Measuring Information Propagation in Literary Social Networks
This work addresses the task of understanding information dynamics in literature, providing insights into character roles and gender representation, but it is incremental as it builds on existing social network analysis with a new dataset and domain-specific application.
The paper tackled the problem of modeling information propagation in literary social networks by developing a new pipeline and analyzing over 5,000 works of fiction, finding that information flows through characters in structural holes and that women fill this role more frequently than men.
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.