What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric

Enrico Liscio; Oscar Araque; Lorenzo Gatti; Ionut Constantinescu; Catholijn M. Jonker; Kyriaki Kalimeri; Pradeep K. Murukannaiah

What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric

Enrico Liscio, Oscar Araque, Lorenzo Gatti, Ionut Constantinescu, Catholijn M. Jonker, Kyriaki Kalimeri, Pradeep K. Murukannaiah

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

35 Downloads (Pure)

Abstract

Moral rhetoric influences our judgement. Although social scientists recognize moral expression as domain specific, there are no systematic methods for analyzing whether a text classifier learns the domain-specific expression of moral language or not. We propose Tomea, a method to compare a supervised classifier's representation of moral rhetoric across domains. Tomea enables quantitative and qualitative comparisons of moral rhetoric via an interpretable exploration of similarities and differences across moral concepts and domains. We apply Tomea on moral narratives in thirty-five thousand tweets from seven domains. We extensively evaluate the method via a crowd study, a series of cross-domain moral classification comparisons, and a qualitative analysis of cross-domain moral expression.

Original language	English
Title of host publication	Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher	Association for Computational Linguistics (ACL)
Pages	14113-14132
Number of pages	20
Edition	2023
ISBN (Electronic)	9781959429722
Publication status	Published - 2023
Event	61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 61

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
Volume	1
ISSN (Print)	0736-587X

Conference

Conference	61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
Abbreviated title	ACL 2023
Country/Territory	Canada
City	Toronto
Period	9/07/23 → 14/07/23

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ArticleFinal published version, 1.1 MBLicence: CC BY

https://aclanthology.org/2023.acl-long.789Licence: CC BY

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Liscio, E., Araque, O., Gatti, L., Constantinescu, I., Jonker, C. M., Kalimeri, K., & Murukannaiah, P. K. (2023). What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2023 ed., pp. 14113-14132). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 1). Association for Computational Linguistics (ACL). https://aclanthology.org/2023.acl-long.789

Liscio, Enrico ; Araque, Oscar ; Gatti, Lorenzo et al. / What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023. ed. Association for Computational Linguistics (ACL), 2023. pp. 14113-14132 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

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title = "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric",

abstract = "Moral rhetoric influences our judgement. Although social scientists recognize moral expression as domain specific, there are no systematic methods for analyzing whether a text classifier learns the domain-specific expression of moral language or not. We propose Tomea, a method to compare a supervised classifier's representation of moral rhetoric across domains. Tomea enables quantitative and qualitative comparisons of moral rhetoric via an interpretable exploration of similarities and differences across moral concepts and domains. We apply Tomea on moral narratives in thirty-five thousand tweets from seven domains. We extensively evaluate the method via a crowd study, a series of cross-domain moral classification comparisons, and a qualitative analysis of cross-domain moral expression.",

author = "Enrico Liscio and Oscar Araque and Lorenzo Gatti and Ionut Constantinescu and Jonker, {Catholijn M.} and Kyriaki Kalimeri and Murukannaiah, {Pradeep K.}",

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Liscio, E, Araque, O, Gatti, L, Constantinescu, I, Jonker, CM, Kalimeri, K & Murukannaiah, PK 2023, What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric. in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023 edn, Proceedings of the Annual Meeting of the Association for Computational Linguistics, vol. 1, Association for Computational Linguistics (ACL), pp. 14113-14132, 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023, Toronto, Canada, 9/07/23. <https://aclanthology.org/2023.acl-long.789>

What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric. / Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo et al.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023. ed. Association for Computational Linguistics (ACL), 2023. p. 14113-14132 (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Araque, Oscar

AU - Gatti, Lorenzo

AU - Constantinescu, Ionut

AU - Jonker, Catholijn M.

AU - Kalimeri, Kyriaki

AU - Murukannaiah, Pradeep K.

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N2 - Moral rhetoric influences our judgement. Although social scientists recognize moral expression as domain specific, there are no systematic methods for analyzing whether a text classifier learns the domain-specific expression of moral language or not. We propose Tomea, a method to compare a supervised classifier's representation of moral rhetoric across domains. Tomea enables quantitative and qualitative comparisons of moral rhetoric via an interpretable exploration of similarities and differences across moral concepts and domains. We apply Tomea on moral narratives in thirty-five thousand tweets from seven domains. We extensively evaluate the method via a crowd study, a series of cross-domain moral classification comparisons, and a qualitative analysis of cross-domain moral expression.

AB - Moral rhetoric influences our judgement. Although social scientists recognize moral expression as domain specific, there are no systematic methods for analyzing whether a text classifier learns the domain-specific expression of moral language or not. We propose Tomea, a method to compare a supervised classifier's representation of moral rhetoric across domains. Tomea enables quantitative and qualitative comparisons of moral rhetoric via an interpretable exploration of similarities and differences across moral concepts and domains. We apply Tomea on moral narratives in thirty-five thousand tweets from seven domains. We extensively evaluate the method via a crowd study, a series of cross-domain moral classification comparisons, and a qualitative analysis of cross-domain moral expression.

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BT - Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Liscio E, Araque O, Gatti L, Constantinescu I, Jonker CM, Kalimeri K et al. What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023 ed. Association for Computational Linguistics (ACL). 2023. p. 14113-14132. (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric

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