# Political Leaning Inference through Plurinational Scenarios

arXiv: https://arxiv.org/abs/2406.07964 · HTML: https://mlsanity.com/paper/2406.07964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [A Long-Term Analysis of Polarization on Twitter](https://mlsanity.com/paper/1703.02769/) (2017)
- [Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey](https://mlsanity.com/paper/2005.09649/) (2020)
- [No Echo in the Chambers of Political Interactions on Reddit](https://mlsanity.com/paper/2102.05477/) (2021)
- [Social Analysis of Young Basque Speaking Communities in Twitter](https://mlsanity.com/paper/2109.03487/) (2021)
- [Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections](https://mlsanity.com/paper/2005.04411/) (2020)
- [QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs](https://mlsanity.com/paper/2011.01181/) (2020)
- [Language Independent Stance Detection: Social Interaction-based Embeddings and Large Language Models](https://mlsanity.com/paper/2210.05715/) (2022)
- [Generalizing Political Leaning Inference to Multi-Party Systems: Insights from the UK Political Landscape](https://mlsanity.com/paper/2312.01738/) (2023)

## Abstract (arXiv)

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.