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.

翻译：影响偏好因用户人口而异,利用人口信息为用户语言偏好提供了重要提示。 在本文中,我们利用用户人口,并提议EmpathBERT(EmpathBERT),这是基于BERT进行同情预测的人口意识框架。 通过几项比较实验,我们表明EmpathBERT超越了传统的机器学习和深层次学习模式,并说明了用户人口统计对于预测用户对刺激性新闻文章的反应中的同情和忧虑的重要性。我们还强调了通过开发影响意识模型来预测用户人口特征,影响信息在回应中的重要性。