Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2023

Abstract

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.

Keywords

Stance detection, Natural language processing, COVID-19, Distant supervision

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 15th ACM Web Science Conference 2023

First Page

23

Last Page

32

ISBN

979-8-4007-0089-7

Identifier

10.1145/3578503.3583606

Publisher

ACM

City or Country

New York, NY, United States

Copyright Owner and License

Authors

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