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Identifying adverse reactions following COVID-19 vaccination in Korea using data from active surveillance: a text mining approach
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Hye Ah Lee, Bomi Park, Chung Ho Kim, Yeonjae Kim, Hyunjin Park, Seunghee Jun, Hyelim Lee, Seunghyun Lewis Kwon, Yesul Heo, Hyungmin Lee, Hyesook Park, COVID-19 Vaccine Safety Research Committee
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Epidemiol Health. 2025;47:e2025034. Published online June 30, 2025
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DOI: https://doi.org/10.4178/epih.e2025034
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 Abstract
Summary
 PDF Supplementary Material
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Abstract
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 coronavirus disease 2019 (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 <1.0%) 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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Summary
Korean summary
* 본 연구는 COVID-19 백신 접종 후 자가 보고된 증상을 텍스트 마이닝으로 분석하여, 이전에 알려지지 않았던 부작용을 확인하였습니다.
* 본 연구 결과는 백신 부작용에 대한 이해를 높이고, 향후 연구 및 감시 체계 개선에 중요한 통찰력을 제공합니다.
Key Message
* This study used text mining of self-reported symptoms following COVID-19 vaccination to identify previously unrecognized adverse reactions.
* Our findings enhance the understanding of vaccine side effects and provide valuable insights for future research and surveillance system improvement.
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KSE
