Digital Dating: Online Profile Content of Older and Younger Adults
Bibliographic record
Abstract
OBJECTIVES: Older adults are utilizing online dating websites in increasing numbers. Adults of different ages may share motivations for companionship and affection, but dating profiles may reveal differences in adults' goals. Theories addressing age-related changes in motivation suggest that younger adults are likely to emphasize themselves, achievements, attractiveness, and sexuality. Older adults are likely to present themselves positively and emphasize their existing relationships and health. METHOD: We collected 4,000 dating profiles from two popular websites to examine age differences in self-presentations. We used stratified sampling to obtain a sample equally divided by gender, aged 18-95 years. We identified 12 themes in the profiles using Linguistic Inquiry and Word Count software (Pennebaker, Booth, & Francis, 2007). RESULTS: Regression analyses revealed that older adults were more likely to use first-person plural pronouns (e.g., we, our) and words associated with health and positive emotions. Younger adults were more likely to use first-person singular pronouns (e.g., I, my) and words associated with work and achievement. DISCUSSION: Findings suggest that younger adults enhance the "self" when seeking romantic partnership. In contrast, older adults are more positive in their profiles and focus more on connectedness and relationships to others.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".