On the Mechanics of NFT Valuation: AI Ethics and Social Media

• Published in: arXiv.org
• Main Authors: Zhang, Luyao, Sun, Yutong, Quan, Yutong, Cao, Jiaxun, Tong, Xin
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 21.07.2023
• Subjects: Blockchain; Computer Science - Computers and Society; Computer Science - Distributed, Parallel, and Cluster Computing; Cryptography; Data exchange; Digital media; Empirical analysis; Ethics; Gender; Interdisciplinary studies; Mechanics; Mechanics (physics); Non-fungible tokens; Robust control; Social networks; Valuation
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2307.10201

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.