# Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks

arXiv: https://arxiv.org/abs/2404.10228 · HTML: https://mlsanity.com/paper/2404.10228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015)
- [We Don't Speak the Same Language: Interpreting Polarization through Machine Translation](https://mlsanity.com/paper/2010.02339/) (2020)
- [Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks](https://mlsanity.com/paper/2207.08349/) (2022)
- [Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study](https://mlsanity.com/paper/2103.10979/) (2021)
- [Affective Polarization in Online Climate Change Discourse on Twitter](https://mlsanity.com/paper/2008.13051/) (2020)

## Abstract (arXiv)

The high volume and rapid evolution of content on social media present major challenges for studying the stance of social media users. In this work, we develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the user-user interaction graph. In the first stage, a simple and efficient heuristic for stance labeling uses the user-hashtag bipartite graph to iteratively update the stance association of user and hashtag nodes via a label propagation mechanism. This set of soft labels is then integrated with the user-user interaction graph to train a graph neural network (GNN) model using semi-supervised learning. We evaluate this method on two large-scale datasets containing tweets related to climate change from June 2021 to June 2022 and gun control from January 2022 to January 2023. Our experiments demonstrate that enriching text-based embeddings of users with network information from the user interaction graph using our semi-supervised GNN method outperforms both classifiers trained on user textual embeddings and zero-shot classification using LLMs such as GPT4. We discuss the need for integrating nuanced understanding from social science with the scalability of computational methods to better understand how polarization on social media occurs for divisive issues such as climate change and gun control.