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

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.

Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends

Early detection of emerging research trends could potentially revolutionise the way research is done. For this reason, trend analysis has become an area of paramount importance in academia and industry. This is due to the significant implications for research funding and public policy. The literature presents several emerging approaches to detecting new research trends. Most of these approaches rely mainly on citation counting. While citations have been widely used as indicators of emerging research topics, they suffer from some limitations. For instance, citations can take months to years to progress and then to reveal trends. Furthermore, they fail to dig into paper content. To overcome this problem, we introduce Leap2Trend, a novel approach to instant detection of research trends. Leap2Trend relies on temporal word embeddings ( word2vec) to track the dynamics of similarities between pairs of keywords, their rankings and respective uprankings (ascents) over time. We applied Leap2Trend to two scientific corpora on different research areas, namely computer science and bioinformatics and we evaluated it against two gold standards Google Trends hits and Google Scholar citations. The obtained results reveal the effectiveness of our approach to detect trends with more than 80% accuracy and 90% precision in some cases. Such significant findings evidence the utility of our Leap2Trend approach for tracking and detecting emerging research trends instantly