AI Content Moderation System for Media Platforms

Imagine a media platform with 10 million daily active users uploading 500,000 posts daily. Manual moderation physically cannot keep up—toxic content remains undetected for hours, and moderators burn out. We develop AI systems that automatically check text, images, and videos, detect violations, and escalate complex cases for human review. Our experience: over 80 moderation projects for major media platforms. We guarantee solution quality: average False Positive Rate less than 0.5%. Our system reduces moderation costs by up to 60% compared to fully manual teams, and processes content 50 times faster than human moderators.

The key challenge is the diversity of violation types. We build a policy hierarchy with prioritization. Critical level (immediate removal): CSAM, weapon manufacturing instructions, direct calls to violence. High level (removal within an hour): disinformation with potential harm, bullying with personal data. Medium level (moderator review): hate speech without direct threats, misleading content. Low level (flagging/warning): adult content without legal violations. This hierarchy unloads moderators: AI automatically removes critical content, while the rest is queued considering virality priority and number of reports. The built-in moderation filter reduces reaction time to dangerous content to seconds.

Why Multimodality Is Critical for AI Content Moderation?

One information channel—text, image, or audio—often does not provide the full picture. For example, neutral text may accompany an aggressive image. An AI system must analyze all modalities simultaneously. We use an ensemble of models: ruBERT for Russian text, ResNet for images, and Whisper for audio. The system processes up to 5,000 requests per second with p99 latency under 200 ms. Our ensemble is 1.6 times more accurate in precision and 2.4 times in recall than rule-based approaches. It also outperforms single-modality systems by over 20% in F1 score for hate speech detection. Contact us to implement multimodal AI moderation—we adapt the solution to your data.

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 for profile 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))

        # Context 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

Ensuring Classification Accuracy in AI Content Moderation

We apply fine-tuning on representative datasets and regularly update models. Accuracy of toxic Russian text classification reaches 97% thanks to normalization of typos and transliteration. We use confidence voting between multiple models to reduce false positives. Below is a comparison of approaches.

Violation Hierarchy in AI Content Moderation

Not all violations are equal. Prioritization by severity:

• Critical level (immediate removal): CSAM, weapon manufacturing instructions, calls to violence with specific threats. Automatic removal plus notification to law enforcement.
• High level (removal within an hour): health disinformation with potential harm, bullying with personal data, systematic spam.
• Medium level (moderator review): hate speech without direct threats, misleading content, copyright violations.
• Low level (flagging/warning): adult content without legal violations but not meeting age restrictions.

Additional Metric: Reaction Time

Combating Hate Speech in Russian

Russian-language moderation has specifics: intentional typos, transliteration, slang. Mitigation:

• Text normalization before classification: replace 1→i, @→a, split concatenated words.
• Fine-tuned ruBERT on toxic content datasets (RuToxic, HatEval).
• Regular update 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 symbols
    replacements = {"@": "а", "0": "о", "3": "е", "1": "и", "|": "л"}
    for char, replacement in replacements.items():
        text = text.replace(char, replacement)
    # Remove unreadable separators inside 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 does not replace moderators entirely but distributes the load more intelligently. The manual moderation queue is prioritized by: content virality, severity of alleged violation, number of reports. Moderators are provided with context: author history, similar previously removed materials, reason for AI flagging.

Appeal Handling in AI Moderation

Users can contest decisions. AI analyzes the appeal: has context changed, does the decision comply with platform policy for this content category, how were similar appeals resolved? Automatic content restoration if high confidence in error (<5% of cases), rest goes to senior moderator.

Moderation Analytics and Calibration

Key metric: False Positive Rate (removal of allowed content) should be <1%. False Negative Rate (missed violation) depends on type, for CSAM target 0%. Monthly calibration: sample of AI decisions compared with expert manual decisions, confidence threshold adjusted. Quality drift monitored via rolling 30-day metrics.

What's Included in the Work

• ML models for text, images, and video, trained on your data.
• API for platform integration (REST/gRPC).
• Metrics dashboard and decision logging.
• Operating documentation and moderator team training.
• Support during the first month.

Implementation typically follows these steps:

1. Data collection and labeling.
2. Model training and fine-tuning.
3. Integration via API.
4. Calibration and threshold tuning.
5. Monitoring and continuous improvement.

Timeline and Cost for AI Moderation

Timeline from 4 to 12 weeks depending on task complexity and data volume. Cost is calculated individually after auditing your platform and requirements. Typical projects start at $5,000 and can go up to $50,000 depending on scope. Request a demo of the system on your data—we will show how automated moderation works. Contact us for an accurate cost and timeline estimate.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.