Quality Scoring

Quality Scoring analyzes articles using heuristic metrics to compute quality scores ranging from 0-100. This helps surface high-quality content and filter low-quality articles.

Overview

The quality scorer evaluates articles across multiple dimensions:

• Depth: Word count, paragraph structure, technical content (code blocks, diagrams)
• References: External links, academic citations, reputable domains
• Author Authority: Author credentials and expertise (planned)
• Domain Reputation: Feed source quality and reliability
• Engagement: Read time estimates and user signals (planned)

Architecture

Scoring Components

Depth Score (0-100)

Evaluates content depth based on:

• Word Count: Higher scores for longer articles (500+ words)
• Structure: Rewards well-organized content with multiple paragraphs
• Technical Content: Bonus points for code blocks (```) and images
• Headings: Recognition of structured content with markdown headings

Example:

# Article with 1500 words, 5 paragraphs, code blocks → Depth Score: 85

Reference Score (0-100)

Assesses external citations:

• External Links: Minimum 3 links recommended
• Academic Citations: DOI, arXiv references weighted highly
• Reputable Domains: .edu, .org domains receive bonus points

Example:

# Article with 5 links, 2 from arxiv.org → Reference Score: 75

Domain Score (0-100)

Based on feed reputation:

• High-Quality Feeds: arXiv, Nature, Science, ACM journals → 90
• Standard Feeds: General tech blogs → 60
• Unknown Feeds: Default score → 50

Overall Score

Weighted combination of component scores:

overall_score = (
    depth_score * 0.25 +
    reference_score * 0.20 +
    author_score * 0.15 +
    domain_score * 0.25 +
    engagement_score * 0.15
)

Usage

CLI Commands

Process Quality Scoring

Run quality scoring manually on unprocessed articles:

aiwebfeeds nlp quality

Options:

• --batch-size: Number of articles to process (default: 100)
• --force: Reprocess all articles, ignoring existing scores

# Process 50 articles
aiwebfeeds nlp quality --batch-size 50

# Reprocess all articles
aiwebfeeds nlp quality --force

View Statistics

aiwebfeeds nlp stats

Shows processing status for all NLP operations including quality scoring.

Python API

from ai_web_feeds.nlp import QualityScorer
from ai_web_feeds.config import Settings

scorer = QualityScorer(Settings())

article = {
    "id": 1,
    "title": "Attention Is All You Need",
    "content": "The Transformer architecture...",  # Long article
    "feed_id": "arxiv-nlp"
}

scores = scorer.score_article(article)
# Returns: {
#     "overall_score": 85,
#     "depth_score": 90,
#     "reference_score": 75,
#     "author_score": 50,
#     "domain_score": 90,
#     "engagement_score": 60
# }

Batch Processing

Quality scoring runs automatically every 30 minutes via APScheduler:

from ai_web_feeds.nlp.scheduler import NLPScheduler
from apscheduler.schedulers.asyncio import AsyncIOScheduler

scheduler = AsyncIOScheduler()
nlp_scheduler = NLPScheduler(scheduler)
nlp_scheduler.register_jobs()
scheduler.start()

Database Schema

article_quality_scores Table

CREATE TABLE article_quality_scores (
    article_id INTEGER PRIMARY KEY,
    overall_score INTEGER NOT NULL CHECK(overall_score BETWEEN 0 AND 100),
    depth_score INTEGER CHECK(depth_score BETWEEN 0 AND 100),
    reference_score INTEGER CHECK(reference_score BETWEEN 0 AND 100),
    author_score INTEGER CHECK(author_score BETWEEN 0 AND 100),
    domain_score INTEGER CHECK(domain_score BETWEEN 0 AND 100),
    engagement_score INTEGER CHECK(engagement_score BETWEEN 0 AND 100),
    computed_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (article_id) REFERENCES feed_entries(id) ON DELETE CASCADE
);

Processed Flags

Feed entries track processing status:

ALTER TABLE feed_entries ADD COLUMN quality_processed BOOLEAN DEFAULT FALSE;
ALTER TABLE feed_entries ADD COLUMN quality_processed_at DATETIME;

Configuration

Configure quality scoring in config.py or via environment variables:

class Phase5Settings(BaseSettings):
    quality_batch_size: int = 100  # Articles per batch
    quality_cron: str = "*/30 * * * *"  # Every 30 minutes
    quality_min_words: int = 100  # Minimum words to score

Environment Variables:

PHASE5_QUALITY_BATCH_SIZE=100
PHASE5_QUALITY_MIN_WORDS=100

Performance

• Throughput: ~100 articles/minute
• Memory: <50MB for batch of 100 articles
• Storage: ~100 bytes per article score

Future Enhancements

Planned improvements for quality scoring:

1. Author Authority: H-index, publication history, expert verification
2. Engagement Metrics: Read time tracking, shares, comments
3. Machine Learning: Train models on user feedback to refine scoring
4. Domain Reputation: Crowdsourced feed quality ratings

Troubleshooting

No Articles Being Scored

Symptom: aiwebfeeds nlp stats shows 0 quality processed.

Solution:

# Check if articles exist
aiwebfeeds feeds list

# Manually trigger scoring
aiwebfeeds nlp quality --batch-size 10

Low Scores for Good Articles

Symptom: High-quality articles receiving low scores.

Cause: Missing metadata (author, feed reputation not configured).

Solution: Update domain scoring logic in quality_scorer.py to recognize your feeds.

See Also

• Entity Extraction - Extract named entities from articles
• Sentiment Analysis - Classify article sentiment
• Topic Modeling - Discover subtopics automatically