How does AI filter crypto news articles for relevance?
Category:AI Integration & Development
Quick Answer
AI filters crypto news by analyzing article content with NLP — scoring relevance based on topic keywords, named entities (tokens, protocols, people), sentiment, source credibility, and freshness. It then deduplicates similar stories so only the most informative version surfaces.
Detailed Answer
The AI Filtering Pipeline for Crypto News
Filtering 10,000+ daily crypto articles down to what matters requires a multi-stage AI pipeline:
Stage 1: Content Analysis
- Named Entity Recognition (NER): Identifies mentions of specific tokens (BTC, ETH), protocols (Uniswap, Aave), people, and companies
- Topic Classification: Categorizes articles into buckets — DeFi, regulation, market analysis, technology, etc.
- Language Detection: Filters non-English or machine-translated spam content
Stage 2: Quality Scoring
| Signal | Weight | What It Measures |
|---|---|---|
| Source reputation | High | Historical accuracy, domain authority |
| Content depth | Medium | Article length, data points, original analysis |
| Freshness | Medium | Time since publication, breaking news boost |
| Sentiment signal | Low | Strong positive/negative = potentially newsworthy |
| Originality | High | Is this original reporting or rehashed content? |
Stage 3: Deduplication
When 50 outlets cover the same Bitcoin ETF story, the system:
- Groups articles by semantic similarity (cosine similarity on embeddings)
- Selects the most informative version (longest, most data points)
- Links related coverage for users who want multiple perspectives
Stage 4: Personalization
Optional user-level filtering based on:
- Portfolio holdings (prioritize news about tokens you hold)
- Reading history (learn topic preferences)
- Alert thresholds (only show critical news during busy times)
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