algo-seo-backlink

# Backlink Quality Assessment

## Overview

Backlink analysis evaluates incoming links by quality metrics (DA/DR, relevance, anchor text diversity, toxicity) to assess a site's off-page SEO strength. Quality assessment is heuristic-based using third-party metrics (Moz DA, Ahrefs DR) as PageRank proxies.

## When to Use

**Trigger conditions:**
- Auditing a site's backlink profile for SEO health
- Identifying and disavowing toxic or spammy links
- Planning link building strategy based on competitor analysis

**When NOT to use:**
- When optimizing on-page content (use content SEO)
- When computing actual PageRank from raw link graphs (use PageRank algorithm)

## Algorithm

```
IRON LAW: Backlink QUALITY Outweighs Quantity
One link from a high-authority, topically relevant domain is worth
more than hundreds from low-quality sites. Evaluate every link on:
1. Authority (DA/DR of linking domain)
2. Relevance (topical match between linking and target pages)
3. Placement (editorial in-content > footer/sidebar)
4. Anchor text (natural diversity > exact-match keyword stuffing)
```

### Phase 1: Input Validation
Export backlink data from Ahrefs, Moz, or Search Console. Required fields: referring domain, DA/DR, anchor text, link type (dofollow/nofollow), first seen date.
**Gate:** Complete backlink export with authority metrics.

### Phase 2: Core Algorithm
1. Deduplicate by referring domain (one link per domain for analysis)
2. Score each link: authority (0-100) × relevance (0-1) × placement weight
3. Flag toxic links: DA < 10, irrelevant foreign language, link farm patterns, PBN indicators
4. Compute profile metrics: total referring domains, DR distribution, anchor text diversity index

### Phase 3: Verification
Cross-reference flagged toxic links against known spam databases. Verify anchor text distribution follows natural pattern (branded > URL > keyword > misc).
**Gate:** Toxic links identified, anchor profile analyzed.

### Phase 4: Output
Return profile assessment with link quality distribution and action items.

## Output Format

```json
{
  "profile": {"referring_domains": 450, "avg_dr": 35, "toxic_count": 23, "anchor_diversity": 0.78},
  "actions": [{"type": "disavow", "domains": ["spam1.com"], "reason": "link farm pattern"}],
  "metadata": {"tool": "ahrefs", "export_date": "2025-01-15"}
}
```

## Examples

### Sample I/O
**Input:** 500 backlinks, 200 referring domains
**Expected:** Distribution: 15% DR 60+, 40% DR 20-59, 45% DR 0-19. Flag 23 toxic domains for disavow.

### Edge Cases
| Input | Expected | Why |
|-------|----------|-----|
| All links from one domain | Low profile diversity | Single-source dependency is risky |
| 90% exact-match anchors | Anchor text penalty risk | Unnatural anchor pattern |
| Zero backlinks | Focus on content first | Can't optimize what doesn't exist |

## Gotchas

- **DA/DR are third-party estimates**: They approximate PageRank but are NOT Google metrics. Two tools often disagree on the same domain's authority.
- **Nofollow still matters**: Google treats nofollow as a "hint." A nofollow link from a DR 90 site still has SEO value, just less than dofollow.
- **Disavow carefully**: Google's disavow tool is a last resort. Disavowing legitimate links harms your own profile. Only disavow clearly toxic/spammy links.
- **Anchor text manipulation**: Exact-match anchor text used to be a ranking factor; now it's a spam signal. Natural profiles have mostly branded and URL anchors.
- **Temporal patterns**: Sudden spikes in backlinks (e.g., 100 links in one day) trigger spam filters. Natural link acquisition is gradual.

## References

- For link toxicity scoring methodology, see `references/toxicity-scoring.md`
- For competitor backlink gap analysis, see `references/competitor-gap.md`