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


  • Weimin Gao School of Computer Science and Engineering, Central South University, ChangSha, China 410083 and Department of Computer Science and Engineering, Hunan Institute of Technology, HengYang, China 421002
  • Jiaming Zhong College of Economic and Management, Xiangnan University, Chenzhou, China 423000
  • Yuan Xiao College of Economic and Management, Xiangnan University, Chenzhou, China 423000



Mining and application; Network group events; Simulation; SIR(Susceptible Infected Recovered Model).


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.


Download data is not yet available.

Author Biographies

Weimin Gao , School of Computer Science and Engineering, Central South University, ChangSha, China 410083 and Department of Computer Science and Engineering, Hunan Institute of Technology, HengYang, China 421002

Weimin Gao received the B.E. degrees from Nanhua University, China, in 1999 and the master’s degrees from the School of Information Science and Engineering, Hunan University, China,in 2007. He is currently pursuing the Ph.D. degree with the School of Computer Science and Engineering, Central South University, China. His current research interest is data center network.

Jiaming Zhong, College of Economic and Management, Xiangnan University, Chenzhou, China 423000

Jiaming Zhong received the B.S. degree from Hunan Normal University. Now he is a professor at Xiangnan University. His research interest is intelligent information processing. He has published more than 40 papers.

Yuan Xiao , College of Economic and Management, Xiangnan University, Chenzhou, China 423000

Yuan Xiao received the M.S. degree from Chongqing University of Technology in 2010. Now she is a lecturer at Xiangnan University. Her research interests include Financial Big Data and Intelligent Financial. She has published more than 10 papers.


Du Junfei. Analysis on the Types of Network Group Events. International Press, 2009 (7).

Lu Wengang, Zhang ting. Research on the Management of Online Rumors in Mass Incidents under the Background of Internet – Based on the analysis of the “March 30” PX Event in Maoming, Guangdong. Research on petition and Social Contradictions, 2017, 000(004): 124–142.

Liu Chun-xiang, Jiang Yao-hui. Wise Govern Approach to the Governments Response to Network Mass Disturbances under the Perspective of Discourse Theory. Journal of Social Science of Hunan Normal University, 2012(01): 10–13.

Peng bo. Network communitive event rule analysis and guard strategy research. Shanghai: Shanghai jiao tong university, 2008.

Peng Zhihui. Analysis of the Research Path of Internet Public Opinion. Library Journal 000.012 (2016): 63–68.

Shi Bo. Study on the Internal Evolution Mechanism of Public Opinion in Public Crisis Events. Journal of Information 29.004 (2010): 41–45.

Yang Jiuhua. A Discussion on the Mode, Cause and Prevention of Network Mass Incidents. Journal of Chongqing Institute of Socialism 11.4 (2009): 89–92.

He Guoping. The Mobilization Model of Internet Group Incidents and Its Public Opinion Guidance. Research on Ideological and Political Work 09 (2009): 37–39.

Milgram, Travers Stanley. An experimental study of the small world problem. Sociometry 32.4 (1969): 425–443.

Xie yungeng, Liu rui, QIAO Rui, et al. Research Overview of Big Data and Social Public Opinion.New media and society 04 (2014): 134–155.

Fan Guohua, Jia Xiaona. Early-warning mechanism of network Mass disturbance. People’s Tribune 000(32) (2010): 158–159.

Liu qiaoling, Li jin, Xiao renbin. Prediction of Internet public opinion spreading trend based on parameter inversion: Taking Sina Weibo as an example. Computer Applications 37(5) (2017): 1419–1423.

Xie shuyun, Quan xiaosong, Shen yuncheng. Construction of the Network Public Opinion evaluation model under the big data environment. Journal of Guiyang University (Natural Science Edition), 2016 (1): 54–57.

Dong jingwei. Research on influence mechanism in the dynamic evolution process of network opinion based on complex network. Harbin: Harbin Institute of Technology, 2016.

Song huanying. Review on the Application of Big Data in the Network Public Opinion Research in China. Information Search, 2020: 93–99.

Yan daocheng. Research on Network Public Opinion in Mass Incidents. Xinhua Publishing House, 2013.

Yi Xing. Group events and Network Public Opinion Research Review. Journal of Political Science and Law, 2014, 31(4): 101–107.

Lin Ling. Communication Mechanism and Coping Strategies of Network Mass Events. Academia Bimestrie, 2010(5): 19–24.

Ma yan. Study on the Method of Micro-blogging Public Opinion Hotspots Mining in Big Data. Journal of Modern Information, 2014, 34(11): 29–33.

Jiang xinhao. The guiding strategy of mass incidents network public opinion. Legal system and society 000.007 (2020): 121–122.

Zeng runxi, Wang guohua, Chen qiang. The Governance of Internet Society under the Relationship between State and Society. Journal of Beijing institute of technology Social sciences edition, 2010, 12(5): 121–125.

YuanXue. Innovation of government behavior mode in network social management in the era of big data. Journal of Guangdong Institute of Public Administration, 2013, 25(4): 25–30.

Hou Jundong, Xiao Renbin. Structural Reversal of Network Public Opinion in Collective Behaviors: Internal Mechanism and Actual Representations. Journal of Social Sciences, 2017 (11): 80–90.

Zhang wei, He mingsheng, etc. On the dynamics of network public opinion: A research based on Weisbuch-Deffuant Model. Information Magazine, 2013, pp. 43–48.

Zhang rui. Dilemmas of Cyber Society Governance in China and Corresponding Strategies: From the Perspective of Public Reason. Suzhou: Suzhou university, 2016.

Wang linlin. Research on Dissemination and Government Response of Internet Public Opinion from Unexpected Public Events based on the System Dynamics Model. Shanghai: East China Norma University, 2016.

Zhang mingshan, Zhang yingchun. Effect model of the network public feeling spreading on mass emergencies. Journal of Southwest University for Nationalities Natural Science Edition, 2011, 37(3): 331–335.

Zhai jie. Micro-blog Emergency Network Public Opinion research on Rule discovery and Prediction method. Dalian: Dalian University of Technology, 2016.

Wen hong. Logic Interaction between Public Opinion Orientation and Government Response in Internet Group Crisis Events—Emotional Analysis Based on Big Data of Snow Village Event. Political Studies, 2019 (1): 77–90.