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Exploring the political pulse of a country using data science tools

Author

Listed:
  • Miguel G. Folgado

    (Universidad de Valencia-CSIC)

  • Veronica Sanz

    (Universidad de Valencia-CSIC
    University of Sussex)

Abstract

In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71–75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis.

Suggested Citation

  • Miguel G. Folgado & Veronica Sanz, 2022. "Exploring the political pulse of a country using data science tools," Journal of Computational Social Science, Springer, vol. 5(1), pages 987-1000, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00157-1
    DOI: 10.1007/s42001-021-00157-1
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    References listed on IDEAS

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    1. Jake M. Hofman & Duncan J. Watts & Susan Athey & Filiz Garip & Thomas L. Griffiths & Jon Kleinberg & Helen Margetts & Sendhil Mullainathan & Matthew J. Salganik & Simine Vazire & Alessandro Vespignani, 2021. "Integrating explanation and prediction in computational social science," Nature, Nature, vol. 595(7866), pages 181-188, July.
    2. Ricardo Francisco Reier Forradellas & Sergio Luis Náñez Alonso & Javier Jorge-Vazquez & Marcela Laura Rodriguez, 2020. "Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction," Social Sciences, MDPI, vol. 10(1), pages 1-20, December.
    3. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
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