Party Prediction for Twitter
Kellin Pelrine Anne Imouza Zachary Yang Jacob-Junqi Tian Sacha Lévy Gabrielle Desrosiers-Brisebois Aarash Feizi Cécile Amadoro André Blais Jean-François Godbout Reihaneh Rabbany
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.