Skip to main content
Log in

Modelling and simulation of misconduct information diffusion in web forum

  • Complex Science Management
  • Published:
Wuhan University Journal of Natural Sciences

Abstract

Using the method of analogy, this paper built the social information field, information field force model and the information diffusion dynamics model to study the dynamic mechanism and the law of the movement regarding how misconduct information moves among nodes in the web forum. It also constructed the web forum misconduct information diffusion complex network simulation model to study the diffusion intensity of misconduct information and its influencing factors. The conclusion is that, under the force of the field, the information flows from the high potential node to the low potential node, during which resistance is generated inside and outside the diffusion channel. In the complex network of the web forum, the diffusion intensity of misconduct information displays an increasing trend as the possibility of reconnection among broken nodes becomes higher. The main factor that determines the diffusion intensity of the misconduct information is the average shortest path. It also increases when the interaction frequency turns higher.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wimmer R D, Dominick J R. Mass Media Research:An Introduction [M]. Seventh edition. Belmont: Wadsworth Publishing Company, 2003:54–60.

    Google Scholar 

  2. Lasswell H D. The Structure and Function of Communication in Society: In the Communication of Ideas [M]. New York: Institute for Religious and Social Studies, 1948.

    Google Scholar 

  3. Daley D J, Kendall D G. Stochastic rumors[J]. IMA Journal of Applied Mathematics, 1965, 1(1): 42–55.

    Article  Google Scholar 

  4. Daley D J, Gani J. Epidemic Modeling [M]. Cambridge: Cambridge University Press, 2000.

    Google Scholar 

  5. Maki D P, Thompson M. Mathematical Models and Applications: with Emphasis on the Social, Life, and Management Sciences [M]. Englewood Cliffs: Prentice Hall, 1973.

    Google Scholar 

  6. Zhang F, Li L, Xuan H Y. Survey of transmission models of infectious diseases [J]. Systems Engineering Theory & Practice, 2011, 31(9): 1736–1744(Ch).

    CAS  Google Scholar 

  7. Woo J, Chen H. An event-driven SIR model for topic diffusion in web forums [C] //Proc of IEEE International Conference on Intelligence and Security Informatics, 2012: 108–113.

    Google Scholar 

  8. Wang J L, Liu S Q, Zheng B W, et al. Qualitative and bifurcation analysis using an SIR model with a saturated treatment function[J]. Mathematical and Computer Modeling, 2012, 55: 710–722.

    Article  Google Scholar 

  9. Zhang Y C, Liu Y, Zhang H F. The research of information dissemination model on online social network [J]. Acta Physica Sinica, 2011, 60(5): 60–66(Ch).

    Google Scholar 

  10. Borge-Holthoefer J, Meloni S, Goncalves B, et al. Emergence of influential spreaders in modified rumor models [J]. Journal of Statistical Physics, 2013, 151(1/2): 383–393.

    Article  Google Scholar 

  11. Chen J. Research of Communication Subjects of Social Network Site Based on WEB2.0 Internet Background [D]. Xiangtan: Xiangtan University, 2013(Ch).

    Google Scholar 

  12. Chen B, Tang X Y, Yu L. Identifying method for opinion leaders in social network based on competency model [J]. Journal on Communications, 2014, 35(11): 89–96(Ch).

    CAS  Google Scholar 

  13. Cao L. The ten characteristics of micro-blog communication and its impact on speech ecology[J]. Journalism Review, 2011, (9): 29–34(Ch).

    Google Scholar 

  14. Yi C Q, Bao Y, Xue Y, et al. Research on mechanism of large-scale information dissemination based on Sina Weibo [J]. Journal of Frontiers of Computer Science & Technology, 2013, 7(6): 551–561(Ch).

    Google Scholar 

  15. Cao J X, Wu J L, Shi W, et al. Sina microblog information diffusion analysis and prediction[J]. Chinese Journal of Computers, 2014, (4):779–790(Ch).

    Google Scholar 

  16. Hao Z, Huang C, Cai R, et al. User interest related information diffusion pattern mining in microblog [J]. Pattern Recognition & Artificial Intelligence, 2016, 29(10): 924–935 (Ch).

    Google Scholar 

  17. Jia H, Xiao R, Yao H, et al. Characteristic analysis of information propagation pattern in online social network [J]. Journal of Computer Applications, 2013, 33(1):105–107 (Ch).

    Article  Google Scholar 

  18. Liu Q Z. A Study on the Influencing Factors of Social Network Information Dissemination: A Case Study of Sina and Micro-Blog [D]. Xi’an: Shaanxi University of Finance and Economics, 2016(Ch).

    Google Scholar 

  19. Zhang H F. Research on Information Dissemination and Viewpoint Evolution in Micro-Blog Network [D]. Beijing: Beijing Jiaotong University, 2015(Ch).

    Google Scholar 

  20. Huang H C, Jiang A L, Hu M. Analysis of information dissemination model based on social network [J]. Application Research of Computers, 2016, 33(9): 2738–2742(Ch).

    Google Scholar 

  21. Yuan G. Analysis of the causes and motivations of network rumors in emergencies—A case study of social media [J]. Media, 2016, (21): 80–83(Ch).

    Google Scholar 

  22. Li H. Research on the Causes of Micro-Blog Rumor and Its Communication Model [D]. Shijiazhuang: Hebei University, 2014(Ch).

    Google Scholar 

  23. Zhuang P. The characteristics and countermeasures of rumor spreading in the media environment [J]. Brand, 2015, (1): 37-37(Ch).

  24. Zhao Q W, Ma Y Y. A probe into the characteristics of We Chat rumor propagation in the We-media age [J]. Journal of News Research, 2014, (16): 11-11(Ch).

  25. Jo D G. Diffusion of rumors on the Internet [J]. The Information Society Review, 2002, (1): 76–77.

    Google Scholar 

  26. Peng C, Li M X. Channels and governance of rumor spreading [J]. Shang Qing, 2013, (43):239-239(Ch).

  27. Nicholas D F, Nicole R M, Jerry S M, et al. Rumor about cancer: content, sources, coping, transmission, and belief [J]. Journal of Health Communication, 2012, (1): 83–84.

    Google Scholar 

  28. Xu B L. Network rumor spreading from audience psychology [J]. Journal of News Research, 2016, 6(6):98-98(Ch).

    Google Scholar 

  29. Meng F R. Research on Rumor Propagation Model in Social Network [D]. Nanjing: Nanjing University of Posts and Telecommunications, 2013(Ch).

    Google Scholar 

  30. Polanyi M. Personal knowledge: Towards a post-critical philosophy [J]. British Journal of Educational Studies, 1958, 20(3): 429.

    Google Scholar 

  31. Pan D B. Field Economics [M]. Wuhan: Hubei People’s Press, 1994(Ch).

    Google Scholar 

  32. Nonaka I, Konno N. The concept of “Ba”: Building a foundation for knowledge creation [J]. California Management Review, 1998, 40(3): 40–54.

    Article  Google Scholar 

  33. Zhang B. On construction, measurement and evolution of knowledge field: A perspective of knowledge flow [J]. Journal of Intelligence, 2011(Ch).

    Google Scholar 

  34. Luo X B. Description of the field[C]//Proceedings of Hubei Society of Physics and Annual Meeting of the 2015. Wuhan: Wuhan Physics Society. 2015: 318–332(Ch).

    Google Scholar 

  35. Yu Y D. Mathematical representation and demonstration of advantage agglomeration in regional coordinate economic field [J]. China Business (On Economic Theory), 2006, (2): 3–9(Ch).

    Google Scholar 

  36. Isard W. Introduction to regional science [J]. Canadian Journal of Economics/Revue Canadienne Deconomique, 1975, 9(3): 542.

    Google Scholar 

  37. Long Q Y. Gravity model analysis of urban interaction [J]. Journal of Central South University of Forestry & Technology (Social Sciences), 2005, 16(5): 48–49(Ch).

    Google Scholar 

  38. Hasson J A, Tinbergen J. Shaping the world economy: Suggestions for an international economic policy [J]. Economica, 1962, 31(123):327.

    Article  Google Scholar 

  39. Poyhonen P. A tentative model for the flows of trade between countries [J]. Weltwirtschatftliches Archive, 1963, 90: 93–100.

    Google Scholar 

  40. Ahn Y Y, Han S, Kwak H, et al. Analysis of topological characteristics of huge online social networking services[C]// International Conference on World Wide Web. New York: ACM, 2007:835–844.

    Google Scholar 

  41. Ha J, Kim S W, Kim S W, et al. An analysis on information diffusion through Blog Cast, in a blogosphere [J]. Information Sciences, 2015, 290(C):45–62.

    Article  Google Scholar 

  42. Hu H B. Research on the Structure, Evolution and Dynamics of Online Social Networks [D]. Shanghai: Shanghai Jiao Tong University, 2010(Ch).

    Google Scholar 

  43. Zhang B. Small world phenomenon of knowledge flowing in complex networks [J]. Journal of Guangxi Normal University, 2010, 28(4): 15–20(Ch).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhan Zheng.

Additional information

Foundation item: Supported by the National Natural Science Foundation for Young Scientists of China (71503188), Key Research Institute of Humanities & Social Science in Hubei Higher Education Institutions (DSS20160105), Subsidy Fund of Grain Science and Technology Innovation and Scientific and Technological Achievements Transformation Set by Grain Bureau of Hubei Province, and Wuhan Social Science Consortium Project

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, W., Zheng, Z. & Xu, X. Modelling and simulation of misconduct information diffusion in web forum. Wuhan Univ. J. Nat. Sci. 22, 541–548 (2017). https://doi.org/10.1007/s11859-017-1286-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11859-017-1286-6

Key words

CLC number

Navigation