IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i21p5264-5278.html
   My bibliography  Save this article

Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling

Author

Listed:
  • Mitrović, Marija
  • Tadić, Bosiljka

Abstract

We present an analysis of the empirical data and the agent-based modeling of the emotional behavior of users on the Web portals where the user interaction is mediated by posted comments, like Blogs and Diggs. We consider the dataset of discussion-driven popular Diggs, in which all comments are screened by machine-learning emotion detection in the text, to determine positive and negative valence (attractiveness and aversiveness) of each comment. By mapping the data onto a suitable bipartite network, we perform an analysis of the network topology and the related time-series of the emotional comments. The agent-based model is then introduced to simulate the dynamics and to capture the emergence of the emotional behaviors and communities. The agents are linked to posts on a bipartite network, whose structure evolves through their actions on the posts. The emotional states (arousal and valence) of each agent fluctuate in time, subject to the current contents of the posts to which the agent is exposed. By an agent’s action on a post its current emotions are transferred to the post. The model rules and the key parameters are inferred from the considered empirical data to ensure their realistic values and mutual consistency. The model assumes that the emotional arousal over posts drives the agent’s action. The simulations are preformed for the case of constant flux of agents and the results are analyzed in full analogy with the empirical data. The main conclusions are that the emotion-driven dynamics leads to long-range temporal correlations and emergent networks with community structure, that are comparable with the ones in the empirical system of popular posts. In view of pure emotion-driven agents actions, this type of comparisons provide a quantitative measure for the role of emotions in the dynamics on real blogs. Furthermore, the model reveals the underlying mechanisms which relate the post popularity with the emotion dynamics and the prevalence of negative emotions (critique). We also demonstrate how the community structure is tuned by varying a relevant parameter in the model. All data used in these works are fully anonymized.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:21:p:5264-5278
    DOI: 10.1016/j.physa.2012.06.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437112004918
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2012.06.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jim Giles, 2011. "Social science lines up its biggest challenges," Nature, Nature, vol. 470(7332), pages 18-19, February.
    2. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    3. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    4. M. Mitrović & B. Tadić, 2010. "Bloggers behavior and emergent communities in Blog space," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(2), pages 293-301, January.
    5. F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    2. Chołoniewski, Jan & Sienkiewicz, Julian & Leban, Gregor & Hołyst, Janusz A., 2019. "Modeling of temporal fluctuation scaling in online news network with independent cascade model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 129-144.
    3. Wang, Xingyuan & Qin, Xiaomeng, 2016. "Asymmetric intimacy and algorithm for detecting communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 569-578.
    4. Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.

    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. Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    2. 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.
    3. 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.
    4. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    5. Müller-Hansen, Finn & Lee, Yuan Ting & Callaghan, Max & Jankin, Slava & Minx, Jan C., 2022. "The German coal debate on Twitter: Reactions to a corporate policy process," Energy Policy, Elsevier, vol. 169(C).
    6. Daesik Kim & Chung Joo Chung & Kihong Eom, 2022. "Measuring Online Public Opinion for Decision Making: Application of Deep Learning on Political Context," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
    7. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    8. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.
    9. Cohen, Scott & Stienmetz, Jason & Hanna, Paul & Humbracht, Michael & Hopkins, Debbie, 2020. "Shadowcasting tourism knowledge through media: Self-driving sex cars?," Annals of Tourism Research, Elsevier, vol. 85(C).
    10. Zhang, Xuetong & Zhang, Weiguo, 2023. "Information asymmetry, sentiment interactions, and asset price," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    11. Indy Wijngaards & Martijn Burger & Job van Exel, 2019. "The promise of open survey questions—The validation of text-based job satisfaction measures," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-22, December.
    12. Junegak Joung & Ki-Hun Kim & Kwangsoo Kim, 2021. "Data-Driven Approach to Dual Service Failure Monitoring From Negative Online Reviews: Managerial Perspective," SAGE Open, , vol. 11(1), pages 21582440209, January.
    13. Ema Kušen & Mark Strembeck, 2021. "“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters," Journal of Computational Social Science, Springer, vol. 4(2), pages 531-565, November.
    14. Wen Zhang & Daniel R. Fesenmaier, 2018. "Assessing emotions in online stories: comparing self-report and text-based approaches," Information Technology & Tourism, Springer, vol. 20(1), pages 83-95, December.
    15. Sejung Park & Jin-A Choi, 2023. "Comparing public responses to apologies: examining crisis communication strategies using network analysis and topic modeling," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3603-3620, August.
    16. Simon Albrecht & Bernhard Lutz & Dirk Neumann, 2020. "The behavior of blockchain ventures on Twitter as a determinant for funding success," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 241-257, June.
    17. Jun Lee & Adam Jatowt & Kyoung‐Sook Kim, 2021. "Discovering underlying sensations of human emotions based on social media," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 417-432, April.
    18. F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
    19. Sergey Smetanin, 2022. "Pulse of the Nation: Observable Subjective Well-Being in Russia Inferred from Social Network Odnoklassniki," Mathematics, MDPI, vol. 10(16), pages 1-38, August.
    20. Widerstedt, Barbro & Månsson, Jonas & Rosdahl, Jonatan, 2018. "A warm welcome? Access to advisory services for men and women," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 100-110.

    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:eee:phsmap:v:391:y:2012:i:21:p:5264-5278. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.