  Affect preferences vary with user demographics, and tapping into demographic
information provides important cues about the users' language preferences. In
this paper, we utilize the user demographics, and propose EmpathBERT, a
demographic-aware framework for empathy prediction based on BERT. Through
several comparative experiments, we show that EmpathBERT surpasses traditional
machine learning and deep learning models, and illustrate the importance of
user demographics to predict empathy and distress in user responses to
stimulative news articles. We also highlight the importance of affect
information in the responses by developing affect-aware models to predict user
demographic attributes.
