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.

该研究提出了一种利用社交信息生成关系嵌入的新方法，该方法可以应用于任何语言和目标，有助于在社交网络上进行立场检测。实验证明，将我们的关系嵌入与文本方法相结合可以显著提高性能。