Who says what? Content and participation characteristics in an online depression community

Highlights

• Topics cover themes of depression, mental health, treatment and relationships.

• Less active users talk about (romantic) relationships, feelings and studying.

• More active users engage in small talk and meta conversations.

• Offering support or suggesting treatment is more common in short comments.

Abstract

Background

An increasingly important source of informal help for people with depression are online depression communities. This study investigates the prevailing topics in an online depression community and how they are related to participation styles.

Methods

A topic model with 26 topics of N = 16,291 posts and N = 71,543 comments of N = 20,037 users in a depression forum on Reddit was created using Latent Dirichlet allocation (LDA). The topics’ proportions in the corpus were correlated with five participation measures, i.e. sum of scores, number of comments, posts to comments ratio, posting frequency, and word count.

Results

The most common topics were Feelings, Motivation, The Community on Reddit, and Time. There were many significant, small to moderate correlations between topic proportions and participation style measures. The topics Feelings, Offering Support, and Small Talk generated a bigger response in the form of scores and comments. Talking about the past and relationships was more common in longer posts, whereas small talk, offering emotional support, and employing cognitive strategies was more readily found in short comments. Lower posting frequency was related to talking about feelings and romantic relationships.

Limitations

No information on users’ demographics or mental health status was available. Topic modeling cannot capture elements of style and tone of text.

Conclusions

A wide spectrum of topics was uncovered in the topic modeling. Patterns in the correlations point to users with different participation styles preferring different topics. Results of this study can aid the development of online interventions for depression.

Introduction

Depression is one of the most common health conditions worldwide with the biggest impact on disability (World Health Organization, 2017). Nevertheless, there is a large treatment gap for depression with at least 4 in 5 people with depression not having access to the minimally adequate treatment (Thornicroft et al., 2018). Increasingly, people turn to the internet for informal help, often in the form of peer-to-peer online communities for depression (Naslund et al., 2016). A number of reasons for participating in these communities have been identified in the literature: they provide access to health information (Barney, Griffiths, & Banfield, 2011), allow sharing one's own experiences and receiving inspiration from similar others (Nimrod, 2013), and reduce the risk of exposure to stigmatization due to anonymity (Wright and Rains, 2013). Additionally, tentative evidence for their effectiveness in reducing depressive symptoms was found in a meta-analysis (Griffiths et al., 2009). Apart from effects on the symptom level, online communities are said to foster empowerment, self-confidence, sense of control, and positive feelings in their users (Barak et al., 2008). Reviews have been conducted on studies of online communities that provide peer support for groups with conditions other than mental disorders: individuals with chronic illness (Kingod et al., 2017) and individuals with cancer (van Eenbergen et al., 2017). While participation in these communities only leads to marginal improvements in various outcomes such as anxiety, general well-being or quality of life, both reviews reported that connecting with others with similar conditions, building social ties, and sharing experiences is seen as beneficial by their users. Another group that benefits from the emotional and informational support available in online communities are first-time parents and pregnant women in their transition to parenthood as well as parents of sick children (Niela-Vilén et al., 2014). The former group can improve their self-efficacy and parenting skills and lower their parenting stress, while the latter report better mental well-being through online peer support.

A better understanding of which topics are discussed in online depression communities and how these topics are received by users is needed to better evaluate their potential benefits and risks (Griffiths et al., 2009). This study focuses on two areas of the research on online depression communities: the content users create and their participation style. Analyses of content of online depression communities show the kind of support they provide and which topics are discussed (Carron-Arthur et al., 2016; Rains et al., 2015). Studying user participation can reveal the social dynamics and interconnectedness in an online depression community (Carron-Arthur et al., 2015).

Analyses of the content of online depression communities typically involve human judges that read and rate each user contribution on categories like emotional or informational support using established coding systems (for a review see Rains et al., 2015). These kinds of qualitative content analyses are only feasible for small datasets. In large-scale datasets, automated text analysis methods, supervised or unsupervised, can identify themes and topics with minimal researcher input (Grimmer & Stewart, 2013). Topic modeling with Latent Dirichlet allocation (LDA; Blei, Ng, & Jordan, 2003), an unsupervised, bottom-up method, is used to model latent topics in a corpus of texts based on the distribution and co-occurrence of words in texts. Topic modeling has been applied in a number of diverse areas such as to predict early psychiatric readmissions from narrative discharge notes in electronic health records (Rumshisky et al., 2016), to summarize large corpora of texts such as newspaper articles about thirdhand smoke (Liu et al., 2019) or drug safety databases (Yu et al., 2014). It has also shown promise in helping researchers identify relevant studies for systematic reviews (Mo et al., 2015). In the area of online health communities topic modeling has been used to study associations between the content in a weight loss community and weight changes of its users (Pappa et al., 2017), to show that highly engaged users favor other topics than less engaged users (Carron-Arthur et al., 2016), and to create machine learning models that can sort content or users into different mental health areas and diagnoses including depression (Gaur et al., 2018; Nguyen et al., 2017; Nguyen et al., 2014).

Users’ participation in online depression communities is highly variable (Carron-Arthur et al., 2014). Highly engaged users contribute many posts over a long time, while other users can be active for a few days or hours only. Going beyond measuring activity levels, a number of participation styles like “Opinion leader”, “Topic-focused responder” or “Information provider” have been identified, with the content users created as a measure for their participation style (for a review of participation styles see Carron-Arthur et al., 2015). Irrespective of the definition, core members are seen as essential for the development and sustainability of an online community (Young, 2013). In the present study, the participation of users was measured using not only the quantity and length of their contributions, but also whether they are more likely to reply to another user than start a new conversation and how big the response to a post in the form of ratings and replies is.

Only a few studies have analyzed content and participation styles of online depression communities together. One study showed that “super users”, i.e. high frequency users, are more likely to provide support and help than to talk about clinical topics such as medication or treatment (Carron-Arthur et al., 2016). Others found associations between prolonged participation in an online depression community and improvements in emotional states, lexical diversity and readability (Park and Conway, 2017; 2018).

The present study extends the research on content and participation styles in online depression communities by studying a large corpus of posts and comments from one community. Specifically, it aims to identify the prevailing topics in this community and to analyze how the prevalence of these topics is related to measures of participation style. The analysis entails two steps. First, topic modeling with LDA is used to uncover latent topics in the corpus of posts and comments. Second, we correlate the percentage each topic takes up in the corpus with a number of participation measures to answer questions such as: which topics in posts are rated most positively, which topics stimulate discussions in the community, how does content in posts and in comments differ, which topics are more prominent among more active, influential users and how is the length of a post or comment related to its content.