Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers

We manually annotated 9734 tweets that were posted by users who reported their pregnancy on Twitter, and used them to train, evaluate, and deploy deep neural network classifiers (F -score=0.93) to detect tweets that report having a child with attention-deficit/hyperactivity disorder (678 users), aut...

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Bibliographic Details
Published in:Journal of medical Internet research 2024-03, Vol.26 (4), p.e50652-e50652
Main Authors: Klein, Ari Z, Gutiérrez Gómez, José Agustín, Levine, Lisa D, Gonzalez-Hernandez, Graciela
Format: Article
Language:English
Subjects:
Asthma - epidemiology
Autism
Autism Spectrum Disorder
Child
Epidemiology
Female
Health aspects
Humans
Neural networks
Neural Networks, Computer
Pregnancy
Research Letter
Social Media
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Summary:We manually annotated 9734 tweets that were posted by users who reported their pregnancy on Twitter, and used them to train, evaluate, and deploy deep neural network classifiers (F -score=0.93) to detect tweets that report having a child with attention-deficit/hyperactivity disorder (678 users), autism spectrum disorders (1744 users), delayed speech (902 users), or asthma (1255 users), demonstrating the potential of Twitter as a complementary resource for assessing associations between pregnancy exposures and childhood health outcomes on a large scale.
ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/50652