Max Semenchuk
@maxsemenchuk
šØ New case study drop: Weāve been working on a Sybil detection algorithm for Farcaster, with real-world use cases in @optimismgrants Airdrops and Citizen House governance. Hereās what we foundāand why @openrank might be your best friend. š§µš š https://govgraph.fyi/blog/sybilrank-for-identifying-sybils-in-farcaster-case-study
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Max Semenchuk
@maxsemenchuk
š§ Why this matters: Sybil accounts threaten both airdrop fairness and governance integrity. We analyzed on-chain activity across 50k+ addresses in OP Airdrop 5. Goal: Detect real humans in a network thatās open by design.
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Max Semenchuk
@maxsemenchuk
š We used data like: ā¢ POAPs ā¢ Attestations (e.g., citizens, GitHub-linked IDs) ā¢ Farcaster social graph ā¢ SAFE multisigs ā¢ OpenRank scores Turns out: 76% of Airdrop 5 addresses showed some meaningful activity š
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Max Semenchuk
@maxsemenchuk
š Key Insight: @OpenRank was the strongest signal for identifying humans. Why? Itās based on follower-following relationsāharder to fake at scale. Also had the widest coverage (57%) vs. attestations (~43%) or POAPs (~35%).
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Max Semenchuk
@maxsemenchuk
š¤ Are these perfect predictors? Nope. But they correlate with airdrop rewards: ā¢ Attestations: r=0.21 ā¢ OpenRank: r=0.19 ā¢ POAPs: r=0.1 These can act as āhealth checksā for any new drop or governance expansion.
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