Are older adults adapting to new forms of communication? A study on emoji adoption across the adult lifespan
Bibliographic record
Abstract
Recent evidence suggests that as a form of non-verbal communication, emojis play critical communicative functions. As such, emojis can help users of all ages meet their social and emotional needs when interacting online. The present study advances our understanding of the factors that influence use of emojis across the adult lifespan. We investigated how age influences several facets of emoji use (frequency of use, diversity of use, ease of interpretation, and interpretation accuracy). We also explored putative mediators of the relationship between age and emoji use by drawing from the literature on technology acceptance. 240 adults, 18–80 years of age, participated in the study. Older users were less likely to use emojis, less likely to use a diversity of emojis, and found emojis less easy to use. Age predicted reduced accuracy of interpretation for only two of the eight emojis tested. Perceived ease of use, Technology self-efficacy, and Expertise with social exchange platforms mediated age-related effects. We conclude that older users have the motivation and ability to utilize emojis but lack the confidence to adopt this new mode of communication. Developers should consider making unambiguous emojis more accessible to facilitate intergenerational interactions on online platforms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".