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Narraport: Narrative-Based Interactions and Report Generation with Large Datasets

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Interactive Storytelling (ICIDS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13138))

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Abstract

There is an increasing demand for rapid content filtering in relation to topics like digital forensics for legal cases, cybersecurity, and social media conduct monitoring. While there have been significant advances in algorithms and frameworks for media processing, this task requires an ensemble of tools and algorithms that are not well-understood by human analysts, thereby reducing their trustworthiness. In this paper, we present a novel perspective on this problem through the development of an intelligent system that generates reports from large email datasets in the form of short stories. The stories generated by the system are based on identifiable plot structures in popular media. These structures are used as semantic sensemaking templates to organize data for further filtering and triage. The end-to-end system, accessible through an interactive dashboard, incorporates unsupervised annotation modules (such as speech acts and sentiment), topic discovery, communication network analysis, character personality profiles, and automated text and visualization generators. This emerging application prototype is developed and internally deployed in collaboration with analysts and researchers actively working in this area.

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Authors and Affiliations

  1. North Carolina State University, Raleigh, NC, USA

    Colin M. Potts & Arnav Jhala

Authors
  1. Colin M. Potts
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  2. Arnav Jhala
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Corresponding author

Correspondence to Colin M. Potts .

Editor information

Editors and Affiliations

  1. National University of Singapore, Singapore, Singapore

    Alex Mitchell

  2. Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Mirjam Vosmeer

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Potts, C.M., Jhala, A. (2021). Narraport: Narrative-Based Interactions and Report Generation with Large Datasets. In: Mitchell, A., Vosmeer, M. (eds) Interactive Storytelling. ICIDS 2021. Lecture Notes in Computer Science(), vol 13138. Springer, Cham. https://doi.org/10.1007/978-3-030-92300-6_11

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  • DOI: https://doi.org/10.1007/978-3-030-92300-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92299-3

  • Online ISBN: 978-3-030-92300-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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