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Extent prediction of the information and influence propagation in online social networks

Author

Listed:
  • Raúl M. Ortiz-Gaona

    (Polytechnic University of Catalonia (UPC)
    Universidad de Cuenca)

  • Marcos Postigo-Boix

    (Polytechnic University of Catalonia (UPC))

  • José L. Melús-Moreno

    (Polytechnic University of Catalonia (UPC))

Abstract

We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verify that the messages to inform and influence, as well as their importance, produce different propagation behaviors in an online social network. We carried out laboratory tests with our model and with the baseline models Linear Threshold and Independent Cascade, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. Our model can contribute to the development of models that maximize the propagation of messages; to predict the spread of viruses in computer networks, mobile telephony and online social networks.

Suggested Citation

  • Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.
    • Handle: RePEc:spr:comaot:v:27:y:2021:i:2:d:10.1007_s10588-020-09309-6
    DOI: 10.1007/s10588-020-09309-6
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    References listed on IDEAS

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    1. Ning Nan & Robert Zmud & Emre Yetgin, 2014. "A complex adaptive systems perspective of innovation diffusion: an integrated theory and validated virtual laboratory," Computational and Mathematical Organization Theory, Springer, vol. 20(1), pages 52-88, March.
    2. Dustin L. Arendt & Leslie M. Blaha, 2015. "Opinions, influence, and zealotry: a computational study on stubbornness," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 184-209, June.
    3. Samadi, Mohammadreza & Nikolaev, Alexander & Nagi, Rakesh, 2016. "A subjective evidence model for influence maximization in social networks," Omega, Elsevier, vol. 59(PB), pages 263-278.
    4. Sohei Ito & Dominik Vymětal & Roman Šperka & Michal Halaška, 2018. "Process mining of a multi-agent business simulator," Computational and Mathematical Organization Theory, Springer, vol. 24(4), pages 500-531, December.
    5. Alexandra Bekiari & Nikolaos Hasanagas, 2015. "Verbal Aggressiveness Exploration through Complete Social Network Analysis: Using Physical Education Students¡¯ Class as an Illustration," International Journal of Social Science Studies, Redfame publishing, vol. 3(3), pages 30-49, May.
    6. Fan, W. & Yeung, K.H., 2011. "Online social networks—Paradise of computer viruses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 189-197.
    7. Hernan Mondani, 2018. "The underlying geometry of organizational dynamics: similarity-based social space and labor flow network communities," Computational and Mathematical Organization Theory, Springer, vol. 24(3), pages 378-400, September.
    8. Rong, Ke & Hu, Guangyu & Lin, Yong & Shi, Yongjiang & Guo, Liang, 2015. "Understanding business ecosystem using a 6C framework in Internet-of-Things-based sectors," International Journal of Production Economics, Elsevier, vol. 159(C), pages 41-55.
    9. Samadi, Mohammadreza & Nagi, Rakesh & Semenov, Alexander & Nikolaev, Alexander, 2018. "Seed activation scheduling for influence maximization in social networks," Omega, Elsevier, vol. 77(C), pages 96-114.
    10. António Fonseca & Jorge Louçã, 2018. "Explaining the emergence of online popularity through a model of information diffusion," Computational and Mathematical Organization Theory, Springer, vol. 24(2), pages 169-187, June.
    11. Declan Mungovan & Enda Howley & Jim Duggan, 2011. "The influence of random interactions and decision heuristics on norm evolution in social networks," Computational and Mathematical Organization Theory, Springer, vol. 17(2), pages 152-178, May.
    12. Postigo-Boix, Marcos & Melús-Moreno, José L., 2018. "A social model based on customers’ profiles for analyzing the churning process in the mobile market of data plans," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 571-592.
    13. Jordi Paniagua & Rafael Rivelles & Juan Sapena, 2019. "Social Determinants of Success: Social Media, Corporate Governance and Revenue," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    14. Matthew Babcock & Ramon Alfonso Villa Cox & Sumeet Kumar, 2019. "Diffusion of pro- and anti-false information tweets: the Black Panther movie case," Computational and Mathematical Organization Theory, Springer, vol. 25(1), pages 72-84, March.
    15. Shahadat Uddin & Arif Khan & Liaquat Hossain & Mahendra Piraveenan & Sven Carlsson, 2015. "A topological framework to explore longitudinal social networks," Computational and Mathematical Organization Theory, Springer, vol. 21(1), pages 48-68, March.
    16. Bolthausen, Erwin & Wüthrich, Mario V., 2013. "Bernoulli's Law of Large Numbers," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 43(02), pages 73-79, May.
    17. Qiaoyun Yun & Peter A. Gloor, 2015. "The web mirrors value in the real world: comparing a firm’s valuation with its web network position," Computational and Mathematical Organization Theory, Springer, vol. 21(4), pages 356-379, December.
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