Summary of Two-stage Stance Labeling: User-hashtag Heuristics with Graph Neural Networks, by Joshua Melton et al.
Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks
by Joshua Melton, Shannon Reid, Gabriel Terejanu, Siddharth Krishnan
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a two-stage stance labeling method for analyzing the opinions of social media users on sensitive topics like climate change and gun control. The approach combines the user-hashtag bipartite graph with the user-user interaction graph to train a graph neural network (GNN) model using semi-supervised learning. This allows for more accurate predictions by incorporating nuanced understanding from social science. The method is evaluated on two large-scale datasets, outperforming both text-based embeddings and zero-shot classification using GPT4. The paper highlights the need for integrating computational methods with social science insights to better understand polarization on social media. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to figure out what people think about important topics like climate change and gun control on social media. It uses special tools called graphs that show how users interact with each other and what hashtags they use. The researchers developed a new method that combines these graphs with some machine learning magic to make predictions about people’s opinions. They tested this method on two big datasets and found that it worked better than previous methods. This research is important because it can help us understand why people get so divided on social media. |
Keywords
» Artificial intelligence » Classification » Gnn » Graph neural network » Machine learning » Semi supervised » Zero shot
