Presentation on 'Modeling the Spread of Information within Novels'

On February 26, 2020, we welcomed David Bamman (UC Berkeley) for his presentation, "Modeling the Spread of Information Within Novels."

Understanding the ways in which information flows through social networks is important for questions of influence--including tracking the spread of cultural trends and disinformation and measuring shifts in public opinion. Much work in this space has focused on networks where nodes, edges and information are all directly observed (such as Twitter accounts with explicit friend/follower edges and retweets as instances of propagation); in this talk, I will focus on the comparatively overlooked case of information propagation in implicit networks--where we seek to discover single instances of a message passing from person A to person B to person C, only given a depiction of their activity in text.

Literature in many ways presents an ideal domain for modeling information propagation described in text, since it depicts a largely closed universe in which characters interact and speak to each other. At the same time, it poses several wholly distinct challenges--in particular, both the length of literary texts and the subtleties involved in extracting information from fictional works pose difficulties for NLP systems optimized for other domains. In this talk, I will describe our ongoing work in measuring information propagation in these implicit networks, and detail an NLP pipeline for discovering it, focusing in detail on new datasets we have created for tagging characters and their coreference in text. This is joint work with Matt Sims, Olivia Lewke, Anya Mansoor, Sejal Popat and Sheng Shen.