IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v48y2014i2p745-754.html
   My bibliography  Save this article

Understanding information propagation on online social tagging systems: a case study on Flickr

Author

Listed:
  • Jason Jung

Abstract

Online social networking services (SNS) have been regarded as one of the most powerful online communication channels to propagate information to other users. It means that the online social networking services are providing users with efficient features (e.g., searching, managing and visualizing new information). It is important for many online collaborative applications to understand how the information can be propagated via such social media. Thus, we want to focus on a social tagging system (e.g., Flickr) where users can easily exchange resources as well as their tags. In this paper, given a certain tag, a social pulse can be established by counting (i) the number of users and (ii) the number of resources over time. More importantly, we assume that information can be propagated by (iii) inducibility from other tags by comparing social pulses. To conduct experimentation, a tag-based searching system (called Tagoole) has been implemented to collect a dataset from Flickr. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Jason Jung, 2014. "Understanding information propagation on online social tagging systems: a case study on Flickr," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 745-754, March.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:2:p:745-754
    DOI: 10.1007/s11135-012-9799-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-012-9799-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-012-9799-8?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. Traud, Amanda L. & Mucha, Peter J. & Porter, Mason A., 2012. "Social structure of Facebook networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4165-4180.
    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. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    2. Jiashun Jin & Zheng Tracy Ke & Shengming Luo, 2022. "Improvements on SCORE, Especially for Weak Signals," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 127-162, June.
    3. Saxena, Rakhi & Kaur, Sharanjit & Bhatnagar, Vasudha, 2019. "Identifying similar networks using structural hierarchy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Luca Braghieri & Ro'ee Levy & Alexey Makarin, 2022. "Social Media and Mental Health," American Economic Review, American Economic Association, vol. 112(11), pages 3660-3693, November.
    5. Ma, Shujie & Su, Liangjun & Zhang, Yichong, 2020. "Detecting Latent Communities in Network Formation Models," Economics and Statistics Working Papers 12-2020, Singapore Management University, School of Economics.
    6. Ciotti, Valerio & Bianconi, Ginestra & Capocci, Andrea & Colaiori, Francesca & Panzarasa, Pietro, 2015. "Degree correlations in signed social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 422(C), pages 25-39.
    7. Wang, Mingyan & Zeng, An & Cui, Xiaohua, 2022. "Collective user switching behavior reveals the influence of TV channels and their hidden community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    8. Yuan, Wei-Guo & Liu, Yun, 2015. "A mixing evolution model for bidirectional microblog user networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 167-179.
    9. Yi-Shan Sung & Dashun Wang & Soundar Kumara, 0. "Uncovering the effect of dominant attributes on community topology: A case of facebook networks," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
    10. Karimi, Fariba & Ramenzoni, Verónica C. & Holme, Petter, 2014. "Structural differences between open and direct communication in an online community," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 263-273.
    11. Yakir Berchenko & Jonathan D. Rosenblatt & Simon D. W. Frost, 2017. "Modeling and analyzing respondent‐driven sampling as a counting process," Biometrics, The International Biometric Society, vol. 73(4), pages 1189-1198, December.
    12. Li, Jin-Yue & Li, Xiang & Li, Cong, 2021. "The Kronecker-clique model for higher-order clustering coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    13. Sun, Xin & Dong, Junyu & Tang, Ruichun & Xu, Mantao & Qi, Lin & Cai, Yang, 2015. "Topological evolution of virtual social networks by modeling social activities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 259-267.
    14. Elizabeth McConnell & Bálint Néray & Bernie Hogan & Aaron Korpak & Antonia Clifford & Michelle Birkett, 2018. "“Everybody Puts Their Whole Life on Facebook”: Identity Management and the Online Social Networks of LGBTQ Youth," IJERPH, MDPI, vol. 15(6), pages 1-19, May.
    15. Jing Yang & Jun Wang & Mengyang Gao, 2023. "Community Evolution Analysis Driven by Tag Events: The Special Perspective of New Tags," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    16. Hanbaek Lyu & Yacoub H. Kureh & Joshua Vendrow & Mason A. Porter, 2024. "Learning low-rank latent mesoscale structures in networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    17. Drago, Carlo & Amidani Aliberti, Livia & Carbonai, Davide, 2014. "Measuring Gender Differences in Information Sharing Using Network Analysis: the Case of the Austrian Interlocking Directorship Network in 2009," Climate Change and Sustainable Development 178241, Fondazione Eni Enrico Mattei (FEEM).
    18. Marya Bazzi & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2014. "Community detection in temporal multilayer networks, with an application to correlation networks," Papers 1501.00040, arXiv.org, revised Dec 2017.
    19. He, Dongxiao & Wang, Hongcui & Jin, Di & Liu, Baolin, 2016. "A model framework for the enhancement of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 602-612.
    20. Vicente, R. & Susemihl, A. & Jericó, J.P. & Caticha, N., 2014. "Moral foundations in an interacting neural networks society: A statistical mechanics analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 124-138.

    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:qualqt:v:48:y:2014:i:2:p:745-754. 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.