The high volume and rapid evolution of content on social media present major
challenges for studying the stance of social media users. In this work, we
develop a two stage stance labeling method that utilizes the user-hashtag
bipartite graph and the user-user interaction graph. In the first stage, a
simple and efficient heuristic for stance labeling uses the user-hashtag
bipartite graph to iteratively update the stance association of user and
hashtag nodes via a label propagation mechanism. This set of soft labels is
then integrated with the user-user interaction graph to train a graph neural
network (GNN) model using semi-supervised learning. We evaluate this method on
two large-scale datasets containing tweets related to climate change from June
2021 to June 2022 and gun control from January 2022 to January 2023.
Experiments demonstrate that our user-hashtag heuristic and the semi-supervised
GNN method outperform zero-shot stance labeling using LLMs such as GPT4.
Further analysis illustrates how the stance labeling information and
interaction graph can be used for evaluating the polarization of social media
interactions on divisive issues such as climate change and gun control.

通过使用用户 - 标签二分图和用户 - 用户交互图，开发了一个两阶段立场标签方法，首先使用用户 - 标签二分图的启发式机制更新用户和标签节点的立场关联，然后将这些标签与用户 - 用户交互图结合，使用半监督学习训练图神经网络模型。实验证明，该方法在评估气候变化和枪支管制等分裂性议题在社交媒体上的极化互动时优于零样本立场标签技术。