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Modeling collective blogging dynamics of popular incidental topics

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Abstract

An extended susceptible-infective (SI) epidemic model is presented in this paper to describe the collective blogging behavior on popular incidental topics. Our model has two major extensions over the classic SI model: in the new model, different blog writers get interested in a specific topic with different probabilities, while in a classic SI model, the infection probability of a disease between any two individuals is identical; the new model takes into consideration the impact of external mainstream media on blog writers, while in a classical SI model, spreading of diseases is merely based on personal contacts between individuals. The new model is capable of explaining the widely observed early burst and heavy tail of topic propagation velocity. The proposed model has a closed-form solution when the individual interest is of uniform distribution with the external influence assumed constant. We validate the proposed model using ten topics from two different data sets: Sina Blog and LiveJournal Blogspace, the results indicating that our model fits the topic propagation velocity and predicts the propagation trend very well.

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References

  1. Adar E, Zhang L, Adamic LA, Lukose RM (2004) Implicit structure and the dynamics of blogspace. In: Workshop on the Weblogging Ecosystem

  2. Asmussen, S (eds) (2003) Applied probability and queues. Springer, New York

    MATH  Google Scholar 

  3. Barthélemy M, Barrat A, Pastoras-Satorras R, Vespignani A (2004) Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. Phys Rev Lett 92: 178701

    Article  Google Scholar 

  4. Bass FM (1969) A new product growth model for consumer durables. Manag Sci 15: 215–227

    Article  MATH  Google Scholar 

  5. CNNIC. 27th China internet development statistics report. http://www.cnnic.cn/research/bgxz/tjbg/201101/t20110120_20302.html

  6. Daley DJ, Gani J (1999) Epidemic modelling: an introduction. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  7. Fung GPC, Yu JX, Yu PS, Lu H (2005) Parameter free bursty events detection in text streams. In: VLDB

  8. Gao C, Liu J, Zhong N (2011) Network immunization and virus propagation in email networks: experimental evaluation and analysis. Knowl Inf Syst (KAIS) 27: 253–279

    Article  MathSciNet  Google Scholar 

  9. Goetz M, Leskovec J, Mcglohon M, Faloutsos C (2009) Modeling blog dynamics. In: Proceedings of the Third international conference on weblogs and social media (ICWSM)

  10. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12: 211–223

    Article  Google Scholar 

  11. Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83: 1420–1443

    Article  Google Scholar 

  12. Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: WWW

  13. Hirsch MJ, Pardalos PM, Murphey R (2011) Dynamics of information systems. Springer, New york

    Google Scholar 

  14. Iribarren JL, Moro E (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev lett 103: 38702

    Article  Google Scholar 

  15. Karrer B, Newman MEJ (2010) A message passing approach for general epidemic models. Phys Rev E 82: 016101

    Article  MathSciNet  Google Scholar 

  16. Kisilevich S, Ang CS, Last M (2011) Large-scale analysis of self-disclosure patterns among online social networks users: a Russian context. Knowl Inf Syst (KAIS). doi:10.1007/s10115-011-0443-z

  17. Kleinberg J (2002) Bursty and hierarchical structure in streams. In: KDD

  18. Kleinberg J (2006) Temporal dynamics of on-line information streams. In: Garofalakis M, Gehrke J, Rastogi R (eds) Data stream management: processing high-speed data streams. Springer, New York

    Google Scholar 

  19. Leskovec J, Backstrom L, Kleinberg J (2009) Meme-tracking and the dynamics of the news cycle. In: KDD

  20. Leskovec J, McGlohon M, Faloutsos C, Glance N, Hurst M (2007) Cascading behavior in large blog graphs. In: Proceedings of the 7th SIAM international conference on data mining (SDM)

  21. LiveJournal. http://www.livejournal.com

  22. Liu H, Lin Y, Han J (2011) Methods for mining frequent items in data streams: an overview. Knowl Inf Syst (KAIS) 26: 1–30

    Article  Google Scholar 

  23. Mahajan V, Muller E, Bass FM (1990) New product diffusion models in marketing: a review and directions for research. J Mark 54: 1–26

    Article  Google Scholar 

  24. Min B, Goh KI, Vazquez A (2011) Spreading dynamics following bursty human activity patterns. Phys Rev E 83: 036102

    Article  Google Scholar 

  25. Moreno Y, Nekovee M, Pacheco AF (2004) Dynamics of rumor spreading in complex networks. Phys Rev E 69: 066130

    Article  Google Scholar 

  26. Pastor-Satorras R, Vespignani A (2001) Epidemic dynamics and endemic states in complex networks. Phys Rev E 63: 066117

    Article  Google Scholar 

  27. Pastor-Satomas R, Vespignani A (2002) Epidemic dynamics in finite scale-free networks. Phys Rev E 65: 035108

    Article  Google Scholar 

  28. Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86: 3200–3203

    Article  Google Scholar 

  29. Pittel B (1987) On spreading a rumor. SIAM J Appl Math 47: 213–223

    Article  MathSciNet  MATH  Google Scholar 

  30. Rodriguez MG, Leskovec J, Krause A (2010) Inferring networks of diffusion and influence. In: KDD

  31. Sina Blog. http://blog.sina.com.cn

  32. Thai MT, Pardalos PM (2011) Handbook of optimization in complex networks. Springer, New York

    Google Scholar 

  33. Tsai FS, Zhang Y (2011) D2S: Document-to-sentence framework for novelty detection. Knowl Inf Syst (KAIS) 29: 419–433

    Article  Google Scholar 

  34. The Nielsen Company. BlogPulse. http://www.blogpulse.com/

  35. Yan G, Zhou T, Wang J, Fu ZQ, Wang BH (2005) Epidemic spread in weighted scale-free networks. Chin Phys Lett 22: 510–513

    Article  Google Scholar 

  36. Yang J, Leskovec J (2010) Modeling information diffusion in implicit networks. In: ICDM

  37. Zhao L, Yuan R, Guan X, Li M (2009) Modeling and analysis of incidental topic propagation in blogosphere. In: Proceedings of 13th international conference on human-computer interaction

  38. Zhao L, Yuan R, Guan X, Jia Q (2009) Bursty propagation model for incidental events in blog networks. J Softw 20: 1384–1392

    Article  Google Scholar 

  39. Zhou Y, Guan X, Zhang Z, Zhang B (2008) Predicting the tendency of topic discussion on the online social networks using a dynamic probability model. In: Proceedings of the hypertext 2008 workshop on collaboration and collective intelligence

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Authors and Affiliations

  1. Center for Intelligent and Networked Systems and TNLIST Lab, Tsinghua University, Beijing, 100084, China

    Li Zhao, Xiaohong Guan & Ruixi Yuan

  2. Ministry of Education Key Laboratory for Intelligent Network and Network Security, Xi’an Jiaotong University, Xi’an, 710049, China

    Xiaohong Guan

Authors
  1. Li Zhao
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  2. Xiaohong Guan
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  3. Ruixi Yuan
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Corresponding author

Correspondence to Li Zhao.

Additional information

The research presented in this paper is supported in part by the National Natural Science Foundation (60921003, 60736027) and 111 International Collaboration Program, of China.

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Zhao, L., Guan, X. & Yuan, R. Modeling collective blogging dynamics of popular incidental topics. Knowl Inf Syst 31, 371–387 (2012). https://doi.org/10.1007/s10115-011-0470-9

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