Classificação de Coleções de NFTs Explorando Metadados e Aprendizagem de Máquina

• Samuel de Oliveira Ribeiro UFPI
• Dayan Ramos Gomes UFPI
• Nara Raquel D. Andrade UFPI
• Emanuel Aurélio F. de Miranda UFPI
• Glauber Dias Gonçalves UFPI

DOI: https://doi.org/10.5753/cblockchain.2024.3172

Resumo

Non-fungible Tokens (NFTs) are digital objects with unique identities and ownership verified via blockchain networks. The digital arts and media industry has embraced NFTs for their secure features, such as defining authorship, transfer, and royalties, which can be programmed into smart contracts. The classification of NFTs is crucial for their commercialization but often relies on the author’s definition, which may be prone to errors, or on expert evaluation. In this work, we analyze NFT collection classes based on metadata extracted from OpenSea, the largest NFT platform. We assess the efficiency of supervised machine learning to identify the most relevant attributes of these collections and classify them into the nine most popular categories on the platform. Our results demonstrate the challenges of automating classification to assist users and platform curators, achieving a promising accuracy of 67% and an F1 score of 72% in the best cases.

Referências

Aspembitova, A. T., Feng, L., and Chew, L. Y. (2021). Behavioral structure of users in cryptocurrency market. PLOS ONE, 16(1):1–19.

Breiman, L. (1996). Bagging predictors. Machine learning, 24(2):123–140.

Casale-Brunet, S., Ribeca, P., Doyle, P., and Mattavelli, M. (2021). Networks of ethereum non-fungible tokens: A graph-based analysis of the erc-721 ecosystem. In 2021 IEEE International Conference on Blockchain, pages 188–195. IEEE.

Costa, D., La Cava, L., and Tagarelli, A. (2023). Show me your nft and i tell you how it will perform: Multimodal representation learning for nft selling price prediction. In Proceedings of the ACM Web Conference 2023, pages 1875–1885.

Hu, T., Liu, X., Chen, T., Zhang, X., Huang, X., Niu, W., Lu, J., Zhou, K., and Liu, Y. (2021). Transaction-based classification and detection approach for ethereum smart contract. Information Processing Management, 58(2):102462.

Nadini, M., Alessandretti, L., Di Giacinto, F., Martino, M., Aiello, L. M., and Baronchelli, A. (2021). Mapping the nft revolution: market trends, trade networks, and visual features. Scientific reports, 11(1):20902.

Valadares, J. A., Villela, S. M., Bernardino, H. S., Gonçalves, G. D., and Vieira, A. B. (2023). Mapping user behaviors to identify professional accounts in ethereum using semi-supervised learning. Expert Systems with Applications, 229:120438.

Wang, Q., Li, R., Wang, Q., and Chen, S. (2021). Non-fungible token (nft): Overview, evaluation, opportunities and challenges.

Xu, G., Guo, B., Su, C., Zheng, X., and Liang, K. (2020). Am i eclipsed? a smart detector of eclipse attacks for ethereum. Computers Security, 88:101604.