Investigating mental and physical disorders associated with COVID-19 in online health forums
Rashmi Patel, Fabrizio Smeraldi, Maryam Abdollahyan, Jessica Irving, Conrad Bessant
Abstract
Open-access reader
Rashmi Patel, Fabrizio Smeraldi, Maryam Abdollahyan, Jessica Irving, Conrad Bessant
Abstract
Open-access reader
Objectives: 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. Setting and design: We obtained data from 739,434 posts by 53,134 unique users of three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1st January 2020 to 31st May 2020. Using NLP, we analysed the content of posts related to COVID-19. Primary outcome measures: (i) Proportion of forum posts containing COVID-19 keywords (ii) Proportion of forum users making their very first post about COVID-19 (iii) Number of COVID-19 related posts containing content related to physical and mental health comorbidities Results: Posts discussing COVID-19 and related comorbid disorders spiked in early- 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. The pandemic and corresponding public response has had a significant impact on posters' queries regarding mental health. Conclusions: We demonstrate it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.
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Objectives: 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. Setting and design: We obtained data from 739,434 posts by 53,134 unique users of three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1st January 2020 to 31st May 2020. Using NLP, we analysed the content of posts related to COVID-19. Primary outcome measures: (i) Proportion of forum posts containing COVID-19 keywords (ii) Proportion of forum users making their very first post about COVID-19 (iii) Number of COVID-19 related posts containing content related to physical and mental health comorbidities Results: Posts discussing COVID-19 and related comorbid disorders spiked in early- 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. The pandemic and corresponding public response has had a significant impact on posters' queries regarding mental health. Conclusions: We demonstrate it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.
Key concepts: Mental health, Coronavirus disease 2019 (COVID-19), Pandemic, Social media, Public health, Population, Psychology, Computer-assisted web interviewing