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A sequential game-theoretic study of the retweeting behavior in Sina Weibo

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Abstract

Retweeting behavior is a key mechanism for information diffusion in microblogging services such as Sina Weibo. It is the mechanism of retweeting that leads to the fast and wide diffusion of information in Weibo. Game theory has been applied to various fields as an important tool for studying user’s behavior. In this paper, a sequential game model has been applied on studying Weibo user’s retweeting behavior. We propose a retweeting game model of Weibo users. By Markov perfect equilibrium strategy we estimate the retweeting probability of the message, of which the continuity and existence have been proved. Comparing the retweeting game model with the Weibo data, we find that the retweeting game model can be used to describe the user’s retweeting behaviors. Experimental results show that in the retweeting game model the trust factor \(\delta \) and the amount of information k have a great influence on the retweeting rate.

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References

  1. Ghiassi M, Skinner J, Zimbra D (2013) Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst Appl 40(16):6266–6282

  2. Naveed N, Gottron T, Kunegis J, Alhadi AC (2011) Searching microblogs: coping with sparsity and document quality. In: Proceedings of the 20th ACM international conference on information and knowledge management. ACM, New York, pp 183–188

  3. Fowler JH, Christakis NA (2010) Cooperative behavior cascades in human social networks. Proc Natl Acad Sci 107(12):5334–5338

    Article  Google Scholar 

  4. Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Rev Mod Phys 81(2):591

    Article  Google Scholar 

  5. Boyd D, Golder S, Lotan G (2010) Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: 43rd Hawaii international conference on system sciences (HICSS). IEEE, pp 1–10

  6. Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web (WWW’10). ACM, New York, pp 591–600

  7. Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. ACM SIGMOD Rec 42(2):17–28

    Article  Google Scholar 

  8. Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with twitter: what 140 characters reveal about political sentiment. ICWSM 10:178–185

    Google Scholar 

  9. Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: IEEE second international conference on social computing (SocialCom), pp 177–184

  10. Xin W, Toshio O (2011) Utilizing learning process to improve recommender system for group learning support. Neural Comput Appl 20:611–621

    Article  Google Scholar 

  11. Lehmann J, Gonçalves B, Ramasco JJ, Cattuto C (2012) Dynamical classes of collective attention in twitter. In: Proceedings of the 21st international conference on World Wide Web. ACM, pp 251–260

  12. Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web. ACM, New York, pp 695–704

  13. Liu C, Zhang ZK (2014) Information spreading on dynamic social networks. Commun Nonlinear Sci Numer Simul 19(4):896–904

    Article  MathSciNet  Google Scholar 

  14. Yang Z, Guo J, Cai K, Tang J, Li J, Zhang L, Su Z (2010) Understanding retweeting behaviors in social networks. In: Proceedings of the 19th ACM international conference on information and knowledge management. ACM, New York, pp 1633–1636

  15. Hong L, Doumith AS, Davison BD (2013) Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM, pp 557–566

  16. Sohn JS, Chung IJ (2013) Dynamic foaf management method for social networks in the social web environment. J Supercomput 66(2):633–648

    Article  Google Scholar 

  17. Jiang F, Rho S, Chen BW, Du X, Zhao D (2014) Face hallucination and recognition in social network services. J Supercomput 71(6):2035–2049

    Article  Google Scholar 

  18. Yan Z, Chen Y, Shen Y (2014) Percontrep: a practical reputation system for pervasive content services. J Supercomput 70(3):1051–1074

    Article  Google Scholar 

  19. Jun S, Kim D, Jeon M, Rho S, Hwang E (2014) Social mix: automatic music recommendation and mixing scheme based on social network analysis. J Supercomput 71(6):1933–1954

    Article  Google Scholar 

  20. Barthwal R, Misra S, Obaidat MS (2013) Finding overlapping communities in a complex network of social linkages and internet of things. J Supercomput 66(3):1749–1772

    Article  Google Scholar 

  21. Rahman MA, Kim HN, El Saddik A, Gueaieb W (2012) A context-aware multimedia framework toward personal social network services. Multimed Tools Appl 71(3):1717–1747

    Google Scholar 

  22. Kim M, Park SO (2013) Group affinity based social trust model for an intelligent movie recommender system. Multimed Tools Appl 64(2):505–516

    Article  Google Scholar 

  23. Jin L, Zhang K, Lu J, Lin YR (2014) Towards understanding the gamification upon users scores in a location-based social network. Multimed Tools Appl 71(3):1531–1555

  24. Lin C, He J, Zhou Y, Yang X, Chen K, Song L (2013) Analysis and identification of spamming behaviors in sina weibo microblog. In: Proceedings of the 7th workshop on social network mining and analysis. ACM, p 5

  25. Qu Y, Huang C, Zhang P, Zhang J (2011) Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In: Proceedings of the ACM 2011 conference on computer supported cooperative work. ACM, New York, pp 25–34

  26. Chen J, She J (2012) An analysis of verifications in microblogging social networks-Sina Weibo. In: 32nd international conference on distributed computing systems workshops (ICDCSW). IEEE, pp 147–154

  27. Guan W, Gao H, Yang M, Li Y, Ma H, Qian W, Cao Z, Yang X (2014) Analyzing user behavior of the micro-blogging website sina weibo during hot social events. Phys A Stat Mech Appl 395:340–351

    Article  Google Scholar 

  28. Gao Q, Abel F, Houben GJ, Yu Y (2012) A comparative study of users microblogging behavior on Sina Weibo and Twitter. In: User modeling, adaptation, and personalization. Springer, New York, pp 88–101

  29. Ma H, Wei J, Qian W, Yu C, Xia F, Zhou A (2013) On benchmarking online social media analytical queries. In: First international workshop on graph data management experiences and systems. ACM, p 10

  30. Veenstra AS, Iyer N, Hossain MD, Park J (2014) Time, place, technology: Twitter as an information source in the wisconsin labor protests. Comput Human Behav 31:65–72

    Article  Google Scholar 

  31. Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. In: Proceedings of the 21st international conference on world wide web. ACM, New York, pp 519–528

  32. Goel S, Watts DJ, Goldstein DG (2012) The structure of online diffusion networks. In: Proceedings of the 13th ACM conference on electronic commerce, pp 623–638, ACM

  33. Jenders M, Kasneci G, Naumann F (2013) Analyzing and predicting viral tweets. In: Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, pp 657–664

  34. Zaman TR, Herbrich R, Van Gael J, Stern D (2010) Predicting information spreading in twitter. In: Workshop on computational social science and the wisdom of crowds, NIPS, vol 104. Citeseer, pp 17599–17601

  35. Chen BW, Chen CY, Wang JF (2013) Smart homecare surveillance system: behavior identification based on state-transition support vector machines and sound directivity pattern analysis. IEEE Trans Syst Man Cybern Syst 43(6):1279–1289

    Article  Google Scholar 

  36. Ji W, Chen B, Chen Y, Kung S (2014) Profit improvement in wireless video broadcasting system: a marginal principle approach. IEEE Trans Mob Comput 99:1. doi:10.1109/TMC.2014.2362919

  37. Chen L, Leneutre J (2009) A game theoretical framework on intrusion detection in heterogeneous networks. IEEE Trans Inf Forensics Secur 4(2):165–178

    Article  Google Scholar 

  38. Doraszelski U, Satterthwaite M (2010) Computable Markov-perfect industry dynamics. RAND J Econ 41(2):215–243

    Article  MathSciNet  Google Scholar 

  39. Duggan J, Kalandrakis T (2012) Dynamic legislative policy making. J Econ Theory 147(5):1653–1688

    Article  MathSciNet  MATH  Google Scholar 

  40. Fearon JD (2011) Self-enforcing democracy. Q J Econ 126(4):1661–1708

    Article  Google Scholar 

  41. Azzimonti M (2011) Barriers to investment in polarized societies. Am Econ Rev 101(5):2182–2204

    Article  Google Scholar 

  42. Liang X, Xiao Y (2013) Game theory for network security. Commun Surv Tutor IEEE 15(1):472–486

    Article  Google Scholar 

  43. Halac M (2012) Relational contracts and the value of relationships. Am Econ Rev 102(2):750–779

    Article  Google Scholar 

  44. Garcia A, Shen Z (2010) Equilibrium capacity expansion under stochastic demand growth. Oper Res 58(1):30–42

    Article  MathSciNet  MATH  Google Scholar 

  45. Klein N (2013) Strategic learning in teams. Games Econ Behav 82:636–657

    Article  MATH  Google Scholar 

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Correspondence to Ru Wang.

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Wang, R., Cai, W. A sequential game-theoretic study of the retweeting behavior in Sina Weibo. J Supercomput 71, 3301–3319 (2015). https://doi.org/10.1007/s11227-015-1456-2

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