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A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics

Author

Listed:
  • Xieling Chen

    (Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong 999077, China)

  • Di Zou

    (Department of English Language Education, The Education University of Hong Kong, Hong Kong 999077, China)

  • Haoran Xie

    (Department of Computing and Decision Sciences, Lingnan University, Hong Kong 999077, China)

  • Gary Cheng

    (Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong 999077, China)

Abstract

Computers in Human Behavior ( CHB ) is a well-established source with a wide range of audiences in the field of human interactions with computers and has been one of the most widely acknowledged and leading venues with significant scientific impact for more than 35 years. This review provides an overview of the status, trends, and particularly the thematic structure of the CHB by adopting bibliometrics and structural topic modeling on 5957 studies. Specifically, we analyzed the trend of publications, identified major institutions and countries/regions, detected scientific collaboration patterns, and uncovered important topics. Significant findings were presented. For example, the contribution of the USA and Open University of Netherlands was highlighted. Important research topics such as e-commerce , social interactions and behaviors , public opinion and social media , cyberbullying , online sexual issues , and game and gamification were identified. This review contributes to the CHB community by justifying the interest in human behavior issues concerning computer use and identifying future research lines on this topic.

Suggested Citation

  • Xieling Chen & Di Zou & Haoran Xie & Gary Cheng, 2021. "A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4859-:d:543711
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    References listed on IDEAS

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    1. Cynthia Van Hee & Gilles Jacobs & Chris Emmery & Bart Desmet & Els Lefever & Ben Verhoeven & Guy De Pauw & Walter Daelemans & Véronique Hoste, 2018. "Automatic detection of cyberbullying in social media text," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.
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    4. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    5. Paul T E Cusack, 2020. "The Human Brain," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24261-24266, October.
    6. Xieling Chen & Juan Chen & Gary Cheng & Tao Gong, 2020. "Topics and trends in artificial intelligence assisted human brain research," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-27, April.
    7. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
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    2. Robert Djogbenou & Vissého Adjiwanou & Solène Lardoux, 2024. "Exploring Sixty-Two Years of Research on Immigrants’ Integration Using Structural Topic Modeling-Based Bibliometric Analysis," Journal of International Migration and Integration, Springer, vol. 25(4), pages 1797-1824, December.

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