ARTEMIS: Detecting Airdrop Hunters in NFT Markets with a Graph Learning System

Chenyu Zhou, Hongzhou Chen, Hao Wu, Junyu Zhang, Wei Cai

Keywords: Airdrop Hunters, Web3, NFTs, Graph Neural Network, Multimodal Deep Learning

Abstract: As Web3 projects leverage airdrops to incentivize participation, airdrop hunters tactically amass wallet addresses to capitalize on token giveaways. This poses challenges to the decentralization goal. Current detection approaches tailored for cryptocurrencies overlook non-fungible tokens (NFTs) nuances. We introduce ARTEMIS, an optimized graph neural network system for identifying airdrop hunters in NFT transactions. ARTEMIS captures NFT airdrop hunters through: (1) a multimodal module extracting visual and textual insights from NFT metadata using Transformer models; (2) a tailored node aggregation function chaining NFT transaction sequences, retaining behavioral insights; (3) engineered features based on market manipulation theories detecting anomalous trading. Evaluated on decentralized exchange Blur's data, ARTEMIS significantly outperforms baselines in pinpointing hunters. This pioneering computational solution for an emergent Web3 phenomenon has broad applicability for blockchain anomaly detection.

Track: Security

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Submission Guidelines Blind: Yes

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Student Author: Yes

Submission Number: 1716