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

arXiv: https://arxiv.org/abs/2410.15743 · HTML: https://mlsanity.com/paper/2410.15743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Political Text Scaling Meets Computational Semantics](https://mlsanity.com/paper/1904.06217/) (2019)
- [Understanding Politics via Contextualized Discourse Processing](https://mlsanity.com/paper/2012.15784/) (2020)
- [Optimizing text representations to capture (dis)similarity between political parties](https://mlsanity.com/paper/2210.11989/) (2022)
- [Additive manifesto decomposition: A policy domain aware method for understanding party positioning](https://mlsanity.com/paper/2305.10136/) (2023)

## Built on by (most cited first, 1 of 1)

- [Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms](https://mlsanity.com/paper/2511.13238/) (2025)

## Abstract (arXiv)

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.