# Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020

arXiv: https://arxiv.org/abs/2303.12029 · HTML: https://mlsanity.com/paper/2303.12029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 21 Mar 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Semantics-aware BERT for Language Understanding](https://mlsanity.com/paper/1909.02209/) (2019)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://mlsanity.com/paper/1908.08167/) (2019)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Stance Detection in Web and Social Media: A Comparative Study](https://mlsanity.com/paper/2007.05976/) (2020)
- [Sentiment Analysis of Twitter Data: A Survey of Techniques](https://mlsanity.com/paper/1601.06971/) (2016)
- [Predicting Anti-Asian Hateful Users on Twitter during COVID-19](https://mlsanity.com/paper/2109.07296/) (2021)
- [#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter](https://mlsanity.com/paper/1603.01973/) (2016)
- [Predicting Crowd Behavior with Big Public Data](https://mlsanity.com/paper/1402.2308/) (2014)
- [A Few Topical Tweets are Enough for Effective User-Level Stance Detection](https://mlsanity.com/paper/2004.03485/) (2020)
- [Partisan Sharing: Facebook Evidence and Societal Consequences](https://mlsanity.com/paper/1409.7254/) (2014)
- [Modeling Political Activism around Gun Debate via Social Media](https://mlsanity.com/paper/2205.00308/) (2022)

## Built on by (most cited first, 3 of 3)

- [Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions](https://mlsanity.com/paper/2505.08464/) (2025)
- [Enhancing Stance Classification on Social Media Using Quantified Moral Foundations](https://mlsanity.com/paper/2310.09848/) (2023)
- [Predicting User Stances from Target-Agnostic Information using Large Language Models](https://mlsanity.com/paper/2409.14395/) (2024)

## Abstract (arXiv)

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people's stance, which we consider as their online behavior in each topic-related event. Our results reveal a strong connection between the stances toward the three topical events and demonstrate the power of past behaviors in predicting one's future behavior.