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

Autor:	Zhang, Hong, Kwak, Haewoon, Gao, Wei, An, Jisun
Rok vydání:	2023
Předmět:	Computer Science - Social and Information Networks Computer Science - Computation and Language
Druh dokumentu:	Working Paper
DOI:	10.1145/3578503.3583606
Popis:	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. Comment: 10 pages, 2 pages, WebSci 2023, April 30-May 1, 2023, Evanston, TX, USA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.12029 Zobrazit plný text záznamu View this record from Arxiv