We Are Launching our Own NFT! Characterizing Fashion NFT Transactions-Preliminary Results

Paper presented @BRAINS2023, conference held in Paris (October 2023).

Abstract. Blockchain technology and Non Fungible Tokens (NFTs) have been a hot topic for several years now, as proven by the multitude of brands launching their own NFT projects. In this paper, we will consider some popular fashion NFT collections, namely: adidas Originals Into the Metaverse, AMBUSH OFFICIAL POW! REBOOT, Azuki x AMBUSH IKZ, CULT & RAIN – The Genesis Collection, Dolce& Gabbana: DGFamily, Dolce& Gabbana: DGFamily Glass Box, Chito x Givenchy NFT, MUGLER – We Are All Angel, RTFKT x Nike Dunk Genesis CRYPTOKICK, Prada Timecapsule. First, we will analyze and examine if we can find salient characteristics of transactions pertaining to these collections. Second, we will attempt to propose a first taxonomy of fashion NFT transactions. From the results, we can state that most transactions occur at the NFT launch and that they belong to the Memberships category. Secondly, the results show that we can propose a taxonomy of four transaction groups or clusters. The findings can have practical implications for both researchers and practitioners, indeed the results: (i) can be a stepping stone for future research on (fashion) NFTs, (ii) can help practitioners analyze transactions using our preliminary taxonomy.

Assessing the impact of network factors and Twitter data on Ethereum’s popularity

I recently published a paper entitled “Assessing the impact of network factors and Twitter data on Ethereum’s popularity” in Blockchain: Research and Applications.

Abstract. In March 2021, we witnessed a surge in Bitcoin price. The cause seemed to be a tweet by Elon Musk. Are other blockchains as sensitive to social media as Bitcoin? And more precisely, could Ethereum’s popularity be explained using social media data?

This work aims to explore the determinants of Ethereum’s popularity. We use both data from Etherscan to retrieve the relevant historic Ethereum factors and Twitter data. Our sample consists of data ranging from 2015 to 2022. We use Ordinary Least Squares to assess the relationship between these factors (Ethereum characteristics and Twitter data) and Ethereum’s popularity.

Our findings show that Ethereum’s popularity—translated here by the number of daily new addresses—is related to the following elements: the Ether (ETH) price, the transaction fees, and the polarity of tweets related to Ethereum.

The results could have multiple practical implications for both researchers and practitioners. First of all, we believe that it will enable readers to better understand the technology of Ethereum and its stake. Secondly, it will help the community identify pointers for anticipating or explaining the popularity of existing or future platforms. And finally, the results could help in understanding the factors facilitating the design of future platforms.

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Feel free to share any comment or question you might have regarding the document itself or the topic in general.

Cheers!

Sarah