ArticleOnline journal of public health informatics2016
Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates.
Article in Online journal of public health informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social media based surveillance systems for healthcare using machine learning: A systematic review.Journal of biomedical informatics · 2020Pooled it
- Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic.Annals of operations research · 2022Article
- Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review.Yearbook of medical informatics · 2021Review
- Article
- Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.PloS one · 2021Article
- Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.Frontiers in digital health · 2021Review
- Article
- Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data.Yearbook of medical informatics · 2019Article
- Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study.Journal of medical Internet research · 2018Observational
- Social Media as a New Vital Sign: Commentary.Journal of medical Internet research · 2018Article
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1 author.
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Abstract
backgroundInfluenza (flu) surveillance using Twitter data can potentially save lives and increase efficiency by providing governments and healthcare organizations with greater situational awareness. However, research is needed to determine the impact of Twitter users' misdiagnoses on surveillance
objectiveThis study establishes the importance of Twitter users' misdiagnoses by showing that Twitter flu surveillance in the United States failed during the 2011-2012 flu season, estimates the extent of misdiagnoses, and tests several methods for reducing the adverse effects of misdiagnoses.
methodsMetrics representing flu prevalence, seasonal misdiagnosis patterns, diagnosis uncertainty, flu symptoms, and noise were produced using Twitter data in conjunction with OpenSextant for geo-inferencing, and a maximum entropy classifier for identifying tweets related to illness. These metrics were tested for correlations with World Health Organization (WHO) positive specimen counts of flu from 2011 to 2014.
resultsTwitter flu surveillance erroneously indicated a typical flu season during 2011-2012, even though the flu season peaked three months late, and erroneously indicated plateaus of flu tweets before the 2012-2013 and 2013-2014 flu seasons. Enhancements based on estimates of misdiagnoses removed the erroneous plateaus and increased the Pearson correlation coefficients by .04 and .23, but failed to correct the 2011-2012 flu season estimate. A rough estimate indicates that approximately 40% of flu tweets reflected misdiagnoses.
conclusionsFurther research into factors affecting Twitter users' misdiagnoses, in conjunction with data from additional atypical flu seasons, is needed to enable Twitter flu surveillance systems to produce reliable estimates during atypical flu seasons.
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