Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks
This work addresses stance detection for social media researchers, offering a scalable computational approach to study polarization on divisive issues, though it is incremental in combining existing techniques.
The authors tackled the problem of stance labeling on social media by developing a two-stage method that combines user-hashtag heuristics with graph neural networks, achieving improved performance over text-based classifiers and zero-shot LLMs like GPT4 on climate change and gun control datasets.
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.