AnthroScore: A Computational Linguistic Measure of Anthropomorphism
Myra Cheng Kristina Gligoric Tiziano Piccardi Dan Jurafsky
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.