When Cute Becomes Criminal: Emoji, Threats and Online Grooming
Bibliographic record
Abstract
Emoji are widely used and are frequently perceived as cute or benign adjuncts to online communications.Employed to humanize truncated digital messages by conveying humor, emotion, and sociability, emoji perform a far more sinister role when used to convey threats or to facilitate the sexual exploitation of minors.These activities exploit the emotive function of emoji and/or their role in facilitating trust, albeit for a criminal purpose.This paper explores the role of emoji in both threats and online grooming.Through a review of a sampling of criminal cases from diverse jurisdictions, we examine relevant prosecutions and find that emoji are being increasingly recognized as a facilitator or adjunct to criminal threats and unlawful sexual solicitation made on online platforms such as Facebook and Instagram or through private messaging.The review also examines the multiple and diverse ways in which evidence of emoji has been admitted in criminal trials, raising contentious (but hitherto largely unrecognized) issues in relation to the application of the best evidence rule.While noting the distinctive opportunities, challenges, and problems posed in relation to how to interpret and best represent these stylized visual representations in criminal proceedings, the article concludes that despite these various difficulties, imposing criminal liability for threats or unlawful solicitation conveyed by
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".