IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v95y2022i8d10.1140_epjb_s10051-022-00385-y.html
   My bibliography  Save this article

Collective attention dynamic induced by novelty decay

Author

Listed:
  • Zhenpeng Li

    (Taizhou University)

  • Xijin Tang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zhenjie Hong

    (Wenzhou University)

Abstract

We investigate Chinese BBS-Tianya Club collective users’ behaviors empirically and confirm that emerging attention on posts follows q-exponential novelty decay. We analytically derive a general model of reply asymptotic behavior, showing that the microscopic rate of change obeys the Gibrat proportional effect and q-exponential novelty decay. Rigorous statistical comparison and tests confirm that the empirically observed distribution of replies is subject to power-law distribution with an exponential cutoff, which is consistent with our theoretical analysis, suggesting that the proposed model effectively describes the collective replying dynamics on BBS. Graphical Abstract

Suggested Citation

  • Zhenpeng Li & Xijin Tang & Zhenjie Hong, 2022. "Collective attention dynamic induced by novelty decay," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(8), pages 1-11, August.
  • Handle: RePEc:spr:eurphb:v:95:y:2022:i:8:d:10.1140_epjb_s10051-022-00385-y
    DOI: 10.1140/epjb/s10051-022-00385-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-022-00385-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1140/epjb/s10051-022-00385-y?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. Young-Ho Eom & Michelangelo Puliga & Jasmina Smailović & Igor Mozetič & Guido Caldarelli, 2015. "Twitter-Based Analysis of the Dynamics of Collective Attention to Political Parties," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    2. Cristian Candia & C. Jara-Figueroa & Carlos Rodriguez-Sickert & Albert-László Barabási & César A. Hidalgo, 2019. "The universal decay of collective memory and attention," Nature Human Behaviour, Nature, vol. 3(1), pages 82-91, January.
    3. de Souza, AndréM.C. & Tsallis, Constantino, 1997. "Student's t- and r-distributions: Unified derivation from an entropic variational principle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 236(1), pages 52-57.
    4. José M Miotto & Eduardo G Altmann, 2014. "Predictability of Extreme Events in Social Media," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-7, November.
    5. Raphael H Heiberger, 2015. "Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor's 100," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-14, August.
    6. Albert-László Barabási, 2005. "The origin of bursts and heavy tails in human dynamics," Nature, Nature, vol. 435(7039), pages 207-211, May.
    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. Lu, Xi & Mo, Hongming & Deng, Yong, 2015. "An evidential opinion dynamics model based on heterogeneous social influential power," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 98-107.
    2. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    3. Darko Cherepnalkoski & Andreas Karpf & Igor Mozetič & Miha Grčar, 2016. "Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-27, November.
    4. Simon DeDeo, 2016. "Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions," Future Internet, MDPI, vol. 8(3), pages 1-23, July.
    5. Zhou, Bin & Xie, Jia-Rong & Yan, Xiao-Yong & Wang, Nianxin & Wang, Bing-Hong, 2017. "A model of task-deletion mechanism based on the priority queueing system of Barabási," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 415-421.
    6. Chen, Ning & Zhu, Xuzhen & Chen, Yanyan, 2019. "Information spreading on complex networks with general group distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 671-676.
    7. Koen Zwet & Ana I. Barros & Tom M. Engers & Peter M. A. Sloot, 2022. "Emergence of protests during the COVID-19 pandemic: quantitative models to explore the contributions of societal conditions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    8. Qianqian Liu & Qun Wang, 2017. "A comparative study on uncooperative search models in survivor search and rescue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 843-857, November.
    9. Kota Yamada & Atsunori Kanemura, 2020. "Simulating bout-and-pause patterns with reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-21, November.
    10. Muaz Niazi & Amir Hussain, 2011. "Agent-based computing from multi-agent systems to agent-based models: a visual survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 479-499, November.
    11. Bent Flyvbjerg & Alexander Budzier & Daniel Lunn, 2021. "Regression to the tail: Why the Olympics blow up," Environment and Planning A, , vol. 53(2), pages 233-260, March.
    12. Timur Gareev & Irina Peker, 2023. "Quantity versus quality in publication activity: knowledge production at the regional level," Papers 2311.08830, arXiv.org.
    13. Pan, Junshan & Hu, Hanping & Liu, Ying, 2014. "Human behavior during Flash Crowd in web surfing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 212-219.
    14. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    15. Gui, Jun & Zheng, Zeyu & Fu, Dianzheng & Fu, Yang & Liu, Zhi, 2021. "Long-term correlations and multifractality of toll-free calls in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    16. Ma, Yin-Jie & Jiang, Zhi-Qiang & Podobnik, Boris, 2022. "Predictability of players’ actions as a mechanism to boost cooperation," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    17. Zhao, Huiyan & Zhang, Chongqi, 2019. "Minimum distance parameter estimation for SDEs with small α-stable noises," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 301-311.
    18. Zhenpeng Li & Luo Li, 2023. "The Generation Mechanism of Degree Distribution with Power Exponent >2 and the Growth of Edges in Temporal Social Networks," Mathematics, MDPI, vol. 11(13), pages 1-11, June.
    19. Markelov, Oleg & Nguyen Duc, Viet & Bogachev, Mikhail, 2017. "Statistical modeling of the Internet traffic dynamics: To which extent do we need long-term correlations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 48-60.
    20. Biton, Dionessa C. & Tarun, Anjali B. & Batac, Rene C., 2020. "Comparing spatio-temporal networks of intermittent avalanche events: Experiment, model, and empirical data," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).

    More about this item

    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:spr:eurphb:v:95:y:2022:i:8:d:10.1140_epjb_s10051-022-00385-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.