A Computation Model of Micro-Blog Information Credibility Based on Bayesian Network

  • Authors

    • Haiyan Rui
    • Jiayue Zhao
    • Chengcheng Li
    • Fengming Liu
    2018-09-07
    https://doi.org/10.14419/ijet.v7i3.19.16984
  • Bayesian network, Microblog, credibility, Netica

  • Abstract

    With the rapid development, Microblog as an important interactive media, has become a kind of transmission carrier of the false information. Therefore, the research significance of Micro-blog information credibility becomes more and more important today. In this paper, different representative factors are selected from three facets--text contents, information dissemination and information source--which influence the information credibility of Micro-blog. We choose Netica software to build Bayesian network model and use the rumors grabbed from Sina Weibo as experimental data in order to get the relationship between conditions and phenomena from the changes of probability distribution in Bayesian network. On the basis of this, we find the influences of the representative factors on the subjective credibility of objective unreliable information.

     

     

  • References

    1. [1] Castillo C, Mendoza M, Poblete B. Information credibility on Twitter. Proceedings of the 20th International Conference on World Wide Web. New York:ACM,2011, 675-684.

      [2] Hend S. Alâ€Khalifa, Rasha M. Alâ€Eidan. An experimental system for measuring the credibility of news content in Twitter. International Journal of Web Information System,2011,7(2) :130-151.

      [3] Mingxia Gao. Chinese microblogging credibility assessment method based on evidence theory. 2014.

      [4] Gupta, Aditi, and P. Kumaraguru. "Credibility ranking of tweets during high impact events." 2012:2-8.

      [5] Gupta M, Zhao Pengxiang, Han Jiawei. Evaluating event credibility on Twitter. Proceedings of the Twelfth SIAM International Conference on Data Mining. Anaheim: Omni Press,2012.

      [6] BIAN Xianhua, CHEN Liang, ZHENG Qianbin. Reliability Research Based on Text Context and Community Structure, Journal of Chongqing University of Technology ( Natural Soienc;e), 2013,27(01):57-61.

      [7] GAO Mingxia，CHEN Furong. Credibility evaluating method of Chinese microblog based on information fusion. Journal of Computer Applications, 2016, 36( 8) : 2071-2075,2081.

      [8] Chen Si.Study on communication mechanism of internet rumors in weibo-take sina weiboas an exsample. University of Electronic Science and Technology of China, 2015.

      [9] Jiang Shengyi, Chen Dongyi, Pang Guansong, Wu Meiling, Wang Lianxi. Research Review of Information Credibility Analysis on Microblog.Library and Information Service,2013, 57(12):136-142.

      [10] Don W. Stacks,‎ Michael B. Salwen. An Integrated Approach to Communication Theory and Research,1996,25(1):94-96.

      [11] Ding Kezhi. Research on information credibility of social network. Central China Normal University.2015.

      [12] Liu Qingsong. Study on the reliability analysis method of Chinese microblog information. Beijing Information Science and Technology University,2015.

      [13] LMD Campos, JM Fernández-Luna, JF Huete. Huete. Clustering terms in the Bayesian network retrieval model: a new approach with two term-layers. Applied Soft Computing, 2004,4 (2) :149-158.

      [14] Yang Jiqiong Design and implementation of bayesian network structure based on Netica. Yunnan University,2007.

      [15] Berthier Ribeiro-Neto, Iimerio Silva, Richard Muntz. Bayesian network models for IR. Physica-Verlag HD , 2000 , 50 :259-291.

      [16] Martins A C. Mobility and social network effects on extremist opinions. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2008, 78(3 Pt 2):036104.

      [17] Hien Trang Nuyen, Weiliang Zhao, Jian Yang. A Trust and Reputation Model Based on Bayesian Network for Web Services. In Proceedings of the International Conference on Web Services(ICWS), 2010, 304-305.

      [18] HU Zhi-gang, FU Yi, XIAO Peng, HU Zhou-jun. Bayesian Network-based Grid QoS Trustworthiness Evaluation Method. Computer Engineering,2009, 35(7): 32-34.

      [19] CHEN Jing, FU Jing-qi. Bayesian Network's Application in Fire Alarm System. Instrumentation Technology, 2011, 10:47-51.

      [20] Si Guannan, Ren Yuhan, Xu Jing, Yang Jufeng. A Dependability Evaluation Model for Internet ware Based on Bayesian Network.Journal of Computer Research and Development, 2012, 49(5):1028-1038.

      [21] Ding Y S, Liu F M, Tang B Y, Context-Sensitive Trust Computing in Distributed Environmentsâ€, Knowledge-Based Systems, 2012(28) 105-114.

      [22] Liu F M, Li X, Ding Y S, Zhao H F, Liu X Y, Ma Y H, Tang B Y, A Social Network-Based Trust-Aware Propagation Model for P2P Systems, Knowledge-Based Systems, 2013(41), 8–15.

      [23] Liu F M, Wang L, Gao L, Li H X, Zhao H F, Sok Khim Men. A Web Service Trust Evaluation Model Based on Small-World Networks, Knowledge-Based Systems, 2014(57) 161-167.

      [24] Liu F M, Wang L, Henric Johnson, Zhao H F, Analysis of Network Trust Dynamics Based on Evolutionary Game, Scientia Iranica, Transaction E: Industrial Engineering, 2015(22.6): 2548-2557.

      [25] Liu F M, Zhu X Q, Hu Y X, Ren L H, Henric Johnson, A Cloud Theory-Based Trust Computing Model in Social Networks. Entropy 2017, 19(1), 11.

      [26] LIANG Hong-quan, WU Wei. Research of trust evaluation model based on dynamic Bayesian network. Journal on Communications, 2013, 34(9):68-76.

      [27] LIN Qing, DAI Huijun,REN Dewang. A Quantitative Trust Assessment Method Based on Bayesian Network. COMPUTEï¼² TECHNOLOGY AND DEVELOPMENT,2016, 26(12):132-136.

      [28] Chen Hui. Research and Analysis of Microblog’s False Topic Based on Bayesian Model. Shandong University, 2013.

      [29] Qazvinian V, Rosengren E, Radev D R, Mei Q. Rumor has it: Identifying misinformation in microblogs. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing.Edinburgh:ACL,2011.

      [30] Wang A H. Don't follow me: Spam detection in Twitter. International Conference on Security and Cryptography. IEEE, 2011:142-151.

      [31] Zhang Jianfei. The Study of the Method and Arithmetic of Learning Bayesian Networks. Northeast Normal University 2005.

  • Downloads

  • How to Cite

    Rui, H., Zhao, J., Li, C., & Liu, F. (2018). A Computation Model of Micro-Blog Information Credibility Based on Bayesian Network. International Journal of Engineering & Technology, 7(3.19), 33-38. https://doi.org/10.14419/ijet.v7i3.19.16984

    Received date: 2018-08-06

    Accepted date: 2018-08-06

    Published date: 2018-09-07