@conference{tomea2023liscio-gsi-conference-2023,
author = "Enrico Liscio and Araque, Oscar and Lorenzo Gatti and Ionut Constantinescu and Catholijn Jonker and Kyriaki Kalimeri and Pradeep Kumar",
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.",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
doi = "http://dx.doi.org/10.18653/v1/2023.acl-long.789",
editor = "    Association for Computational Linguistics",
isbn = "978-1-959429-72-2",
keywords = "machine learning;natural language processing;moral foundations theory;lexicons",
pages = "14113–14132",
title = "{W}hat does a {T}ext {C}lassifier {L}earn about {M}orality? {A}n {E}xplainable {M}ethod for {C}ross-{D}omain {C}omparison of {M}oral {R}hetoric",
url = "https://aclanthology.org/2023.acl-long.789/",
year = "2023",
}