Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions and express their political views, presenting researchers with an invaluable resource for analyzing public opinion. In this paper, we focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors and convert the task into a Graph Neural Network (GNN)-based node classification problem. Then, we introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors, which are also tweet nodes due to the graph's bipartite nature, effectively leveraging user behavioral information. The experimental results show that our proposed model significantly outperforms several competitive baselines. Further analyses demonstrate the significance of user behavioral information and the effectiveness of skip aggregation.

本文从 2020 年美国总统大选入手，利用 Twitter 平台收集了一组数据集，使用图神经网络以及节点分类的方法来研究公众的政见以及观点传播。研究结果展示了用户行为信息在推特政治意见检测中的重要性以及信息聚合中跨层级信息关联提升模型性能的有效性。

通过二分图分析在推特中检测政治观点：一种跳过聚合的图卷积方法