Mental health-related communication in a virtual community: text mining analysis of a digital exchange platform during the Covid-19 pandemic

Abstract

Abstract Background Virtual communities played an important role in mental health and well-being during the Covid-19 pandemic by providing access to others and thereby preventing loneliness. The pandemic has accelerated the urge for digital solutions for people with pre-existing mental health problems. So far, it remains unclear how the people concerned communicate with each other and benefit from peer-to-peer support on a moderated digital platform. Objective The aim of the project was to identify and describe the communication patterns and verbal expression of users on the inCLOUsiv platform during the first lockdown in 2020. Methods Discussions in forums and live chats on inCLOUsiv were analysed using text mining, which included frequency, correlation, n-gram and sentiment analyses. Results The communication behaviour of users on inCLOUsiv was benevolent and supportive; and 72% of the identified sentiments were positive. Users addressed the topics of ‘corona’, ‘anxiety’ and ‘crisis’ and shared coping strategies. Conclusions The benevolent interaction between users on inCLOUsiv is in line with other virtual communities for Covid-19 and the potential for peer-to-peer support. Users can benefit from each other’s experiences and support each other. Virtual communities can be used as an adjuvant to existing therapy, particularly in times of reduced access to local health services.

Keywords

• Virtual communities
• Sentiment analysis
• Text mining
• Covid-19
• Mental health