Development of AI System for Content Moderation on Media Platforms
Moderation of user content at scale of millions of publications per day is impossible without automation. An AI system processes text, images, and video, identifies platform policy violations, and passes edge cases to manual review.
Violation Hierarchy and Policies
Not all violations are equal. Prioritization by severity:
Critical Level (immediate removal): CSAM, weapons manufacturing instructions, calls to violence with specific threats. Automatic removal + law enforcement notification.
High Level (removal within hour): health misinformation with potential harm, bullying with personal data, systematic spam.
Medium Level (moderator review): hate speech without direct threats, misleading content, copyright violations.
Low Level (labeling/warning): adult content without legal violations but not age-appropriate.
Multimodal Moderation
class ContentModerationSystem:
def __init__(self):
self.text_classifier = TextModerationClassifier()
self.image_classifier = ImageModerationClassifier() # NSFW, violence
self.audio_classifier = AudioModerationClassifier() # hate speech in voice
self.context_analyzer = ContextAnalyzer() # account context, history
def moderate(self, content: UserContent) -> ModerationDecision:
signals = []
if content.text:
signals.append(self.text_classifier.classify(content.text))
if content.images:
for img in content.images:
signals.append(self.image_classifier.classify(img))
if content.audio:
transcript = self.speech_to_text(content.audio)
signals.append(self.text_classifier.classify(transcript))
# Contextual analysis: author history, content type, audience
context = self.context_analyzer.analyze(content.author_id, content.channel_type)
return self.make_decision(signals, context)
class ModerationDecision(BaseModel):
action: str # allow / flag / remove / escalate
violation_categories: list[str]
confidence: float
requires_human_review: bool
reasoning: str # for decision audit
appeal_eligible: bool
Hate Speech Handling in Russian
Russian language moderation has specifics: intentional typos, transliteration, jargon. Mitigation:
- Text normalization before classification: 1→i, @ → а, compound word splitting
- Fine-tuned ruBERT on toxic content dataset (RuToxic, HatEval)
- Regular updates of euphemism dictionary and new slang forms
- Separate model for implicit toxicity (sarcasm, indirect insults)
def normalize_text(text: str) -> str:
text = text.lower()
# Replace leetspeak and characters
replacements = {"@": "а", "0": "о", "3": "е", "1": "и", "|": "л"}
for char, replacement in replacements.items():
text = text.replace(char, replacement)
# Remove unreadable separators within words (X.X.X → XXX)
text = re.sub(r'\b(\w)\.\1\b', lambda m: m.group(1)*3, text)
return text
Manual Moderation and Queue Management
AI system doesn't fully replace moderators — distributes workload smarter. Manual moderation queue prioritized by: content virality (more views, more urgent), violation severity, number of user reports.
Moderators provided with context: author history, similar previously removed content, why AI flagged.
Appeals Handling
Users can contest decision. AI analyzes appeal:
- Has context changed (author provided additional information)?
- Does decision match platform policy for content category?
- Similar appeals on similar content — how were they resolved?
Automatic content restoration with high confidence in error (< 5% of cases), rest — to senior moderator.
Analytics and Calibration
Key metric: False Positive Rate (allowed content removed) — must be < 1%. False Negative Rate (violation missed) — depends on type, for CSAM target 0%.
Monthly calibration: sample of AI decisions compared with expert manual decisions, confidence threshold adjusted. Quality drift tracked via 30-day rolling metrics.