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A dynamic influence model of social network hotspot based on grey system

一种基于灰色系统理论的热点话题用户行为影响力模型

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Abstract

The outbreak of hotspot in social network may contain complex dynamic genesis. Using user behavior data from hotspots in social network, we study how different user groups play different roles for a hotspot topic. Firstly, by analyzing users’ behavior records, we mine group situation that promotes the hotspot. Several major attributions in a hotspot outbreak, such as individual, peer and group triggers, are defined formally according to the view-point of social identity, social interaction, retweet depth and opinion leader. Secondly, for the problem of the uneven and sparse data in each stage of hotspot topic’s life cycle, we propose a dynamic influence model based on grey system to formalize the effect of different groups. Then the process of hotspot evolution driven by distinct crowd is showed dynamically. The experimental result confirms that the model is able not only to qualify users’ influence on a hotspot topic but also to predict effectively an upcoming change in a hotspot topic.

摘要

创新点

  1. 1.

    基于在线用户群体行为关系网络, 考虑话题演化复杂的线上、 线下动力学成因, 针对热点话题普遍存在生命周期各阶段数据不均匀以及稀疏性问题, 利用灰色系统理论基础思想和方法, 构建热点话题用户行为影响力模型, 发现社交网络平台中大众话题变化趋势的背后推动力量.

  2. 2.

    融合微观度量和中观视角的研究方法, 依据用户关系特性划分网络群体, 通过时间离散化及时间切片方法, 提出一种动态的热点话题用户行为影响力评估模型. 使其能够动态化、 阶段化展现不同用户群体在热点话题产生、发展、消亡生命周期演化过程中的量化推动影响力.

  3. 3.

    本文提出的用户行为影响力模型不仅能够根据在线社交网络的话题演化相关驱动属性, 挖掘每个热点话题在生命周期中的不同时间段内动态的推动群体, 而且能够对下一时间段的话题互动变化量进行预测, 为舆情管控、 网络水军的发现提供有力依据.

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References

  1. Guille A, Hacid H, Favre C, et al. Information diffusion in online social networks: a survey. ACM SIGMOD Rec, 2013, 42: 17–28

    Article  Google Scholar 

  2. Li D, Xu Z, Li S, et al. Link recommendation for promoting information diffusion in social networks. In: Proceedings of the 22nd International Conference on World Wide Web Companion, Brazil, 2013. 185–186

    Google Scholar 

  3. Agrawal D, Budak C, El Abbadi A. Information diffusion in social networks: observing and affecting what society cares about. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. New York: ACM, 2011. 2609–2610

    Google Scholar 

  4. Neto S M B, Cavalin P R, Pinhanez C S, et al. Reaction times for user behavior models in microblogging online social networks. In: Proceedings of the 2013 Workshop on Data-driven User Behavioral Modelling and Mining from Social Media, San Francisco, 2013

    Google Scholar 

  5. Bakshy E, Rosenn I, Marlow C, et al. The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, Lyon, 2012. 519–528

    Chapter  Google Scholar 

  6. Rodriguez M G, Leskovec J, Schölkopf B. Structure and dynamics of information pathways in online media. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining, Rome, 2013. 23–32

    Google Scholar 

  7. Kwak H, Lee C, Park H, et al. What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, Raleigh, 2010. 591–600

    Chapter  Google Scholar 

  8. Wu S, Hofman J M, Mason W A, et al. Who says what to whom on twitter. In: Proceedings of the 20th International Conference on World Wide Web, Hyderabad, 2011. 705–714

    Chapter  Google Scholar 

  9. Venkatanathan J, Karapanos E, Kostakos V, et al. A network science approach to modelling and predicting empathy. In: Proceedings of 2013 International Conference on Advances in Social Networks Analysis and Mining, Niagara Falls, 2013. 1395–1400

    Google Scholar 

  10. He X R, Kempe D. Stability of influence maximization. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014. 1256–1265

    Google Scholar 

  11. Romero D M, Galuba W, Asur S, et al. Influence and passivity in social media. Lect Notes Comput Sci, 2011, 6913: 18–33

    Article  Google Scholar 

  12. Ren J, Cheng Z, Shen J, et al. Influences of influential users: an empirical study of music social network. In: Proceedings of International Conference on Internet Multimedia Computing and Service, Xiamen, 2014

    Google Scholar 

  13. Tang J, Sun J, Wang C, et al. Social influence analysis in large-scale networks. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Paris, 2009. 807–816

    Chapter  Google Scholar 

  14. Aral S, Walker D. Identifying influential and susceptible members of social networks. Science, 2012, 337: 337–341

    Article  MathSciNet  Google Scholar 

  15. Matsumura N, Ohsawa Y, Ishizuka M. Influence diffusion model in text-based communication. Trans Jap Soc Artif Intell, 2002, 17: 259–267

    Article  Google Scholar 

  16. Sharifi B, Hutton M, Kalita J. Experiments in microblog summarization. In: Proceedings of the 2010 IEEE 2nd International Conference on Social Computing, Minneapolis, 2010. 49–56

    Chapter  Google Scholar 

  17. Mishne G, de Rijke M. Capturing global mood levels using blog posts. In: Proceedings of AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs, Palo Alto, 2006. 145–152

    Google Scholar 

  18. Cataldi M, Mittal N, Aufaure M A. Estimating domain-based user influence in social networks. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing. New York: ACM, 2013. 1957–1962

    Chapter  Google Scholar 

  19. Saez-Trumper D, Comarela G, Almeida V, et al. Finding trendsetters in information networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012. 1014–1022

    Chapter  Google Scholar 

  20. Jung Y, Gray R, Lampe C, et al. Favors from facebook friends: unpacking dimensions of social capital. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 2013. 11–20

    Chapter  Google Scholar 

  21. Gomez Rodriguez M, Leskovec J, Krause A. Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington DC, 2010. 1019–1028

    Chapter  Google Scholar 

  22. Kibanov M, Atzmueller M, Scholz C, et al. Temporal evolution of contacts and communities in networks of face-to-face human interactions. Sci China Inf Sci, 2014, 57: 032103

    Article  Google Scholar 

  23. Lin Y I, Liu S. A historical introduction to grey systems theory. In: Proceedings of 2004 IEEE International Conference on Systems, Man and Cybernetics. Washington DC: IEEE, 2004. 2403–2408

    Google Scholar 

  24. Cha M, Benevenuto F, Gummadi P K, et al. Measuring user influence in twitter: the million follower fallacy. In: Proceedings of International AAAI Conference on Weblogs and Social Media, Washington DC, 2010. 10–17

    Google Scholar 

  25. Li F, Du T C. Who is talking? An ontology-based opinion leader identification framework for word-Of-mouth marketing in online social blogs. Decis Support Syst, 2011, 51: 190–197

    Article  Google Scholar 

  26. Papagelis M, Murdock V, van Zwol R. Individual behavior and social influence in online social systems. In: Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia. New York: ACM, 2011. 241–250

    Chapter  Google Scholar 

  27. Yuan N J, Zheng Y, Zhang L, et al. T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans Knowl Data Eng, 2013, 25: 2390–2403

    Article  Google Scholar 

  28. Lu L, Zhang Y, Yeung C H, et al. Leaders in social networks, the delicious case. Plos One, 2011, 6: e21202

    Article  Google Scholar 

  29. Pakzad F, Abhari A. Characterization of user networks in facebook. In: Proceedings of the 2010 Spring Simulation Multiconference, San Diego, 2010

    Google Scholar 

  30. Park B, Lee K, Kang N. The impact of influential leaders in the formation and development of social networks. In: Proceedings of the 6th International Conference on Communities and Technologies. New York: ACM, 2013. 8–15

    Chapter  Google Scholar 

  31. Tang X, Yang C C. Ranking user influence in healthcare social media. ACM Trans Intell Syst Technol, 2012, 3: 565–582

    Article  Google Scholar 

  32. Tang J, Wu S, Sun J. Confluence: conformity influence in large social networks. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2013. 347–355

    Chapter  Google Scholar 

  33. Carrington P J, Scott J, Wasserman S. Models and Methods in Social Network Analysis. Cambridge: Cambridge University Press, 2005. 37

    Book  Google Scholar 

  34. Dong Y, Ke Q, Cai Y, et al. Teledata: data mining, social network analysis and statistics analysis system based on cloud computing in telecommunication industry. In: Proceedings of the 3rd International Workshop on Cloud Data Management. New York: ACM, 2011. 41–48

    Chapter  Google Scholar 

  35. Deng J L. Introduction to grey system theory. J Grey Syst, 1989, 1: 1–24

    MATH  Google Scholar 

  36. Meerschaert M M. Mathematical Modeling. Oxford: Academic Press, 1993

    MATH  Google Scholar 

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Authors and Affiliations

  1. Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, 400065, China

    YunPeng Xiao, Jing Ma & YanBing Liu

  2. Swiss Federal Institute of Technology (EPFL), Lausanne, CH-1015, Switzerland

    ZhiXian Yan

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  1. YunPeng Xiao
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  2. Jing Ma
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  3. YanBing Liu
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Correspondence to YunPeng Xiao.

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Xiao, Y., Ma, J., Liu, Y. et al. A dynamic influence model of social network hotspot based on grey system. Sci. China Inf. Sci. 58, 1–12 (2015). https://doi.org/10.1007/s11432-015-5439-y

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