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

arXiv 2304.11367 ↗First posted 22 Apr 2023cs.SI cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 22 Apr 2023.

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References · 20

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8 more
  1. 2022Robust Models are less Over-Confidentcited by 25
  2. 2022KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Mediacited by 20
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