Topology of Thematic Communities in Online Social Networks: A Comparative Study

  • Valentina Guleva
  • Danila Vaganov
  • Daniil Voloshin
  • Klavdia Bochenina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10860)

Abstract

The network structure of communities in social media significantly affects diffusion processes which implement positive or negative information influence on social media users. Some of the thematic communities in online social networks may provide illegal services or information in them may cause undesired psychological effects; moreover, the topology of such communities and behavior of their members are influenced by a thematic. Nevertheless, recent research does not contain enough detail about the particularities of thematic communities formation, or about the topological properties of underlying friendship networks. To address this gap, in this study we analyze structure of communities of different types, namely, carders, commercial sex workers, substance sellers and users, people with radical political views, and compare them to the ‘normal’ communities (without a single narrow focus). We discovered that in contrast to ordinary communities which have positive assortativity (as expected for social networks), specific thematical communities are significantly disassortative. Types of anomalous communities also differ not only in content but in structure. The most specific are the communities of radicalized individuals: it was shown that they have the highest connectivity and the larger part of nodes within a friendship graph.

Keywords

Network topology Data analysis Online social media Normal communities Anomalous communities Subscribers friendship networks 

Notes

Acknowledgements

This research was financially supported by Ministry of Education and Science of the Russian Federation, Agreement #14.578.21.0196 (03.10.2016). Unique Identification RFMEFI57816X0196.

References

  1. 1.
    Hu, H.B., Wang, X.F.: Disassortative mixing in online social networks. EPL (Europhys. Lett.) 86(1) (2009). Article no. 18003CrossRefGoogle Scholar
  2. 2.
    Rowe, M., Saif, H.: Mining pro-ISIS radicalisation signals from social media users. In: ICWSM, pp. 329–338 (2016)Google Scholar
  3. 3.
    Lau, R.Y., Xia, Y., Ye, Y.: A probabilistic generative model for mining cybercriminal networks from online social media. IEEE Comput. Intell. Mag. 9(1), 31–43 (2014)CrossRefGoogle Scholar
  4. 4.
    Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Discov. 29(3), 626–688 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Savage, D., Zhang, X., Yu, X., Chou, P., Wang, Q.: Anomaly detection in online social networks. Soc. Netw. 39, 62–70 (2014)CrossRefGoogle Scholar
  6. 6.
    Elsharkawy, S., Hassan, G., Nabhan, T., Roushdy, M.: Effectiveness of the k- core nodes as seeds for influence maximisation in dynamic cascades. Int. J. Comput. 2 (2017)Google Scholar
  7. 7.
    Quax, R., Apolloni, A., Sloot, P.M.: The diminishing role of hubs in dynamical processes on complex networks. J. R. Soc. Interface 10(88) (2013). Article no. 20130568CrossRefGoogle Scholar
  8. 8.
    Pei, S., Muchnik, L., Andrade Jr., J.S., Zheng, Z., Makse, H.A.: Searching for superspreaders of information in real-world social media. Sci. Rep. 4, (2014). Article no. 5547Google Scholar
  9. 9.
    Liu, Y., Jin, X., Shen, H., Cheng, X.: Do rumors diffuse differently from non-rumors? A systematically empirical analysis in sina weibo for rumor identification. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 407–420. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57454-7_32CrossRefGoogle Scholar
  10. 10.
    Ratkiewicz, J., Conover, M., Meiss, M.R., Gonçcalves, B., Flammini, A., Menczer, F.: Detecting and tracking political abuse in social media. ICWSM 11, 297–304 (2011)Google Scholar
  11. 11.
    Varol, O., Ferrara, E., Menczer, F., Flammini, A.: Early detection of promoted campaigns on social media. EPJ Data Sci. 6(1), 13 (2017)CrossRefGoogle Scholar
  12. 12.
    Bindu, P., Mishra, R., Thilagam, P.S.: Discovering spammer communities in twitter. J. Intell. Inf. Syst. 1–25 (2018)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Valentina Guleva
    • 1
  • Danila Vaganov
    • 1
  • Daniil Voloshin
    • 1
  • Klavdia Bochenina
    • 1
  1. 1.ITMO UniversitySaint PetersburgRussia

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