Visualizing Literature Review Theme Evolution on Timeline Maps: Comparison Across Disciplines

Chunlei Tang; Yangyong Zhu; Suzanne V. Blackley; Joseph M. Plasek; Meihan Wan; Li Zhou; Jing Ma; David W. Bates

doi:10.1109/ACCESS.2019.2925706

IEEE Access (Jan 2019)

Visualizing Literature Review Theme Evolution on Timeline Maps: Comparison Across Disciplines

Chunlei Tang,
Yangyong Zhu,
Suzanne V. Blackley,
Joseph M. Plasek,
Meihan Wan,
Li Zhou,
Jing Ma,
David W. Bates

Affiliations

Chunlei Tang: ORCiD; Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Yangyong Zhu: Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
Suzanne V. Blackley: Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Joseph M. Plasek: ORCiD; Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Meihan Wan: Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
Li Zhou: Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Jing Ma: ORCiD; Department of Population Medicine, Harvard Medical School, Boston, MA, USA
David W. Bates: ORCiD; Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

DOI: https://doi.org/10.1109/ACCESS.2019.2925706
Journal volume & issue: Vol. 7
pp. 90597 – 90607

Abstract

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Data-driven visualization techniques can be utilized to enhance the literature review process across different disciplines. In our work, 910 articles were retrieved using keyword search from bibliographic databases of two different disciplines (computer science: DBLP and medicine: MEDLINE) between 2001 and 2016. These articles' titles were processed using dynamic latent Dirichlet allocation to generate a set of themes/topics, which were subsequently classified and assigned to regions in a spatiotemporal geographical map. Resulting data visualizations from both repositories were manually reviewed by independent annotators. The results from the DBLP and MEDLINE were comparable and, taken together, suggest potential benefits of increased future interaction amongst multidisciplinary fields. Our findings indicate that spiral timeline maps have the potential to help researchers acquire or compare knowledge efficiently without prior domain knowledge.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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