Emergence and Structure of Cybercommunities

Part of the Springer Optimization and Its Applications book series (SOIA, volume 57)


We study topology of bipartite networks representing high-resolution data of the online communications of users on Blogs and similar Web portals. User communities occurring in connection with certain popular posts, movies etc., are detected by spectral analysis of these networks. Due to indirect nature of the online interactions between users, further information about the structure of the communities is inferred by text analysis of the related comments. We employ the emotion classifier based on machine-learning methods and trained for this type of data, to determine the emotional contents of text of each post and comment within a given community. Combined with the network theory, in this way we are able to unravel the role of emotion expressed in the text for the patterns of user behavior, which leads to the emergence of collective states with the appearance of communities, and their internal structure and evolution. All data are fully anonymized. No information about user IDs are given.



The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7-ICT-2008-3 under grant agreement n o 231323 (CYBEREMOTIONS). B.T. also thanks support from the national program P1-0044 (Slovenia). Special thanks to G. Paltoglou for providing user-friendly emotion classifier for Blogs.


  1. 1.
    Adrianson, L.: Gender and computer-mediated communication: group processes in problem solving. Computers in Human Behavior 17(1), 71–94 (2001). DOI DOI:10.1016/S0747- 5632(00)00033-9. URL
  2. 2.
    Berners-Lee, T., Hall, W., Hendler, J., Shadbolt, N., Weitzner, J.: Creating a Science of the Web. Science 313, 769–771 (2006)CrossRefGoogle Scholar
  3. 3.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics Reports 424, 175–308 (2006). DOI 10.1016/j. physrep.2005.10.009MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bollobas, B.: Modern Graph Theory. Springer-Verlafg, Berlin Heidelberg (1998)MATHCrossRefGoogle Scholar
  5. 5.
    Brumfiel, G.: Breaking the convention? Nature 459, 1050–1051 (2009)CrossRefGoogle Scholar
  6. 6.
    Cattuto, C., Barrat, A., Baldassarri, A., Schehr, G., Loreto, V.: Collective dynamics of social annotation. PNAS 106, 10511–10515 (2009)Google Scholar
  7. 7.
    Cho, A.: Ourselves and Our Interactions: The Ultimate Physics Problem? Science 325 (2009)Google Scholar
  8. 8.
    Crane, R., Schweitzer, F., Sornette, D.: Power Law Signature of Media Exposure in Human Response Waiting Time Distributions. Phys. Rev. E 81(5), 056101 (2010)CrossRefGoogle Scholar
  9. 9.
    Dodds, P., Danforth, C.: Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents. Journal of Happiness Studies 11(4), 441–456 (2009)CrossRefGoogle Scholar
  10. 10.
    Donato, D., Leonardi, S., Millozzi, S., Tsaparas, P.: Mining the inner structure of the Web graph. Journal of Physics A: Mathematical and Theoretical. 41, 224,017 (2008)Google Scholar
  11. 11.
    Donetti, L., Muñoz, M.A.: Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics: Theory and Experiment 10 (2004)Google Scholar
  12. 12.
    Evans, T.S., Lambiotte, R.: Line Graphs, Link Partitions and Overlapping Communities. Phys. Rev. E 80(1), 016105 (2009). DOI 10.1103/PhysRevE.80.016105Google Scholar
  13. 13.
    Fortunato, S.: Community detection in graphs. Physics Reports 486(3-5), 75–174 (2010). DOI DOI:10.1016/j.physrep.2009.11.002MathSciNetCrossRefGoogle Scholar
  14. 14.
    Fowler, J.H., Christakis, N.A.: Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the framingham heart study. British Medicine Journal 337, a2338 (2008). DOI 10.1136/bmj.a2338. URL
  15. 15.
    Fu, F., Liu, L., Yang, K., Wang, L.: The structure of self-organized blogosphere. arXiv:0607361 (2006)Google Scholar
  16. 16.
    Grujić, J.: Movies recommendation networks as bipartite graphs. Lecture Notes in Computer Science 5102 (2008)Google Scholar
  17. 17.
    Grujić, J., Mitrović, M., Tadić, B.: Mixing patterns and communities on bipartite graphs on web-based social interactions. In: Digital Signal Processing, 2009 16th International Conference on, pp. 1–8 (2009). DOI 10.1109/ICDSP.2009.5201238Google Scholar
  18. 18.
    Kleinberg, J.: The Convergence of Social and technological Networks. Communications of the ACM 51, 66–72 (2008)CrossRefGoogle Scholar
  19. 19.
    Kujawski, B., Holyst, J., Rodgers, G.: Growing trees in internet news groups and forums. Phys. Rev. E 76, 036103– + (2007)Google Scholar
  20. 20.
    Lambiotte, R., Ausloos, M.: Uncovering collective listening habits and music genres in bipartite networks. Physical Review E 72, 066107 (2005). URL doi:  10.1103/PhysRevE.72.066107
  21. 21.
    Lambiotte, R., Ausloos, M.: On the genre-fication of music: a percolation approach (long version). The European Physical Journal B 50, 183 (2006)CrossRefGoogle Scholar
  22. 22.
    Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Cascading behavior in large blog graphs (2007). URL
  23. 23.
    Liu, L., Fu, F., Wang, L.: Information propagation and self-organized consensus in the blogosphere: a game theoretical approach (2007)
  24. 24.
    Lorenz, J.: Universality of movie rating distributions. Eur. Phys. Journal B 71(2), 251–258 (2008). DOI 10.1140/epjb/e2009-00283-3CrossRefGoogle Scholar
  25. 25.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, 1 edn. Cambridge University Press (2008). URL
  26. 26.
    Manning, C.D., Schüetze, H.: Foundations of Statistical Natural Language Processing, 1 edn. The MIT Press (1999). URL
  27. 27.
    Mitrović, M., Paltoglou, G., Tadić, B.: Networks and emotion-driven user communities on popular blogs. Eur. Phys. Journal B 77, 597–609 (2010)CrossRefGoogle Scholar
  28. 28.
    Mitrović, M., Paltoglou, G., Tadić, B.: Quantitative analysis of bloggers colelctive behavior powered by emotions. Journal of Statistical Mechanics: Theory and Experiment 2011(02), P02005 (2011)CrossRefGoogle Scholar
  29. 29.
    Mitrović, M., Tadić, B.: Search of Weighted Subgraphs on Complex Networks with Maximum Likelihood Methods. Lecture Notes in Computer Science 5102, 551–558 (2008)CrossRefGoogle Scholar
  30. 30.
    Mitrović, M., Tadić, B.: Bloggers behavior and emergent communities in blog space. Eur. Phys. Journal B 73(2), 293–301 (2009). DOI 10.1140/epjb/e2009-00431-9CrossRefGoogle Scholar
  31. 31.
    Mitrović, M., Tadić, B.: Spectral and dynamical properties in classes of sparse networks with mesoscopic inhomogeneities. Phys. Rev. E 80(2), 026123– + (2009). DOI 10.1103/PhysRevE.80.026123Google Scholar
  32. 32.
    Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67, 026126 (2003). URL
  33. 33.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Now Publishers Inc (2008). URL
  34. 34.
    Panzarasa, P., Opsahl, T., Carley, K.: Patterns and dynamics of users’ behavior and interactions: network analysis of and online community. Journal of the American Society for Information Science and Technology 60, 911–932 (2009)CrossRefGoogle Scholar
  35. 35.
    Sano, Y., Takayasu, M.: Macroscopic and microscopic statistical properties observed in blog entries. Journal of Economic Interaction and coordination 5(2), 10 (2009)Google Scholar
  36. 36.
    Tadić, B.: Dynamics of directed graphs: the world-wide Web. Physica A 293, 273–284 (2001)MATHCrossRefGoogle Scholar
  37. 37.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment Strenght Detection in Short Informal Text. Journal of the American Society for Information Science and Technology 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  38. 38.
    Thelwall, M., Byrne, A., Goody, M.: Which types of news story attract bloggers? Informetionresearch 12, 327 + –22 (2007)Google Scholar
  39. 39.
    Tsallis, C., Bukman, D.J.: Anomalous diffusion in the presence of external forces: Exact time-dependent solutions and their thermostatistical basis. Phys. Rev. E 54(3), R2197–R2200 (1996). DOI 10.1103/PhysRevE.54.R2197CrossRefGoogle Scholar
  40. 40.
    Zhou, T., Jiang, L., Su, R., Zhang, Y.: Effect of initial configuration on network-based recommendation. Eyrophys. Lett. 81, 58004 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Theoretical PhysicsJožef Stefan InstituteLjubljanaSlovenia

Personalised recommendations