Airdrops represent a pivotal strategic instrument for Web3 projects, serving to distribute free tokens and motivate early adoption. However, the popularity of these tokens has fueled the emergence of airdrop hunters—individuals who exploit multiple transactions to acquire disproportionate amounts of tokens unfairly. This phenomenon threatens the integrity and fairness of the Web3 community. Current detection methods struggle with high false-positive rates, harming legitimate users, and require significant computational resources for training. Furthermore, these methods face challenges in adapting to the evolving tactics of airdrop hunters, leading to diminished detection accuracy and efficiency. We introduce ARTEMIX, a community-boosting-based framework that integrates custom-engineered features and community detection techniques to identify airdrop hunters in NFT transactions. Using data from the Blur NFT market, ARTEMIX demonstrates superior accuracy and efficiency, outperforming existing graph-based inference models, achieving an F1 score of 0.898. This approach provides a scalable and effective solution to anomaly detection in the Web3 ecosystem, promoting a more secure and equitable environment for token distributions.