Skip to main content
SpringerLink
Log in

Precocious identification of popular topics on Twitter with the employment of predictive clustering

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The present paper outlines a novel approach to predict popularity of topics for social network Twitter; the method is designed to identify precociously the topics able to demonstrate “explosive” growth in popularity. First of all, the predictive clustering method ascertains real (not written in hash-tags!) topics of tweets and then predicts popularity rates for the topics. The same clustering algorithm is employed both to ascertain the real topic of a message and to cluster segments of time series (in order to predict topics popularity), namely, maximum likelihood adaptive neural system based upon modelling field theory. In the course of wide-ranging simulation, typical variants of “pre-explosive” dynamics were revealed; some of them were turned out to be equal to heuristic techniques to predict topics popularity well known for PR community collaborating with the network (“crab,” “Pesavento’s butterfly,” etc.).

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Kietzmann J, Hermkens K (2011) Social media? Get serious! Understanding the functional building blocks of social media. Bus Horiz 54:241–251

    Article  Google Scholar 

  2. Fu X, Shen Y (2014) Study of collective user behaviour in Twitter: a fuzzy approach. Neural Comput Appl 25(7–8):1603–1614

    Article  Google Scholar 

  3. Rill S, Reinel D, Scheidt J, Zicari RV (2014) PoliTwi: early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowl-Based Syst 69:24–33

    Article  Google Scholar 

  4. Bravo-Marquez F, Mendoza M, Poblete B (2014) Meta-level sentiment models for big social data analysis. Knowl-Based Syst 69:86–99

    Article  Google Scholar 

  5. Fan J, Yao Q (2003) Non-linear time series. Nonparametric and parametric methods. Springer, New York

    Book  MATH  Google Scholar 

  6. Blockeel H, De Raedt L, Ramon J (1998) Top-down induction of clustering trees. In: 15th International conference on machine learning, pp 55–63

  7. Gromov VA, Borisenko EA (2015) Predictive clustering on non-successive observations for multi-step ahead chaotic time series prediction. Neural Comput Appl 26(8):1827–1838

    Article  Google Scholar 

  8. Gromov VA, Shulga AN (2012) Chaotic time series prediction with employment of ant colony optimization. Expert Syst Appl 39:8474–8478

    Article  Google Scholar 

  9. Martınez-Alvarez F, Troncoso A, Riquelme JC, Riquelme JM (2011) Energy time series forecasting based on pattern sequence similarity. IEEE Trans Knowl Data Eng 23(8):1230–1243

    Article  Google Scholar 

  10. Dzeroski S, Gjorgjioski V, Slavkov I, Struyf J (2006) Analysis of time series data with predictive clustering trees. In: Proceedings of the 5th international conference on knowledge discovery in inductive databases, pp 63–80

  11. Martınez-Alvarez F, Troncoso A, Riquelme JC, Riquelme JM (2007) Partitioning-clustering techniques applied to the electricity price time series. In: Intelligent data engineering and automated learning—IDEAL-2007. Springer

  12. Widiputra H, Kho H, Pears R, Kasabov N (2009) A novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction. Neural Inf Process 5864:114–121

    Article  Google Scholar 

  13. Widiputra H, Pears R, Kasabov N (2011) Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends. Adv Knowl Discov Data Min 6635:161–172

    Google Scholar 

  14. Phu L, Anh DT (2011) Motif-based method for initialization the K-means clustering for time series data. In: Wang D, Reynolds M (eds) AI 2011: Advances in artificial intelligence. Springer, New York, pp 11–20

    Chapter  Google Scholar 

  15. Lu W, Yang J, Liu X, Pedrycz W (2014) The modeling and prediction of time series based on synergy of high-order fuzzy cognitive map and fuzzy c-means clustering. Knowl-Based Syst 70:242–255

    Article  Google Scholar 

  16. Kasabov NK (2015) Evolving connectionist systems for adaptive learning and knowledge discovery: trends and directions. Knowl-Based Syst. doi:10.1016/j.knosys.2014.12.032

    Google Scholar 

  17. Perlovsky L (2001) Neural networks and intellect. Oxford University Press, New York

    Google Scholar 

  18. Aghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering—a decade review. Inf Syst 53:16–38

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Oles Honchar Dnepropetrovsk National University, Gagarina av., 72, Dnepropetrovsk, 49000, Ukraine

    Vasilii A. Gromov & Anton S. Konev

Authors
  1. Vasilii A. Gromov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anton S. Konev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vasilii A. Gromov.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gromov, V.A., Konev, A.S. Precocious identification of popular topics on Twitter with the employment of predictive clustering. Neural Comput & Applic 28, 3317–3322 (2017). https://doi.org/10.1007/s00521-016-2256-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2256-1

Keywords

Search

Navigation