International Journal of Information Science and Management (IJISM)

Forecasting the Subject Trend of International Library and Information Science Research by 2030 Using the Deep Learning Approach

Document Type : Articles

Authors

Farzaneh Ghanadi Nezhad 1
Farideh Osareh 2
Mohammad Reza Ghane 3

1 Ph.D. Candidate in Knowledge and Information Science, Shahid Chamran University of Ahvaz,

2 Professor, Department of Knowledge and Information Science, Shahid Chamran University of Ahvaz, Ahvaz

3 Associate Prof. of LIS, Department of Evaluation and Collection Development, Regional Information Center for Science & Technology (RICeST), Shiraz, Iran

Abstract
This study seeks to forecast the subject trend of library and information science research until 2030 based on modeling previous research topics in this field, which has been done with a text mining and in-depth learning approach. After pre-processing and thematic classification of the studies, deep neural network algorithms were used to model previous studies and forecast future topics. The study population included 90,311 journal articles in library and information science publications indexed on the Web of Science website from 1945-2020. All research processes were implemented in the Python programming language. The findings showed that the largest number of studies in the future would be related to Internet and web studies, and the growth rate of these topics will be higher in the future. However, topics related to libraries and their work processes and other traditional disciplines such as theoretical foundations will have a lower growth rate in library and information science studies. As a result, knowledge of important future issues, while helping to plan for future research, can identify study gaps and investment opportunities in the R&D sector, thereby assisting researchers, universities, and relevant research institutes in selecting projects intelligently.https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.26.9

Keywords

Subject Forecasting
Research Trend
Subject Trend
Future Topics
Library and Information Science
Deep Learning

  • Receive Date 02 January 2022
  • Accept Date 03 December 2022

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