(T-ToCODE): A Framework for Trendy Topic Detection and Community Detection for Information Diffusion in Social Network

  • Reena PagareEmail author
  • Akhil Khare
  • Shankar Chaudhary
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1042)


The increased use of social network generates a huge amount of data. Extracting useful information from this huge data available is the need of today. Study and analysis of this data generated provide insight into the behavior of the customers or users and thus will be beneficial to increase the sales of products or understand customers. To achieve the same, we propose a novel framework which will extract trendy topics, identify communities related to these trendy, topics, and also identify influential or seed nodes in communities. The framework intends to find the list of topics which are popular, second, find trend-driven communities, and from these trend-driven communities find nodes which act as seed nodes and thus dominate the spread of information in the community. Analysis of real-world data is done and results are compared with baseline approaches.


Community detection Information diffusion Topic detection Trend topics Social network 



The authors declare that they have no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards. Informed consent was obtained from all individual participants included in the study.


  1. 1.
    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 (KDD ‘03), pp. 137–146. ACM, New York, NY, USA (2003).
  2. 2.
    Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Deng, X., Graham, F.C. (eds.) Proceedings of the 3rd international conference on Internet and network economics (WINE'07), pp. 306–311. Springer, Berlin, Heidelberg (2007)Google Scholar
  3. 3.
    Bonchi, F.: Influence propagation in social networks: a data mining perspective. In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 2(1) pp. 2–2 (2011)Google Scholar
  4. 4.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, pp. 57–66 2001.
  5. 5.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web (TWEB) 1(1), 1–39 (2007)CrossRefGoogle Scholar
  6. 6.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining—WSDM ’10, p. 241 (2010). Available at:
  7. 7.
    Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probability ies for independent cascade model. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 67–75 (2008)Google Scholar
  8. 8.
    Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘10), 1029–1038. ACM, New York, NY, USA (2010).
  9. 9.
    Liu, L., et al.: Modelling of information diffusion on social networks with applications to WeChat. Physica A Stat. Mech. Appl. 496, 318–329 (2018)CrossRefGoogle Scholar
  10. 10.
    Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: WSDM ’11, pp. 45–54 (2011)Google Scholar
  11. 11.
    Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York (2010)CrossRefGoogle Scholar
  12. 12.
    Kim, Jooho, Hastak, Makarand: Social network analysis: characteristics of online social networks after a disaster. Int. J. Inf. Manag. 38(1), 86–96 (2018)CrossRefGoogle Scholar
  13. 13.
    Tu, H.T., Nguyen, K.P.: Differential information diffusion model in social network. In: Asian Conference on Intelligent Information and Database Systems. Springer, Cham (2018)CrossRefGoogle Scholar
  14. 14.
    Shi, J., et al.: Determinants of users’ information dissemination behavior on social networking sites: an elaboration likelihood model perspective. Internet Res. 28(2), 393–418 (2018)CrossRefGoogle Scholar
  15. 15.
    Hu, W., et al.: Who will share my image?: Predicting the content diffusion path in online social networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM (2018)Google Scholar
  16. 16.
    Yang, Jaewon, Leskovec, Jure: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)CrossRefGoogle Scholar
  17. 17.
    Tu, H.T., Nguyen, K.P.: Competitive information diffusion model in social network with negative information propagation. In: Asian Conference on Intelligent Information and Database Systems. Springer, Cham (2018)CrossRefGoogle Scholar
  18. 18.
    Liu, X., Liu, C.: Information Diffusion and Opinion Leader Mathematical Modeling Based on Microblog. IEEE Access (2018)Google Scholar
  19. 19.
    Beydoun, G., et al.: Disaster management and information systems: insights to emerging challenges. Inf. Syst. Front. 20, 1–4 (2018)CrossRefGoogle Scholar
  20. 20.
    Liang, Y., Kee, K.F.: Developing and validating the ABC framework of information diffusion on social media. New Media Soc. 20(1), 272–292 (2018)CrossRefGoogle Scholar
  21. 21.
    Jiang, C., Chen, Y., Liu, K.J.R.: Evolutionary dynamics of information diffusion over social networks. IEEE Trans. Signal Process. 62(17), 4573–4586 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Romero, Galuba, W., Asur, S., Huberman, B.: Influence and passivity in social media. In: ECML/PKDD ’11, pp. 18–33 (2011)Google Scholar
  23. 23.
    Lim, K.W., Chen, C., Buntine, W.: Twitter-network topic model: a full Bayesian treatment for social network and text modeling (2016). arXiv preprint arXiv:1609.06791
  24. 24.
    Jain, S., Mohan, G., Sinha, A.: Network diffusion for information propagation in online social communities. In: 2017 Tenth International Conference on Contemporary Computing (IC3). IEEE (2017)Google Scholar
  25. 25.
    Stai, E., et al.: Temporal dynamics of information diffusion in twitter: modeling and experimentation. IEEE Trans. Comput. Soc. Syst. 5(1), 256–264 (2018)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Ye, X., et al.: Open source social network simulator focusing on spatial meme diffusion. In: Human Dynamics Research in Smart and Connected Communities. pp. 203–222. Springer, Cham (2018)CrossRefGoogle Scholar
  27. 27.
    Jalayer, M., Azheian, M., Kermani, M.A.M.A.: A hybrid algorithm based on community detection and multi attribute decision making for influence maximization.”. Comput. Ind. Eng. 120, 234–250 (2018)CrossRefGoogle Scholar
  28. 28.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  29. 29.
    AlSumait, L., Barbará, D., Domeniconi, C.: On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In: 2008 Eighth IEEE International Conference on Data Mining, Pisa, pp. 3–12 (2008).
  30. 30.
    Cordeiro, M.: Twitter event detection: Combining wavelet analysis and topic inference summarization. In: Doctoral Symposium on Informatics Engineering, DSIE (2012)Google Scholar
  31. 31.
    Liu, G., Xu, X., Zhu, Y., Li, L.: An improved latent dirichlet allocation model for hot topic extraction. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp. 470–476. Sydney, NSW (2014).
  32. 32.
    Wu, C., Wu, B., Wang, B.: Event evolution model based on random walk model with hot topic extraction. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds.) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science, vol. 10086. Springer, Cham (2016)CrossRefGoogle Scholar
  33. 33.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10, P10008 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.PAHERUdaipurIndia
  2. 2.MVSR COEHyderabadIndia

Personalised recommendations