Skip to main content
SpringerLink
Log in

Modelling Voting Behaviour During a General Election Campaign Using Dynamic Bayesian Networks

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12981))

Included in the following conference series:

  • 1851 Accesses

Abstract

This work aims to develop a Machine Learning framework to predict voting behaviour. Data resulted from longitudinally collected variables during the Portuguese 2019 general election campaign. Naïve Bayes (NB), and Tree Augmented Naïve Bayes (TAN) and three different expert models using Dynamic Bayesian Networks (DBN) predict voting behaviour systematically for each moment in time considered using past information. Even though the differences found in some performance comparisons are not statistically significant, TAN and NB outperformed DBN experts’ models. The learned models outperformed one of the experts’ models when predicting abstention and two when predicting right-wing parties vote. Specifically, for the right-wing parties vote, TAN and NB presented satisfactory accuracy, while the experts’ models were below 50% in the third evaluation moment.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dalton, R.J., Wattenberg, M.P.: Parties Without Partisans: Political Change in Advanced Industrial Democracies. Oxford University Press, Oxford (2002)

    Book  Google Scholar 

  2. Rahat, G., Sheafer, T.: The personalizations of politics: Israel, 1949–2003. Polit. Commun. 24(1), 65–80 (2007). https://doi.org/10.1080/10584600601128739

    Article  Google Scholar 

  3. Van Aelst, P., Sheafer, T., Stanyer, J.: The personalization of mediated political communication: a review of concepts, operationalizations and key findings. Journalism 13(2), 203–220 (2012)

    Article  Google Scholar 

  4. Langer, A.I.: A historical exploration of the personalisation of politics in the print media: the British prime ministers (1945–1999). Parliam. Aff. 60(3), 371–387 (2007)

    Article  Google Scholar 

  5. Adam, S., Maier, M.: Personalization of politics a critical review and agenda for research. Ann. Int. Commun. Assoc. 34(1), 213–257 (2016). https://doi.org/10.1080/23808985.2010.11679101

    Article  Google Scholar 

  6. Spisak, B.R., Van Der Laken, P.A., Doornenbal, B.M.: Finding the right fuel for the analytical engine: expanding the leader trait paradigm through machine learning? Leadersh. Q. (2019). https://doi.org/10.1016/j.leaqua.2019.05.005

    Article  Google Scholar 

  7. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier, San Francisco (2014)

    Google Scholar 

  8. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997). https://doi.org/10.1023/A:1007465528199

    Article  MATH  Google Scholar 

  9. Gama, J., Ferreira, A.C.P.D.L., Carvalho, D., Faceli, K., Lorena, A.C., Oliveira, M.: Extração de conhecimento de dados: data mining. Edições Sílabo (2015)

    Google Scholar 

  10. Needham, C.J., Bradford, J.R., Bulpitt, A.J., Westhead, D.R.: A primer on learning in Bayesian networks for computational biology. PLoS Comput. Biol. 3(8), 1409–1416 (2007). https://doi.org/10.1371/journal.pcbi.0030129

    Article  Google Scholar 

  11. Duan, Z., Wang, L.: K-dependence Bayesian classifier ensemble. Entropy 19(12), 651 (2017). https://doi.org/10.3390/e19120651

    Article  MathSciNet  Google Scholar 

  12. Heintzman, N., Kleinberg, S.: Using uncertain data from body-worn sensors to gain insight into type 1 diabetes. J. Biomed. Inform. 63, 259–268 (2016). https://doi.org/10.1016/j.jbi.2016.08.022

    Article  Google Scholar 

  13. Yu, K., Li, J., Liu, L.: A review on algorithms for constraint-based causal discovery, pp. 1–17 (2016). http://arxiv.org/abs/1611.03977

  14. Murphy, K., Mian, S.: Modeling gene expression data using dynamic Bayesian networks. BT-Technical Report, Computer Science Division. University of California, Berkeley (1999)

    Google Scholar 

  15. Zhu, S., Wang, Y.: Hidden markov induced dynamic Bayesian network for recovering time evolving gene regulatory networks. Sci. Rep. 5, 1–17 (2015). https://doi.org/10.1038/srep17841

    Article  Google Scholar 

  16. Jia, Y., Huan, J.: Constructing non-stationary dynamic Bayesian networks with a flexible lag choosing mechanism. BMC Bioinformatics 11(Suppl. 6), 1–13 (2010). https://doi.org/10.1186/1471-2105-11-27

    Article  Google Scholar 

  17. Uusitalo, L.: Advantages and challenges of Bayesian networks in environmental modelling. Ecol. Model. 203(3–4), 312–318 (2007). https://doi.org/10.1016/j.ecolmodel.2006.11.033

    Article  Google Scholar 

  18. Marcot, B.G., Holthausen, R.S., Raphael, M.G., Rowland, M.M., Wisdom, M.J.: Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. For. Ecol. Manage. 153(1–3), 29–42 (2001). https://doi.org/10.1016/S0378-1127(01)00452-2

    Article  Google Scholar 

  19. Martín De Santa Olalla, F.J., Domínguez, A., Artigao, A., Fabeiro, C., Ortega, J.F.: Integrated water resources management of the hydrogeological unit ‘Eastern Mancha’ using Bayesian belief networks. Agric. Water Manag. 77(1–3), 21–36 (2005). https://doi.org/10.1016/j.agwat.2004.09.029.

  20. Dagum, P., Galper, A., Horvitz, E.: Dynamic network models for forecasting. Uncertainty in Artificial Intelligent, pp. 41–48 (1992)

    Google Scholar 

  21. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  22. Iman, R.L., Davenport, J.M.: Approximations of the critical region of the fbietkan statistic. Commun. Stat. Methods 9(6), 571–595 (1980)

    Article  Google Scholar 

  23. García, S., Herrera, F.: An extension on ‘statistical comparisons of classifiers over multiple data sets’ for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)

    MATH  Google Scholar 

  24. Shaffer, J.P.: Modified sequentially rejective multiple test procedures. J. Am. Stat. Assoc. 81(395), 826–831 (1986). https://doi.org/10.1080/01621459.1986.10478341

    Article  MATH  Google Scholar 

  25. da Silva, F.F., Costa, P.: Do we need warm leaders? Exploratory study of the role of voter evaluations of leaders’ traits on turnout in seven European countries. Eur. J. Polit. Res. 58(1), 117–140 (2019). https://doi.org/10.1111/1475-6765.12273

    Article  Google Scholar 

  26. Silver, B.D., Anderson, B.A., Abramson, P.R.: Who overreports voting? Am. Polit. Sci. Rev. 80(2), 613–624 (1986). https://doi.org/10.2307/1958277

    Article  Google Scholar 

  27. Kuhn, M., Johnson, K.: Applied Predictive Modeling, vol. 26, Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6849-3

  28. Henderson, J.A.: Hookworm eradication as a natural experiment for schooling and voting in the American South. Polit. Behav. 40(2), 467–494 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by national funds through FCT - Foundation for Science and Technology, I.P., within the scope of the project PACTO – “The impact of Political leaders’ Attributes and Campaign TOne on voting behaviour: a multimodal perspective” (PTDC/CPO-CPO/28886/2017).

Author information

Authors and Affiliations

  1. Faculty of Economics, University of Porto, Porto, Portugal

    Patrício Costa & João Gama

  2. Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal

    Patrício Costa

  3. ICVS/3B’s–P.T. Government Associate Laboratory, Braga/Guimarães, Portugal

    Patrício Costa

  4. Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal

    Patrício Costa

  5. Faculty of Sciences, University of Porto, Porto, Portugal

    Ana Rita Nogueira

  6. LIAAD - INESC TEC, Porto, Portugal

    Ana Rita Nogueira & João Gama

Authors
  1. Patrício Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ana Rita Nogueira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Gama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrício Costa .

Editor information

Editors and Affiliations

  1. ISEP/GECAD, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  2. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Francisco S. Melo

  3. DETI/IEETA, University of Aveiro, Aveiro, Portugal

    Nuno Lau

  4. FEUP/LIACC, University of Porto, Porto, Portugal

    Henrique Lopes Cardoso

  5. FEUP/LIACC, University of Porto, Porto, Portugal

    Luís Paulo Reis

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Costa, P., Nogueira, A.R., Gama, J. (2021). Modelling Voting Behaviour During a General Election Campaign Using Dynamic Bayesian Networks. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86230-5_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86229-9

  • Online ISBN: 978-3-030-86230-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation