  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. Our
experiments demonstrate that enriching text-based embeddings of users with
network information from the user interaction graph using our semi-supervised
GNN method outperforms both classifiers trained on user textual embeddings and
zero-shot classification using LLMs such as GPT4. We discuss the need for
integrating nuanced understanding from social science with the scalability of
computational methods to better understand how polarization on social media
occurs for divisive issues such as climate change and gun control.
