Advertisement

Capturing the Dynamics of Hashtag-Communities

  • Philipp Lorenz
  • Frederik Wolf
  • Jonas Braun
  • Nataša Djurdjevac Conrad
  • Philipp Hövel
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

Online media have a huge impact on public opinion, economics and politics. Every day, billions of posts are created and comments are written, covering a broad range of topics. Especially the format of hashtags, as a discrete and condensed version of online content, is a promising entry point for in-depth investigations. In this work we provide a set of methods from static community detection as well as novel approaches for tracing the dynamics of topics in time dependent data. We build temporal and weighted co-occurence networks from hashtags. On static snapshots we infer the community structure using customized methods. We solve the resulting bipartite matching problem between adjacent timesteps, by taking into account higher order memory. This results in a matching that is robust to temporal fluctuations and instabilities of the static community detection. The proposed methodology, tailored to uncover the detailed dynamics of groups of hashtags is adjustable and by that broadly applicable to reveal the temporal behavior of various online topics.

Notes

Acknowledgements

P. Lorenz and P. Hövel acknowledge the support by Deutsche Forschungsgemeinschaft (DFG) in the framework of the Collaborative Research Center 910. We thank A. Koher, V. Belik, J. Siebert, and C. Bauer for fruitful discussions.

References

  1. 1.
    Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)CrossRefGoogle Scholar
  2. 2.
    Asur, S., Parthasarathy, S., Ucar, D.: An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans. Knowl. Discov. Data (TKDD) 3(4), 16 (2009)Google Scholar
  3. 3.
    Au Yeung, C.m., Gibbins, N., Shadbolt, N.: Contextualising tags in collaborative tagging systems. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT ’09, pp. 251–260. ACM, New York, NY, USA.  https://doi.org/10.1145/1557914.1557958. (2009)
  4. 4.
    Aynaud, T., Fleury, E., Guillaume, J.L., Wang, Q.: Communities in evolving networks: definitions, detection, and analysis techniques. In: Dynamics on and of Complex Networks, Vol. 2, pp. 159–200. Springer (2013)Google Scholar
  5. 5.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks (2009)Google Scholar
  6. 6.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 2008(10), P10008 (2008)Google Scholar
  7. 7.
    Cancho, R.F.i., Solé, R.V.: The small world of human language. Proc. R. Soc. Lond. B: Biol. Sci. 268(1482), 2261–2265 (2001).  https://doi.org/10.1098/rspb.2001.1800
  8. 8.
    Cazabet, R., Amblard, F., Hanachi, C.: Detection of overlapping communities in dynamical social networks. In: 2010 IEEE Second International Conference on Social Computing, pp. 309–314.  https://doi.org/10.1109/socialcom.2010.51. (2010)
  9. 9.
    Cazabet, R., Takeda, H., Hamasaki, M., Amblard, F.: Using dynamic community detection to identify trends in user-generated content. Soc. Netw. Anal. Min. 2(4), 361–371 (2012).  https://doi.org/10.1007/s13278-012-0074-8 CrossRefGoogle Scholar
  10. 10.
    Chakraborty, A., Ghosh, S., Ganguly, N.: Detecting overlapping communities in folksonomies. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, HT ’12, pp. 213–218. ACM, New York, NY, USA.  https://doi.org/10.1145/2309996.2310032 (2012)
  11. 11.
    Djurdjevac, N., Bruckner, S., Conrad, T.O., Schütte, C.: Random walks on complex modular networks12. JNAIAM 6(1–2), 29–50 (2011)MathSciNetMATHGoogle Scholar
  12. 12.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities in dynamic social networks. In: 2010 International Conference on Advances in Social Networks Analysis and Mining, pp. 176–183.  https://doi.org/10.1109/asonam.2010.17. (2010)
  14. 14.
    Hopcroft, J., K., O., Kulis, B., Selman, B.: Tracking evolving communities in large linked networks. Proc. Natl. Acad. Scie. 101(suppl 1), 5249–5253 (2004)Google Scholar
  15. 15.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Quart. 2(1–2), 83–97 (1955)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Metzner, P., Schütte, C., Vanden-Eijnden, E.: Transition path theory for markov jump processes. Multiscale Model. Simul. 7(3), 1192–1219 (2009).  https://doi.org/10.1137/070699500 MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577 (2006)CrossRefGoogle Scholar
  18. 18.
    Palla, G., Barabasi, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446, 664 (2007)CrossRefGoogle Scholar
  19. 19.
    Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)CrossRefGoogle Scholar
  20. 20.
    Papadopoulos, S., Kompatsiaris, Y., Vakali, A.: A graph-based clustering scheme for identifying related tags in folksonomies. In: Proceedings of the 12th International Conference on Data Warehousing and Knowledge Discovery, DaWaK’10, pp. 65–76. Springer, Berlin, (2010)Google Scholar
  21. 21.
    Peixoto, T.P.: Hierarchical block structures and high-resolution model selection in large networks. Phys. Rev. X 4, 011047 (2014).  https://doi.org/10.1103/physrevx.4.011047
  22. 22.
    Rosvall, M., Bergstrom, C.T.: Mapping change in large networks. PloS one 5(1), e8694 (2010)CrossRefGoogle Scholar
  23. 23.
    Rosvall, M., Esquivel, A.V., Lancichinetti, A., West, J.D., Lambiotte, R.: Memory in network flows and its effects on spreading dynamics and community detection. Nat. Commun. 5, 4630 (2014)CrossRefGoogle Scholar
  24. 24.
    Sarich, M., Djurdjevac, N., Bruckner, S., Conrad, T.O., Schütte, C.: Modularity revisited: A novel dynamics-based concept for decomposing complex networks. J. Comput. Dyn. 1(1), 191–212 (2014)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Sekara, V., Stopczynski, A., Lehmann, S.: Fundamental structures of dynamic social networks. Proc. Natl. Acad. Sci. USA 113(36), 9977–9982 (2016).  https://doi.org/10.1073/pnas.1602803113 CrossRefGoogle Scholar
  26. 26.
    Tantipathananandh, C., Berger-Wolf, T., Kempe, D.: A framework for community identification in dynamic social networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’07, pp. 717–726. ACM, New York, NY, USA.  https://doi.org/10.1145/1281192.1281269. (2007)

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Philipp Lorenz
    • 1
  • Frederik Wolf
    • 1
  • Jonas Braun
    • 2
  • Nataša Djurdjevac Conrad
    • 3
  • Philipp Hövel
    • 1
  1. 1.Institute of Theoretical PhysicsTechnical University BerlinBerlinGermany
  2. 2.Department of PhysicsHumboldt-Universität zu BerlinBerlinGermany
  3. 3.Zuse Institute Berlin (ZIB)BerlinGermany

Personalised recommendations