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Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis

Overview of attention for article published in Journal of the American Medical Informatics Association, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
76 Mendeley
Title
Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis
Published in
Journal of the American Medical Informatics Association, February 2016
DOI 10.1093/jamia/ocv190
Pubmed ID
Authors

Ulrike Deetjen, John A Powell

Abstract

This research examines the extent to which informational and emotional elements are employed in online support forums for 14 purposively sampled chronic medical conditions and the factors that influence whether posts are of a more informational or emotional nature. Large-scale qualitative data were obtained from Dailystrength.org. Based on a hand-coded training dataset, all posts were classified into informational or emotional using a Bayesian classification algorithm to generalize the findings. Posts that could not be classified with a probability of at least 75% were excluded. The overall tendency toward emotional posts differs by condition: mental health (depression, schizophrenia) and Alzheimer's disease consist of more emotional posts, while informational posts relate more to nonterminal physical conditions (irritable bowel syndrome, diabetes, asthma). There is no gender difference across conditions, although prostate cancer forums are oriented toward informational support, whereas breast cancer forums rather feature emotional support. Across diseases, the best predictors for emotional content are lower age and a higher number of overall posts by the support group member. The results are in line with previous empirical research and unify empirical findings from single/2-condition research. Limitations include the analytical restriction to predefined categories (informational, emotional) through the chosen machine-learning approach. Our findings provide an empirical foundation for building theory on informational versus emotional support across conditions, give insights for practitioners to better understand the role of online support groups for different patients, and show the usefulness of machine-learning approaches to analyze large-scale qualitative health data from online settings.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Unknown 72 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 24%
Student > Master 14 18%
Student > Doctoral Student 8 11%
Researcher 8 11%
Student > Bachelor 7 9%
Other 21 28%
Readers by discipline Count As %
Medicine and Dentistry 18 24%
Psychology 15 20%
Social Sciences 10 13%
Unspecified 9 12%
Computer Science 7 9%
Other 17 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 08 July 2016.
All research outputs
#1,631,770
of 12,027,349 outputs
Outputs from Journal of the American Medical Informatics Association
#571
of 1,984 outputs
Outputs of similar age
#53,244
of 293,997 outputs
Outputs of similar age from Journal of the American Medical Informatics Association
#24
of 55 outputs
Altmetric has tracked 12,027,349 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,984 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. This one has gotten more attention than average, scoring higher than 71% 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 293,997 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 55 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 56% of its contemporaries.