ArticleJournal of medical Internet research2021
Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Leveraging Large Language Models for Infectious Disease Surveillance-Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis.Journal of medical Internet research · 2025Article
- Exploring the potential of online social listening for noncommunicable disease monitoring.PeerJ · 2025Article
- Internet-based surveillance to track trends in seasonal allergies across the United States.PNAS nexus · 2024Article
- A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets.JMIR formative research · 2024Article
- Development of an early alert model for pandemic situations in Germany.Scientific reports · 2023Article
- Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study.Journal of medical Internet research · 2023Article
- Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts.Journal of medical Internet research · 2023Article
- Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.International journal of data science and analytics · 2023Article
- Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning-Based Solution.JMIR medical informatics · 2022Article
- SEED: Symptom Extraction from English Social Media Posts using Deep Learning and Transfer Learning.medRxiv : the preprint server for health sciences · 2022Article
- Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline.JMIR public health and surveillance · 2022Article
- Dynamics of social media behavior before and after SARS-CoV-2 infection.Frontiers in public health · 2022Article
- Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic.Computer methods and programs in biomedicine · 2021Article
- Predicting Norovirus in the United States Using Google Trends: Infodemiology Study.Journal of medical Internet research · 2021Article
- Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes.Journal of medical Internet research · 2021Article
- Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study.Journal of medical Internet research · 2021Article
- Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights.Journal of medical Internet research · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundIn the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone.
objectiveThe objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention.
methodsBeginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out "reported speech" (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020.
resultsInterannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F
conclusionsWe have made the 13,714 tweets identified in this study, along with each tweet's time stamp and US state-level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.