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Networks and emotion-driven user communities at popular blogs

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  • M. Mitrović
  • G. Paltoglou
  • B. Tadić

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. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Suggested Citation

  • M. Mitrović & G. Paltoglou & B. Tadić, 2010. "Networks and emotion-driven user communities at popular blogs," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 597-609, October.
    • Handle: RePEc:spr:eurphb:v:77:y:2010:i:4:p:597-609
    DOI: 10.1140/epjb/e2010-00279-x
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    References listed on IDEAS

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    1. Yukie Sano & Misako Takayasu, 2010. "Macroscopic and microscopic statistical properties observed in blog entries," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(2), pages 221-230, December.
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    Cited by:

    1. Mitrović, Marija & Tadić, Bosiljka, 2012. "Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5264-5278.
    2. Sanda Martinčić-Ipšić & Edvin Močibob & Matjaž Perc, 2017. "Link prediction on Twitter," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-21, July.
    3. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 0. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 0, pages 1-19.
    4. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 2017. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 17(1), pages 101-119, March.
    5. Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    6. Anna Chmiel & Julian Sienkiewicz & Mike Thelwall & Georgios Paltoglou & Kevan Buckley & Arvid Kappas & Janusz A Hołyst, 2011. "Collective Emotions Online and Their Influence on Community Life," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    7. Chmiel, Anna & Sobkowicz, Pawel & Sienkiewicz, Julian & Paltoglou, Georgios & Buckley, Kevan & Thelwall, Mike & Hołyst, Janusz A., 2011. "Negative emotions boost user activity at BBC forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2936-2944.

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