Are you fluent in sexual emoji?😉: Exploring the use of emoji in romantic and sexual contexts
Bibliographic record
Abstract
This research presents an exploratory study of how individuals use emoji, specifically in sexually suggestive contexts. Emoji are small images that depict emotions, concepts, or items that are used in computer-mediated communication in order to add context, emotion, and personality to messages. The dataset consists of 693 participants recruited via online social networks and forums. Results indicate that the use of emoji play a significant role in the sending and receiving of sexually suggestive messages; of individuals who have sent these messages, 51% report that the use of emoji led to the sexually suggestive behaviour and 54% report that emoji appear in their messages sometimes, often, or always. The three most common object emoji last sent and received in a sexually suggestive message are the tongue (👅), the eggplant (🍆), and the sweat droplets (💦), while the three most common face emoji last sent and received in this context are the smirking face (😏), the winking face (😉), and the blowing a kiss face (😘). Additionally, this study demonstrates that extraversion and number of casual sexual partners is significantly related to the use of sexually suggestive emoji, as both extraversion and numbers of casual sexual partners account for 5.9% of the shared variance in the use of sexual emoji. This research provides empirical information that may be used to guide future research into the use of emoji in computer-mediated 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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".