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LitStoryTeller+: an interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox

Author

Listed:
  • Qing Ping

    (Drexel University)

  • Chaomei Chen

    (Drexel University)

Abstract

The continuing growth of scientific publications has posed a double-challenge to researchers, to not only grasp the overall research trends in a scientific domain, but also get down to research details embedded in a collection of core papers. Existing work on science mapping provides multiple tools to visualize research trends in domain on macro-level, and work from the digital humanities have proposed text visualization of documents, topics, sentences, and words on micro-level. However, existing micro-level text visualizations are not tailored for scientific paper corpus, and cannot support meso-level scientific reading, which aligns a set of core papers based on their research progress, before drilling down to individual papers. To bridge this gap, the present paper proposes LitStoryTeller+, an interactive system under a unified framework that can support both meso-level and micro-level scientific paper visual storytelling. More specifically, we use entities (concepts and terminologies) as basic visual elements, and visualize entity storylines across papers and within a paper borrowing metaphors from screen play. To identify entities and entity communities, named entity recognition and community detection are performed. We also employ a variety of text mining methods such as extractive text summarization and comparative sentence classification to provide rich textual information supplementary to our visualizations. We also propose a top-down story-reading strategy that best takes advantage of our system. Two comprehensive hypothetical walkthroughs to explore documents from the computer science domain and history domain with our system demonstrate the effectiveness of our story-reading strategy and the usefulness of LitStoryTeller+.

Suggested Citation

  • Qing Ping & Chaomei Chen, 2018. "LitStoryTeller+: an interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1887-1944, September.
  • Handle: RePEc:spr:scient:v:116:y:2018:i:3:d:10.1007_s11192-018-2803-x
    DOI: 10.1007/s11192-018-2803-x
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    References listed on IDEAS

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    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Cody Dunne & Ben Shneiderman & Robert Gove & Judith Klavans & Bonnie Dorr, 2012. "Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2351-2369, December.
    3. David Chavalarias & Jean-Philippe Cointet, 2013. "Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    4. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    5. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    6. Cody Dunne & Ben Shneiderman & Robert Gove & Judith Klavans & Bonnie Dorr, 2012. "Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2351-2369, December.
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    Cited by:

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    2. Dugué, Nicolas & Perez, Anthony, 2022. "Direction matters in complex networks: A theoretical and applied study for greedy modularity optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

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