The Evolution of AI Dependency in Scientometric Research

Li, Muyan; Duan, Wenzhuo; Hou, Jingrui; Liu, Xingshen; Wang, Ping

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https://hdl.handle.net/2142/117357 Copy

Description

Title

The Evolution of AI Dependency in Scientometric Research

Author(s)

• Li, Muyan
• Duan, Wenzhuo
• Hou, Jingrui
• Liu, Xingshen
• Wang, Ping

Issue Date

2023-03-13

Keyword(s)

• Scientometrics
• Artificial intelligence
• Dependency analysis and Technology lag time

Abstract

The use of AI methodologies, particularly machine learning and deep learn-ing, is increasing in scientometrics research for processing large and com-plex data structures. This has resulted in the phenomenon of AI dependency in scientometrics. In this study, we analysed bibliographic data from the Journal of Informetrics and Scientometrics, two core scientometric journals, using the Web of Science database. We created an AI technology term lexi-con and studied the trend and adoption rate of AI dependency. Our findings show that scientometric research is becoming more dependent on AI, with a decreasing lag time for adopting new methods. In the coming years, deep learning-based scientometric studies will become a prominent research trend.

Publisher

iSchools

Series/Report Name or Number

iConference 2023 Proceedings

Type of Resource

• Other
• text

Language

eng

Handle URL

https://hdl.handle.net/2142/117357

Copyright and License Information

Copyright 2023 is held by Muyan Li, Wenzhuo Duan, Jingrui Hou, Xingshen Liu and Ping Wang. Copyright permissions, when appropriate, must be obtained directly from the authors.

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