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.

社交媒体用户通过与其他用户的互动、自发声明或参与网络社区表达其政治偏好，因此像 Twitter 这样的社交网络是研究计算科学方法在政治学习推理方面的宝贵数据源。本研究聚焦于西班牙的三个多样化地区（巴斯克地区、加泰罗尼亚和加利西亚），探索用于分析不断演变和复杂的政治格局所需的多党分类的各种方法，并将其与二元左右派方法进行比较。我们采用两步法，通过从转发中获取的无监督用户表示来进行政治倾向检测。对于由标记用户及其互动组成的新收集和筛选数据集的全面实验证明，将关系嵌入作为政治意识形态检测的表示方法，在有限的训练数据下，在二元和多党框架中都具有有效性。最后，数据可视化展示了关系嵌入捕捉复杂的组内和组间政治共鸣的能力。