Measuring Information Propagation in Literary Social Networks
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| Authors | Matthew Sims, David Bamman |
| Journal/Conference Name | arXiv preprint |
| Paper Category | Artificial Intelligence |
| Paper 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. |
| Date of publication | 2020 |
| Code Programming Language | Python |
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