Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

On the usage of epidemiological models for information diffusion over twitter

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

The way information spreads through online social networks is popularly considered to be similar to how viruses spread through a population. In this work, we study the suitability of using epidemiological models to model the spread of hashtags over Twitter by analyzing the nature of the spread. First, we define two extensions of the popular SIR model called Exo-SIR and Exo-SIS and their variants and study all the prominent hashtags in the dataset. Then, we study the hashtags that are about events and that are not about events separately. We found that the predominant nature of the spread of information over Twitter is endogenous. However, it is exogenous in the absence of events. The predominant nature of the spread of epidemics is endogenous. This implies that the usage of epidemiological models to model spread of information over Twitter is appropriate only if the spread is during events.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. https://networkx.org/documentation/stable/reference/generated/networkx.generators.random_graphs.barabasi_albert_graph.html#networkx.generators.random_graphs.barabasi_albert_graph.

References

  • Anderson RM, May RM (1992) Infectious diseases of humans: Dynamics and control. OUP Oxford

  • Bansal R, Paka WS, Sengupta S, Chakraborty T, et al (2021) Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of covid-19 fake tweets. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 188–200. Springer

  • Barabási A-L (2013) Network science. Philos Transact Royal Soc A: Math, Phys Eng Sci 371(1987):20120375

    Article  Google Scholar 

  • Brauer F, Castillo-Chavez C (2011) Mathematical models in population biology and epidemiology. Texts in Applied Mathematics. Springer New York

  • Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Rev Modern Phys 81(2):591

    Article  Google Scholar 

  • Chen Y-C, Lu P-E, Chang C-S, Liu T-H (2020) A time-dependent SIR model for COVID-19 with undetectable infected persons. IEEE Transact Netw Sci Eng 7(4):3279–3294

    Article  MathSciNet  Google Scholar 

  • Cheng J-J, Liu Y, Shen B, Yuan W-G (2013) An epidemic model of rumor diffusion in online social networks. Eur Phys J B 86(1):1–7

    Article  MathSciNet  Google Scholar 

  • Cinelli M, Quattrociocchi W, Galeazzi A, Valensise CM, Brugnoli E, Schmidt AL, Zola P, Zollo F, Scala A (2020) The covid-19 social media infodemic. Sci Rep 10(1):1–10

    Article  Google Scholar 

  • Comito C (2021) How covid-19 information spread in us? the role of twitter as early indicator of epidemics. IEEE Trans Serv Comput 15(3):1193–1205

    Article  Google Scholar 

  • Comito C, Falcone D, Talia D (2017) A peak detection method to uncover events from social media. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 459–467. IEEE

  • Dandekar R, Rackauckas C, Barbastathis G (2020) A machine learning-aided global diagnostic and comparative tool to assess effect of quarantine control in COVID-19 spread. Patterns 1(9):100145

    Article  Google Scholar 

  • Fujita K, Medvedev A, Koyama S, Lambiotte R, Shinomoto S (2018) Identifying exogenous and endogenous activity in social media. Phys Rev E 98(5):052304

    Article  Google Scholar 

  • Goffman W, Newill V (1964) Generalization of epidemic theory. Nature 204(4955):225–228

    Article  Google Scholar 

  • Hatua A, Nguyen TT, Sung AH (2017) Information diffusion on twitter: pattern recognition and prediction of volume, sentiment, and influence. In: Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 157–167

  • Jin F, Dougherty E, Saraf P, Cao Y, Ramakrishnan N (2013) Epidemiological modeling of news and rumors on twitter. In: Proceedings of the 7th Workshop on Social Network Mining and Analysis, pp. 1–9

  • Jung SY, Jo H, Son H, Hwang HJ (2020) Real-world implications of a rapidly responsive COVID-19 spread model with time-dependent parameters via deep learning: Model development and validation. J Med Internet Res 22(9):19907

    Article  Google Scholar 

  • Kabir KA, Kuga K, Tanimoto J (2019) Analysis of sir epidemic model with information spreading of awareness. Chaos, Solitons Fractals 119:118–125

    Article  MathSciNet  Google Scholar 

  • Kaxiras E, Neofotistos G (2020) Multiple epidemic wave model of the COVID-19 pandemic: modeling study. J Med Internet Res 22(7):20912

    Article  Google Scholar 

  • Kendall DG (1956) Deterministic and stochastic epidemics in closed populations. In: Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, vol. 4, pp. 149–165. University of California Press Berkeley

  • Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing Papers of a Mathematical and Physical Character 115(772), 700–721. The Royal Society London

  • Kiss IZ, Broom M, Craze PG, Rafols I (2010) Can epidemic models describe the diffusion of topics across disciplines? J Informetr 4(1):74–82

    Article  Google Scholar 

  • Kleinberg J (2008) The convergence of social and technological networks. Commun ACM 51(11):66–72

    Article  Google Scholar 

  • Kumar P, Sinha A (2021) Information diffusion modeling and analysis for socially interacting networks. Social Netw Anal Min 11(1):1–18

    Article  MathSciNet  Google Scholar 

  • Li M, Wang X, Gao K, Zhang S (2017) A survey on information diffusion in online social networks: models and methods. Information 8(4):118

    Article  Google Scholar 

  • Maleki M, Mead E, Arani M, Agarwal N (2021) Using an epidemiological model to study the spread of misinformation during the black lives matter movement. arXiv preprint arXiv:2103.12191

  • Mathur A, Gupta CP (2020) Dynamic seiz in online social networks: epidemiological modeling of untrue information. Int J Adv Comput Sci Appl 11:577–585

    Google Scholar 

  • Naaman M, Becker H, Gravano L (2011) Hip and trendy: characterizing emerging trends on twitter. J Am Soc Inform Sci Technol 62(5):902–918

    Article  Google Scholar 

  • Prasad Peri subrahmanya hari (2021) COVID-19 disease spread modeling by QSIR method: the parameter optimal control approach. Clinical Epidemiology and Global Health, 100934

  • Raafat RM, Chater N, Frith C (2009) Herding in humans. Trends Cogn Sci 13(10):420–428

    Article  Google Scholar 

  • Razaque A, Rizvi S, Almiani M, Al Rahayfeh A, et al (2019) State-of-art review of information diffusion models and their impact on social network vulnerabilities. J King Saud Univ-Comput Inform Sci. Elsevier

  • Rodrigues HS (2016) Application of sir epidemiological model: new trends. arXiv preprint arXiv:1611.02565

  • Sivaraman NK, Gaur M, Baijal S, Muthiah SB, Sheth A (2022) Exo-sir: an epidemiological model to analyze the impact of exogenous spread of infection. Int J Data Sci Analyt, 1–16. Springer

  • Sivaraman NK, Gaur M, Baijal S, Rupesh CV, Muthiah SB, Sheth A (2020) Exo-sir: an epidemiological model to analyze the impact of exogenous infection of covid-19 in india. arXiv preprint arXiv:2008.06335

  • Sivaraman NK, Muthiah SB, Agarwal P, Todwal L (2020) Social synchrony in online social networks and its application in event detection from twitter data. In: 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 451–456. IEEE

  • Stai E, Milaiou E, Karyotis V, Papavassiliou S (2018) Temporal dynamics of information diffusion in twitter: Modeling and experimentation. IEEE Transactions on Computational Social Systems 5(1), 256–264. IEEE

  • Tolles J, Luong T (2020) Modeling epidemics with compartmental models. Jama

  • Walker PG, Whittaker C, Watson OJ, Baguelin M, Winskill P, Hamlet A, Djafaara BA, Cucunubá Z, Mesa DO, Green W, et al (2020) The impact of COVID-19 and strategies for mitigation and suppression in low-and middle-income countries. Science. American Association for the Advancement of Science

  • Wang Y, Zheng B (2014) On macro and micro exploration of hashtag diffusion in twitter. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 285–288. IEEE

  • Woo J, Chen H (2016) Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. SpringerPlus 5(1):1–19

    Article  Google Scholar 

  • Xiong X, Li Y, Qiao S, Han N, Wu Y, Peng J, Li B (2018) An emotional contagion model for heterogeneous social media with multiple behaviors. Physica A 490:185–202

    Article  MathSciNet  Google Scholar 

  • Yang D, Liao X, Wei J, Chen G, Cheng X (2019) Modeling information diffusion with the external environment in social networks. J Internet Technol 20(2):369–377

    Google Scholar 

  • Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction, 248. Cambridge University Press

  • Zakary O, Bidah S, Rachik M, Ferjouchia H (2020) Mathematical model to estimate and predict the COVID-19 infections in morocco: Optimal control strategy. J Appl Math 2020. Hindawi

Download references

Author information

Authors and Affiliations

  1. The LNM Institute of Information Technology, Jaipur, India

    Nirmal Kumar Sivaraman, Shivansh Baijal & Sakthi Balan Muthiah

Authors
  1. Nirmal Kumar Sivaraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shivansh Baijal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sakthi Balan Muthiah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nirmal Kumar Sivaraman.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Simulation results

Appendix: Simulation results

See Tables 9, 10, 11.

Table 9 Plots of the simulation results set 1
Full size table
Table 10 Plots of the simulation results set 2
Full size table
Table 11 Plots of the simulation results set 3
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sivaraman, N.K., Baijal, S. & Muthiah, S.B. On the usage of epidemiological models for information diffusion over twitter. Soc. Netw. Anal. Min. 13, 133 (2023). https://doi.org/10.1007/s13278-023-01130-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-023-01130-8

Keywords

Search

Navigation