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

Health and Risk Communication (45) JMIR Theme Issue: COVID-19 Special Issue (2452) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Outbreak and Pandemic Preparedness and Management (1727) Infectious Diseases (non-STD/STI) (1575) Infodemiology and Infoveillance (1349) Natural Language Processing (692) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (383)

Published on in Vol 2, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32449, first published .
Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

Authors of this article:

Christopher Marshall1 Author Orcid Image ;   Kate Lanyi1 Author Orcid Image ;   Rhiannon Green1 Author Orcid Image ;   Georgina C Wilkins1 Author Orcid Image ;   Fiona Pearson1 Author Orcid Image ;   Dawn Craig1 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Journals

  1. Loudovikou A, Tsagkaris C, Papakosta V, Papazoglou A, Moysidis D. The impact of the COVID-19 pandemic on language deserves more attention: evidence from university students in Greece—a research note. Discover Mental Health 2022;2(1) View
  2. Maghsoudi A, Nowakowski S, Agrawal R, Sharafkhaneh A, Kunik M, Naik A, Xu H, Razjouyan J. Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre– and Peri–COVID-19 Pandemic Retrospective Study. Journal of Medical Internet Research 2022;24(12):e41517 View
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  4. Zhu J, Yalamanchi N, Jin R, Kenne D, Phan N. Investigating COVID-19’s Impact on Mental Health: Trend and Thematic Analysis of Reddit Users’ Discourse. Journal of Medical Internet Research 2023;25:e46867 View
  5. White B, Gombert A, Nguyen T, Yau B, Ishizumi A, Kirchner L, León A, Wilson H, Jaramillo-Gutierrez G, Cerquides J, D’Agostino M, Salvi C, Sreenath R, Rambaud K, Samhouri D, Briand S, Purnat T. Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study. JMIR Infodemiology 2023;3:e47317 View
  6. Mieles Toloza I, Delgado Meza J, Acevedo-Suárez J. Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios. Revista Politécnica 2024;53(1):57 View
  7. Jacaruso L. Insights into the nutritional prevention of macular degeneration based on a comparative topic modeling approach. PeerJ Computer Science 2024;10:e1940 View
  8. Ravaut M, Zhao R, Phung D, Qin V, Milovanovic D, Pienkowska A, Bojic I, Car J, Joty S. Targeting COVID-19 and Human Resources for Health News Information Extraction: Algorithm Development and Validation. JMIR AI 2024;3:e55059 View

Books/Policy Documents

  1. Allami H, Akyüz S. Decision Making in Healthcare Systems. View