  We present the task of modeling information propagation in literature, in
which we seek to identify pieces of information passing from character A to
character B to character C, only given a description of their activity in text.
We describe a new pipeline for measuring information propagation in this domain
and publish a new dataset for speaker attribution, enabling the evaluation of
an important component of this pipeline on a wider range of literary texts than
previously studied. Using this pipeline, we analyze the dynamics of information
propagation in over 5,000 works of fiction, finding that information flows
through characters that fill structural holes connecting different communities,
and that characters who are women are depicted as filling this role much more
frequently than characters who are men.
