Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives
Elisa Kreiss, Zijian Wang, Christopher E. Potts · 2020
Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies.How does the language of these reports act on readers?We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from Englishlanguage newspapers in the U.S. For Suspect-Guilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings.Sus-pectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments.We use SuspectGuilt to train and assess predictive models which validate the usefulness of the corpus, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and spanlevel annotations.Such models might be used as tools for understanding the societal effects of crime reporting.