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Modeling Topical Information Diffusion over Microblog Networks

  • Kuntal Dey
  • Hemank Lamba
  • Seema Nagar
  • Shubham Gupta
  • Saroj Kaushik
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Traditional information spread and activation models on social networks, fail to take user interests towards specific content (topics) into account. To this, we propose a predictive topical spreading activation model (TopSPA). Following cues from the well-known spreading activation (SPA) model, we design the TopSPA algorithm to include the affinity of users to given topics. TopSPA utilizes the social connection structures of users, along with their topic affinities, to model the information flow. We use topic-based skew in energy seeding and energy propagation resistance in the network to form our overall information diffusion model. We empirically validate our model on multiple social event datasets on Twitter, predicting information diffusion over the social graph with a high accuracy.

References

  1. 1.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM, pp. 65–74. ACM (2011)Google Scholar
  2. 2.
    Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. In: ICDM (2012)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Google Scholar
  4. 4.
    Bourigault, S., Lagnier, C., Lamprier, S., Denoyer, L., Gallinari, P.: Learning social network embeddings for predicting information diffusion. In: WSDM, pp. 393–402. ACM (2014)Google Scholar
  5. 5.
    Bourigault, S., Lamprier, S., Gallinari, P.: Representation learning for information diffusion through social networks: an embedded cascade model. In: WSDM, pp. 573–582. ACM (2016)Google Scholar
  6. 6.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: the million follower fallacy. ICWSM 10, 10–17 (2010)Google Scholar
  7. 7.
    Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S.,Nanavati, A.A., Joshi, A.: Social ties and their relevance to churn in mobiletelecom networks. In: EDBT, pp. 668–677. ACM (2008)Google Scholar
  8. 8.
    Fei, H., Jiang, R., Yang, Y., Luo, B., Huan, J.: Content based social behavior prediction: a multi-task learning approach. In: CIKM, pp. 995–1000 (2011)Google Scholar
  9. 9.
    Grabowicz, P.A., Ganguly, N., Gummadi, K.P.: Distinguishing between topical and non-topical information diffusion mechanisms in social media. In: ICWSM, pp. 151–160 (2016)Google Scholar
  10. 10.
    Halberstam, Y., Knight, B.: Homophily, group size, and the diffusion of political information in social networks: Evidence from twitter. J. Public Econ. 143, 73–88 (2016)Google Scholar
  11. 11.
    Jiang, C., Chen, Y., Liu, K.R.: Evolutionary dynamics of information diffusion over social networks. IEEE Trans. Signal Process. 62(17), 4573–4586 (2014)Google Scholar
  12. 12.
    Kuo, T.T., Hung, S.C., Lin, W.S., Peng, N., Lin, S.D., Lin, W.F.: Exploiting latent information to predict diffusions of novel topics on social networks. In: ACL (2012)Google Scholar
  13. 13.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social media or a news media. In: Proceedings of the WWW (2010)Google Scholar
  14. 14.
    Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM (2010)Google Scholar
  15. 15.
    McCallum, A.K.: Mallet: a machine learning for language toolkit. http://mallet.cs.umass.edu (2002)
  16. 16.
    Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: SIGKDD, pp. 33–41 (2012)Google Scholar
  17. 17.
    Nagar, S., Narang, K., Mehta, S., Subramaniam, L., Dey, K.: Topical discussions on unstructured microblogs: analysis from a geographical perspective. In: WISE (2013)Google Scholar
  18. 18.
    Nagar, S., Seth, A., Joshi, A.: Characterization of social media response to natural disasters. In: Proceedings of the WWW (2012)Google Scholar
  19. 19.
    Narang, K., Nagar, S., Mehta, S., Subramaniam, L.V., Dey, K.: Discovery and analysis of evolving topical social discussions on unstructured microblogs. In: ECIR (2013)Google Scholar
  20. 20.
    Nematzadeh, A., Ferrara, E., Flammini, A., Ahn, Y.Y.: Optimal network modularity for information diffusion. Phys. Rev. Lett. 113(8), 088,701 (2014)Google Scholar
  21. 21.
    Nguyen, J.H., Hu, B., Gnnemann, S., Ester, M.: Finding contexts of social influence in online social networks. In: The 7th SNA-KDD Workshop—SNA-KDD’13 (2013)Google Scholar
  22. 22.
    Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: WWW, pp. 695–704 (2011)Google Scholar
  23. 23.
    Tang, J., Wu, S., Sun, J.: Confluence: Conformity influence in large social networks. In: KDD (2013)Google Scholar
  24. 24.
    Wu, H., Bu, J., Chen, C., Wang, C., Qiu, G., Zhang, L., Shen, J.: Modeling dynamic multi-topic discussions in online forums. In: AAAI Conference on Artificial Intelligence (2010)Google Scholar
  25. 25.
    Yang, J., Leskovec, J.: Modeling information diffusion in implicit networks. In: ICDM, pp. 599–608 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kuntal Dey
    • 1
  • Hemank Lamba
    • 2
  • Seema Nagar
    • 1
  • Shubham Gupta
    • 3
  • Saroj Kaushik
    • 4
  1. 1.IBM ResearchDelhiIndia
  2. 2.Carnegie Mellon UniversityPittsburghUSA
  3. 3.IIITDelhiIndia
  4. 4.Indian Institute of TechnologyDelhiIndia

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