Tapping twitter: A meta-method of the qualitative health literature using social media as a data collection tool
Bibliographic record
Abstract
Background Social media, such as Twitter, Facebook and YouTube, are modern web-based platforms that facilitate communication and information-sharing. Approximately 70% of Canadians use social media – a percentage that is even higher among young adults. Online content is a primary source of healthcare information for internet-using adults. A 2012 survey indicated that 89% of adult Canadians use the internet to find information about health issues and symptoms. There is an ideal fit between those who use the internet as a primary channel for accessing health information and those who want to track user groups and compare information concerning individual attitudes and behaviour about health issues. Thus, social media are fast becoming an innovative data source and data collection tool for researching health issues. What is less clear is how qualitative health researchers design and execute studies using social media, the quality of data generated, the trustworthiness and credibility of results, and necessary ethical considerations. Objectives This paper will present the findings of a meta-method of qualitative health studies that used social media as a data source and/or data collection tool. Methods A meta-method examines the epistemological and methodological underpinnings and the procedural rules for engaging in qualitative research. Our primary goal is to provide insight into the methodological strengths and limitations of using social media when engaging in qualitative health research. This meta-method was conducted according to the guidelines advanced by Paterson et al. Six databases were searched for English-language articles published between 2006 and 2012 using search terms to identify qualitative research studies that used social media as a data collection tool and/or data source. Eligible studies were analyzed thematically and compared for credibility, trustworthiness, transparency, and clarity of design. Results Major themes emerging from the inductive comparative analysis of the selected studies will show how and under what conditions social media were used to collect data to study a health issue, the associated ethical and other challenges associated with executing the study, and observations concerning the quality of the research process. Conclusions This meta-method indicates that social media as a data source and/or collection tool can make a valuable contribution to health knowledge if methodological standards for qualitative health research are rigorously followed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.231 | 0.326 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".