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Discovering and tracking query oriented active online social groups in dynamic information network

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Abstract

The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies.

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Notes

  1. http://www.facebook.com

  2. http://www.twitter.com

  3. https://plus.google.com

  4. https://github.com/splorp/wordpress-comment-blacklist/blob/master/blacklist.txt

  5. https://gate.ac.uk/wiki/twitie.html

  6. https://www.noslang.com/

  7. https://www.webopedia.com/quick_ref/Twitter_Dictionary_Guide.asp

  8. https://github.com/EFord36/normalise

  9. A political protest movement comprising autonomous groups affiliated by their militant opposition to fascism and other forms of extreme right-wing ideology.

  10. A man who is readily upset or offended by progressive attitudes that conflict with his more conventional or conservative views.

  11. http://aspell.net/

  12. https://lucene.apache.org

  13. We use the Greek lowercase letter kappa (κ) to refer the number of clusters produced by k-means algorithm, and the English lowercase letter k to refer the k-core of the social graph G.

  14. http://snap.stanford.edu/data/twitter7.html

  15. We do not include IGM-Hashtag in efficiency results as the computation times for both IGM-Topic and IGM-Hashtag are same. So IGM-Topic is denoted as IGM in efficiency results. We reported the effectiveness comparison between IGM-Topic and IGM-Hashtag in Section 7.3

  16. An adjacency matrix of a network is represented by A, where Auv = 0 means there’s no edge (no interaction) between nodes u and v and Auv= 1 means there is an edge between the two.

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Acknowledgments

This work is supported by the ARC Discovery Projects DP170104747, DP160102412 and DP160102114.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia

    Md Musfique Anwar & Chengfei Liu

  2. Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia

    Jianxin Li

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  1. Md Musfique Anwar
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  2. Chengfei Liu
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Correspondence to Md Musfique Anwar.

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This article belongs to the Topical Collection: Special Issue on Social Computing and Big Data Applications

Guest Editors: Xiaoming Fu, Hong Huang, Gareth Tyson, Lu Zheng, and Gang Wang

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Anwar, M.M., Liu, C. & Li, J. Discovering and tracking query oriented active online social groups in dynamic information network. World Wide Web 22, 1819–1854 (2019). https://doi.org/10.1007/s11280-018-0627-5

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