  The ever-growing textual records of contemporary social issues, often
discussed online with moral rhetoric, present both an opportunity and a
challenge for studying how moral concerns are debated in real life. Moral
foundations theory is a taxonomy of intuitions widely used in data-driven
analyses of online content, but current computational tools to detect moral
foundations suffer from the incompleteness and fragility of their lexicons and
from poor generalization across data domains. In this paper, we fine-tune a
large language model to measure moral foundations in text based on datasets
covering news media and long- and short-form online discussions. The resulting
model, called Mformer, outperforms existing approaches on the same domains by
4--12% in AUC and further generalizes well to four commonly used moral text
datasets, improving by up to 17% in AUC. We present case studies using Mformer
to analyze everyday moral dilemmas on Reddit and controversies on Twitter,
showing that moral foundations can meaningfully describe people's stance on
social issues and such variations are topic-dependent. Pre-trained model and
datasets are released publicly. We posit that Mformer will help the research
community quantify moral dimensions for a range of tasks and data domains, and
eventually contribute to the understanding of moral situations faced by humans
and machines.
