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

Digital Health Reviews (1318) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1552) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1049) Big Data (241)

Published on in Vol 25 (2023)

This is a member publication of University of Bristol (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42734, first published .
Methodologies for Monitoring Mental Health on Twitter: Systematic Review

Methodologies for Monitoring Mental Health on Twitter: Systematic Review

Methodologies for Monitoring Mental Health on Twitter: Systematic Review

Authors of this article:

Nina H Di Cara1, 2 Author Orcid Image ;   Valerio Maggio2, 3 Author Orcid Image ;   Oliver S P Davis2, 3, 4 Author Orcid Image ;   Claire M A Haworth1, 4 Author Orcid Image
Article Authors Cited by (11) Tweetations (6) Metrics

Journals

  1. Thakur N. Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox. Big Data and Cognitive Computing 2023;7(2):116 View
  2. McGarry K. Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos. Information 2023;14(7):408 View
  3. Colella S, Dufourt F, Hildebrand V, Vivès R. Mental health effects of COVID-19 lockdowns: A Twitter-based analysis. Economics & Human Biology 2023;51:101307 View
  4. Wang Y, Deng H, Gao S, Li T, Wang F. A Fresh Perspective on Examining Population Emotional Well-Being Trends by Internet Search Engine: An Emerging Composite Anxiety and Depression Index. International Journal of Environmental Research and Public Health 2024;21(2):202 View
  5. Florath J, Chanussot J, Keller S. Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis. ISPRS International Journal of Geo-Information 2024;13(4):107 View
  6. Zhu Z. Maternal mental health monitoring in an online community: a natural language processing approach. Behaviour & Information Technology 2024:1 View
  7. Benjachairat P, Senivongse T, Taephant N, Puvapaisankit J, Maturosjamnan C, Kultananawat T. Classification of suicidal ideation severity from Twitter messages using machine learning. International Journal of Information Management Data Insights 2024;4(2):100280 View
  8. Merayo N, Ayuso-Lanchares A, González-Sanguino C. Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram. PeerJ Computer Science 2024;10:e2251 View
  9. Bokolo B, Liu Q. Advanced Comparative Analysis of Machine Learning and Transformer Models for Depression and Suicide Detection in Social Media Texts. Electronics 2024;13(20):3980 View
  10. Di Cara N, Joinson D, Mummé M, Tanner A, Maggio V, Thomas R, Haworth C, Boyd A, Davis O. Twitter data as a linked digital phenotype in the Avon Longitudinal Study of Parents and Children. Wellcome Open Research 2024;9:688 View

Books/Policy Documents

  1. Haldar S, El-Gayar O, El-Gayar S. Impact of Digital Solutions for Improved Healthcare Delivery. View