A dependency-based machine learning approach to the identification of research topics: a case in COVID-19 studies

Trends and hot topics in linguistics studies from 2011 to 2021: A bibliometric analysis of highly cited papers

High citations most often characterize quality research that reflects the foci of the discipline. This study aims to spotlight the most recent hot topics and the trends looming from the highly cited papers (HCPs) in Web of Science category of linguistics and language & linguistics with bibliometric analysis. The bibliometric information of the 143 HCPs based on Essential Citation Indicators was retrieved and used to identify and analyze influential contributors at the levels of journals, authors, and countries. The most frequently explored topics were identified by corpus analysis and manual checking. The retrieved topics can be grouped into five general categories: multilingual-related, language teaching, and learning related, psycho/pathological/cognitive linguistics-related, methods and tools-related, and others. Topics such as bi/multilingual(ism), translanguaging, language/writing development, models, emotions, foreign language enjoyment (FLE), cognition, anxiety are among the most frequently explored. Multilingual and positive trends are discerned from the investigated HCPs. The findings inform linguistic researchers of the publication characteristics of the HCPs in the linguistics field and help them pinpoint the research trends and directions to exert their efforts in future studies.

LDA-based topic modeling for COVID-19-related sports research trends

Introduction

The COVID-19 pandemic could generate a turning point for introducing a new system for sports participation and business. The purpose of this study is to explore trends and topic structures of COVID-19-related sports research by analyzing the relevant literature.

Methods

Sports studies related to COVID-19 were collected in searching international academic databases. After the pre-processing step using the refinement and morpheme analysis function of the Net Miner program, topic modeling and social network analysis were used to analyze Journal Citation Reports found using the search term 'COVID-19 sports'.

Results

As a result, this study used subject modeling to reveal important potential topics in COVID-19-related sports research articles. 'Sports participation', 'elite players', and 'sports industry' were macroscopically classified, and detailed research topics could be identified from each division.

Conclusion

This study revealed important latent topics from COVID-19-related sports research articles using topic modeling. The results of the research elucidate the structure of academic knowledge on this topic and provide guidance for future research.

Motivation in second language acquisition: A bibliometric analysis between 2000 and 2021

As one of the most important constructs of individual differences in second language learning, motivation has garnered a lot of attention in the area of Second Language Acquisition (SLA). Nevertheless, little bibliometric research has been conducted to provide a systematic overview of this line of research, which will help researchers to better understand how motivation-related research in SLA has evolved over the past 22 years and where it might push the boundaries of SLA research into in the future. In this study, three types of bibliometric analyses (i.e., co-citation analysis, citation analysis, and keyword analysis) were performed to identify the prominent scholarly documents, authors, venues of publications, and research topics that have been highly influential in the research of motivation in SLA between 2000 and 2021. Results from scientific network maps and keyword analysis suggest significant changes in the topic over the past 22 years. The results in this study also indicate an evident continuity of theoretic development in L2 language learning motivation research. Moreover, an air of active use of qualitative approaches has been detected in L2 language learning motivation research in the last 22 years.

Thematic evolution of coronavirus disease: a longitudinal co-word analysis

Purpose

Using science mapping analysis approach and co-word analysis, the present study explores and visualizes research fields and thematic evolution of the coronavirus. Based on this method, one can get a picture of the real content of the themes in the mentioned thematic area and identify the main minor and emerging themes.

Design/methodology/approach

This study was conducted based on co-word science mapping analysis under a longitudinal study (from 1988 to 2020). The collection of documents in this study was further divided into three subperiods: 1988–1998, 1999–2009 and 2010–2020. In order to perform science mapping analysis based on co-word bibliographic networks, SciMAT was utilized as a bibliometric tool. Moreover, WoS, PubMed and Scopus bibliographic databases were used to download all records.

Findings

In this study, strategic diagrams were demonstrated for the coronavirus research for a chronological period to assess the most relevant themes. Each diagram depended on the sum of documents linked to each research topic. In the first period (1988–1998), the most centralizations were on virology and evaluation of coronavirus structure and its structural and nonstructural proteins. In the second period (1999–2009), with due attention to high population density in eastern Asia and the increasing number of people affected with the new generation of coronavirus (named severe acute respiratory syndrome virus or SARS virus), publications have been concentrated on “antiviral activity.” In the third period (2010–2020), there was a tendency to investigate clinical syndromes, and most of the publications and citations were about hot topics like “severe acute respiratory syndrome,” “coronavirus” and “respiratory tract disease.” Scientometric analysis of the field of coronavirus can be regarded as a roadmap for future research and policymaking in this important area.

Originality/value

The originality of this research can be considered in two ways. First, the strategic diagrams of coronavirus are drawn in four thematic areas including motor cluster, basic and transversal cluster, highly developed cluster and emerging and declining cluster. Second, COVID-19 is mentioned as a hot topic of research.