How We Measure
Authority

We map publicly available data and the connectivity between it, across tens of thousands of topics, to determine who genuinely holds authority in any field and how fast that authority is growing.

Coverage

A Few Per Cent of the Web, Held Properly

We do not try to hold the whole web. We hold the small share of it that originates what the rest repeats: the people and organisations who publish first, including the long tail of working practitioners that a popularity-ranked index buries. It is a deliberate slice, not a sample, which is why it still answers the kind of question you would otherwise take to a search engine.

The size of that slice is the point. A full-web crawler has to decide how often to revisit a billion pages, and the arithmetic forces a revisit interval measured in days. Our surface is orders of magnitude smaller and we know whose it is, so the accounts that matter can be watched continuously instead of swept occasionally. Less to recrawl is not a cost saving we pass on; it is the reason the index can be current at all. It is also why this runs in Europe on our own hardware: an index that tried to hold everything would make EU-only infrastructure prohibitive.

Holding that slice properly is the part that took years. We have had continuous, compliant API access to the major platforms for long enough to have real historical depth, and every year compounds: a profile enriched over years carries signal quality that one assembled last month cannot match, and each new source creates fresh cross-references across every profile already collected. Recreating it today means navigating API restrictions, policy changes and historical gaps that did not exist when we started, whatever the compute budget.

40M+experts
  • X
  • YouTube
  • LinkedIn
  • Instagram
  • Bluesky
  • Reddit
  • Wikipedia
  • OpenAlex
  • and more
Multi-Source Ingestion
Social media presence, podcasts, and news coverage pulled via compliant API access.
Identity Resolution
Cross-platform profiles linked into a single entity, regardless of how many surfaces a person appears on.
Signal Extraction
Interactions, citations, and mentions extracted and weighted by source quality and topic relevancy.
Authority Score
A normalized score and signal growth rate output for every indexed entity, updated continuously.
Identity Graph

One Person, Many Sources. One Score.

An authority rarely exists on a single platform. A climate researcher may publish academic papers, host a podcast, maintain a YouTube channel, appear in news coverage, and hold an active LinkedIn presence. Each surface contains a fragment of the full picture.

Our identity resolution layer links those fragments into a single unified entity profile. The result is a score that reflects the whole person, not just whatever corner of the internet a query happens to surface first.

Critically, every new data source we add does not just enrich new profiles: it creates new cross-references across every entity already in the index. Five platforms is not five times the data, it is the connections between those five sources for every entity, compounding with each addition. That is why the index becomes harder to replicate with every platform we integrate, not easier.

ENTITYPROFILEYOUTUBELINKEDINNEWSPRESSPODCASTSACADEMIC
One unified profile from five independent sources
Topics

Tens of Thousands of Topics, Built Bottom-Up

We do not score authority in a vacuum. Every entity is evaluated within a structured topic taxonomy that spans tens of thousands of fields, niches, and sub-disciplines. Topic relevancy is derived from the content an entity produces and the interactions that the entity receives.

That taxonomy is bottom-up. Topics are derived from the content and connection patterns observed in the graph itself, then curated by topic experts. New topics emerge as clusters become dense enough to warrant separation; existing topics merge when their connection patterns collapse. So emerging fields like AI safety, prompt engineering and mRNA therapeutics appear as soon as they have signal, rather than waiting for a static taxonomy to be updated.

Topic density matters too: an entity that is highly connected within a dense, well-indexed cluster carries a different signal than one operating in a sparse niche. Both are scored, but the context shapes how the signals are read.

Topic Classification
Topic relevancy confirmed
Matched
Connected entities in same topic
Confirmed
Topic density: high
Indexed
Topic relevancy unclear
Unscored
Entity excluded from topic
  • Irrelevant voices
  • Low-credibility sources
  • Spam and bots
  • Off-topic
  • Expert Article
  • Research institution Paper
  • Industry expert Video
  • Organisation Report
The Score

Connectivity Determines Score

Authority is not self-declared. It is derived from how an entity is connected to others within its topic. The more interactions an entity has with relevant, high-authority sources, the stronger its score; low-quality connections carry little weight, while well-connected, relevant sources compound it. Source quality itself comes from the authority score of the connected entities, calibrated by topic relevancy and the density of the cluster they belong to.

So follower counts and surface-level engagement do not drive the score. What matters is the quality and relevancy of the entities in your connection graph, and whether those connections are meaningful within your topic context.

That produces two numbers. The authority score measures how actively peers and audiences recognise someone's expertise on a topic. It reflects online recognition rather than credentials, so a highly credentialed but less active figure can score below a smaller, highly engaged niche voice. Signal growth tracks the rate at which that score is changing: rising growth means an entity is gaining relevant interactions faster than the topic average, which makes it a leading indicator of where authority in a field is shifting.

authority(e, topic)=
∑c ∈ connections(e) authority(c, topic)× relevance(c, topic)× quality(c)
  • connections who interacts with the entity, across every platform we hold
  • relevance how close that entity sits to this topic, not to the entity
  • quality what the connecting entity is itself worth, which is this same number, one step out

Simplified: the production weighting, decay and cluster-density calibration are not published.

Authority Score Over Time
t₀T0.50.81.0
Score growth as relevant connections accumulate
Document Scoring

A Score for a Person Becomes a Score for a Document

A per-topic score for a person is also a ranking signal for anything that person publishes. Resolve the byline on an article, the authors on a paper or the account behind a post, look up what each of them is worth on the subject of that document, and you have a weight that no amount of page-level analysis can produce.

This is why the identity graph is the hard part rather than the ranking. Scoring a document by its author requires knowing, reliably and at scale, that the handle on a post, the byline on an article and the name on a paper are the same person. That is the work described above, and it is what the weight is built on.

The publication still counts. A masthead is a real signal about editorial process, and it stays one of the inputs. What changes is that it stops being the only one: a specialist correspondent writing inside their field and a generalist writing outside theirs are no longer indistinguishable because they share a domain name.

Weighting one article

  • DocumentArticle on mRNA stability
  • Byline resolved1 author, 3 platforms
  • Author on this topicHigh
  • Author off topicNot counted
  • PublicationOne input, not the basis
Hard Problems

Bootstrapping, and Whether the Score Is Gameable

Every connectivity-based ranking system faces the same bootstrap problem: authority is derived from links to other authorities, so where does the first authority come from? Our answer is a multi-source seeding process per topic, combining academic citation graphs, established editorial output and cross-platform identity persistence. Seed sets are not static: they are continuously audited and re-validated as the graph evolves, with decay applied to entities that lose relevant connections and growth signals applied to new entrants.

The second question is whether the result can be faked. Authority scoring is gameable in principle, like any ranking system. The three structural defences alongside make it expensive in practice.

We do not claim the system is unfakeable. We claim that faking it costs more than it returns.

Three structural defences
  • Connectivity weighting: a new account's score depends on whether high-authority entities engage with it, not on its own activity. Buying followers does not move the score.
  • Cross-platform requirement: a single-platform stunt does not produce a unified profile, since identity resolution requires consistent presence across at least two independent sources.
  • Signal-growth tracking: anomalous spikes are flagged for review rather than rewarded.
Gaming Attempt
Bought 50k followers
No effect
Single-platform spike
No unified profile
Coordinated mention burst
Flagged for review
Score unchanged. Cost exceeds return.

Access the Authority Index

Query authority scores for any indexed entity via our API.