mlsanityData to 7 Oct 2026

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

arXiv 2102.00272 ↗First posted 30 Jan 2021cs.LG cs.CLcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 15 of its 55 references that are in the map. First posted on arXiv 30 Jan 2021.

Builds on

15 of 55 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  4. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,041
  5. 2013Network In Networkcited by 600
  6. 2019Text Summarization with Pretrained Encoderscited by 553
  7. 2019How to Fine-Tune BERT for Text Classification?cited by 409
  8. 2019Multi-Task Deep Neural Networks for Natural Language Understandingcited by 349
  9. 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 209
  10. 2018Adversarial Removal of Demographic Attributes from Text Datacited by 166
  11. 2019MoEL: Mixture of Empathetic Listenerscited by 91
  12. 2019GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledgecited by 78
3 more
  1. 2018Modeling Empathy and Distress in Reaction to News Storiescited by 52
  2. 2017Annotating and Modeling Empathy in Spoken Conversationscited by 10
  3. 2019Learning Word Ratings for Empathy and Distress from Document-Level User Responsescited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 4; 2025: 4; 2026: 1.

17 papers.

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  1. 2023Nationality Bias in Text Generationcited by 50
  2. 2023Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articlescited by 16
  3. 2022Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformerscited by 10
  4. 2021Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Modelscited by 5
  5. 2022A Critical Reflection and Forward Perspective on Empathy and Natural Language Processingcited by 4
  6. 2024Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?cited by 4
  7. 2025Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanishcited by 4
  8. 2025Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communicationcited by 4
  9. 2024Modeling Empathetic Alignment in Conversationcited by 2
  10. 2024Turn-Level Empathy Prediction Using Psychological Indicatorscited by 1
  11. 2022Sentiment Analysis: Predicting Yelp Scorescited by 0
  12. 2022On the Limitations of Sociodemographic Adaptation with Transformerscited by 0
5 more
  1. 2023Empathy and Distress Detection using Ensembles of Transformer Modelscited by 0
  2. 2024Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Modelscited by 0
  3. 2025Toward Human-Centered Readability Evaluationcited by 0
  4. 2025Empathy Level Prediction in Multi-Modal Scenario with Supervisory Documentation Assistancecited by 0
  5. 2026Generating Place-Based Compromises Between Two Points of Viewcited by 0