Measuring Information Propagation in Literary Social Networks

Matthew Sims, David Bamman


Abstract
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.
Anthology ID:
2020.emnlp-main.47
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
642–652
Language:
URL:
https://aclanthology.org/2020.emnlp-main.47
DOI:
10.18653/v1/2020.emnlp-main.47
Bibkey:
Cite (ACL):
Matthew Sims and David Bamman. 2020. Measuring Information Propagation in Literary Social Networks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 642–652, Online. Association for Computational Linguistics.
Cite (Informal):
Measuring Information Propagation in Literary Social Networks (Sims & Bamman, EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.47.pdf
Video:
 https://slideslive.com/38939220
Code
 dbamman/litbank +  additional community code
Data
LitBank