AnthroScore: A Computational Linguistic Measure of Anthropomorphism

Myra Cheng, Kristina Gligoric, Tiziano Piccardi, Dan Jurafsky


Abstract
Anthropomorphism, or the attribution of human-like characteristics to non-human entities, has shaped conversations about the impacts and possibilities of technology. We present AnthroScore, an automatic metric of implicit anthropomorphism in language. We use a masked language model to quantify how non-human entities are implicitly framed as human by the surrounding context. We show that AnthroScore corresponds with human judgments of anthropomorphism and dimensions of anthropomorphism described in social science literature. Motivated by concerns of misleading anthropomorphism in computer science discourse, we use AnthroScore to analyze 15 years of research papers and downstream news articles. In research papers, we find that anthropomorphism has steadily increased over time, and that papers related to language models have the most anthropomorphism. Within ACL papers, temporal increases in anthropomorphism are correlated with key neural advancements. Building upon concerns of scientific misinformation in mass media, we identify higher levels of anthropomorphism in news headlines compared to the research papers they cite. Since AnthroScore is lexicon-free, it can be directly applied to a wide range of text sources.
Anthology ID:
2024.eacl-long.49
Volume:
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
807–825
Language:
URL:
https://aclanthology.org/2024.eacl-long.49
DOI:
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Cite (ACL):
Myra Cheng, Kristina Gligoric, Tiziano Piccardi, and Dan Jurafsky. 2024. AnthroScore: A Computational Linguistic Measure of Anthropomorphism. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 807–825, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
AnthroScore: A Computational Linguistic Measure of Anthropomorphism (Cheng et al., EACL 2024)
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