Modeling Social Influence in Mobile Messaging Apps

  • Songmei YuEmail author
  • Sofya Poger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11512)


Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. With the exponential growth of online social network services especially mobile messaging apps, users around the world are logging in to messaging apps to not only chat with friends but also to connect with brands, browse merchandise, and watch content. Mobile chat apps boast a number of distinct characteristics that make their audiences particularly appealing to businesses and marketers, including their size, retention and usage rates, and user demographics. The combined user base of the top four chat apps is larger than the combined user base of the top four social networks. Therefore, it makes great sense to analyze user behavior and social influence in mobile messaging apps. In this paper, we focus on computational aspects of measuring social influence of groups formed in mobile messaging apps. We describe the special features of mobile messaging apps and present challenges. We address the challenges by proposing a temporal weighted data model to measure the group influence in messaging apps by considering their special features, with implementation and evaluation in the end.


Social influence Mobile messaging apps Influence modeling 


  1. 1.
    Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceeding of the 14th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 7–15 (2008)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Buckley, C., Voorhees, E.M.: Retrieval evaluation with incomplete information. In: SIGIR 2004, pp. 25–32 (2004)Google Scholar
  5. 5.
    Chu, C.-T., et al.: Map-reduce for machine learning on multicore. In: Proceedings of the 18th Neural Information Processing Systems (NIPS 2006) (2006)Google Scholar
  6. 6.
    Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 160–168 (2008)Google Scholar
  7. 7.
    Craswell, N., de Vries, A.P., Soboroff, I.: Overview of the TREC-2005 enterprise track. In: TREC 2005 Conference Notebook, pp. 199–205 (2005)Google Scholar
  8. 8.
    Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceeding of the 16th International Conference on World Wide Web (WWW 2007) (2007)Google Scholar
  9. 9.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation (OSDI 2004), p. 10 (2004)Google Scholar
  10. 10.
    Dourisboure, Y., Geraci, F., Pellegrini, M.: Extraction and classification of dense communities in the web. In: WWW 2007, pp. 461–470 (2007)Google Scholar
  11. 11.
    Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), pp. 150–160 (2000)Google Scholar
  12. 12.
    Frey, B.J., Dueck, D.: Mixture modeling by affinity propagation. In: Proceedings of the 18th Neural Information Processing Systems (NIPS 2006), pp. 379–386 (2006)Google Scholar
  13. 13.
    Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: Proceedings of the 17th International World Wide Web Conference (WWW 2008), pp. 101–110 (2008)Google Scholar
  14. 14.
    Papadimitriou, S., Disco, S.J.: Distributed co-clustering with map-reduce. In: Proceedings of IEEE International Conference on Data Mining (ICDM 2008) (2008)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Felician UniversityLodiUSA

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