How jumblewisp scores trust

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Kind-30023 (Article)

2026-04-23T13:13:09Z

Your Web of Trust

When you log in, jumblewisp builds a Web of Trust (WoT) from your social graph:

  • 1 hop: everyone you follow
  • 2 hops: everyone your follows follow

This gives you a pool of typically thousands of pubkeys — your extended network.

Two Signals

For any given account, we track two things:

  • Follows — how many of your direct follows also follow them
  • Mutes — how many people in your WoT have muted them

The Formula

We use Bayesian-smoothed exponential decay:

followRate = follows / myFollowSetSize muteRate = mutes / wotSize priorRate = 2%

smoothedRatio = (muteRate + priorRate × 10%) / (followRate + priorRate) score = round(100 × exp(−5 × smoothedRatio))

Dividing by myFollowSetSize means signal strength scales with your curation habits. If you follow 100 people, a single follow carries real weight. If you follow 10,000, it contributes far less — because mass-following is a weaker endorsement.

The prior keeps scores stable for accounts with little data. The exponential decay means even a small mute-to-follow ratio drops the score sharply.

What the Numbers Mean

| Situation | Score | | ------------------------------- | ----- | | Well-followed, never muted | ~87 | | Unknown to your network | 1 | | Muted by many, followed by none | 0 |

A score of 1 means the account is simply unknown — no one in your network follows or mutes them. A score of 0 means your WoT has actively flagged them with no countervailing follows.

Why This Approach

Your follow and mute graph is already a distributed reputation system — jumblewisp just does the math. No central authority decides who's trustworthy. Your network does.

Raw JSON

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      "How jumblewisp scores trust"
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      "jumblewisp shows you a trust score (0–100) next to every account. Here's how it works."
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  "content": "## Your Web of Trust\nWhen you log in, jumblewisp builds a **Web of Trust (WoT)** from your social graph:\n\n- **1 hop:** everyone you follow\n- **2 hops:** everyone your follows follow\n\nThis gives you a pool of typically thousands of pubkeys — your extended network.\n## Two Signals\nFor any given account, we track two things:\n\n- **Follows** — how many of your direct follows also follow them\n- **Mutes** — how many people in your WoT have muted them\n## The Formula\nWe use Bayesian-smoothed exponential decay:\n\n```\nfollowRate = follows / myFollowSetSize muteRate = mutes / wotSize priorRate = 2%\n\nsmoothedRatio = (muteRate + priorRate × 10%) / (followRate + priorRate) score = round(100 × exp(−5 × smoothedRatio))\n```\n\nDividing by `myFollowSetSize` means signal strength scales with your curation habits. If you follow 100 people, a single follow carries real weight. If you follow 10,000, it contributes far less — because mass-following is a weaker endorsement.\n\nThe prior keeps scores stable for accounts with little data. The exponential decay means even a small mute-to-follow ratio drops the score sharply.\n## What the Numbers Mean\n\n| Situation                       | Score |\n| ------------------------------- | ----- |\n| Well-followed, never muted      | ~87   |\n| Unknown to your network         | 1     |\n| Muted by many, followed by none | 0     |\n\nA score of **1** means the account is simply unknown — no one in your network follows or mutes them. A score of **0** means your WoT has actively flagged them with no countervailing follows.\n## Why This Approach\nYour follow and mute graph is already a distributed reputation system — jumblewisp just does the math. No central authority decides who's trustworthy. Your network does.",
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