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International Conference on Social Informatics

SocInfo 2018: Social Informatics pp 102–116Cite as

An Agent-Based Modelling Approach to Analyse the Public Opinion on Politicians

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11185))

Abstract

A politician’s popularity can be measured by polls or by measuring the amount of times a politician is mentioned on the Internet in a positive or negative manner. This paper introduces an approach to an agent-based computational model to model a politician’s popularity within a population that participates on the Internet over time. A particle swarm optimization algorithm is used for parameter tuning to identify the characteristics of all agents based on the analysis of public opinions on a politician found on the Internet. The properties of the network are verified by applying a social network analysis. A mathematical analysis is used to get more in depth understanding on the model and to verify its correctness.

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References

  1. Acemoglu, D., Ozdaglar, A.: Opinion dynamics and learning in social networks. Dyn. Games Appl. 1(1), 3–49 (2011)

    Article  MathSciNet  Google Scholar 

  2. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  3. Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: ICWSM, vol. 8, pp. 361–362 (2009)

    Google Scholar 

  4. DiGrazia, J., McKelvey, K., Bollen, J., Rojas, F.: More tweets, more votes: Social media as a quantitative indicator of political behavior. PLoS ONE 8(11), e79449 (2013)

    Article  Google Scholar 

  5. Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence models, analysis, and simulation. J. Artif. Soc. Soc. Simul. 5(3) (2002)

    Google Scholar 

  6. Kim, J.: Philosophy of Mind. Westview Press, Boulder (1996)

    Google Scholar 

  7. Kuipers, B.J.: Commonsense reasoning about causality: deriving behavior from structure. Artif. Intell. 24, 169–203 (1984)

    Article  Google Scholar 

  8. Kuipers, B.J., Kassirer, J.P.: How to discover a knowledge representation for causal reasoning by studying an expert physician. In: Proceedings of the 8th International Joint Conference on Artificial Intelligence, IJCAI 1983, William Kaufman, Los Altos, CA, pp. 49–56 (1983)

    Google Scholar 

  9. Lazarsfeld, P.F., Gaudet, H., Berelson, B.: The People’s Choice: How the Voter Makes Up His Mind in a Presidential Campaign. Columbia University Press, New York (1965)

    Google Scholar 

  10. Metaxas, P.T., Mustafaraj, E., Gayo-Avello, D.: How (not) to predict elections. In: Proceedings of the Third International Conference on Social Computing, SocialCom 2011, pp. 165–171. IEEE (2011)

    Google Scholar 

  11. Nickerson, D.W.: Is voting contagious? evidence from two field experiments. Am. Polit. Sci. Rev. 102(01), 49–57 (2008)

    Article  Google Scholar 

  12. Pearl, J.: Causality. Cambridge University Press, New York (2000)

    MATH  Google Scholar 

  13. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. An overview. Swarm Intell. 1, 33–57 (2007)

    Article  Google Scholar 

  14. Sang, E.T.K., Bos, J.: Predicting the 2011 Dutch senate election results with twitter. In: Proceedings of the workshop on semantic analysis in social media, pp. 53–60. Association for Computational Linguistics (2012)

    Google Scholar 

  15. Treur, J.: Dynamic modeling based on a temporal–causal network modeling approach. Biologically Inspired Cogn. Architectures 16, 131–168 (2016)

    Article  Google Scholar 

  16. Treur, J.: Network-Oriented Modelling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45213-5

    Book  MATH  Google Scholar 

  17. Watts, D.J., Dodds, P.S.: Influentials, Networks, and Public Opinion Formation. J. Consum. Res. 34(4), 441–458 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Behavioural Informatics Group, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Thijs M. A. Brouwers, John P. T. Onneweer & Jan Treur

Authors
  1. Thijs M. A. Brouwers
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  2. John P. T. Onneweer
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  3. Jan Treur
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Corresponding author

Correspondence to Jan Treur .

Editor information

Editors and Affiliations

  1. University of Koblenz, Koblenz, Germany

    Steffen Staab

  2. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

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Brouwers, T.M.A., Onneweer, J.P.T., Treur, J. (2018). An Agent-Based Modelling Approach to Analyse the Public Opinion on Politicians. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11185. Springer, Cham. https://doi.org/10.1007/978-3-030-01129-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-01129-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01128-4

  • Online ISBN: 978-3-030-01129-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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