Evolution of Discussion Topics in Online Depression Self-Help Groups Before, During, and After COVID-19 Lockdown in China

• Published in: The Usage and Impact of ICTs During the Covid-19 Pandemic pp. 170 - 193
• Main Authors: Sun, Honglei Lia, Fichman, Pnina
• Format: Book Chapter
• Language: English
• Published: United Kingdom Routledge 2023; Taylor & Francis Group
• Edition: 1
• Subjects: Online depression community; Public Health Emergency Response; CoP Literature; LDA Topic Modeling; LDA; Singular Pronouns; Health Information Behavior; COVID-19; LDA Model; Low Health Literacy Group; Entire Dataset; LIWC Lexicon; Vaccine Hesitancy; language features; Social informatics; Depression Group; Topic evolution; ICT Consequence; Major Depression; Discussion Topics; ICT Governance; Social Informatics Research; Public Engagement; Instrumental Support; Major Depression Group; Virtual CoP; LDA Topic; Information Behavior
• ISBN: 9781032139746; 9781032139753; 1032139757; 1032139749
• DOI: 10.4324/9781003231769-10

Online communities play a vital role in coping with mental health problems and have become a popular place for depressed individuals to communicate with others. Yet, little is known about online depression self-help communities, the topics that are commonly being discussed among members of these groups, and if and how topics have evolved during the COVID-19 pandemic. Topics may shift not only because of socio-psychological factors but also due to other contextual factors, such as global pandemics and strict health measures that come with them. At times of crisis, such as the COVID-19 lockdown, topics may attract a higher volume of participation and/or change in scope and range. We address this gap by analyzing 2,999 posts and 25,173 comments submitted over a period of three months to two online depression self-help groups in China. We traced the evolution of topics over time-before, during, and after the COVID-19 lockdown-and uncovered topic similarities and variations between the groups during the three stages. Then, we investigated the language features of common concerns and each group's main topics in each stage. We discuss and explain our findings in light of the COVID-19 lockdown in China. We conclude with the implications of the study for research and practice and suggest future research directions. This chapter describes an evolutionary pattern of discussion topics and analyze the language features in online depression communities, grounded in data from two online depression self-help groups in China that were active before, during, and after the COVID-19 lockdown. Online communities play a vital role in coping with mental health problems and have become a popular place for depressed individuals to communicate with others. Language use patterns mirror mental states and psychopathological characteristics. Tausczik and Pennebaker argue that not only is the language the most effective way for individuals to describe themselves, others, and the world, but it is also the most common and reliable way for people to translate their internal thoughts and emotions into a form that others can understand. The COVID-19 pandemic as a public health emergency of global concern has caused a huge psychological impact on individuals.