A Temporal Variable-Scale Clustering Method on Feature Identification for Policy Public-Opinion Management
Volume 35, Issue 3 (2024), pp. 671–686
Pub. online: 26 April 2024
Type: Research Article
Open Access
Open Access
Received
1 January 2024
1 January 2024
Accepted
1 April 2024
1 April 2024
Published
26 April 2024
26 April 2024
Abstract
The development of various digital social network platforms has caused public opinion to play an increasingly important role in the policy making process. However, due to the fact that public opinion hotspots usually change rapidly (such as the phenomenon of public opinion inversion), both the behaviour feature and demand feature of netizens included in the public opinion often vary over time. Therefore, this paper focuses on the feature identification problem of public opinion simultaneously considering the multiple observation time intervals and key time points, in order to support the management of policy-focused online public opinion. According to the variable-scale data analysis theory, the temporal scale space model is established to describe candidate temporal observation scales, which are organized following the time points of relevant policy promulgation (policy time points). After proposing the multi-scale temporal data model, a temporal variable-scale clustering method (T-VSC) is put forward. Compared to the traditional numerical variable-scale clustering method, the proposed T-VSC enables to combine the subjective attention of decision-makers and objective timeliness of public opinion data together during the scale transformation process. The case study collects 48552 raw public opinion data on the double-reduction education policy from Sina Weibo platform during Jan 2023 to Nov 2023. Experimental results indicate that the proposed T-VSC method could divide netizens that participate in the dissemination of policy-focused public opinion into clusters with low behavioural granularity deviation on the satisfied observation time scales, and identify the differentiated demand feature of each netizen cluster at policy time points, which could be applied to build the timely and efficient digital public dialogue mechanism.
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Biographies
Wang Ai
A. Wang received her PhD degree in 2021 in management science and engineering and she is currently an associate professor in the School of Humanities and Social Science at University of Science and Technology Beijing, China. She has published papers in respected journals like Future Generation Computer Systems, Applied Soft Computing, Studies in Informatics and Control. Her research interests focus on data mining, intelligent decision making, as well as emergency management.
Gao Xuedong
X. Gao received his bachelor degree from Nankai University, China, in 1983, and the PhD degree from Belarusian State University in 1993. He is currently a professor in the Department of Management Science and Engineering, School of Economics and Management at University of Science and Technology Beijing, China. His research interests include management process optimization, data mining, intelligent decision making.
Tang Mincong
M. Tang graduated from The Chinese University of Hong Kong in 2011 with a PhD in management information systems. His main research areas include information management and systems, artificial intelligence, modelling and simulation in operations and transportation. Dr. Tang currently serves as an editor for several journals, including the International Journal of Computers Communications and Control, International Journal of RF Technologies, Journal of Computing and Information Technology. He has previously worked as a researcher at the International Research Center for Informatics Research at Beijing Jiaotong University and is now a chair professor at Xuzhou University of Technology, visiting professor at the Industrial University of Ho Chi Minh City in Vietnam. His research has been published in numerous international journals, including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Consumer Electronics, Eletronic Markets, Advances in Production Engineering and Management, Information Technology and Management, Information and Management, Journal of Applied Research and Technology, International Journal of Computers, Communications and Control, Studies in Informatics and Control, Applied Soft Computing, Future Generation Computer Systems.
