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Trust + Merit Analyzers

The forum's trust + merit systems are dense graphs. Looking at one user's trust tab tells you a fraction of the picture. Community-built analyzers let you see the structure — who trusts whom, who's giving merit to whom, where the influence concentrates.

Trust DAG visualizers

Several community projects render DefaultTrust + extended trust as an interactive graph:

  • Nodes = users
  • Edges = trust relationships (positive / negative)
  • Edge weight = volume of feedback exchanged

What you learn:

  • Who's actually in DT (vs. who claims to be) — though the forum's own trust list is the authority on this, so check there first
  • Which DT members are most influential (most others trust them)
  • Where trust clusters are isolated — a group whose feedback stays inside the group is a pattern worth reading more carefully, not a finding in itself
  • Which users have trust that only runs one way, at scale

Merit-flow trackers

Similar visualization, applied to merit:

  • Who's giving merit to whom
  • Which active users distribute the most sMerit
  • Boards where merit concentrates (often a leading indicator of where high-quality content lives)
  • Users whose merit comes mostly from a single small group

Practical uses

Do

  • Before joining a campaign, check the manager's trust graph for healthy structure
  • When evaluating a service provider, look at the dispersion of their positive trust (many sources = better than one cluster)
  • Use merit-flow data to find active curators in your area of interest
  • Cross-reference: a 'great' user whose trust comes from only one tight cluster is worth a closer read

Don't

  • Take graph data as ground truth — it's a snapshot, not real-time
  • Use these tools to harass — same data accelerates doxxing
  • Trust shallow analysis — read the actual feedback content, not just the graph

Reading a trust graph

Healthy patterns:

  • Diverse sources of positive trust (many DT members, many non-DT members)
  • Reasonable in/out ratio (a user who trusts others moderately as well as receives trust)
  • Long-running connections (multi-year edges)

Patterns worth a second look:

None of these prove anything. They tell you where to read more carefully.

  • All of someone's positive feedback comes from a small group who also vouch for each other. This can mean collusion — it can also just mean a small local community that trades together.
  • Trust only flows one way, at scale.
  • A cluster of new positive feedback appears around an account shortly before it applies for something.

If a pattern concerns you, read the actual feedback and the reasons given. If you still have doubts, do not trade — declining is always available, and it does not require you to accuse anyone of anything.

Limitations

  • Graph tools reflect public trust feedback only; they can't see PM-level commitments
  • They reflect quantitative structure but not qualitative content
  • They're maintained by individuals — can break or get out of date
Quick knowledge check — this quiz needs JavaScript. The lesson above is complete without it.