Identifying adverse reactions following COVID-19 vaccination from data collected through active surveillance: a text mining approach.

Publication date: Jun 30, 2025

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. 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. 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

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Semantics

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