Using Twitter for Data Collection With Health-Care Consumers
Bibliographic record
Abstract
Background: Twitter is one of the most popular social media platforms. The growing use of Twitter by health-care consumers creates a novel venue to understand patient experiences. To understand the potential for this platform to be utilized in patient- and family-oriented health research, this study reviewed published literature on the use of Twitter in health research. Methods: In collaboration with the research team, a research librarian designed and implemented a search strategy in eight databases. Primary and secondary screenings were conducted using predetermined criteria by one reviewer. A second reviewer verified screening decisions in 10% of the studies. Evidence tables were created to synthesize across the following study elements: research design, data collection techniques, analytic approaches, and author’s insights on Twitter as a data collection method. Descriptive narrative analysis was used to synthesize data. Results: The search strategy captured 618 articles; 233 were eliminated in primary screening and 366 articles were eliminated during secondary screening. Verification by the second reviewer resulted in very good agreement (κ = .980). Seventeen articles were included in the final data set. Synthesis across the studies demonstrated that Twitter is currently used to search and mine research data, while active recruitment strategies on Twitter are just beginning to emerge. Conclusion: The novelty of Twitter for study recruitment and data collection with health-care consumers presents advantages and challenges that differ from traditional methods of data collection.
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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 | 0.140 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".