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

Paper Title

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

Publication Info

• Topic area: Cryptocurrency security and governance
• Keywords: Airdrop hunters, blockchain security, decentralized finance, Delphi method, on-chain analysis, socio-technical systems, user behavior profiling, threshold reference distributions, governance fairness, explainability

Background and Problem

• Problem / challenge: The term "airdrop hunter" lacks a standardized, operational definition, leading to inconsistent labeling, poor generalization across contexts, and weak explainability in detection methods.
• Significance: Airdrop hunters distort metrics, dilute rewards for genuine users, and undermine fairness, trust, and governance in cryptocurrency ecosystems.
• Motivation and related work: Prior detection methods (heuristics, graph clustering, machine learning) have shown promise but rely on ad hoc definitions, making results difficult to compare across projects and contexts. This paper seeks to establish a reproducible baseline for defining and detecting airdrop hunters.

Solution

• Proposed approach: A Delphi-based framework combining expert consensus, open/axial coding, and operationalized indicators to define and detect airdrop hunters.
• Novelty:
  1. Formalized definition of "airdrop hunter" with six systematic contrasts to regular users.
  2. Creation of 15 computable indicators (11 hunter-behavior signals, 4 human-ness counter-evidence signals) validated through Delphi rounds.
  3. Introduction of context-aware threshold reference distributions for detection and governance.
• Procedure and key techniques:
  • Phase 1: Open-ended expert survey analyzed via open and axial coding to derive behavioral dimensions and indicators.
  • Phase 2: Two Delphi rounds to assess indicator importance and propose thresholds, reporting medians, quartiles, and dispersion statistics.
  • Indicators categorized into operational patterns (e.g., scripted trading, fund consolidation) and human-ness counter-evidence (e.g., natural trading behavior, asset diversity).

Results

• Concrete findings:
  • Five operational indicators (e.g., Automated Scripted Trading, Batch New-Wallet Activation) and four human-ness counter-evidence indicators reached importance consensus.
  • Thresholds reported as reference distributions (e.g., BW median = 10 wallets activated, RF median = 50% consolidation ratio).
• Advantage over baselines: Provides a unified definition, reproducible computation recipes, and interpretable rationales, addressing inconsistencies and explainability gaps in prior methods.
• Experiments / evaluation: Delphi rounds with 10 external experts; importance and threshold ratings for 15 indicators; thresholds treated as context-sensitive reference distributions rather than universal constants.
• Limitations and future work:
  • Gender and regional biases in expert panel composition.
  • Thresholds not yet validated on real on-chain datasets.
  • AI-assisted coding risks and potential residual biases.

Summary

This paper introduces a Delphi-based framework to define and detect airdrop hunters, transforming an ambiguous slang term into a reproducible construct. By combining expert consensus with operationalized indicators, the study provides a standardized definition, validated importance ratings, and context-aware threshold distributions. The framework improves detection accuracy, explainability, and governance fairness while enabling adaptation across chains and campaign designs. Future work will focus on validating thresholds on large-scale datasets and addressing panel composition biases.