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Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds

Overview of attention for article published in Journal of Medical Internet Research, March 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 (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

blogs
1 blog
twitter
30 X users
facebook
2 Facebook pages

Citations

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57 Dimensions

Readers on

mendeley
260 Mendeley
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Title
Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds
Published in
Journal of Medical Internet Research, March 2016
DOI 10.2196/jmir.5391
Pubmed ID
Authors

Eradah O Hamad, Marie Y Savundranayagam, Jeffrey D Holmes, Elizabeth Anne Kinsella, Andrew M Johnson

Abstract

Twitter's 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 260 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Colombia 1 <1%
Austria 1 <1%
Unknown 257 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 63 24%
Student > Master 33 13%
Researcher 31 12%
Student > Doctoral Student 19 7%
Student > Bachelor 19 7%
Other 61 23%
Unknown 34 13%
Readers by discipline Count As %
Social Sciences 65 25%
Medicine and Dentistry 37 14%
Computer Science 22 8%
Psychology 19 7%
Business, Management and Accounting 15 6%
Other 55 21%
Unknown 47 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 04 December 2021.
All research outputs
#1,584,771
of 26,587,829 outputs
Outputs from Journal of Medical Internet Research
#1,185
of 8,373 outputs
Outputs of similar age
#24,616
of 314,611 outputs
Outputs of similar age from Journal of Medical Internet Research
#19
of 66 outputs
Altmetric has tracked 26,587,829 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,373 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.4. This one has done well, scoring higher than 85% 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 314,611 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 66 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 71% of its contemporaries.