EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction

TL;DR

EmpathBERT is a BERT-based framework that leverages user demographic information to improve empathy and distress prediction in responses to news articles, outperforming traditional models.

Contribution

The paper introduces EmpathBERT, a novel demographic-aware BERT-based model that enhances empathy prediction by incorporating user demographic data.

Findings

EmpathBERT surpasses traditional machine learning models.

Demographic information significantly improves empathy prediction.

Affect-aware models effectively predict user demographics.

Abstract

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.