Cultural Influences on Perceptions of Emotions Depicted in Emojis
Bibliographic record
Abstract
Previous research suggests that people from different cultures weigh cues in the eyes versus mouth differently while interpreting emotions. In Western cultures, where overt emotional display is the norm, people weigh the mouth region more heavily when interpreting facial emotional expression in comparison with people from Eastern cultures. By contrast, in Eastern cultures, where subtle emotion display is the norm, people weigh the eyes region more heavily in comparison with people from Western cultures. Emojis are frequently used paralinguistic cues that convey emotions. Here, we report the results of an online quasiexperimental study in which emotion cues in the eyes and mouth regions of emojis were manipulated to test for differences in the perception of emotions among Westerners and Easterners (N = 427). Consistent with previous research, relative to one another, Westerners' and Easterners' ratings of the emotional valence (i.e., happiness/sadness) of emojis were influenced more heavily by the mouth and eyes, respectively. Thus, the present study adds to the literature suggesting cultural differences in the use of mouth versus eye cues to interpret emotions and supports the notion that these differences extend to paralinguistic cues such as emojis and, consequently, have implications for digital communication.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".