Skip to main content
Top
Published in: BMC Psychiatry 1/2016

Open Access 01-12-2016 | Research article

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

Authors: Bradley Carron-Arthur, Julia Reynolds, Kylie Bennett, Anthony Bennett, Kathleen M. Griffiths

Published in: BMC Psychiatry | Issue 1/2016

Login to get access

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’.
Literature
1.
go back to reference Fox S. Peer-to-peer Health Care. Washington: PewResearchCentre; 2011. Fox S. Peer-to-peer Health Care. Washington: PewResearchCentre; 2011.
2.
go back to reference Fox S, Duggan M. Health Online 2013. Washington: PewResearchCentre; 2013. Fox S, Duggan M. Health Online 2013. Washington: PewResearchCentre; 2013.
3.
go back to reference Fox S, Jones S. The social life of health information. Washington: PewResearchCenter; 2009. Fox S, Jones S. The social life of health information. Washington: PewResearchCenter; 2009.
4.
go back to reference Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A. Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. Br Med J. 2004;328(7449):1166. PMID: 15142921.CrossRef Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A. Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. Br Med J. 2004;328(7449):1166. PMID: 15142921.CrossRef
5.
go back to reference Griffiths KM, Calear AL, Banfield M. Systematic Review on Internet Support Groups (ISGs) and Depression (1): Do ISGs Reduce Depressive Symptoms? J Med Internet Res. 2009;11(3):e40. PMID: 19793719.CrossRefPubMedPubMedCentral Griffiths KM, Calear AL, Banfield M. Systematic Review on Internet Support Groups (ISGs) and Depression (1): Do ISGs Reduce Depressive Symptoms? J Med Internet Res. 2009;11(3):e40. PMID: 19793719.CrossRefPubMedPubMedCentral
6.
go back to reference Griffiths KM, Mackinnon AJ, Crisp DA, Christensen H, Bennett K, Farrer L. The Effectiveness of an Online Support Group for Members of the Community with Depression: A Randomised Controlled Trial. PLoS One. 2012;7(12):e53244. Griffiths KM, Mackinnon AJ, Crisp DA, Christensen H, Bennett K, Farrer L. The Effectiveness of an Online Support Group for Members of the Community with Depression: A Randomised Controlled Trial. PLoS One. 2012;7(12):e53244.
7.
go back to reference Barak A, Boniel-Nissim M, Suler J. Fostering empowerment in online support groups. Comput Hum Behav. 2008;24(5):1867–83.CrossRef Barak A, Boniel-Nissim M, Suler J. Fostering empowerment in online support groups. Comput Hum Behav. 2008;24(5):1867–83.CrossRef
8.
go back to reference Brouwer W, Kroeze W, Crutzen R, de Nooijer J, de Vries N, Brug J, et al. Which Intervention Characteristics are Related to More Exposure to Internet-Delivered Healthy Lifestyle Promotion Interventions? A Systematic Review. J Med Internet Res. 2011;13(1):e2. Brouwer W, Kroeze W, Crutzen R, de Nooijer J, de Vries N, Brug J, et al. Which Intervention Characteristics are Related to More Exposure to Internet-Delivered Healthy Lifestyle Promotion Interventions? A Systematic Review. J Med Internet Res. 2011;13(1):e2.
9.
go back to reference Crisp DA, Griffiths KM, Mackinnon AJ, Bennett K, Christensen H. An online intervention for reducing depressive symptoms: Secondary benefits for self-esteem, empowerment and quality of life. Psychiatry Res. 2014;216(1):60–6.CrossRefPubMed Crisp DA, Griffiths KM, Mackinnon AJ, Bennett K, Christensen H. An online intervention for reducing depressive symptoms: Secondary benefits for self-esteem, empowerment and quality of life. Psychiatry Res. 2014;216(1):60–6.CrossRefPubMed
10.
go back to reference Griffiths KM, Calear AL, Banfield M, Tam A. Systematic Review on Internet Support Groups (ISGs) and Depression (2): What Is Known About Depression ISGs? J Med Internet Res. 2009;11(3):e41. Griffiths KM, Calear AL, Banfield M, Tam A. Systematic Review on Internet Support Groups (ISGs) and Depression (2): What Is Known About Depression ISGs? J Med Internet Res. 2009;11(3):e41.
11.
go back to reference Young C. Community management that works: how to build and sustain a thriving online health community. J Med Internet Res. 2013;15(6):e119. PMID: 23759312.CrossRefPubMedPubMedCentral Young C. Community management that works: how to build and sustain a thriving online health community. J Med Internet Res. 2013;15(6):e119. PMID: 23759312.CrossRefPubMedPubMedCentral
12.
go back to reference Carron-Arthur B, Cunningham JA, Griffiths KM. Describing the distribution of engagement in an Internet support group by post frequency: A comparison of the 90-9-1 Principle and Zipf’s Law. Internet Interventions. 2014;1(4):165–8. doi:10.1016/j.invent.2014.09.003.CrossRef Carron-Arthur B, Cunningham JA, Griffiths KM. Describing the distribution of engagement in an Internet support group by post frequency: A comparison of the 90-9-1 Principle and Zipf’s Law. Internet Interventions. 2014;1(4):165–8. doi:10.​1016/​j.​invent.​2014.​09.​003.CrossRef
13.
go back to reference Carron-Arthur B, Reynolds J, Bennett K, Bennett A, Cunningham JA, Griffiths KM. Community structure of a mental health Internet support group: modularity of user thread thread participation. JMIR Ment Health. 2016;3(2):e20. Carron-Arthur B, Reynolds J, Bennett K, Bennett A, Cunningham JA, Griffiths KM. Community structure of a mental health Internet support group: modularity of user thread thread participation. JMIR Ment Health. 2016;3(2):e20.
14.
go back to reference Nimrod G. The membership life cycle in online support groups. Int J Commun. 2012;6:1245–61. Nimrod G. The membership life cycle in online support groups. Int J Commun. 2012;6:1245–61.
15.
go back to reference Salem D, Bogat G, Reid C. Mutual help goes on-line. J Community Psychol. 1997;25(2):189–207.CrossRef Salem D, Bogat G, Reid C. Mutual help goes on-line. J Community Psychol. 1997;25(2):189–207.CrossRef
16.
go back to reference Carron-Arthur B, Ali K, Cunningham JA, Griffiths KM. From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities. J Med Internet Res. 2015;17(12):e271. Carron-Arthur B, Ali K, Cunningham JA, Griffiths KM. From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities. J Med Internet Res. 2015;17(12):e271.
17.
18.
go back to reference van Mierlo T, Voci S, Lee S, Fournier R, Selby P. Superusers in social networks for smoking cessation: analysis of demographic characteristics and posting behavior from the Canadian Cancer Society’s smokers' helpline online and StopSmokingCenter.net. J Med Internet Res. 2012;14(3):e66. PMID: 22732103.CrossRefPubMedPubMedCentral van Mierlo T, Voci S, Lee S, Fournier R, Selby P. Superusers in social networks for smoking cessation: analysis of demographic characteristics and posting behavior from the Canadian Cancer Society’s smokers' helpline online and StopSmokingCenter.net. J Med Internet Res. 2012;14(3):e66. PMID: 22732103.CrossRefPubMedPubMedCentral
19.
go back to reference Preece J. Sociability and usability in online communities: determining and measuring success. Behav Inf Technol. 2001;20(5):347–56.CrossRef Preece J. Sociability and usability in online communities: determining and measuring success. Behav Inf Technol. 2001;20(5):347–56.CrossRef
20.
go back to reference Cutrona SC, Suhr J, MacFarlane R. Interpersonal transactions and the psychological sense of support. In: Duck S, Silver R, editors. Personal Relationship and Social Support. London: Sage; 1990. p. 30–45. Cutrona SC, Suhr J, MacFarlane R. Interpersonal transactions and the psychological sense of support. In: Duck S, Silver R, editors. Personal Relationship and Social Support. London: Sage; 1990. p. 30–45.
21.
go back to reference Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310–57.CrossRefPubMed Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310–57.CrossRefPubMed
22.
go back to reference Roberts LJ, Luke DA, Rappaport J, Seidman E, Toro PA, Reischl TM. Charting uncharted terrain: a behavioral observation system for mutual help groups. Am J Community Psychol. 1991;19(5):715–37.CrossRefPubMed Roberts LJ, Luke DA, Rappaport J, Seidman E, Toro PA, Reischl TM. Charting uncharted terrain: a behavioral observation system for mutual help groups. Am J Community Psychol. 1991;19(5):715–37.CrossRefPubMed
23.
go back to reference Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res. 2003;3:993–1022. Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res. 2003;3:993–1022.
24.
go back to reference Nguyen T, Phung D, Dao B, Venkatesh S, Berk M. Affective and Content Analysis of Online Depression Communities. Affect Comput IEEE Trans. 2014;5(3):217–26.CrossRef Nguyen T, Phung D, Dao B, Venkatesh S, Berk M. Affective and Content Analysis of Online Depression Communities. Affect Comput IEEE Trans. 2014;5(3):217–26.CrossRef
25.
go back to reference Dao B, Nguyen T, Phung D, Venkatesh S. Effect of Mood, Social Connectivity and Age in Online Depression Community via Topic and Linguistic Analysis. In: Benatallah B, Bestavros A, Manolopoulos Y, Vakali A, Zhang Y, editors. Web Information Systems Engineering – WISE 2014. Lecture Notes in Computer Science. 8786. Thessaloniki: Springer International Publishing; 2014. p. 398–407. Dao B, Nguyen T, Phung D, Venkatesh S. Effect of Mood, Social Connectivity and Age in Online Depression Community via Topic and Linguistic Analysis. In: Benatallah B, Bestavros A, Manolopoulos Y, Vakali A, Zhang Y, editors. Web Information Systems Engineering – WISE 2014. Lecture Notes in Computer Science. 8786. Thessaloniki: Springer International Publishing; 2014. p. 398–407.
26.
go back to reference Mitchell M, Hollingshead K, Coppersmith G. Quantifying the Language of Schizophrenia in Social Media. Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Denver: Association for Computational Linguistics; 2015. Mitchell M, Hollingshead K, Coppersmith G. Quantifying the Language of Schizophrenia in Social Media. Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Denver: Association for Computational Linguistics; 2015.
27.
go back to reference Schwartz AH, Eichstaedt J, Kern ML, Park G, Sap M, Stillwell D, et al. Towards Assessing Changes in Degree of Depression through Facebook. Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Baltimore: Association for Computational Linguistics; 2014. Schwartz AH, Eichstaedt J, Kern ML, Park G, Sap M, Stillwell D, et al. Towards Assessing Changes in Degree of Depression through Facebook. Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Baltimore: Association for Computational Linguistics; 2014.
28.
go back to reference Zhang L, Huang X, Liu T, Li A, Chen Z, Zhu T. Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users. In: Zu Q, Hu B, Gu N, Seng S, editors. Human Centered Computing. Lecture Notes in Computer Science. 8944. Switzerland: Springer International Publishing; 2015. p. 549–59. Zhang L, Huang X, Liu T, Li A, Chen Z, Zhu T. Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users. In: Zu Q, Hu B, Gu N, Seng S, editors. Human Centered Computing. Lecture Notes in Computer Science. 8944. Switzerland: Springer International Publishing; 2015. p. 549–59.
30.
go back to reference Evans MS. A computational approach to qualitative analsyis in large textual datasets. PLoS One. 2014;9(2):e87908. Evans MS. A computational approach to qualitative analsyis in large textual datasets. PLoS One. 2014;9(2):e87908.
31.
go back to reference Mimmo D, Wallach HM, Talley E, Leenders M, McCallum A. Optimizing semantic coherence in topic models. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Edinburgh: Association for Computational Linguistics; 2011. Mimmo D, Wallach HM, Talley E, Leenders M, McCallum A. Optimizing semantic coherence in topic models. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Edinburgh: Association for Computational Linguistics; 2011.
32.
go back to reference AlSumait L, Barbara D, Gentle J, Domenico C. Topic significance ranking of lda generative models. In: Buntine W, Grobelnik M, Mladenic D, Shawe-Taylor J, editors. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer; 2009. p. 67–82.CrossRef AlSumait L, Barbara D, Gentle J, Domenico C. Topic significance ranking of lda generative models. In: Buntine W, Grobelnik M, Mladenic D, Shawe-Taylor J, editors. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer; 2009. p. 67–82.CrossRef
33.
go back to reference Holt-Lunstad J, Uchino BN. Social Support and Health. In: Glanz K, Rimer BK, Viswanath K, editors. Health Behavior: Theory, Research, and Practice. San Francisco: John Wiley and Sons; 2015 Holt-Lunstad J, Uchino BN. Social Support and Health. In: Glanz K, Rimer BK, Viswanath K, editors. Health Behavior: Theory, Research, and Practice. San Francisco: John Wiley and Sons; 2015
34.
go back to reference Jacobson N, Greenley D. What Is Recovery? A Conceptual Model and Explication. Psychiatr Serv. 2001;52(4):482–5.CrossRefPubMed Jacobson N, Greenley D. What Is Recovery? A Conceptual Model and Explication. Psychiatr Serv. 2001;52(4):482–5.CrossRefPubMed
35.
go back to reference Barney LJ, Griffiths KM, Banfield M. Explicit and implicit information needs of people with depression: a qualitative investigation of problems reported on an online depression support forum. BMC Psychiatry. 2011;11(88). Barney LJ, Griffiths KM, Banfield M. Explicit and implicit information needs of people with depression: a qualitative investigation of problems reported on an online depression support forum. BMC Psychiatry. 2011;11(88).
36.
go back to reference Griffiths KM, Reynolds J, Vassallo S. An Online, Moderated Peer-to-Peer Support Bulletin Board for Depression: User-Perceived Advantages and Disadvantages. JMIR Mental Health. 2015;2(2):e14. Griffiths KM, Reynolds J, Vassallo S. An Online, Moderated Peer-to-Peer Support Bulletin Board for Depression: User-Perceived Advantages and Disadvantages. JMIR Mental Health. 2015;2(2):e14.
37.
go back to reference Liu B. Sentiment Analysis and Subjectivity. In: Indurkhya N, Damerau FJ, editors. Handbook of Natural Language Processing. Boca Raton: Taylor and Francis; 2010 Liu B. Sentiment Analysis and Subjectivity. In: Indurkhya N, Damerau FJ, editors. Handbook of Natural Language Processing. Boca Raton: Taylor and Francis; 2010
38.
go back to reference Wang X, Zuo Z, Zhao K. The Evolution of User Roles in Online Health Communities – A Social Support Perspective. Pacific Asia Conference on Information Systems; 2015; Singapore. Wang X, Zuo Z, Zhao K. The Evolution of User Roles in Online Health Communities – A Social Support Perspective. Pacific Asia Conference on Information Systems; 2015; Singapore.
39.
go back to reference Wang C, Kraut R, Levine J. Eliciting and Receiving Online Support: Using Computer-Aided Content Analysis to Examine the Dynamics of Online Social Support. J Med Internet Res. 2015;17(4):e99. Wang C, Kraut R, Levine J. Eliciting and Receiving Online Support: Using Computer-Aided Content Analysis to Examine the Dynamics of Online Social Support. J Med Internet Res. 2015;17(4):e99.
40.
go back to reference Wang X, Zhao K, Street N. Social Support and User Engagement in Online Health Communities. In: Zheng X, Zeng D, Chen H, Zhang Y, Xing C, Neill D, editors. Smart Health. Lecture Notes in Computer Science. 8549: Beijing: Springer International Publishing; 2014. p. 97–110. Wang X, Zhao K, Street N. Social Support and User Engagement in Online Health Communities. In: Zheng X, Zeng D, Chen H, Zhang Y, Xing C, Neill D, editors. Smart Health. Lecture Notes in Computer Science. 8549: Beijing: Springer International Publishing; 2014. p. 97–110.
Metadata
Title
What’s all the talk about? Topic modelling in a mental health Internet support group
Authors
Bradley Carron-Arthur
Julia Reynolds
Kylie Bennett
Anthony Bennett
Kathleen M. Griffiths
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Psychiatry / Issue 1/2016
Electronic ISSN: 1471-244X
DOI
https://doi.org/10.1186/s12888-016-1073-5

Other articles of this Issue 1/2016

BMC Psychiatry 1/2016 Go to the issue