Voting Booklet Bias: Stance Detection in Swiss Federal Communication
Eric Egli Noah Mamié Eyal Liron Dolev Mathias Müller
In this study, we use recent stance detection methods to study the stance
(for, against or neutral) of statements in official information booklets for
voters. Our main goal is to answer the fundamental question: are topics to be
voted on presented in a neutral way?
  To this end, we first train and compare several models for stance detection
on a large dataset about Swiss politics. We find that fine-tuning an M-BERT
model leads to the best accuracy. We then use our best model to analyze the
stance of utterances extracted from the Swiss federal voting booklet concerning
the Swiss popular votes of September 2022, which is the main goal of this
project.
  We evaluated the models in both a multilingual as well as a monolingual
context for German, French, and Italian. Our analysis shows that some issues
are heavily favored while others are more balanced, and that the results are
largely consistent across languages.
  Our findings have implications for the editorial process of future voting
booklets and the design of better automated systems for analyzing political
discourse. The data and code accompanying this paper are available at
https://github.com/ZurichNLP/voting-booklet-bias.