From Slang to Standards: Consensus-Driven Airdrop Hunter Definition as a Baseline for Cryptocurrency Ecosystem Security and Governance

Abstract

Cryptocurrency airdrops power the growth and governance of the cryptocurrency ecosystem, yet attract airdrop hunters, who coordinate wallets, script interactions, and cash out quickly, distorting metrics and fairness. Prior detection strands (heuristics/clustering, light-supervised community partitioning, and graph learning) face three fundamentals: inconsistent definitions, weak explainability, and poor cross-context generalization. We distill expert knowledge into a computable, interpretable baseline: open/axial coding of expert narratives followed by two Delphi rounds to (1) formalize a consensus, operational definition with six contrasts to regular users; (2) derive 15 measurable indicators spanning operations and fund-flow, tempered by human-ness counter-evidence; and (3) report thresholds as reference distributions (medians, quartiles). The baseline supplies shared semantics and computation for labeling/evaluation, yields inspectable why-flagged rationales for audit and governance, and offers context-aware guidance across chains, campaign designs, and market phases, thereby strengthening on-chain security while informing the design of socio-technical systems perceived as fair, trustworthy, and resistant to strategic misuse.