@inproceedings{buechel-etal-2018-modeling,
title = "Modeling Empathy and Distress in Reaction to News Stories",
author = "Buechel, Sven and
Buffone, Anneke and
Slaff, Barry and
Ungar, Lyle and
Sedoc, Jo{\~a}o",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1507/",
doi = "10.18653/v1/D18-1507",
pages = "4758--4765",
abstract = "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."
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Modeling Empathy and Distress in Reaction to News Stories
%A Buechel, Sven
%A Buffone, Anneke
%A Slaff, Barry
%A Ungar, Lyle
%A Sedoc, João
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F buechel-etal-2018-modeling
%X 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.
%R 10.18653/v1/D18-1507
%U https://aclanthology.org/D18-1507/
%U https://doi.org/10.18653/v1/D18-1507
%P 4758-4765
Markdown (Informal)
[Modeling Empathy and Distress in Reaction to News Stories](https://aclanthology.org/D18-1507/) (Buechel et al., EMNLP 2018)
ACL
- Sven Buechel, Anneke Buffone, Barry Slaff, Lyle Ungar, and João Sedoc. 2018. Modeling Empathy and Distress in Reaction to News Stories. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4758–4765, Brussels, Belgium. Association for Computational Linguistics.