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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Digital Health Reviews (1289) Machine Learning (1465) Natural Language Processing (692)

Published on in Vol 6, No 4 (2020): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21660, first published .
Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

Authors of this article:

Tavleen Singh1 Author Orcid Image ;   Kirk Roberts1 Author Orcid Image ;   Trevor Cohen2 Author Orcid Image ;   Nathan Cobb3 Author Orcid Image ;   Jing Wang4 Author Orcid Image ;   Kayo Fujimoto5 Author Orcid Image ;   Sahiti Myneni1 Author Orcid Image
Article Authors Cited by (14) Tweetations (4) Metrics

Journals

  1. Zingg A, Singh T, Myneni S. Analysis of Online Peripartum Depression Communities: Application of Multilabel Text Classification Techniques to Inform Digitally-Mediated Prevention and Management. Frontiers in Digital Health 2021;3 View
  2. Singh T, Roberts K, Cohen T, Cobb N, Franklin A, Myneni S. Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework. Journal of Biomedical Informatics 2023;140:104324 View
  3. Guraya S, Guraya S, Harkin D, Ryan Á, Mat Nor M, Yusoff M. Medical Education e-Professionalism (MEeP) framework; from conception to development. Medical Education Online 2021;26(1) View
  4. Ricard B, Hassanpour S. 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 2021;23(9):e27314 View
  5. Chang A, Schulz P, Jiao W, Liu M. Obesity-Related Communication in Digital Chinese News From Mainland China, Hong Kong, and Taiwan: Automated Content Analysis. JMIR Public Health and Surveillance 2021;7(11):e26660 View
  6. Camargo Posada A, Monsalve Barrero S, Bermúdez Duarte P. Percepción ciudadana sobre casos de maltrato animal publicados en redes sociales del Instituto Distrital de Protección y Bienestar Animal en Bogotá, Colombia. Revista de Medicina Veterinaria 2023 View
  7. Ardalani A, Daneshvar M. WLCD: a dataset of lifestyle in relation with women’s cancer. BMC Research Notes 2023;16(1) View
  8. Chi Y, Chen H. Investigating Substance Use via Reddit: Systematic Scoping Review. Journal of Medical Internet Research 2023;25:e48905 View
  9. Zingg A, Singh T, Franklin A, Ross A, Selvaraj S, Refuerzo J, Myneni S. Digital health technologies for peripartum depression management among low-socioeconomic populations: perspectives from patients, providers, and social media channels. BMC Pregnancy and Childbirth 2023;23(1) View
  10. Quistberg D. Potential of artificial intelligence in injury prevention research and practice. Injury Prevention 2024;30(2):89 View
  11. Kilpeläinen K, Ståhl T, Ylöstalo T, Keski-Kuha T, Nyrhinen R, Koponen P, Gissler M. Citizens’ digital footprints to support health promotion at the local level—PUHTI study, Finland. European Journal of Public Health 2024;34(4):676 View
  12. Deng T, Urbaczewski A, Lee Y, Barman-Adhikari A, Dewri R. Identifying Marijuana Use Behaviors Among Youth Experiencing Homelessness Using a Machine Learning–Based Framework: Development and Evaluation Study. JMIR AI 2024;3:e53488 View
  13. Derner E, Kučera D, Oliver N, Zahálka J. Can ChatGPT read who you are?. Computers in Human Behavior: Artificial Humans 2024;2(2):100088 View

Books/Policy Documents

  1. Sarker A. Natural Language Processing in Biomedicine. View