Political Leaning Inference through Plurinational Scenarios

TL;DR

This paper presents a novel approach using relational embeddings to infer political leanings from social media interactions, effectively capturing complex multi-party political landscapes in Spain with limited data.

Contribution

It introduces a two-step method employing unsupervised relational embeddings for multi-party political leaning detection, outperforming traditional binary approaches.

Findings

Relational embeddings effectively detect political ideologies.

Method works well with limited training data.

Captures intra-group and inter-group political affinities.

Abstract

Social media users express their political preferences via interaction with other users, by spontaneous declarations or by participation in communities within the network. This makes a social network such as Twitter a valuable data source to study computational science approaches to political learning inference. In this work we focus on three diverse regions in Spain (Basque Country, Catalonia and Galicia) to explore various methods for multi-party categorization, required to analyze evolving and complex political landscapes, and compare it with binary left-right approaches. We use a two-step method involving unsupervised user representations obtained from the retweets and their subsequent use for political leaning detection. Comprehensive experimentation on a newly collected and curated dataset comprising labeled users and their interactions demonstrate the effectiveness of using Relational Embeddings as representation method for political ideology detection in both binary and multi-party frameworks, even with limited training data. Finally, data visualization illustrates the ability of the Relational Embeddings to capture intricate intra-group and inter-group political affinities.