  In this paper, we present a variety of classification experiments related to
the task of fictional discourse detection. We utilize a diverse array of
datasets, including contemporary professionally published fiction, historical
fiction from the Hathi Trust, fanfiction, stories from Reddit, folk tales,
GPT-generated stories, and anglophone world literature. Additionally, we
introduce a new feature set of word "supersenses" that facilitate the goal of
semantic generalization. The detection of fictional discourse can help enrich
our knowledge of large cultural heritage archives and assist with the process
of understanding the distinctive qualities of fictional storytelling more
broadly.
