Self‐disclosure about dementia on social media: toward evidence‐informed guidance
Bibliographic record
Abstract
Abstract Background Social media platforms are increasingly used by people living with dementia and their care partners to seek information and advice, share personal stories, raise awareness, and offer support to others. Engagement with social media is often accompanied by a personal disclosure of a dementia diagnosis or identification as a care partner, but the impact of this disclosure remains unknown. Social media engagement can be beneficial by facilitating peer‐interactions and social support; however experts have raised concerns about the potential for exposure to misinformation and stigma as a result of self‐disclosure. As the popularity of self‐disclosure on social media increases, balancing these risks and benefits is critical to promote healthy and safe social media use for people living with dementia and their care partners. The goal of this project is to deliver an evidence‐based resource to support decision‐making around social media use in dementia. Method As a first step toward addressing this goal, the current project aims to identify the motivations and impact of self‐disclosure on social media. Posts related to self‐disclosure were retrieved from Facebook groups and pages over a six‐month period for analysis. Automated and manual sentiment‐ and model‐based interaction analyses were carried out on the data to characterize posts based on their 1) primary motivation for self‐disclosure, 2) polarity, 3) bids for action, and 4) anonymity of the poster. Result Preliminary findings reveal information‐ and support‐seeking as the most common motivations for self‐disclosure, highlighting the importance of guidance on identifying misinformation and engaging in healthy peer support. Conclusion This work will help empower the dementia community to access support and information safely in the increasingly popular social media spaces to enhance peer supports and access to high‐quality resources.
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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 | 0.095 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".