Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives
Brian Keith, Tanushree Mitra

TL;DR
This paper introduces a novel computational method to represent and extract narratives from online data using a route map metaphor, supported by formal narrative theory and validated through user evaluation.
Contribution
It presents a theory-driven computational narrative representation, a new extraction algorithm, and an evaluation demonstrating its effectiveness for visualizing complex stories.
Findings
Narrative maps effectively communicate complex narratives.
The extraction algorithm maximizes coherence and respects structural constraints.
User evaluations favor the narrative map approach for clarity and insight.
Abstract
Narratives are fundamental to our perception of the world and are pervasive in all activities that involve the representation of events in time. Yet, modern online information systems do not incorporate narratives in their representation of events occurring over time. This article aims to bridge this gap, combining the theory of narrative representations with the data from modern online systems. We make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach. In particular, given the effectiveness of visual metaphors, we employ a route map metaphor to design a narrative map representation. The narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map. Each element of our representation…
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Taxonomy
TopicsTopic Modeling · Artificial Intelligence in Games · Digital Humanities and Scholarship
