  The large majority of the research performed on stance detection has been
focused on developing more or less sophisticated text classification systems,
even when many benchmarks are based on social network data such as Twitter.
This paper aims to take on the stance detection task by placing the emphasis
not so much on the text itself but on the interaction data available on social
networks. More specifically, we propose a new method to leverage social
information such as friends and retweets by generating relational embeddings,
namely, dense vector representations of interaction pairs. Our method can be
applied to any language and target without any manual tuning. Our experiments
on seven publicly available datasets and four different languages show that
combining our relational embeddings with textual methods helps to substantially
improve performance, obtaining best results for six out of seven evaluation
settings, outperforming strong baselines based on large pre-trained language
models.
