  Readers' responses to literature have received scant attention in
computational literary studies. The rise of social media offers an opportunity
to capture a segment of these responses while data-driven analysis of these
responses can provide new critical insight into how people "read". Posts
discussing an individual book on Goodreads, a social media platform that hosts
user discussions of popular literature, are referred to as "reviews", and
consist of plot summaries, opinions, quotes, or some mixture of these. Since
these reviews are written by readers, computationally modeling them allows one
to discover the overall non-professional discussion space about a work,
including an aggregated summary of the work's plot, an implicit ranking of the
importance of events, and the readers' impressions of main characters. We
develop a pipeline of interlocking computational tools to extract a
representation of this reader generated shared narrative model. Using a corpus
of reviews of five popular novels, we discover the readers' distillation of the
main storylines in a novel, their understanding of the relative importance of
characters, as well as the readers' varying impressions of these characters. In
so doing, we make three important contributions to the study of infinite
vocabulary networks: (i) an automatically derived narrative network that
includes meta-actants; (ii) a new sequencing algorithm, REV2SEQ, that generates
a consensus sequence of events based on partial trajectories aggregated from
the reviews; and (iii) a new "impressions" algorithm, SENT2IMP, that provides
finer, non-trivial and multi-modal insight into readers' opinions of
characters.
