This paper is in the following e-collection/theme issue:

Digital Health Reviews (1728) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1914) Natural Language Processing (917) Artificial Intelligence (2136) Machine Learning (2079)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47923, first published .
Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review

Authors of this article:

Karen O'Connor1 Author Orcid Image ;   Su Golder2 Author Orcid Image ;   Davy Weissenbacher3 Author Orcid Image ;   Ari Z Klein1 Author Orcid Image ;   Arjun Magge1 Author Orcid Image ;   Graciela Gonzalez-Hernandez3 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Golder S, Xu D, O’Connor K, Wang Y, Batra M, Hernandez G. Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review. Drug Safety 2025;48(4):321 View
  2. Chedrawi C, Raya K, Kazoun N. Social media role in detecting food supply chain management challenges through blockchain technology. Journal of Asia Business Studies 2025;19(3):635 View

Conference Proceedings

  1. Suman C, Thakur R. 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). Examining the Joint Influence of Sarcasm and Emotion on Gender Prediction View