mlsanityData to 14 Oct 2026

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

arXiv 2303.12029 ↗First posted 21 Mar 2023cs.SI cs.CLcited by 3

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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.

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

15 of 65 references.

References · 15

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3 more
  1. 2020A Few Topical Tweets are Enough for Effective User-Level Stance Detectioncited by 2
  2. 2014Partisan Sharing: Facebook Evidence and Societal Consequencescited by 1
  3. 2022Modeling Political Activism around Gun Debate via Social Mediacited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2025Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directionscited by 6
  2. 2023Enhancing Stance Classification on Social Media Using Quantified Moral Foundationscited by 4
  3. 2024Predicting User Stances from Target-Agnostic Information using Large Language Modelscited by 1