Explains precision, recall, F1 and AUC to balance catching DM threats with avoiding public false positives, and covers dataset and multilingual challenges.
AI flags threats, harassment, and coordinated attacks in social messages using outlier detection and classifiers across 40+ languages.
How biased data, cultural gaps, and feedback loops skew AI moderation—and practical fixes like diverse datasets, adversarial debiasing, XAI, and human review.
How AI moderation automates detection, audit logging, and multilingual DM monitoring to help platforms meet DSA, GDPR, and evolving U.S. laws.
How personalized federated learning tailors on-device AI moderation to reduce false positives, protect user privacy, and detect multilingual threats.
Explains how emojis are repurposed to hide bullying, grooming, and extremist signals—and why context-aware AI moderation is essential to spot harmful patterns.
Guide to building real-time moderation: clear rules, AI + human layers, escalation tiers, event-specific settings, multilingual support, and crisis protocols.
Multilingual AI detects and auto-hides abusive comments and high-risk DMs across 40+ languages to improve user safety and protect reputations.
How regional language, slang and cultural norms affect AI moderation—and how localized models plus human review reduce false positives and missed threats.
Checklist for building, validating, and deploying predictive models to detect online grooming, sextortion, and harassment while ensuring fairness and privacy.
