  Given the development and abundance of social media, studying the stance of
social media users is a challenging and pressing issue. Social media users
express their stance by posting tweets and retweeting. Therefore, the
homogeneous relationship between users and the heterogeneous relationship
between users and tweets are relevant for the stance detection task. Recently,
graph neural networks (GNNs) have developed rapidly and have been applied to
social media research. In this paper, we crawl a large-scale dataset of the
2020 US presidential election and automatically label all users by manually
tagged hashtags. Subsequently, we propose a bipartite graph neural network
model, DoubleH, which aims to better utilize homogeneous and heterogeneous
information in user stance detection tasks. Specifically, we first construct a
bipartite graph based on posting and retweeting relations for two kinds of
nodes, including users and tweets. We then iteratively update the node's
representation by extracting and separately processing heterogeneous and
homogeneous information in the node's neighbors. Finally, the representations
of user nodes are used for user stance classification. Experimental results
show that DoubleH outperforms the state-of-the-art methods on popular
benchmarks. Further analysis illustrates the model's utilization of information
and demonstrates stability and efficiency at different numbers of layers.
