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.
Machine summary:
org/0000-0002-1832-7904 Received: 09 August 2021 Accepted: 06 October 2021 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.
, 2014; Wu & Ren, 2018; Hoz-Correa, Munoz-Leiva & Bakucz, 2018; Lara-Rodriguez, Rojas-Contreras & Oliva, 2019; Pestana, Sânchez & Moutinho, 2019).
The primary studies regarding subject trends in library and information science research analyze articles published in journals at different periods (Tuomaala, Jârvelin & Vakkari, 2014; Luo & McKinney, 2015; Liu & Yang 2019).
They dealt with the subject analysis of dissertations (Zong, Shen, Yuan, Hu, Hou & Deng, 2013; Anna, Mannan, Srirahayu & Mutia, 2018), conference papers (Garner, Davidson & Williams, 2008), the structure and field mapping (Zins, 2007; Figuerola, Marco & Pinto, 2017; Han, 2020), content analysis of a collection of field texts including anicles, dissertations, etc.
Studies have shown topics such as digital libraries, information retrieval, knowledge management, information and communication technology, Internet, web technology, semantic web, social media, search engines, metadata, ontology, emotion analysis, natural language processing, machine learning, data mining, text mining, and web mining are new and emerging fields in the field (Summers, Oppenheim, Meadows, McKnight & Kinnell, 1999; Kumar Sinha, 2016; Soheili, Khasseh & koranian, 2018; Mansourkiaie, Babalhavaegi, Nooshinfard & Soheili 2019; Abdollahzadeh, 2019; Baghmohammad, Mansouri & Cheashmehsohrabi, 2020; Ta#kin, 2021).