  Measuring similarity is a basic task in information retrieval, and now often
a building-block for more complex arguments about cultural change. But do
measures of textual similarity and distance really correspond to evidence about
cultural proximity and differentiation? To explore that question empirically,
this paper compares textual and social measures of the similarities between
genres of English-language fiction. Existing measures of textual similarity
(cosine similarity on tf-idf vectors or topic vectors) are also compared to new
strategies that use supervised learning to anchor textual measurement in a
social context.
