Health Forums and Twitter for Dementia Research: Opportunities and Considerations
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Social media platforms are promising sources for large quantities of participant-driven research data and circumvent some common challenges when conducting dementia research. This study provides a summary of key considerations and recommendations about using these platforms as research tools for dementia. DESIGN: Mixed methods. SETTING: Alzheimer's Society's online Dementia Talking Point forum from inception to April 17, 2018, and Twitter in February and March 2018. PARTICIPANTS: All users of Dementia Talking Point who posted in subforums labeled "I have dementia" and "I care for a person with dementia," and Twitter users whose posts contained the keywords "dementia," "Alzheimer," or "Alzheimer's." MEASUREMENTS: We quantified the average daily number of dementia-related posts on each platform and number of words per post. Guided by a codebook, we conducted thematic content analysis of 5% of the 15,513 posts collected from Dementia Talking Point, and 10% of the 25,948 comprehensible posts from Twitter containing "dementia," "Alzheimer," or "Alzheimer's." We also summarized research-relevant characteristics inherent to platforms and posts. RESULTS: On average, Dementia Talking Point provided less than two new daily dementia-related posts with 213.5 to 241.5 words, compared with 7,883 new daily Twitter posts with 14.5 words. Persons with dementia (PWDs) commonly shared dementia-related concerns (75.7%), experiences (68.6%), and requests for, as well as offers of, information and support (44.3% and 38.6%, respectively). Caregivers commonly shared caregiving experience (67.0%) and requests for information and support (52.5%). Most common dementia-related Twitter posts were derogatory use of the term dementia (14.5%), advocacy, fundraising, and awareness (11.6%), and research dissemination (8.0%). Recommendations about these platforms' unique technical and ethical considerations are outlined. CONCLUSIONS: Understanding the priorities of PWDs and their caregivers remains important to understand how clinicians can best support them. This study will help clinicians and researcher to better leverage online health forums and Twitter for such dementia-related information.
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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 | 0.165 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.022 | 0.053 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".