Stigma of Dementia on Social Media During World Alzheimer’s Awareness Month: Thematic Analysis of Posts
Bibliographic record
Abstract
Background: Dementia-related stigma is a significant global health concern. However, public awareness and education about dementia-related stigma remain limited, especially on social media. Examining dementia-related stigma on social media is critical because it impacts how the public perceives people living with dementia. By understanding dementia-related stigma on social media, we can develop educational strategies to target false stereotypes, beliefs, and misinformation to improve the quality of life of people living with dementia. Objective: This study examines dementia-related stigma on the X platform (formerly Twitter) during World Alzheimer's Month to identify opportunities for intervention to address dementia-related stigma. Methods: A total of 266,211 posts were scraped from X during the World Alzheimer's Awareness Month from September 1-30, 2022, a global advocacy campaign organized by Alzheimer's Disease International. We used filters to exclude non-English content, duplicate posts, and reply posts with missing content. To ensure rigor and trustworthiness in the research, several measures were employed, ranging from peer debriefing sessions to documenting the research process. Results: After filtering the data, 1981 posts were examined using thematic analysis. A total of four main themes were identified including: (i) dementia stereotypes: "a burden to society"; (ii) discrimination and denied dignity: "discrimination exists in public spaces"; (iii) devaluing the lives of people with dementia: "society should legalize euthanasia"; and (iv) countering dementia-related stigma: "break down the stigma." Although the World Alzheimer's Awareness Month is helpful for raising awareness, more research is needed to address dementia-related stigma, stereotypes, and discrimination on social media. Conclusions: By analyzing how stigma manifests on social media, our study sheds light on the dementia education and information needed to address false beliefs, misinformation, and dementia-related stigma. The findings from our study have important implications for policymakers, health professionals, and community advocates working to design awareness campaigns to reduce dementia-related stigma on social media.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".