  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.
