The European Physical Journal B

, Volume 77, Issue 4, pp 597–609

Networks and emotion-driven user communities at popular blogs

Article

Abstract.

Online communications at web portals represents technology-mediated user interactions, leading to massive data and potentially new techno-social phenomena not seen in real social mixing. Apart from being dynamically driven, the user interactions via posts is indirect, suggesting the importance of the contents of the posted material. We present a systematic way to study Blog data by combined approaches of physics of complex networks and computer science methods of text analysis. We are mapping the Blog data onto a bipartite network where users and posts with comments are two natural partitions. With the machine learning methods we classify the texts of posts and comments for their emotional contents as positive or negative, or otherwise objective (neutral). Using the spectral methods of weighted bipartite graphs, we identify topological communities featuring the users clustered around certain popular posts, and underly the role of emotional contents in the emergence and evolution of these communities.

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References

  1. 1.
    T. Berners-Lee, W. Hall, J. Hendler, N. Shadbolt, J. Weitzner, Science 313, 769 (2006) CrossRefGoogle Scholar
  2. 2.
    D. Donato, S. Leonardi, S. Millozzi, P. Tsaparas, J. Phys. A 41, 224017 (2008) CrossRefMathSciNetADSGoogle Scholar
  3. 3.
    B. Tadić, Physica A 293, 273 (2001) MATHCrossRefADSGoogle Scholar
  4. 4.
    J. Kleinberg, Communications of the ACM 51, 66 (2008) CrossRefGoogle Scholar
  5. 5.
    A. Cho, Science 325, 406 (2009) CrossRefADSGoogle Scholar
  6. 6.
    L. Adrianson, Computers in Human Behavior 17, 71 (2001) CrossRefGoogle Scholar
  7. 7.
    C. Cattuto, A. Barrat, A. Baldassarri, G. Schehr, V. Loreto, PNAS 106, 10511 (2009) Google Scholar
  8. 8.
    M. Thelwall, A. Byrne, M. Goody, Information Research 12, 327 (2007) Google Scholar
  9. 9.
    G. Brumfiel, Nature 459, 1050 (2009) CrossRefGoogle Scholar
  10. 10.
    T. Zhou, H.A.T. Kiet, B.J. Kim, B.H. Wang, P. Holme, Europhys. Lett. 82, 28002 (2008) CrossRefADSGoogle Scholar
  11. 11.
    D. Derks, A.H. Fischer, A.E.R. Bos, Comput. Hum. Behav. 24, 766 (2008) CrossRefGoogle Scholar
  12. 12.
    M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, A. Kappas, J. Am. Soc. Inf. Sci. Technol. (in press), 2010 Google Scholar
  13. 13.
    J.H. Fowler, N.A. Christakis, British Medicine Journal 337, a2338 (2008) Google Scholar
  14. 14.
    J. Bollen, A. Pepe, H. Mao, arXiv:0911.1583, 2009 Google Scholar
  15. 15.
    F. Fu, L. Liu, K. Yang, L. Wang, The structure of self-organized blogosphere, arXiv:0607361, 2006 Google Scholar
  16. 16.
    L. Liu, F. Fu, L. Wang, Information propagation and collective consensus in blogosphere: a game theoretical approach, in Web 2.0 - Eine empirische Bestandsaufnahme (Vieweg+Teubner Verlag, 2008), pp. 87–104 Google Scholar
  17. 17.
    J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, M. Hurst, Cascading Behavior in Large Blog Graphs, in Proceedings of 7th SIAM International Conference on Data Mining (SDM) (2007), p. 2940613 Google Scholar
  18. 18.
    Y. Sano, M. Takayasu, Macroscopic and microscopic statistical properties observed in blog entries, J. Economic Interaction and Coordination (2010) Google Scholar
  19. 19.
    M. Mitrović, B. Tadić, Eur. Phys. J. B 73, 293 (2009) CrossRefADSGoogle Scholar
  20. 20.
    S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.U. Hwang, Phys. Rep. 424, 175 (2006) CrossRefMathSciNetADSGoogle Scholar
  21. 21.
    B. Tadić, G.J. Rodgers, S. Thurner, Int. J. Bifurc. Chaos 17, 2363 (2007) MATHCrossRefGoogle Scholar
  22. 22.
    J. Grujić, Lect. Notes Comput. Sci. 5102, 576 (2008) CrossRefGoogle Scholar
  23. 23.
    J. Lorenz, Eur. Phys. J. B 71, 251 (2008) CrossRefMathSciNetADSGoogle Scholar
  24. 24.
    R. Lambiotte, M. Ausloos, Phys. Rev. E 72, 066107 (2005) CrossRefADSGoogle Scholar
  25. 25.
    R. Lambiotte, M. Ausloos, Eur. Phys. J. B 50, 183 (2006) CrossRefADSGoogle Scholar
  26. 26.
    R. Crane, F. Schweitzer, D. Sornette, Phys. Rev. E 81, 056101 (2010) CrossRefADSGoogle Scholar
  27. 27.
    P. Panzarasa, T. Opsahl, K. Carley, J. Am. Soc. Inf. Sci. Technol. 60, 911 (2009) CrossRefGoogle Scholar
  28. 28.
    J. Grujić, M. Mitrović, B. Tadić, IEEE Xplore, 259 (2009) Google Scholar
  29. 29.
    T. Zhou, L. Jiang, R. Su, Y. Zhang, Europhys. Lett. 81, 58004 (2008) CrossRefADSGoogle Scholar
  30. 30.
    B. Kujawski, J. Holyst, G. Rodgers, Phys. Rev. E 76, 036103 (2007) CrossRefADSGoogle Scholar
  31. 31.
    L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan, Group formation in large social networks: membership, growth, and evolution, in KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (ACM, 2006), pp. 44–54 Google Scholar
  32. 32.
    B. Fortuna, M. Grobelnik, D. Mladenic, Ontogen, http://ontogen.ijs.si/, 2008
  33. 33.
    P. Dodds, C. Danforth, Journal of Happiness Studies, 1389 (2009), ISSN 1389-4978 (Print) 1573-7780 (Online) Google Scholar
  34. 34.
    B. Pang, L. Lee, Opinion Mining and Sentiment Analysis (Now Publishers Inc, 2008) Google Scholar
  35. 35.
    M. Mitrović, B. Tadić, Phys. Rev. E 80, 026123 (2009) CrossRefADSGoogle Scholar
  36. 36.
    V. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre, J. Stat. Mech. 2008, P10008 (2008) CrossRefGoogle Scholar
  37. 37.
    S. Fortunato, Phys. Rep. 486, 75 (2010) CrossRefMathSciNetADSGoogle Scholar
  38. 38.
    B. Bollobas, Modern Graph Theory (Springer, 1998) Google Scholar
  39. 39.
    C.D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, 1st edn. (Cambridge University Press, 2008) Google Scholar
  40. 40.
    C.D. Manning, H. Schüetze, Foundations of Statistical Natural Language Processing, 1st edn. (The MIT Press, 1999) Google Scholar
  41. 41.
    I.H. Witten, T.C. Bell, IEEE Transactions on Information Theory 37, 1085 (1991) CrossRefGoogle Scholar
  42. 42.
    C. Macdonald, I. Ounis, The TREC Blogs06 Collection: Creating and Analysing a Blog Test Collection, Technical report, Department of Computing Science, University of Glasgow, 2006 Google Scholar
  43. 43.
    C. Macdonald, I. Ounis, I. Soboroff, Overview of the TREC-2008 blog track, in The Sixteenth Text REtrieval Conference (TREC 2008) Proceedings (2008) Google Scholar
  44. 44.
    A. Lancichinetti, S. Fortunato, Phys. Rev. E 80, 056117 (2009) CrossRefADSGoogle Scholar
  45. 45.
    M. Mitrović, B. Tadić, Lect. Notes Comput. Sci. 5102, 551 (2008) CrossRefGoogle Scholar
  46. 46.
    M. Rosvall, C. Bergstrom, PNAS 105, 1118 (2008) CrossRefADSGoogle Scholar
  47. 47.
    A.N. Samukhin, S.N. Dorogovtsev, J.F.F. Mendes, Phys. Rev. E 77, 036115 (2008) CrossRefMathSciNetADSGoogle Scholar
  48. 48.
    L. Donetti, M.A. Muñoz, J. Stat. Mech.: Theory and Experiment 10, P10012 (2004) CrossRefADSGoogle Scholar
  49. 49.
    M. Mitrović, G. Paltoglou, B. Tadić, Quantitative analysis of bloggers colelctive behavior powered by emotions, in preparation, 2010 Google Scholar
  50. 50.
    G. Grinstein, R. Linsker, Phys. Rev. E 77, 012101 (2008) CrossRefADSGoogle Scholar
  51. 51.
    M. Mitrović, B. Tadić, Network automaton model of bursting emotional behavior on Blogs, preprint (2010) Google Scholar
  52. 52.
    D. Garcia, F. Schweitzer, Emotions in product reviews – Empirics and models, in preparation, 2010 Google Scholar
  53. 53.
    D. Garcia, F. Schweitzer, Eur. Phys. J. B 77, 533 (2010) CrossRefGoogle Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of theoretical physicsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Statistical Cybernetics Research Group, School of Computing and Information Technology University of WolverhamptonWolverhamptonUK

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