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TEVisE: An Interactive Visual Analytics Tool to Explore Evolution of Keywords’ Relations in Tweet Data

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Human-Computer Interaction – INTERACT 2021 (INTERACT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12934))

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Abstract

Recently, a new window to explore tweet data has been opened in TExVis tool through visualizing the relations between the frequent keywords. However, timeline exploration of tweet data, not present in TExVis, could play a critical factor in understanding the changes in people’s feedback and reaction over time. Targeting this, we present our visual analytics tool, called TEVisE. It uses an enhanced adjacency matrix diagram to overcome the cluttering problem in TExVis and visualizes the evolution of frequent keywords and the relations between these keywords over time. We conducted two user studies to find answers of our two formulated research questions. In the first user study, we focused on evaluating the used visualization layouts in both tools from the perspectives of common usability metrics and cognitive load theory. We found better accuracy in our TEVisE tool for tasks related to reading exploring relations between frequent keywords. In the second study, we collected users’ feedback towards exploring the summary view and the new timeline evolution view inside TEVisE. In the second study, we collected users’ feedback towards exploring the summary view and the new timeline evolution view inside TEVisE. We found that participants preferred both view, one to get overall glance while the other to get the trends changes over time.

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Author information

Authors and Affiliations

  1. San Francisco State University, San Francisco, CA, USA

    Shah Rukh Humayoun

  2. University of Kaiserslautern, Kaiserslautern, Germany

    Ibrahim Mansour

  3. Intel Labs, Intel Corporation, Santa Clara, CA, USA

    Ragaad AlTarawneh

Authors
  1. Shah Rukh Humayoun
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  2. Ibrahim Mansour
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  3. Ragaad AlTarawneh
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Corresponding author

Correspondence to Shah Rukh Humayoun .

Editor information

Editors and Affiliations

  1. Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy

    Carmelo Ardito

  2. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Rosa Lanzilotti

  3. Computer Science Department, University of Pisa, Pisa, Italy

    Alessio Malizia

  4. Department of Computer Science, University of York, York, UK

    Helen Petrie

  5. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  6. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Giuseppe Desolda

  7. Microsoft Research, Redmond, WA, USA

    Kori Inkpen

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Humayoun, S.R., Mansour, I., AlTarawneh, R. (2021). TEVisE: An Interactive Visual Analytics Tool to Explore Evolution of Keywords’ Relations in Tweet Data. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12934. Springer, Cham. https://doi.org/10.1007/978-3-030-85613-7_37

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  • DOI: https://doi.org/10.1007/978-3-030-85613-7_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85612-0

  • Online ISBN: 978-3-030-85613-7

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