Ageism during the 2024 U.S. Presidential Election: thematic analysis of tweets
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: When the 2024 U.S. Presidential election was announced, Joe Biden and Donald Trump were two of the oldest candidates in election history. This circumstance created sentiments of ageist political discourse and arguments for presidential age limits. Despite clear ageist discourse during the U.S. election, there is a notable lack of research examining this issue. This study used posts from X (formerly Twitter) to understand ageism on social media during the 2024 U.S. Presidential Election, particularly focusing on the campaign period when the race was between Biden and Trump. RESEARCH DESIGN AND METHODS: Posts were collected from X during the American presidential election campaign from February 11-25, 2024. After filtering out non-English, incomplete, and unrelated posts, 1,254 relevant posts were coded line-by-line and then thematically analyzed. Rigor was established by using multiple strategies ranging from a strong audit trail to using interrater reliability during thematic analysis. RESULTS: Four main themes were identified: (1) old age as an inherent weakness: "they're both too old," (2) dementia-related stigma, (3) dehumanization of older adults: "ancient fossils are running for office," and (4) fear of perceived incompetence. DISCUSSION AND IMPLICATIONS: Our study's findings shed light on how ageist discourse on social media threatens the credibility of older political leaders by shifting the focus from policies to stereotypical age-based attacks. Further research is needed to examine the impact of ageist discourse on electoral campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".