Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers.
Ari Z KleinJosé Agustín Gutiérrez GómezLisa D LevineGraciela Gonzalez-HernandezPublished in: Journal of medical Internet research (2024)
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 1 -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.
Keyphrases
- neural network
- social media
- autism spectrum disorder
- electronic health record
- preterm birth
- big data
- mental health
- pregnancy outcomes
- chronic obstructive pulmonary disease
- pregnant women
- machine learning
- risk assessment
- early life
- artificial intelligence
- attention deficit hyperactivity disorder
- intellectual disability
- high speed
- allergic rhinitis