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A new opinion leaders detecting algorithm in multi-relationship online social networks

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Abstract

Opinion leaders in online social networks are important for various fields such as public opinion propagation, marketing management, administrative science and even politics. There are often many kinds of relationships in an online social network. Detecting and identifying opinion leaders depending on any one kind of relationship is inaccurate. In this paper, node importance analysis in multi-relationship online social networks was proposed by signalling based on Multi-subnet Composited Complex Networks Model, and considering the characteristics of multiple relationships which would interrelate with each other. Through node importance under multiple relationships, the novel opinion leader detecting algorithm in multi-relationship online social networks is proposed and approved to be efficient by experiments described in this paper.

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References

  1. Belz FM, Baumbach W (2010) Netnography as a method of lead user identification. Creat Innov Manag 19(3):304–313

    Article  Google Scholar 

  2. Bilgram V, Brem A, Voigt KI (2008) User-centric innovations in new product development—systematic identification of lead users harnessing interactive and collaborative online-tools. Int J Innov Manag 12(03):419–458

    Article  Google Scholar 

  3. Callaway D, Newman J, Strogatz S (2000) Network robustness and fragility: percolation on random graphs. Phys Rev Lett 85:5468–5471

    Article  Google Scholar 

  4. Ding, C., He, X., Husbands, P., Zha, H., and Simon, H. D. PageRank, HITS and a unified framework for link analysis. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002, pp. 353–354

  5. Freeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41

    Article  Google Scholar 

  6. Haibo H, Xiaofan W (2008) Analysis of online social networks based on complex network theory. Complex Syst Complexity Sci 2(5):1–14

    Google Scholar 

  7. Huang B, Yu G and Karimi H R. The finding and dynamic detection of opinion leaders in social network. Probl Eng 2014

  8. Kim H, Tang J, Aderson R (2012) Centrality prediction in dynamic human contact networks. Comput Netw 56(3):983–996

    Article  Google Scholar 

  9. Kratzer J, Lettl C (2009) Distinctive roles of lead users and opinion leaders in the social networks of schoolchildren. J Consum Res 36(4):646–659

    Article  Google Scholar 

  10. Lazarsfeld PF, Berelson B, Gaudet H (1965) The people’s choice. How the voter makes up his mind in a presidential campaign. Columbia University Press, New York

  11. Mazhari S, Fakhrahmad SM, Sadeghbeygi H (2015) A user-profile-based friendship recommendation solution in social networks. J Inf Sci 41(3):284–295

    Article  Google Scholar 

  12. Milgram S (1967) The small world problem. Psychology today 2(1):60–67

    Google Scholar 

  13. Miller, J. C., Rae, G., Schaefer, F., Ward, L. A., LoFaro, T., and Farahat, A. Modifications of Kleinberg's HITS algorithm using matrix exponentiation and web log records. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 2001, pp. 444–445

  14. Okamoto K, Chen W, Li X (2005) Ranking of closeness centrality for large-scale social networks. Lect Notes Comput Sci 59:186–195

    MATH  Google Scholar 

  15. Opsahl T, Agneessens F, Skvoretz J (2011) Node centrality in weighted networks: generalizing degree and shortest paths. Soc Networks 32(1):245–251

    Google Scholar 

  16. Qingqing Z (2013) Relationship between scores and tags for Chinese books-in the case of Douban book. Chinese J Libr Inf Sci 6(4):40–54

    Google Scholar 

  17. Ravi K, Ravi VA (2015) Survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst 89:14–46

    Article  Google Scholar 

  18. Severyn A, Moschitti A, Uryupina O (2015) Multi-lingual opinion mining on YouTube. Inf Process Manag 52(1):46–60

    Article  Google Scholar 

  19. Song, X, Chi, Y, Hino, K, and Tseng, B. Identifying opinion leaders in the blogosphere. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007, pp. 971–974

  20. Song X, Yan X, Li Y (2015) Modelling liking networks in an online healthcare community: an exponential random graph model analysis approach. J Inf Sci 41(1):89–96

    Article  Google Scholar 

  21. Von Hippel E (1986) Lead users: a source of novel product concepts. Manag Sci 32(7):791–805

    Article  Google Scholar 

  22. Von Hippel E (2007) The sources of innovation. Das Summa Summarum des Management. Gabler, p 111–120

  23. Xia Y and Fan J. Efficient attack strategy to communication networks with partial degree information. 2011 I.E. Int Symp, 2011, pp.1091–1095

  24. Yu X, Wei X, Lin X (2010) Algorithms of BBS opinion leader mining based on sentiment analysis. Web Information Systems and Mining. Springer Berlin Heidelberg, Heidelberg, pp 360–369

    Google Scholar 

  25. Zhai Z, Xu H, Jia P (2008) Identifying opinion leaders in BBS. Web Intelligence Intell Agent Technol:398–401

Download references

Acknowledgements

Many thanks are due to Neal Gilmore and Karen Gilmore for their assistance in language revision. Also, thanks to the editor and all anonymous reviewers for their constructive comments.

Funding

This work is supported by the Humanity and Social Science Youth foundation of Ministry of Education of China (grant no. 15YJC860001). This research is also supported by the Social Science Foundation of Qingdao, China (grant no. QDSKL150437), Statistical Science Research Project of China (grant no. 2015LZ20) and China Postdoctoral Science Foundation Funded Project (grant no. 2015 M580571, grant no. 2016 M590612).

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Authors and Affiliations

  1. College of Data Science and Software Engineering, Qingdao University, Qingdao, 266071, People’s Republic of China

    Gengxin Sun & Sheng Bin

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  1. Gengxin Sun
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  2. Sheng Bin
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Correspondence to Gengxin Sun.

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Sun, G., Bin, S. A new opinion leaders detecting algorithm in multi-relationship online social networks. Multimed Tools Appl 77, 4295–4307 (2018). https://doi.org/10.1007/s11042-017-4766-y

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  • DOI: https://doi.org/10.1007/s11042-017-4766-y

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