A Public Psychological Pressure Index for Social Networks

With the worldwide proliferation of social networks, public opinion analysis of data generated by social networks has become an important field of research. Social networks have become a major platform for public opinion formation and diffusion, and analyzing public opinion through social network da...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.23457-23469
Main Authors: Zhang, Hong-Li, Jin, Rui, Zhang, Yu, Tian, Zhihong
Format: Article
Language:English
Subjects:
Computation
Information entropy
intelligence and security informatics
International trade
Maximum entropy
public information security
Public opinion
public opinion analysis
Public policy
public psychological pressure index
social computing
Social networks
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Summary:With the worldwide proliferation of social networks, public opinion analysis of data generated by social networks has become an important field of research. Social networks have become a major platform for public opinion formation and diffusion, and analyzing public opinion through social network data plays an important role across numerous fields, including political science, economics, commerce, finance, international trade, public policy implementation and so on. Nevertheless, the corresponding quantitative indexes of public opinion analysis have not yet been developed, and the theoretical foundation underpinning such indexes has yet to be established. How to measure public opinion through social network data is a significant problem in need of the development of a series of quantitative assessment indices and social computing methods that can be used to solve this problem. This paper proposes both the concept of a public psychological pressure index and its calculation method, making it a fundamental work in the field of public opinion analysis. The maximum entropy principle is introduced to the social computing domain in this paper and positions it as the theoretical foundation underpinning such indexes.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2969270