Research on Identification of Network Public Opinion's Key Nodes and Analysis on Diffusion Modes in the Context of Micro-blog

  • Jiang Kan ,
  • Tang Zhufa
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  • School of Computer, Electronics & Information, Guangxi University, Nanning 530004

Received date: 2015-09-23

  Revised date: 2015-10-05

  Online published: 2015-10-20

Abstract

[Purpose/significance]This paper aims to improve the accuracy, effectiveness and efficiency of public opinion strategies by mining universal characteristics and laws of transmission paths on public opinion hidden in the massive data.[Method/process]From the perspective of information diffusion effect, this paper proposes a WSD-Rank influential evaluation model by taking an overall consideration of transmission width, speed and depth. Based on the measurement of individual influence, it explores the structural characteristics and evolutional laws of the public opinion transmission model according to the extended structure of information diffusion.[Result/conclusion]The results show that: public opinion transmission presents various diffusion models; different diffusion models have distinct on the transmission coverage, transmission speed, transmission efficiency and transmission life cycle; there are interactive evolution and intersection among diffusion models.

Cite this article

Jiang Kan , Tang Zhufa . Research on Identification of Network Public Opinion's Key Nodes and Analysis on Diffusion Modes in the Context of Micro-blog[J]. Library and Information Service, 2015 , 59(20) : 105 -111 . DOI: 10.13266/j.issn.0252-3116.2015.20.018

References

[1] 韩运荣, 高顺杰. 微博舆论传播模式探究[J].现代传播: 中国传媒大学学报,2012,34(7): 35-39.
[2] 田占伟, 隋玚. 基于复杂网络理论的微博信息传播实证分析[J].图书情报工作, 2012,56(8): 42-46.
[3] Gomez-Rodriguez M, Leskovec J, Krause A. Inferring networks of diffusion and influence[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2010: 1019-1028.
[4] Wu Shaomei, Hofman J M, Mason W A, et al. Who says what to whom on Twitter[C]//Proceedings of the 20th International Conference on World Wide Web. New York: ACM, 2011: 705-714.
[5] 袁毅. 微博客信息传播结构, 路径及其影响因素分析[J]. 图书情报工作, 2011, 55(12): 26-30.
[6] 于洪, 杨显. 微博中节点影响力度量与传播路径模式研究[J]. 通信学报, 2012, 33(S2): 97-102.
[7] 刘继, 李磊. 基于微博用户转发行为的舆情信息传播模式分析[J]. 情报杂志, 2013,32(7): 74-77.
[8] Freeman L C. Centrality in social networks conceptual clarification[J]. Social Networks, 1979, 1(3): 215-239.
[9] Freeman L C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977,40(1): 35-41.
[10] Bonacich P. Factoring and weighting approaches to status scores and clique identification[J]. Journal of Mathematical Sociology, 1972, 2(1): 113-120.
[11] Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine[J]. Computer Networks and ISDN Systems, 1998, 30(1): 107-117.
[12] Weng Jianshu, Lim E P, Jiang Jing, et al. TwitterRank:Finding topic-sensitive influential twitterers[C]//Proceedings of the Third ACM International Conference on Web Search and Data Mining. New York: ACM, 2010: 261-270.
[13] Lü Linyuan, Zhang Yicheng,Yeung C H,et al. Leaders in social networks, the delicious case[J]. PLOS ONE, 2011, 6(6): e21202.doi:10.1371/journal.pone.0021202
[14] Ding Zhaoyun, Jia Yan, Zhou Bin, et al. Measuring the spreadability of users in microblogs[J]. Journal of Zhejiang University Science C, 2013, 14(9): 701-710.
[15] Cha M, Haddadi H, Benevenuto F, et al. Measuring user influence in Twitter: The million follower fallacy[C]//Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. Barcelona: ICWSM,2010:10-17.
[16] Pal A, Counts S. Identifying topical authorities in microblogs[C]//Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. New York: ACM, 2011: 45-54.
[17] 原福永, 冯静, 符茜茜. 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.
[18] Bakshy E, Hofman J M, Mason W A, et al. Everyone's an influencer: quantifying influence on Twitter[C]//Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. New York: ACM, 2011: 65-74.
[19] 林琛. 微博个体信息传播影响力评价模型研究[J].现代图书情报技术, 2014, 30(2): 79-85.
[20] 毛佳昕, 刘奕群, 张敏, 等. 基于用户行为的微博用户社会影响力分析[J].计算机学报, 2014(4): 1-10.
[21] Ghosh R, Lerman K. Predicting influential users in online social networks[EB/OL].[2015-09-20].http://arxiv.org/pdf/1005.4882v1.pdf.
[22] 任晓龙, 吕琳媛. 网络重要节点排序方法综述[J].科学通报, 2014(13): 1175-1197.
[23] Ding Zhaoyun, Jia Yan, Zhou Bin, et al. Mining topical influencers based on the multi-relational network in micro-blogging sites[J]. China Communications, 2013, 10(1): 93-104.

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