Abstract:
NFT or Non-Fungible Token is a token that certifies a digital asset to be unique. A wide range of assets including, digital art, music, tweets, memes, are being sold as NFTs. NFT-related content has been widely shared on social media sites such as Twitter. We aim to understand the dominant factors that influence NFT asset valuation. Towards this objective, we create a first-of-its-kind dataset linking Twitter and OpenSea (the largest NFT marketplace) to capture social media profiles and linked NFT assets. Our dataset contains 245,159 tweets posted by 17,155 unique users, directly linking 62,997 NFT assets on OpenSea worth 19 Million USD. We have made the dataset publicly available.
We analyze the growth of NFTs, characterize the Twitter users promoting NFT assets, and gauge the impact of Twitter features on the virality of an NFT. Further, we investigate the effectiveness of different social media and NFT platform features by experimenting with multiple machine learning and deep learning models to predict an asset’s value. We model the problem as a binary classification as well as an ordinal classification task. Our results show that social media features improve the ordinal classification accuracy by 6% over baseline models that use only NFT platform features. Among social media features, count of user membership lists, number of likes and replies are important features. On the other hand, OpenSea features like offer entered, bids withdrawn, bid entered and is presale turn out to be important predictors.
