Understanding the Intersection of Ageism and Dementia‐Related Stigma During the American Presidential Election: A Thematic Analysis of Social Media Discourse
Bibliographic record
Abstract
BACKGROUND: When the United States (U.S.) Presidential Election was announced in 2024, Joe Biden and Donald Trump were two of the oldest candidates in election history. This circumstance contributed to sentiments of ageism and dementia-related stigma. Although ageism and dementia-related stigma frequently intersect, there is a paucity of research exploring these interconnected issues. This study used tweets from X (formerly Twitter) to understand the intersection of ageism and dementia-related stigma on social media during the 2024 U.S. Presidential Election. METHOD: We purchased a professional developer account to access the official Application Programming Interface (API) of the social media platform X. We collected relevant tweets from X using the Tweepy application in Python during the U.S. Presidential Election campaign from February 11-25, 2024. Using filters, 1,254 relevant posts were analyzed using thematic analysis. Actions were taken to support trustworthiness and rigor such as inter-rater reliability coding and the creation of a thematic map. RESULT: Based on our thematic analysis, four main themes were identified: 1) dehumanization of older adults: "ancient fossils are running for office," 2) dementia-related ridicule: "demented addled candidate," 3) old age as an inherent weakness: "they're both too old; and 4) fear of perceived incompetence: "the fear is palpable." CONCLUSION: Our findings shed light on how social media discourse can threaten the credibility of older political leaders by shifting the focus from campaign policies to dementia-related stigma and ageism. The persistent questioning of the candidates' competence, coupled with ageist rhetoric, may undermine the credibility of older political leaders and public trust in the electoral system. Further research is needed to address the impact of ageism and dementia-related stigma on political leaders' credibility and the larger electoral system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".