Modeling the Spread of Information within Novels - Information Propagation in Implicit Networks

Explore the challenges and methodologies of tracking information propagation in implicit networks within literary texts in this hour-long lecture by David Bamman from UC Berkeley. Delve into the unique difficulties posed by fictional works for NLP systems and learn about innovative approaches to character tagging and coreference resolution. Discover how this research contributes to understanding cultural trends, disinformation spread, and public opinion shifts through the lens of literature. Gain insights into the intersection of natural language processing, cultural analytics, and computational humanities, and understand the potential applications of these techniques beyond literary analysis.

Introduction
NLP and fiction
Computational Humanities
How do we get there
Solutions
Name Entity Recognition
Nested Density Recognition
Project Gutenberg
Black Beauty
Entities
Coreference Resolution
Fundamental Research Question
Open Directions
Questions