1. Key topics in social science research on COVID-19: An automated literature analysis.
- Author
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Xian Cheng, Ying Zhao, and Stephen Shaoyi Liao
- Subjects
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SERIAL publications , *BIBLIOMETRICS , *ARTIFICIAL intelligence , *MENTAL health , *PUBLIC health , *SOCIAL sciences , *DESCRIPTIVE statistics , *MENTAL depression , *RESEARCH funding , *THEMATIC analysis , *STAY-at-home orders , *ANXIETY , *MEDICAL research , *COVID-19 pandemic , *LITERATURE , *DATA mining , *PSYCHOLOGICAL stress , *EVALUATION - Abstract
Background: The COVID-19 pandemic has triggered a significant increase in academic research in the realm of social sciences. As such, there is an increasing need for the scientific community to adopt effective and efficient methods to examine the potential role and contribution of social sciences in the fight against COVID-19. Objectives: This study aims to identify the key topics and explore publishing trends in social science research pertaining to COVID-19 via automated literature analysis. Methods: The automated literature analysis employed utilizes keyword analysis and topic modelling technique, specifically Latent Dirichlet Allocation, to highlight the most relevant research terms, overarching research themes and research trends within the realm of social science research on COVID-19. Results: The focus of research and topics were derived from 9733 full-text academic papers. The bulk of social science research on COVID-19 centres on the following themes: 'Clinical Treatment', 'Epidemic Crisis', 'Mental Influence', 'Impact on Students', 'Lockdown Influence' and 'Impact on Children'. Conclusion: This study adds to our understanding of key topics in social science research on COVID-19. The automated literature analysis presented is particularly useful for librarians and information specialists keen to explore the role and contributions of social science topics in the context of pandemics. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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