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

Overview of attention for article published in BMC Psychiatry, October 2016
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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8 X users

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138 Mendeley
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Title
What’s all the talk about? Topic modelling in a mental health Internet support group
Published in
BMC Psychiatry, October 2016
DOI 10.1186/s12888-016-1073-5
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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'.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 137 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 19%
Student > Master 18 13%
Researcher 16 12%
Student > Doctoral Student 15 11%
Student > Bachelor 6 4%
Other 16 12%
Unknown 41 30%
Readers by discipline Count As %
Psychology 31 22%
Medicine and Dentistry 14 10%
Computer Science 13 9%
Social Sciences 11 8%
Nursing and Health Professions 4 3%
Other 22 16%
Unknown 43 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 June 2017.
All research outputs
#6,462,358
of 23,509,982 outputs
Outputs from BMC Psychiatry
#2,278
of 4,865 outputs
Outputs of similar age
#96,883
of 315,553 outputs
Outputs of similar age from BMC Psychiatry
#46
of 98 outputs
Altmetric has tracked 23,509,982 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 4,865 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 315,553 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 98 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.