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An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications

Author

Listed:
  • Xi Zhang

    (Tianjin University)

  • Xianhai Wang

    (Tianjin University)

  • Hongke Zhao

    (Tianjin University)

  • Patricia Ordóñez de Pablos

    (University of Oviedo)

  • Yongqiang Sun

    (Wuhan University)

  • Hui Xiong

    (Rutgers University)

Abstract

Altmetrics indices are increasingly applied to measure scholarly influence in recent years because they can reflect the influence of research outputs more timely comparing with traditional measurements. Simultaneously, artificial intelligence (AI), as an emerging interdiscipline, has a rapid development in these years. Traditional indices can’t reflect the influence of the AI research outputs quickly, thus more timely altmetrics indices are needed. In this paper, we conduct four studies about altmetrics indices and AI research outputs based on the datasets collected from Altmetric.com and Scopus database. First, we provide a review of the research status in the AI field. Second, we show the AI researches that attracted the most attention. Third, we demonstrate the general effectiveness of altmetrics indices in the AI field. Last, we examine the effectiveness of altmetrics indices for different levels of AI journal papers and AI conference papers. Our results indicate that there is a rapid increase of AI publications and the public has paid more attention to AI research outputs since 2011. It is found that altmetrics indices are effective to discriminate highly cited publications and publications whose citation counts increase quickly. Among all Altmetric sub-indicators, Number of Mendeley readers is the most effective. Moreover, the results indicate that altmetrics indices are more effective in high levels of AI journal papers and AI conference papers. The main contribution of this paper is investigating the effectiveness of altmetrics indices from the perspective of different levels of publications. This study lays the foundation for further investigations about effectiveness of altmetrics indices from new perspectives, and it has important implication for the studies about the impact of social media on the scientific community.

Suggested Citation

  • Xi Zhang & Xianhai Wang & Hongke Zhao & Patricia Ordóñez de Pablos & Yongqiang Sun & Hui Xiong, 2019. "An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1311-1344, June.
    • Handle: RePEc:spr:scient:v:119:y:2019:i:3:d:10.1007_s11192-019-03088-x
    DOI: 10.1007/s11192-019-03088-x
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    8. Farhat Chowdhury & Albert N. Link & Martijn Hasselt, 2022. "Public support for research in artificial intelligence: a descriptive study of U.S. Department of Defense SBIR Projects," The Journal of Technology Transfer, Springer, vol. 47(3), pages 762-774, June.
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    10. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2020. "Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-26, June.
    11. Patricia Ordóñez de Pablos, 2023. "Editorial: Digital economy, intellectual capital and competitiveness. Some lessons for the post-COVID-19 era," International Journal of Learning and Intellectual Capital, Inderscience Enterprises Ltd, vol. 20(1), pages 1-5.
    12. Yulia Turovets & Konstantin Vishnevskiy & Artem Altynov, 2020. "How To Measure Ai: Trends, Challenges And Implications," HSE Working papers WP BRP 116/STI/2020, National Research University Higher School of Economics.
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