---
title: "Modeling Empathy and Distress in Reaction to — Buechel 2018"
description: "The paper addresses text-based empathy prediction, which earlier work handled with private datasets, labels assigned by outside annotators, and a single…"
authors: ["Sven Buechel", "Anneke Buffone", "Barry Slaff", "Lyle Ungar", "João Sedoc"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1507/"
doi: "10.18653/v1/d18-1507"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/buechel-2018-modeling-empathy-distress-reaction-news/"
---
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# Modeling Empathy and Distress in Reaction to News Stories

**Authors:** Sven Buechel, Anneke Buffone, Barry Slaff, [Lyle Ungar](https://md.research.elamur.ai/free/en/authors/lyle-ungar/), [João Sedoc](https://md.research.elamur.ai/free/en/authors/joao-sedoc/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1507/) · [PDF](https://aclanthology.org/D18-1507.pdf) · [DOI 10.18653/v1/d18-1507](https://doi.org/10.18653/v1/d18-1507) · [OpenAlex](https://openalex.org/W2889287254)

**Citations:** 102 citations (openalex-snapshot · 2026-10-14) · [79](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses text-based empathy prediction, which earlier work handled with private datasets, labels assigned by outside annotators, and a single, simplified notion of empathy. The authors built a new public corpus by asking MTurk workers to read news articles chosen to provoke empathic reactions. After each article, participants first rated their own feelings using Batson's Empathic Concern – Personal Distress Scale and then wrote a short message about the story, as if addressed to friends. The labels therefore come from the writers themselves. They also separate two psychologically distinct states: other-oriented empathic concern and self-oriented personal distress. Because each text has only one rater, reliability comes from multi-item scales and is checked with split-half reliability, which turns out high. Three regressors built on FastText embeddings were compared, and a CNN performed best. The work matters because it provides the first shared gold standard for this task and models empathy closer to how psychology defines it.

## Key points
- Corpus construction: 418 news articles, 403 MTurk participants (each read 5 articles, paid 4 USD, median time 32 minutes). After manual filtering, 1860 messages remained, totaling 173,686 NLTK tokens (median 84 tokens per message).
- Ratings use 6 empathy items and 8 distress items, each on a 7-point scale, averaged per construct.
- Empathy and distress correlate only moderately (Pearson r=.451), which supports treating them as separate targets.
- Split-half reliability is r=.875 for empathy and r=.924 for distress.
- Results under 10-fold CV, in Pearson r:
  - Ridge: .385 (empathy) / .410 (distress).
  - FFN: .379 / .401.
  - CNN: .404 / .444, significantly better than the other two.
  - Recurrent models struggled during development because the texts are long.
- Data and code: github.com/wwbp/empathic_reactions

## Interesting ideas
- **Writer-reported labels.** Having authors rate their own emotional state right before writing gives a stronger ground truth than third-party judgments. This applies to any task where the label is an internal state, such as stress or mood.
- **Multi-item scales instead of many annotators.** Averaging several related questionnaire items can stand in for multiple raters when each text has only one annotator. This makes single-annotator datasets more trustworthy.
- **Split-half reliability as an agreement metric.** The scale items are randomly divided into two halves, and the correlation between the half-averages is repeated over many splits. This gives a reliability estimate when standard inter-annotator agreement cannot be computed.
- **Splitting a vague construct into separate targets.** Modeling empathy and distress separately exposed different behavior: the CNN's gain over the centroid models was larger for distress. The authors read this as a sign that word order and composition matter more for distress than for empathy. Breaking fuzzy labels into theory-based components could reveal similar differences in other tasks.
- **A fair comparison setup.** All models were evaluated on identical CV splits with a held-out dev set and paired t-tests across folds. This is a cheap, rigorous way to compare models on small datasets.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

79 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 75 not yet.

- [MIME: MIMicking Emotions for Empathetic Response Generation](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://md.research.elamur.ai/free/en/papers/sharma-2020-computational-approach-understanding-empathy-expressed/)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
- [Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{buechel2018modeling,
  title = {{Modeling Empathy and Distress in Reaction to News Stories}},
  author = {Buechel, Sven and Buffone, Anneke and Slaff, Barry and Ungar, Lyle and Sedoc, João},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1507},
  url = {https://doi.org/10.18653/v1/d18-1507}
}
```

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## References

39 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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