What's all the talk about? Topic modelling in a mental health Internet support group. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Carron-Arthur, Bradley; Reynolds, Julia; Bennett, Kylie; Bennett, Anthony; Griffiths, Kathleen
• Is Part Of: BMC psychiatry. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-12
• Subjects: Internet support group -- Mental health -- Topic modelling -- Latent Dirichlet Allocation -- Super users -- Peer-support; Psychiatry -- Periodicals; 616.89005
• DOI: 10.1186/s12888-016-1073-5
• Languages: English
• ISSNs: 1471-244X

Abstract

Abstract Background The majority of content in an Internet Support Group (ISG) is contributed by 1 % of the users ('super users'). Computational methods, such as topic modelling, can provide a large-scale quantitative objective description of this content. Such methods may provide a new perspective on the nature of engagement on ISGs including the role of super users and their possible effect on other users. Methods A topic model was computed for all posts (N = 131, 004) in the ISG BlueBoard using Latent Dirichlet Allocation. A model containing 25 topics was selected on the basis of intelligibility as determined by diagnostic metrics and qualitative investigation. This model yielded 21 substantive topics for further analysis. Two chi-square tests were conducted separately for each topic to ascertain: (i) if the odds of super users' and other users' posting differed for each topic; and (ii) if for super users the odds of posting differed depending on whether the response was to a super user or to another user. Results The 21 substantive topics covered a range of issues related to mental health and peer-support. There were significantly higher odds that super users wrote content on 13 topics, with the greatest effects being for Parenting Role (OR [95%CI] = 7.97 [7.85–8.10]), Co-created Fiction (4.22 [4.17–4.27]), Mental Illness (3.13 [3.11–3.16]) and Positive Change (2.82 [2.79–2.84]). There were significantly lower odds for super users on 7 topics, with the greatest effects being for the topics Depression (OR = 0.27 [0.27–0.28]), Medication (0.36 [0.36–0.37]), Therapy (0.55 [0.54–0.55]) and Anxiety (0.55 [0.55–0.55]). However, super users were significantly more likely to write content on 5 out of these 7 topics when responding to other users than when responding to fellow super users. Conclusions The findings suggest that super users serve the role of emotionally supportive companions with a focus on topics broadly resembling the consumer/carer model of recovery. Other users engage in topics with a greater focus on experiential knowledge, disclosure and informational support, a pattern resembling the clinical symptom-focussed approach to recovery. However, super users modify their content in response to other users in a manner consistent with being 'active help providers'.