Dementia Research Engagement on Social Media: A Content Analysis
Bibliographic record
Abstract
Abstract Background Social media is a powerful tool for engaging diverse audiences in dementia research, especially healthy individuals who may not be actively seeking dementia research education or opportunities. However, there is little data summarizing current social media practices, content exchange, and ethical considerations in the context of dementia research. To inform ethical dementia research engagement on social media, we characterized current practices by analyzing public Facebook and Twitter posts. Method We searched for and downloaded from Facebook (2‐yr period) and Twitter (1‐yr period) all posts containing dementia research‐related keywords. We retrieved n = 7,990 Facebook posts after filtering for inclusion and exclusion criteria. The Facebook data aided the development of a machine learning model to search Twitter for dementia research‐related posts (n = 600,000). After filtering, we retrieved n = 10,000 top retweeted posts. We performed content analysis on a random sample (10%) of the Facebook and Twitter posts. Codebook development followed a qualitative manual coding strategy. Coding ceased when no new codes emerged from the data. Result The top 5 Facebook user spaces were Public groups (18%), Nonprofit organizations (18%), Medical & health (9%), Communities (8%), and Medical companies (7%). On Twitter, users with academic/research affiliations were the largest group. Sharing knowledge was the primary form of content exchange on Facebook (19%) and Twitter (21%); there were fewer research opportunities (<9%). Most posts contained linked or embedded media in the form of science news articles. Twitter likes (>100,000) were overall higher than Facebook reactions (>45,000). Although dementia ‘prevention/risk’ and ‘treatment’ were major topics for Facebook and Twitter, posts about ‘diagnostics’ had the most Facebook reactions (29%). Justice was a prominent ethics topic regarding inequalities related to race, ethnicity, culture, gender, and intersecting modes of marginalization in dementia research. Conclusion The results demonstrate the importance of social media as an engagement tool of current topics in dementia research and reveal areas of potential for increased engagement. More data is needed on the social media guidelines followed by dementia researchers, especially regarding their effectiveness and contextual application. The next project phase will use these data to inform the development of a consensus on best practices in this area.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".