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Social Scoring Social scoring answers one question: Did this call attract credible attention — relative to what this KOL normally gets? It’s designed to reduce two common traps:
  • “Big account = high score” (not necessarily true)
  • “Botted / low-effort engagement” inflating metrics

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Inputs

Social scoring can use:
  • Views, likes, reposts, replies, bookmarks
  • Follower count (for normalization)
  • Comment text + unique commenter identities (for quality checks)

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Baseline normalization (why big accounts don’t auto-win)

Raw engagement numbers are meaningless without context. OpenKol builds a rolling baseline for each KOL from their recent activity (where available), using ratios like:
  • likes per view
  • reposts per view
  • replies per view
  • bookmarks per view
A new call is scored by comparing its ratios to that KOL’s baseline. Example idea (conceptual):
  • If a KOL usually gets 1.0% reposts per view, a 3.0% repost rate is meaningful.
  • If a KOL already averages 3.0%, then the same number is “normal.”
This is how OpenKol rewards unusual, credible attention — not just audience size.

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Comment quality (how we reduce junk)

Replies can be the noisiest metric, so OpenKol treats them carefully. Typical protections include:
  • Deduplicating repeated / spammy text patterns
  • Weighting by unique commenters (many different people > a few loud accounts)
  • Reducing impact of very low-effort replies
The goal is not to “judge sentiment.” It’s to distinguish real engagement from low-value activity.

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Score composition (0–100)

The social score is a weighted sum of components (capped at 100):
  • Views: up to 20
  • Likes: up to 20
  • Reposts: up to 15
  • Replies: up to 10
  • Bookmarks: up to 15
  • Comment quality: up to 20
Each component is computed by:
  1. converting the raw metric into a ratio (often per view),
  2. comparing it to the KOL baseline,
  3. clamping to a safe range so outliers don’t break the score, then
  4. weighting into points.

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How to interpret Social score

  • High Social doesn’t guarantee the token performed — it means the call drew strong, credible attention.
  • Low Social doesn’t mean the call was “bad” — it may indicate a quiet/early post that still printed (check Chart score).
Best practice:
  • Use Social score to detect attention quality
  • Use Chart score to confirm market outcome

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Why this matters

Apers lose money when they confuse “loud” with “good.” Baseline normalization and comment-quality checks help:
  • prevent large accounts from overwhelming the score
  • reduce obvious low-value engagement
  • surface calls that genuinely broke through compared to a KOL’s normal reach