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Original article Identifying adverse reactions following COVID-19 vaccination from data collected through active surveillance: a text mining approach
Hye Ah Lee1orcid , Bomi Park2orcid , Chung Ho Kim2orcid , Yeonjae Kim2orcid , Hyunjin Park5orcid , Seunghee Jun5orcid , Hyelim Lee5orcid , Seunghyun Lewis Kwon6orcid , Yesul Heo3orcid , Hyungmin Lee3orcid , Hyesook Park4orcid
Epidemiol Health 2025;e2025034
DOI: https://doi.org/10.4178/epih.e2025034 [Accepted]
Published online: June 30, 2025
1Ewha Womans University Mokdong Hospital, Seoul , Korea
2Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Korea
3Division of Immunization Policy, Korea Disease Control and Prevention Agency, Cheongju, Korea
4이화의대 , Seoul, Korea
5Department of Preventive Medicine, College of Medicine, Ewha Womans University, Seoul, Korea
6Division of Immunization Services, Korea Disease Control and Prevention Agency, Cheongju, Korea
Corresponding author:  Hyungmin Lee,
Email: hpark@ewha.ac.kr
Hyesook Park,
Email: hpark@ewha.ac.kr
Received: 19 February 2025   • Revised: 13 May 2025   • Accepted: 17 June 2025
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OBJECTIVES
Unstructured text data collected through vaccine safety surveillance systems can identify previously unreported adverse reactions and provide critical information to enhance these systems. This study explored adverse reactions using text data collected through an active surveillance system following COVID-19 vaccination.
METHODS
We performed text mining on 2,608 and 2,054 records from 2 survey seasons (2023–2024 and 2024–2025), in which participants reported health conditions experienced within 7 days of vaccination using free-text responses. Frequency analysis was conducted to identify key terms, followed by subgroup analyses by sex, age, and concomitant influenza vaccination. In addition, semantic network analysis was used to examine terms reported together.
RESULTS
The analysis identified several common (≥1%) adverse events, such as respiratory symptoms, sleep disturbances, lumbago, and indigestion, which had not been frequently noted in prior literature. Moreover, less frequent (≥0.1% to <1%) adverse reactions affecting the eyes, ears, and oral cavity were also detected. These adverse reactions did not differ significantly in frequency based on the presence or absence of simultaneous influenza vaccination. Co-occurrence analysis and estimation of correlation coefficients further revealed associations between frequently co-reported symptoms.
CONCLUSIONS
This study utilized text mining to uncover previously unrecognized adverse reactions associated with COVID-19 vaccination, thereby broadening our understanding of the vaccine’s safety profile. The insights obtained may inform future investigations into vaccine-related adverse reactions and improve the processing of text data in surveillance systems.


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