@article{f32344c233f844c1be528bdd532a84dd,
title = "Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study",
abstract = "Online health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users. We obtained data from three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1 January 2020 to 31 May 2020. Using NLP, we analysed the content of posts related to COVID-19. (1) Proportion of forum posts containing COVID-19 keywords; (2) proportion of forum users making their very first post about COVID-19; (3) proportion of COVID-19-related posts containing content related to physical and mental health comorbidities. Data from 739 434 posts created by 53 134 unique users were analysed. A total of 35 581 posts (4.8%) contained a COVID-19 keyword. Posts discussing COVID-19 and related comorbid disorders spiked in early March to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. Over a quarter of COVID-19-related thread titles mentioned a physical or mental health comorbidity. We demonstrate that it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. The pandemic and corresponding public response has had a significant impact on posters{\textquoteright} queries regarding mental health. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.",
author = "Rashmi Patel and Fabrizio Smeraldi and Maryam Abdollahyan and Jessica Irving and Conrad Bessant",
note = "Funding Information: RP has received support from a Medical Research Council (MRC) Health Data Research UK Fellowship (MR/S003118/1), a National Institute for Health Research (NIHR) Advanced Fellowship (NIHR301690) and a Starter Grant for Clinical Lecturers (SGL015/1020) supported by the Academy of Medical Sciences, The Wellcome Trust, MRC, British Heart Foundation, Arthritis Research UK, the Royal College of Physicians and Diabetes UK. FS and CB were funded by an Alan Turing Institute (ATI) Fellowship and by an EPSRC COVID-19 Rapid Response Impact Acceleration Fund. Computational resources were funded by a Microsoft Azure Sponsorship through the ATI. Funding Information: Data Research UK Fellowship (MR/S003118/1), a National Institute for Health Research (NIHR) Advanced Fellowship (NIHR301690) and a Starter Grant for Clinical Lecturers (SGL015/1020) supported by the Academy of Medical Sciences, The Wellcome Trust, MRC, British Heart Foundation, Arthritis Research UK, the Royal College of Physicians and Diabetes UK. FS and CB were funded by an Alan Turing Institute (ATI) Fellowship and by an EPSRC COVID-19 Rapid Response Impact Acceleration Fund. Computational resources were funded by a Microsoft Azure Sponsorship through the ATI. Funding Information: Funding RP has received support from a Medical Research Council (MRC) Health Publisher Copyright: {\textcopyright} 2021, British Medical Journal Publishing Group. All rights reserved.",
year = "2021",
month = nov,
day = "5",
doi = "10.1136/bmjopen-2021-056601",
language = "English",
volume = "11",
journal = "BMJ Open",
issn = "2044-6055",
publisher = "BMJ Publishing Group",
number = "11",
}