Reading Between the Glyphs: Unmasking Gen Z’s Coded Cyberbullying on Social Media with AI
Year: 2026
Type: article
Abstract: Adolescents increasingly speak in code online — where a peach ([Formula: see text]) isn’t fruit and “101” isn’t a class. We ask whether these symbol–number–emoji sequences cause measurable harm, how adults misread them and whether AI can detect them without over-policing. We execute three phases — Map, Listen and Detect. In Phase I (Map), we analyzed 5000 posts across platforms: covert-hostility prevalence was 2.52% (95% CI 2.12–2.99), higher on short-form feeds and concentrated in evenings (73%). Hostility resided in combinations — hybrids (47.6%), emoji-only clusters (34.1%) and alphanumeric obfuscations (18.3%) — with mixed-script tricks over-represented. In Phase II (Listen), interviews with 45 Gen Z participants showed exposure (91%), frequent experience (62% often/always) and salient impacts. Adults positively engaged with coded mockery in 58% of cases; 73% believed this amplified reach. In Phase III (Detect), our sequence-aware model (CRISP) achieved AUPRC 0.79 and Recall@1% FPR 0.58 (ECE 0.032), outperforming a text-only baseline (AUPRC 0.52) and a commercial API, especially on covert items (0.74 versus 0.47/0.31) while cutting benign-banter false positives by 41%. Decision-curve analysis favored CRISP across costs. We contribute a semiotic framework (symbols[Formula: see text] → [Formula: see text]sequences[Formula: see text] → [Formula: see text]scenes), a corpus and pipeline that flags coded hostility — including dog-whistles for extremism or drugs — without ethically silencing in-group play. A repository is uploaded to GitHub to show a sample of posts [Formula: see text], an Excel codebook and an AI-generated video (prototype).
Source: Computing Open
Author: Samer Abaddi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7130653266