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

arXiv: https://arxiv.org/abs/2102.00272 · HTML: https://mlsanity.com/paper/2102.00272/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 55 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Adversarial Removal of Demographic Attributes from Text Data](https://mlsanity.com/paper/1808.06640/) (2018)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Modeling Empathy and Distress in Reaction to News Stories](https://mlsanity.com/paper/1808.10399/) (2018)
- [Annotating and Modeling Empathy in Spoken Conversations](https://mlsanity.com/paper/1705.04839/) (2017)
- [Learning Word Ratings for Empathy and Distress from Document-Level User Responses](https://mlsanity.com/paper/1912.01079/) (2019)

## Built on by (most cited first, 17 of 17)

- [Nationality Bias in Text Generation](https://mlsanity.com/paper/2302.02463/) (2023)
- [Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles](https://mlsanity.com/paper/2308.04346/) (2023)
- [Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers](https://mlsanity.com/paper/2210.07362/) (2022)
- [Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models](https://mlsanity.com/paper/2103.06057/) (2021)
- [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://mlsanity.com/paper/2210.16604/) (2022)
- [Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?](https://mlsanity.com/paper/2401.12492/) (2024)
- [Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish](https://mlsanity.com/paper/2502.08458/) (2025)
- [Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication](https://mlsanity.com/paper/2505.21451/) (2025)
- [Modeling Empathetic Alignment in Conversation](https://mlsanity.com/paper/2405.00948/) (2024)
- [Turn-Level Empathy Prediction Using Psychological Indicators](https://mlsanity.com/paper/2407.08607/) (2024)
- [Sentiment Analysis: Predicting Yelp Scores](https://mlsanity.com/paper/2201.07999/) (2022)
- [On the Limitations of Sociodemographic Adaptation with Transformers](https://mlsanity.com/paper/2208.01029/) (2022)
- [Empathy and Distress Detection using Ensembles of Transformer Models](https://mlsanity.com/paper/2312.02578/) (2023)
- [Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models](https://mlsanity.com/paper/2403.00807/) (2024)
- [Toward Human-Centered Readability Evaluation](https://mlsanity.com/paper/2510.10801/) (2025)
- [Empathy Level Prediction in Multi-Modal Scenario with Supervisory Documentation Assistance](https://mlsanity.com/paper/2512.02558/) (2025)
- [Generating Place-Based Compromises Between Two Points of View](https://mlsanity.com/paper/2604.24536/) (2026)

## Abstract (arXiv)

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.