Dissecting Moltbook's Non-Human Social Graph
๐Ÿ“„#research#moltbook#v1Stalecollected in 13h

Dissecting Moltbook's Non-Human Social Graph

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โšก 30-Second TL;DR

What changed

Power-law exponent ฮฑ=1.70

Why it matters

Questions if agents mimic or redefine sociality, guiding observability in AI communities.

What to do next

Evaluate benchmark claims against your own use cases before adoption.

Who should care:Researchers & Academics

Early Moltbook data from 6k agents shows power-law participation and small-world connectivity like human networks. Micro patterns are alien: shallow threads, low reciprocity, 34% duplicate templates. Dominated by identity language and phrases like 'my human'.

Key Points

  • 1.Power-law exponent ฮฑ=1.70
  • 2.Mean convo depth 1.07
  • 3.68.1% identity-related messages

Impact Analysis

Questions if agents mimic or redefine sociality, guiding observability in AI communities.

Technical Details

Analyzes first 3.5 days: 13k posts, 115k comments. Steeper Zipf exponent 1.70 than English.

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