# Modeling Empathy and Distress in Reaction to News Stories

arXiv: https://arxiv.org/abs/1808.10399 · HTML: https://mlsanity.com/paper/1808.10399/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 30 of 52)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
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- [Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark](https://mlsanity.com/paper/2305.14938/) (2023)
- [The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks](https://mlsanity.com/paper/2304.13861/) (2023)
- [Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models](https://mlsanity.com/paper/2311.04915/) (2023)
- [Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations](https://mlsanity.com/paper/2102.08368/) (2021)
- [Empathic Conversations: A Multi-level Dataset of Contextualized Conversations](https://mlsanity.com/paper/2205.12698/) (2022)
- [EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction](https://mlsanity.com/paper/2102.00272/) (2021)
- [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://mlsanity.com/paper/2405.17633/) (2024)
- [MEDIC: A Multimodal Empathy Dataset in Counseling](https://mlsanity.com/paper/2305.02842/) (2023)
- [SoMeLVLM: A Large Vision Language Model for Social Media Processing](https://mlsanity.com/paper/2402.13022/) (2024)
- [AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworker](https://mlsanity.com/paper/2411.02408/) (2024)
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- [Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers](https://mlsanity.com/paper/2210.07362/) (2022)
- [EmpHi: Generating Empathetic Responses with Human-like Intents](https://mlsanity.com/paper/2204.12191/) (2022)
- [Mixture of Cognitive Reasoners: Modular Reasoning with Brain-Like Specialization](https://mlsanity.com/paper/2506.13331/) (2025)
- [Supporting Cognitive and Emotional Empathic Writing of Students](https://mlsanity.com/paper/2105.14815/) (2021)
- [Computational Politeness in Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.12814/) (2024)
- [PVG at WASSA 2021: A Multi-Input, Multi-Task, Transformer-Based Architecture for Empathy and Distress Prediction](https://mlsanity.com/paper/2103.03296/) (2021)
- [SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks](https://mlsanity.com/paper/2402.01980/) (2024)
- [WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction](https://mlsanity.com/paper/2104.09827/) (2021)
- [Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries](https://mlsanity.com/paper/2504.09271/) (2025)
- [Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models](https://mlsanity.com/paper/2103.06057/) (2021)
- [Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods](https://mlsanity.com/paper/2311.00721/) (2023)
- [PsyDI: Towards a Personalized and Progressively In-depth Chatbot for Psychological Measurements](https://mlsanity.com/paper/2408.03337/) (2024)
- [Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces](https://mlsanity.com/paper/2504.13277/) (2025)
- [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://mlsanity.com/paper/2210.16604/) (2022)
- [EmPO: Emotion Grounding for Empathetic Response Generation through Preference Optimization](https://mlsanity.com/paper/2406.19071/) (2024)
- [Impact of Decoding Methods on Human Alignment of Conversational LLMs](https://mlsanity.com/paper/2407.19526/) (2024)

## Abstract (arXiv)

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.