IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/33-soc-2013.html

Comment-based discussion communities In the Russian livejournal and their topical coherence

Author

Listed:
  • Olessia Y. Koltsova

    (National Research University Higher School of Economics)

  • Sergei N. Koltcov

    (National Research University Higher School of Economics)

  • Sergey I. Nikolenko

    (National Research University Higher School of Economics)

Abstract

We study the structure of online discussions in order to uncover latent communities of socially important debate. Our research reveals that discussion communities defined by mutual commenting in the Russian language blogosphere are centered mainly around blog authors as opinion leaders and, to a lesser extent, around a shared topic or topics. We have derived these conclusions from the dataset of 17386 full text posts written by top 2000 LiveJournal bloggers and over 520,000 comments that result in about 4.5 million edges in the network of co-commenting

Suggested Citation

  • Olessia Y. Koltsova & Sergei N. Koltcov & Sergey I. Nikolenko, 2013. "Comment-based discussion communities In the Russian livejournal and their topical coherence," HSE Working papers WP BRP 33/SOC/2013, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:33/soc/2013
    as

    Download full text from publisher

    File URL: http://www.hse.ru/data/2014/01/13/1340844073/33SOC2013.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zakharov, Pavel, 2007. "Diffusion approach for community discovering within the complex networks: LiveJournal study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 550-560.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    3. Henry Farrell & Daniel Drezner, 2008. "The power and politics of blogs," Public Choice, Springer, vol. 134(1), pages 15-30, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohd-Zaid, Fairul & Kabban, Christine M. Schubert & Deckro, Richard F. & White, Edward D., 2017. "Parameter specification for the degree distribution of simulated Barabási–Albert graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 141-152.
    2. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    3. Zheng, Mingbo & Zhang, Xinyu, 2025. "Digitalization and renewable energy development: Analysis based on cross-country panel data," Energy, Elsevier, vol. 319(C).
    4. Elias Carroni & Paolo Pin & Simone Righi, 2020. "Bring a Friend! Privately or Publicly?," Management Science, INFORMS, vol. 66(5), pages 2269-2290, May.
    5. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    6. Baek, Seung Ki & Kim, Tae Young & Kim, Beom Jun, 2008. "Testing a priority-based queue model with Linux command histories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3660-3668.
    7. Freddy Hern�n Cepeda L�pez, 2008. "La topolog�a de redes como herramienta de Seguimiento en el sistema de Pagos de Alto Valor en Colombia," Borradores de Economia 4676, Banco de la Republica.
    8. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    9. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    10. Chang, Chia-ling & Chen, Shu-heng, 2011. "Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics Discussion Papers 2011-25, Kiel Institute for the World Economy.
    11. Lin, Yi & Zhang, Jianwei & Yang, Bo & Liu, Hong & Zhao, Liping, 2019. "An optimal routing strategy for transport networks with minimal transmission cost and high network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 551-561.
    12. Stefano Breschi & Lucia Cusmano, 2002. "Unveiling the Texture of a European Research Area: Emergence of Oligarchic Networks under EU Framework Programmes," KITeS Working Papers 130, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Jul 2002.
    13. He, Xuan & Zhao, Hai & Cai, Wei & Li, Guang-Guang & Pei, Fan-Dong, 2015. "Analyzing the structure of earthquake network by k-core decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 34-43.
    14. Huang, Huilin, 2009. "The degree sequences of an asymmetrical growing network," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 420-425, February.
    15. Gianluca Carnabuci, 2013. "The distribution of technological progress," Empirical Economics, Springer, vol. 44(3), pages 1143-1154, June.
    16. Zhengzheng Pan, 2012. "Opinions and Networks: How Do They Effect Each Other," Computational Economics, Springer;Society for Computational Economics, vol. 39(2), pages 157-171, February.
    17. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    18. Chen, Qinghua & Shi, Dinghua, 2004. "The modeling of scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 240-248.
    19. Srayan Datta & Eytan Adar, 2018. "A generative model for scientific concept hierarchies," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.
    20. López-Pintado, Dunia, 2008. "Diffusion in complex social networks," Games and Economic Behavior, Elsevier, vol. 62(2), pages 573-590, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • Z19 - Other Special Topics - - Cultural Economics - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hig:wpaper:33/soc/2013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.