ArticleJournal of the American Medical Informatics Association : JAMIA2024
Streamlining social media information retrieval for public health research with deep learning.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A patient-centered approach to developing and validating a natural language processing model for extracting patient-reported symptoms.Scientific reports · 2025Article
- Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.Journal of medical Internet research · 2025Article
- Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing.Health data science · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
objectiveSocial media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a Unified Medical Language System (UMLS)-colloquial symptom dictionary from COVID-19-related tweets as proof of concept.
methodsCOVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity samples were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research.
resultsWe identified 498 480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18 226. The final dictionary contains 38 175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons.
conclusionsThis study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret, and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes.
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