Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural
  Networks
Joshua Melton Shannon Reid Gabriel Terejanu Siddharth Krishnan
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.