@inproceedings{hu-collier-2024-individualized,
title = "An Individualized News Affective Response Dataset",
author = "Hu, Tiancheng and
Collier, Nigel",
editor = "Fu, Xiyan and
Fleisig, Eve",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-srw.46",
pages = "555--563",
abstract = "The rise of sensationalism in news reporting, driven by market saturation and online competition, has compromised news quality and trust. At the core of sensationalism is the evocation of affective responses in the readers. Current NLP approaches to emotion detection often overlook the subjective differences in groups and individuals, relying on aggregation techniques that can obscure nuanced reactions. We introduce a novel large-scale dataset capturing subjective affective responses to news headlines. The dataset includes Facebook post screenshots from popular UK media outlets and uses a comprehensive annotation scheme. Annotators report their affective responses, provide discrete emotion labels, assess relevance to current events, and indicate sharing likelihood. Additionally, we collect demographic, personality, and media consumption data. This ongoing dataset aims to enable more accurate models of affective response by considering individual and contextual factors. This work is ongoing and we highly appreciate any feedback.",
}
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<abstract>The rise of sensationalism in news reporting, driven by market saturation and online competition, has compromised news quality and trust. At the core of sensationalism is the evocation of affective responses in the readers. Current NLP approaches to emotion detection often overlook the subjective differences in groups and individuals, relying on aggregation techniques that can obscure nuanced reactions. We introduce a novel large-scale dataset capturing subjective affective responses to news headlines. The dataset includes Facebook post screenshots from popular UK media outlets and uses a comprehensive annotation scheme. Annotators report their affective responses, provide discrete emotion labels, assess relevance to current events, and indicate sharing likelihood. Additionally, we collect demographic, personality, and media consumption data. This ongoing dataset aims to enable more accurate models of affective response by considering individual and contextual factors. This work is ongoing and we highly appreciate any feedback.</abstract>
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%0 Conference Proceedings
%T An Individualized News Affective Response Dataset
%A Hu, Tiancheng
%A Collier, Nigel
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F hu-collier-2024-individualized
%X The rise of sensationalism in news reporting, driven by market saturation and online competition, has compromised news quality and trust. At the core of sensationalism is the evocation of affective responses in the readers. Current NLP approaches to emotion detection often overlook the subjective differences in groups and individuals, relying on aggregation techniques that can obscure nuanced reactions. We introduce a novel large-scale dataset capturing subjective affective responses to news headlines. The dataset includes Facebook post screenshots from popular UK media outlets and uses a comprehensive annotation scheme. Annotators report their affective responses, provide discrete emotion labels, assess relevance to current events, and indicate sharing likelihood. Additionally, we collect demographic, personality, and media consumption data. This ongoing dataset aims to enable more accurate models of affective response by considering individual and contextual factors. This work is ongoing and we highly appreciate any feedback.
%U https://aclanthology.org/2024.acl-srw.46
%P 555-563
Markdown (Informal)
[An Individualized News Affective Response Dataset](https://aclanthology.org/2024.acl-srw.46) (Hu & Collier, ACL 2024)
ACL
- Tiancheng Hu and Nigel Collier. 2024. An Individualized News Affective Response Dataset. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 555–563, Bangkok, Thailand. Association for Computational Linguistics.