How CiteRank works
CiteRank measures how visible a voice is across AI systems and the open web. It's externally grounded — every component checks signals the rest of the world can verify. We don't credit the platform itself.
Methodology v5 · Phase 1 active · Last updated May 4, 2026
What goes into the score
| Dimension | Weight | What it measures | How fast it moves |
|---|---|---|---|
| Peer Standing (PS) | 30 | DR-weighted citations and mentions across the open web | Slow (months) |
| Citation Rate (CR) | 25 | Named citations in answers from ChatGPT, Claude, Gemini, and Perplexity | Volatile (weeks) |
| Topical Authority (TA) | 20 | Whether AI answers name this voice when asked about their declared topics | Volatile (weeks) |
| Content Velocity (CV) | 15 | Posting cadence across owned channels (Substack, podcast, YouTube, etc.) | Direct lever |
| Query Coverage (QC) | 10 | Whether this voice appears across all three AI query archetypes (top voices, frameworks, recommendations) | Volatile (weeks) |
DR-weighted citations and mentions across the open web
Moves: Slow (months)
Named citations in answers from ChatGPT, Claude, Gemini, and Perplexity
Moves: Volatile (weeks)
Whether AI answers name this voice when asked about their declared topics
Moves: Volatile (weeks)
Posting cadence across owned channels (Substack, podcast, YouTube, etc.)
Moves: Direct lever
Whether this voice appears across all three AI query archetypes (top voices, frameworks, recommendations)
Moves: Volatile (weeks)
Each dimension in detail
Peer Standing (PS)· 0–30
What it captures: branded mentions and citations across the open web, weighted by source quality.
How it's computed: DataForSEO branded-mentions search → de-dup → exclude domains the voice owns (see "Self-citation exclusion" below) → tier-weight against an internal quality_sources table (DR 1–10) → normalize 0–30.
Self-citation exclusion: A voice's own website doesn't count. If they list oneusefulthing.org as a controlled domain, mentions on oneusefulthing.org are filtered before scoring.
Why it's slow: PS reflects the cumulative weight of where a voice has been published or referenced. It changes over months, not days.
Citation Rate (CR)· 0–25
What it captures: how often AI assistants cite this voice's owned content when asked about their topics.
How it's computed: Direct queries about the voice on each declared topic ("Tell me about {voice} in the context of {topic}", "What are {voice}'s key contributions to {topic}?") → run against ChatGPT, Claude, Gemini, Perplexity → check if any annotation URL matches the voice's controlled domains → CR = cited probes / total probes × 25.
Anti-gaming guard: if only one of four LLMs cites the voice, the score is halved (cross-platform consistency check).
Why it's volatile: AI citations shift week to week as model providers re-train and re-rank.
Topical Authority (TA)· 0–20
What it captures: whether the voice's name shows up when AI is asked about their topic, without being asked about the voice directly.
How it's computed: Associative queries on each declared topic ("Who are the leading voices on {topic}?") → run against the same four LLMs → 3-state classification on the response: unprompted (full credit, name appears in narrative), on_list (half credit, name in an enumerated list), absent (no credit) → normalize 0–20.
Anti-gaming guard: if fewer than three of four LLMs mention the voice, the score is zeroed (cross-platform consistency check).
Why TA can be 0 even with high CR: CR asks "does AI cite this voice when answering questions ABOUT them"; TA asks "does AI surface this voice when discussing the topic at all." A voice can be widely cited (high CR) but not yet associated with the topic (TA=0).
Content Velocity (CV)· 0–15
What it captures: how active the voice is on owned channels (publishing cadence).
How it's computed: parse RSS feeds (Substack, podcast) and channel APIs (YouTube, Twitter, TikTok, Instagram, LinkedIn) → assemble all post dates → score recency (0–6: most recent post within 7 / 14 / 30 / 60+ days) + frequency (0–5: posts per week over last 90 days) + consistency (0–4: months of activity).
Why it's a direct lever: unlike PS/CR/TA, the voice can move CV by posting more.
Query Coverage (QC)· 0–10
What it captures: whether this voice is surfaced by AI across all three major query archetypes — not just one phrasing of the question.
The three archetypes:
- Top voices — "Who are the leading voices on {topic}?"
- Frameworks — "What frameworks define {topic}?"
- Recommendations — "Who would you recommend I follow for {topic}?"
How it's computed: for each archetype, we check whether at least one LLM probe (across the declared topics and all four engines) returned a classification of unprompted or on_list rather than absent. If all three archetypes show the voice, QC = 10. Two archetypes = 6–7. One = 3. None = 0.
Why it complements TA: TA rewards breadth (how many topics × LLMs return the voice). QC rewards depth of association — a voice can have solid TA by appearing frequently in one query pattern while being invisible to the others. QC catches that gap.
Zero additional LLM cost: QC derives from the same probe results already run for Topical Authority. It adds no inference cost per scoring run.
How we handle run-to-run variance
AI responses vary between runs. The same query asked twice can return different lists. CiteRank's CR and TA dimensions are sampled across multiple runs and aggregated as a rolling 4-week median, with a confidence band reported alongside each score.
A wide confidence band means the AI's answer for this voice is unstable — not necessarily that the voice is invisible. Solo Authority and Team tier runs sample more aggressively to tighten the band.
AI-Recognized — a separate monthly check
Once a month, we ask each LLM "Tell me about {voice} in the context of {topic}." A Claude Haiku judge verifies whether the response accurately describes the voice (matches their LinkedIn role, primary domain, and recent works). If 3 of 4 LLMs return accurate descriptions, the voice earns the AI-Recognized badge.
AI-Recognized doesn't feed the composite score — it's a binary milestone, separate from CiteRank. Designed to give early-stage voices an achievable goal beyond raw score climbing.
What we don't credit
- We exclude self-citations: a voice's own website doesn't add to their PS or CR score.
- We require multi-engine consistency: a single LLM citing the voice doesn't carry the score; ≥3 of 4 are required for full credit on TA, and ≥2 of 4 for full CR.
- We don't credit on-platform activity. CiteRank measures the rest of the world — not what happens on CiteGist itself. The platform cannot credit itself.
How rankings work
Voices are ranked within cohorts — niche × follower-tier — not against the entire platform. A nano-tier (under 25K followers) machine-learning voice is ranked against other nano-tier ML voices, not against an established educator with 500K followers.
Cohorts publish leaderboards only when at least 25 voices are in them. This prevents "Top 5 — only 4 voices" rank-inflation.