Abstract
In microblogosphere, some microbloggers are not only active but also influential, called them key microbloggers, who enable information diffuse faster, wider and deeper. The knowledge of key microbloggers is crucial for developing efficient methods to either hinder the rumor spread or promote useful information dissemination. In this paper, we discuss how to evaluate a microblogger’s influence, investigate microblogging-specific features that constitute a microblogger’s active index, and present a model attempting to quantify key microbloggers. We conduct experiments with data, which was crawled from Sina Weibo, and evaluate ranking accuracy of the proposed model. Experimental results attest that the proposed method is able to identify key microbloggers effectively in the microblogging behavior.
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Aral, S., Walker, D.: Identifying influential and susceptible members of social networks. Science 337, 337–341 (2012)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Saito, K., Kimura, M., Ohara, K., Motoda, H.: Efficient discovery of influential nodes for SIS models in social networks. Knowl. Inf. Syst. 30, 613–635 (2012)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)
Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039–1048. ACM (2010)
Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6, 888–893 (2010)
Subbian, K., Melville, P.: Supervised rank aggregation for predicting influencers in twitter. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust, and 2011 IEEE Third International Conference on Social Computing, pp. 661–665. IEEE (2011)
Zhao, L., Zeng, Y., Zhong, N.: A weighted multi-factor algorithm for microblog search. In: Zhong, N., Callaghan, V., Ghorbani, A.A., Hu, B. (eds.) AMT 2011. LNCS, vol. 6890, pp. 153–161. Springer, Heidelberg (2011)
Nagmoti, R., Teredesai, A., De Cock, M.: Ranking approaches for microblog search. In: Proceedings 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), pp. 153–157, (2010)
Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.-Y.: An empirical study on learning to rank of tweets. In: Proceedings of the 23rd International Conference on Computational Linguistics, Association for Computational Linguistics, Beijing, China, pp. 295–303 (2010)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: the million follower fallacy. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, pp. 10–17 (2010)
Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, ACM, New York, New York, USA, pp. 261–270 (2010)
Lü, L., Zhang, Y.C., Yeung, C.H., Zhou, T.: Leaders in social networks, the delicious case. PLoS ONE 6, e21202 (2011)
Chow, W.S., Chan, L.S.: Social network, social trust and shared goals in organizational knowledge sharing. Inf. Manage. 45, 458–465 (2008)
McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, Citeseer, pp. 41–48 (1998)
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM 2010
Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1, 83–98 (2008)
Kendall, M.G.: A new measure of rank correlation. Biometrika 30, 81–93 (1938)
Acknowledgements
This work was sponsored by the National Key Technology R&D Program (Grant No. 2012BAH18B05). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Huang, L., Wang, W., Chen, X., Chen, J. (2016). A Heuristic Method of Identifying Key Microbloggers. In: Chang, C., Chiari, L., Cao, Y., Jin, H., Mokhtari, M., Aloulou, H. (eds) Inclusive Smart Cities and Digital Health. ICOST 2016. Lecture Notes in Computer Science(), vol 9677. Springer, Cham. https://doi.org/10.1007/978-3-319-39601-9_37
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DOI: https://doi.org/10.1007/978-3-319-39601-9_37
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