Wearing masks implies refuting Trump?: Towards target-specific user stance prediction across events in COVID-19 and US Election 2020

• Main Authors: ZHANG, Hong, KWAK, Haewoon, GAO, Wei, AN, Jisun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Stance detection; Natural language processing; COVID-19; Distant supervision; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8451; https://ink.library.smu.edu.sg/context/sis_research/article/9454/viewcontent/Wearing_masks_implies_refuting_Trump_Towards_target_specific_user_stance_prediction_across_events_in_COVID_19_and_US_Election_2020__1_.pdf
• Institution: Singapore Management University

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.