Please wait a minute...
New Technology of Library and Information Service  2013, Vol. Issue (12): 74-80    DOI: 10.11925/infotech.1003-3513.2013.12.12
Current Issue | Archive | Adv Search |
The Topic Evolution Model of the Public Opinion in Micro-Blogging Network
Li Qing, Zhu Hengmin, Yang Dongchao
College of Economics & Management, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  As the popular development of the micro-blog, which has gradually become the stage where the public opinion occurs and evolves. To analyze the mechanism of that, based on the traditional disease spreading dynamic model named SEIR, this paper proposes an evolution model with the immune function which can represent the characteristic of the micro-blog's fission spreading pattern. In this model, whether a micro-blog's user would re-tweet the message is mainly influenced by his/her impact and the interest's degree to the public opinion. And the authors simulate the parameters in this model to analyze and verify the model presented in this paper. The results show that user's interest to the public opinion is the key factor to affect the spreading extent.
Key wordsSEIR propagation model      Micro-blogging network      Spread evolution of the      public opinion     
Received: 14 August 2013      Published: 08 January 2014
:  C931.6  

Cite this article:

Li Qing, Zhu Hengmin, Yang Dongchao. The Topic Evolution Model of the Public Opinion in Micro-Blogging Network. New Technology of Library and Information Service, 2013, (12): 74-80.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2013.12.12     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2013/V/I12/74

[1] 张彦超, 刘云, 张海峰, 等.基于在线社交网络的信息传播模型[J]. 物理学报, 2011, 60(5):1-7.(Zhang Yanchao, Liu Yun, Zhang Haifeng, et al.The Research of Information Dissemination Model on Online Social Network[J].Acta Physica Sinica, 2011, 60(5):1-7.)
[2] 张彦超.社交网络服务中信息传播模式与舆论演进过程研究[D].北京:北京交通大学, 2012.(Zhang Yanchao.Research on Information Dissemination and Opinion Evolution in the Social Networking Services[D]. Beijing: Beijing Jiaotong University, 2012.)
[3] 汪小帆, 李翔, 陈关荣.网络科学导论[M].北京:高等教育出版社, 2012.(Wang Xiaofan, Li Xiang, Chen Guanrong.Network Science: An Introduction[M].Beijing: Higher Education Press, 2012.)
[4] Xiong F, Liu Y, Zhang Z J, et al.An Information Diffusion Model Based on Retweeting Mechanism for Online Social Media[J].Physics Letters A, 2012, 376(30-31):2103-2108.
[5] Zhou Y.News Spreading Model Based on Micro-Blogging Platform in Network Era[A].//Informatics and Management Science VI[M]. London:Springer, 2013:173-180.
[6] 赵丽, 袁睿翕, 管晓宏, 等.博客网络中具有突发性的话题传播模型[J]. 软件学报, 2009, 20(5):1384-1392.(Zhao Li, Yuan Ruixi, Guan Xiaohong, et al.Bursty Propagation Model for Incidental Events in Blog Networks[J].Journal of Software, 2009, 20(5):1384-1392.)
[7] 李军, 陈震, 黄霁崴.微博影响力评价研究[J]. 信息网络安全, 2012(3):10-13, 27.(Li Jun, Chen Zhen, Huang Jiwei.Micro-blog Impact Evaluation Study[J].Netinfo Security, 2012(3):10-13, 27.)
[8] 原福永, 冯静, 符倩倩.微博用户的影响力指数模型[J]. 现代图书情报技术, 2012(6):60-64.(Yuan Fuyong, Feng Jing, Fu Qianqian.Influence Index Model of Micro-blog User[J].New Technology of Library and Information Service, 2012(6):60-64.)
[9] Cha M, Haddadi H, Benevenuto F, et al.Measuring User Influence in Twitter: The Million Follower Fallacy[C].In:Proceedings of the 4th International AAAI Conference on Weblogs and Social Media.2010:10-17.
[10] Wu X, Wang J.Micro-blog in China: Identify Influential Users and Automatically Classify Posts on Sina Micro-blog[J].Journal of Ambient Intelligence and Humanized Computing, 2012.doi:10.1007/S12652-012-0121-3.
[11] 马知恩, 周义仓, 王稳地, 等.传染病动力学的数学建模与研究[M].北京:科学出版社, 2004.(Ma Zhien, Zhou Yicang, Wang Wendi, et al. Epidemic Dynamics of Mathematical Modeling and Research[M]. Beijing: Science Press, 2004.)
[12] Kermack W O, McKendrick A G. A Contributions to the Mathematical Theory of Epidemics[J]. Proceedings of the Royal Society A, 1927, 115(772):700-721.
[1] Fan Tao,Wang Hao,Wu Peng. Sentiment Analysis of Online Users' Negative Emotions Based on Graph Convolutional Network and Dependency Parsing[J]. 数据分析与知识发现, 2021, 5(9): 97-106.
[2] Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[3] Ma Yingxue,Zhao Jichang. Patterns and Evolution of Public Opinion on Weibo During Natural Disasters: Case Study of Typhoons and Rainstorms[J]. 数据分析与知识发现, 2021, 5(6): 66-79.
[4] Wang Nan,Li Hairong,Tan Shuru. Predicting of Public Opinion Reversal with Improved SMOTE Algorithm and Ensemble Learning[J]. 数据分析与知识发现, 2021, 5(4): 37-48.
[5] Xu Yabin, Sun Qiutian. Identifying Leaders and Dissemination Paths of Public Opinion[J]. 数据分析与知识发现, 2021, 5(2): 32-42.
[6] Cheng Tiejun, Wang Man, Huang Baofeng, Feng Lanping. Predicting Online Public Opinion in Emergencies Based on CEEMDAN-BP[J]. 数据分析与知识发现, 2021, 5(11): 59-67.
[7] Shao Qi,Mu Dongmei,Wang Ping,Jin Chunyan. Identifying Subjects of Online Opinion from Public Health Emergencies[J]. 数据分析与知识发现, 2020, 4(9): 68-80.
[8] Liang Ye,Li Xiaoyuan,Xu Hang,Hu Yiran. CLOpin: A Cross-Lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
[9] Deng Jiangao,Zhang Xuan,Fu Zhu,Wei Qingming. Tracking Online Public Opinion Based on System Dynamics: Case Study of “Xiangshui Explosion Accident”[J]. 数据分析与知识发现, 2020, 4(2/3): 110-121.
[10] Liang Yanping,An Lu,Liu Jing. Topic Resonance of Micro-blogs on Similar Public Health Emergencies[J]. 数据分析与知识发现, 2020, 4(2/3): 122-133.
[11] Ding Shengchun,Yu Fengyang,Li Zhen. Identifying Potential Trending Topics of Online Public Opinion[J]. 数据分析与知识发现, 2020, 4(2/3): 29-38.
[12] Huang Wei,Zhao Jiangyuan,Yan Lu. Empirical Research on Topic Drift Index for Trending Network Events[J]. 数据分析与知识发现, 2020, 4(11): 92-101.
[13] Lin Wang,Ke Wang,Jiang Wu. Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event[J]. 数据分析与知识发现, 2019, 3(4): 42-52.
[14] Jiang Wu,Yinghui Zhao,Jiahui Gao. Research on Weibo Opinion Leaders Identification and Analysis in Medical Public Opinion Incidents[J]. 数据分析与知识发现, 2019, 3(4): 53-62.
[15] Yanshuang Mei,Hengmin Zhu,Jing Wei. A Study on the Mechanism of Media Collaboration on the Spread of Internet Public Opinion[J]. 数据分析与知识发现, 2019, 3(2): 65-71.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn