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Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives

Authors:

Brian Felipe Keith Norambuena,

Tanushree MitraAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue CSCW3

Article No.: 228, Pages 1 - 33

https://doi.org/10.1145/3432927

Published: 05 January 2021 Publication History

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 is backed by a corresponding element from formal narrative theory, thus providing a solid theoretical background to our method. Our approach extracts the underlying graph structure of the narrative map using a novel optimization technique focused on maximizing coherence while respecting structural and coverage constraints. We showcase the effectiveness of our approach by performing a user evaluation to assess the quality of the representation, metaphor, and visualization. Evaluation results indicate that the Narrative Map representation is a powerful method to communicate complex narratives to individuals. Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.

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Index Terms

Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives
1. Applied computing
  1. Computers in other domains
    1. Publishing
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue CSCW3

CSCW

December 2020

1825 pages

EISSN:2573-0142

DOI:10.1145/3446568

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Jeff Nichols
Apple Inc., United States

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Published: 05 January 2021

Published in PACMHCI Volume 4, Issue CSCW3

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Cited By

Molina IKeith BMatus M(2025)A Multimodal Dataset of Fact-Checked News from Chile’s Constitutional Processes: Collection, Processing, and AnalysisData10.3390/data1002001310:2(13)Online publication date: 28-Jan-2025
https://doi.org/10.3390/data10020013
Nouri AHossain M(2025)CoRBS: a dynamic storytelling algorithm using a novel contextualization approach for documents utilizing BERT featuresKnowledge and Information Systems10.1007/s10115-024-02263-867:2(1213-1248)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10115-024-02263-8
Gui FYang JTang YChen HAn N(2024)Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature RefinementApplied Sciences10.3390/app1402091814:2(918)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020918
Plötzky FKiehne NBalke W(2024)Lost in Recursion: Mining Rich Event Semantics in Knowledge GraphsProceedings of the 16th ACM Web Science Conference10.1145/3614419.3644001(354-364)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3614419.3644001
Muller MSaaman Evan der Werf JJeurgens CReijers H(2024)TimeFlows: Visualizing Process Chronologies from Vast Collections of Heterogeneous Information ObjectsResearch Challenges in Information Science10.1007/978-3-031-59465-6_13(203-219)Online publication date: 2-May-2024
https://doi.org/10.1007/978-3-031-59465-6_13
Keith Norambuena BMitra TNorth C(2023)A Survey on Event-Based News Narrative ExtractionACM Computing Surveys10.1145/358474155:14s(1-39)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3584741
Zhou JZhang YLuo QParker ADe Choudhury M(2023)Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human SolutionsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581318(1-20)Online publication date: 19-Apr-2023
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Botzer NWeninger T(2023)Entity graphs for exploring online discourseKnowledge and Information Systems10.1007/s10115-023-01877-865:9(3591-3609)Online publication date: 24-Apr-2023
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