  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.
