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Public opinion is a crucial factor in shaping political decision-making.Nowadays, social media has become an essential platform for individuals toengage in political discussions and express their political views, presentingresearchers with an invaluable resource for analyzing public opinion. In thispaper, we focus on the 2020 US presidential election and create a large-scaledataset from Twitter. To detect political opinions in tweets, we build auser-tweet bipartite graph based on users' posting and retweeting behaviors andconvert the task into a Graph Neural Network (GNN)-based node classificationproblem. Then, we introduce a novel skip aggregation mechanism that makes tweetnodes aggregate information from second-order neighbors, which are also tweetnodes due to the graph's bipartite nature, effectively leveraging userbehavioral information. The experimental results show that our proposed modelsignificantly outperforms several competitive baselines. Further analysesdemonstrate the significance of user behavioral information and theeffectiveness of skip aggregation.

Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach