Is That a Genuine Smile? Emoji-Based Sarcasm Interpretation Across the Lifespan
Bibliographic record
Abstract
Emoji appear to be an important cue to judge whether a statement is sarcastic in computer-mediated communication. In this study, we investigated whether the smiling emoji, an indicator of sarcastic intention in the Chinese culture, exerts an influence on sarcasm interpretation across the lifespan. Statements accompanied with or without a smiling emoji were compared in unambiguous (Experiment 1) and ambiguous (Experiment 2) contexts. The results of Experiment 1 illustrated that for teenagers and the 20-year-olds the smiling emoji enhanced the perceived sarcasm of sarcastic statements significantly. However, there was no difference in interpreting sarcastic statements with or without a smiling emoji in other age groups. Experiment 2 replicated the results of Experiment 1. We found both teenagers and the 20-year-olds were more likely to arrive at a sarcastic interpretation of the ambiguous statement followed by a smiling emoji, which were less frequent in participants aged in their 30s, 40s, 50s and individuals over 60 years old. This might be because people of varying ages differ in decoding the emotions of the emoji. Age-related differences in the use of sarcasm and participants’ experiences with using emoji might be possible factors that were closely related to the interpretation of emoji-based sarcasm.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".