Party Prediction for Twitter

Authors

  • Kellin Pelrine McGill University Mila - Quebec AI Institute
  • Anne Imouza McGill University Mila - Quebec AI Institute
  • Zachary Yang McGill University Mila - Quebec AI Institute
  • Jacob-Junqi Tian McGill University Mila - Quebec AI Institute
  • Sacha Lévy McGill University Mila - Quebec AI Institute
  • Gabrielle Desrosiers-Brisebois Université de Montréal Mila - Quebec AI Institute
  • Aarash Feizi McGill University Mila - Quebec AI Institute
  • Cécile Amadoro Université de Montréal
  • André Blais Université de Montréal
  • Jean-François Godbout Université de Montréal Mila - Quebec AI Institute
  • Reihaneh Rabbany McGill University Mila - Quebec AI Institute

DOI:

https://doi.org/10.1609/icwsm.v18i1.31381

Abstract

A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a predictive model for inferring their political affiliation. The accuracy of this model can change the conclusions of a downstream analysis significantly, yet the choice between different models seems to be made arbitrarily. In this paper, we provide a comprehensive survey and an empirical comparison of the current party prediction practices and propose several new approaches which are competitive with or outperform state-of-the-art methods, yet require less computational resources. Party prediction models rely on the content generated by the users (e.g., tweet texts), the relations they have (e.g., who they follow), or their activities and interactions (e.g., which tweets they like). We examine all of these and compare their signal strength for the party prediction task. This paper lets the practitioner select from a wide range of data types that all give strong performance. Finally, we conduct extensive experiments on different aspects of these methods, such as data collection speed and transfer capabilities, which can provide further insights for both applied and methodological research.

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Published

2024-05-28

How to Cite

Pelrine, K., Imouza, A., Yang, Z., Tian, J.-J., Lévy, S., Desrosiers-Brisebois, G., Feizi, A., Amadoro, C., Blais, A., Godbout, J.-F., & Rabbany, R. (2024). Party Prediction for Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 1174-1192. https://doi.org/10.1609/icwsm.v18i1.31381