Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter

TL;DR

This paper presents a method to analyze political discourse on Twitter by predicting party positions using hashtags to fine-tune text representations, enabling reliable analysis without manual annotation even in low-resource, noisy social media data.

Contribution

It extends manifesto-based political positioning methods to Twitter data by leveraging hashtags for automatic fine-tuning, improving robustness in low-resource scenarios.

Findings

Method produces stable political positioning aligned with manifestos.

Effective even with limited and short-term Twitter data.

No manual annotation required for reliable analysis.

Abstract

Political discourse on Twitter is a moving target: politicians continuously make statements about their positions. It is therefore crucial to track their discourse on social media to understand their ideological positions and goals. However, Twitter data is also challenging to work with since it is ambiguous and often dependent on social context, and consequently, recent work on political positioning has tended to focus strongly on manifestos (parties' electoral programs) rather than social media. In this paper, we extend recently proposed methods to predict pairwise positional similarities between parties from the manifesto case to the Twitter case, using hashtags as a signal to fine-tune text representations, without the need for manual annotation. We verify the efficacy of fine-tuning and conduct a series of experiments that assess the robustness of our method for low-resource scenarios. We find that our method yields stable positioning reflective of manifesto positioning, both in scenarios with all tweets of candidates across years available and when only smaller subsets from shorter time periods are available. This indicates that it is possible to reliably analyze the relative positioning of actors forgoing manual annotation, even in the noisier context of social media.