@inproceedings{reinig-etal-2024-survey,
title = "A Survey on Modelling Morality for Text Analysis",
author = "Reinig, Ines and
Becker, Maria and
Rehbein, Ines and
Ponzetto, Simone",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.245",
pages = "4136--4155",
abstract = "In this survey, we provide a systematic review of recent work on modelling morality in text, an area of research that has garnered increasing attention in recent years. Our survey is motivated by the importance of modelling decisions on the created resources, the models trained on these resources and the analyses that result from the models{'} predictions. We review work at the interface of NLP, Computational Social Science and Psychology and give an overview of the different goals and research questions addressed in the papers, their underlying theoretical backgrounds and the methods that have been applied to pursue these goals. We then identify and discuss challenges and research gaps, such as the lack of a theoretical framework underlying the operationalisation of morality in text, the low IAA reported for manyhuman-annotated resulting resources and the lack of validation of newly proposed resources and analyses.",
}
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<abstract>In this survey, we provide a systematic review of recent work on modelling morality in text, an area of research that has garnered increasing attention in recent years. Our survey is motivated by the importance of modelling decisions on the created resources, the models trained on these resources and the analyses that result from the models’ predictions. We review work at the interface of NLP, Computational Social Science and Psychology and give an overview of the different goals and research questions addressed in the papers, their underlying theoretical backgrounds and the methods that have been applied to pursue these goals. We then identify and discuss challenges and research gaps, such as the lack of a theoretical framework underlying the operationalisation of morality in text, the low IAA reported for manyhuman-annotated resulting resources and the lack of validation of newly proposed resources and analyses.</abstract>
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%0 Conference Proceedings
%T A Survey on Modelling Morality for Text Analysis
%A Reinig, Ines
%A Becker, Maria
%A Rehbein, Ines
%A Ponzetto, Simone
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F reinig-etal-2024-survey
%X In this survey, we provide a systematic review of recent work on modelling morality in text, an area of research that has garnered increasing attention in recent years. Our survey is motivated by the importance of modelling decisions on the created resources, the models trained on these resources and the analyses that result from the models’ predictions. We review work at the interface of NLP, Computational Social Science and Psychology and give an overview of the different goals and research questions addressed in the papers, their underlying theoretical backgrounds and the methods that have been applied to pursue these goals. We then identify and discuss challenges and research gaps, such as the lack of a theoretical framework underlying the operationalisation of morality in text, the low IAA reported for manyhuman-annotated resulting resources and the lack of validation of newly proposed resources and analyses.
%U https://aclanthology.org/2024.findings-acl.245
%P 4136-4155
Markdown (Informal)
[A Survey on Modelling Morality for Text Analysis](https://aclanthology.org/2024.findings-acl.245) (Reinig et al., Findings 2024)
ACL
- Ines Reinig, Maria Becker, Ines Rehbein, and Simone Ponzetto. 2024. A Survey on Modelling Morality for Text Analysis. In Findings of the Association for Computational Linguistics ACL 2024, pages 4136–4155, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.