the 24-day Weibo data associated with the incident of “Malaysia Airlines” loss of contact from big data for model establishment, public opinion prediction, and comprehensive evaluation. The result innovatively shows that, in addition to the initial public opinion that is worthy of attention, the follow-up public opinion is also noteworthy and proves that our model has more practical value.

scholarly journals Research on the Design of Government Affairs Platform in the Context of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qian Huang ◽  
Xue Wen Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Algorithm ◽  
The Internet ◽  
Chinese Government ◽  
Development Status ◽  
Deep Learning Algorithm ◽  
Government Management ◽  
The Government ◽  
The Impact

Big data is a massive and diverse form of unstructured data, which needs proper analysis and management. It is another great technological revolution after the Internet, the Internet of Things, and cloud computing. This paper firstly studies the related concepts and basic theories as the origin of research. Secondly, it analyzes in depth the problems and challenges faced by Chinese government management under the impact of big data. Again, we explore the opportunities that big data brings to government management in terms of management efficiency, administrative capacity, and public services and believe that governments should seize opportunities to make changes. Brainlike computing attempts to simulate the structure and information processing process of biological neural network. This paper firstly analyzes the development status of e-government at home and abroad, studies the service-oriented architecture (SOA) and web services technology, deeply studies the e-government and SOA theory, and discusses this based on the development status of e-government in a certain region. Then, the deep learning algorithm is used to construct the monitoring platform to monitor the government behavior in real time, and the deep learning algorithm is used to conduct in-depth mining to analyze the government's intention behavior.

Digital Degradation

2020 ◽  
pp. 113-136
Author(s):  
Sarah Esther Lageson
Keyword(s):  
Big Data ◽  
Criminal Justice ◽  
Criminal Justice System ◽  
Social Inequality ◽  
Justice System ◽  
Digital Technologies ◽  
The Internet ◽  
Technological Systems ◽  
Big Data Technologies ◽  
The Government

Interviews with more than 100 people whose records appear online show how the ability to manage digital punishment is directly tied to a person’s familiarity with technological systems and their faith in bureaucracy. Instead of confronting the government or the criminal justice system, many people engage in digital avoidance, afraid that any attempts will only make the problem worse. This intersection between the criminal justice system and technology reproduces social inequality at the speed of the internet, disproportionately impacting people who have less access to and command over digital technologies. This chapter discusses the qualities of digital punishment, the strategies people who are experiencing digital punishment deploy to deal with their online stigma, and an explanation for why many people choose to engage in digital avoidance rather than try to have their online record removed. Rooted in theories of the digital divide and the disparate impact of big data technologies, the chapter concludes with a discussion of how digital punishment challenges long-held theories of criminal stigma, desistance, and rehabilitation.

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