What’s all the talk about? Topic modelling in a mental health Internet support group

BMC Psychiatry

Research Article

Kathleen M. Griffiths¹  Julia Reynolds¹  Kylie Bennett¹  Bradley Carron-Arthur¹  Anthony Bennett¹ 

[1] Centre for Mental Health Research, The Australian National University, 63 Eggleston Road, Acton, 2601, Canberra, ACT, Australia;

关键词: Internet support group;    Mental health;    Topic modelling;    Latent Dirichlet Allocation;    Super users;    Peer-support;   

DOI  :  10.1186/s12888-016-1073-5

 received in 2016-05-18, accepted in 2016-10-17,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsA 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.ResultsThe 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.ConclusionsThe 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’.

【 授权许可】

CC BY   
© The Author(s). 2016