Seyhan, BetülSefer, Emre2024-01-292024-01-292023-11-27979-840070240-2http://hdl.handle.net/10679/9109https://doi.org/10.1145/3604237.3626896Non Fungible Tokens (NFTs) are blockchain-based unique digital assets defining ownership deeds. They can characterize various different objects such as collectible, art, and in-game items. In general, NFTs are encoded by blockchains smart contracts, and they are traded via cryptocurrencies. Their price and investors attention on them has remarkably increased especially in 2021, making them promising alternative class of investment. Surprisingly, predicting their prices has only recently started to be analyzed systematically. In this paper, we focus on predicting NFT primary sale price and secondary sale via deep learning. We use multimodal data, NFT images and NFT text characteristics when predicting their prices. Here, we show that contrasting the different and similar (DS) hierarchical features of images and text serves as an important identifying marker for their price, with the consequence that we only need to direct our attention to this aspect when designing a multimodal NFT price predictor. When designing NFT price predictor from multimodal data without using any financial attributes, we come up with Fine-Grained Differences-Similarities Enhancement Network (FG-DSEN), which improves detection with a simple and interpretable structure to enhance the DS aspect between images and text. According to detailed assessment on publicly available NFT dataset, our proposed approach outperforms baselines on both price direction prediction and secondary sale participation prediction according to several machine learning classification metrics.engrestrictedAccessconferenceObject11612300112498270001410.1145/3604237.3626896BERTBlockchainDeep learningNFTs2-s2.0-85179847906