Authors: (1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland; (2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain; (3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland; (4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland; (5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Authors: Authors: (1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland; (2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain; (3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland; (4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland; (5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Table of Links 1 Introduction 1 Introduction 2 State of Art 2 State of Art 3 Projects Selection and Data Collection 3 Projects Selection and Data Collection 3.1 Blockchain data 3.1 Blockchain data 3.2 Twitter data 3.2 Twitter data 4 Data Analysis and Results 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.3 Hashtags 4.3 Hashtags 4.4 The role of the social network community 4.4 The role of the social network community 5 Conclusions and References 5 Conclusions and References This paper presents an analysis of the role of social media, specially Twitter, in the context of non-fungible tokens, better known as NFTs. Such emerging technology framing the creation and exchange of digital object, started years ago with early projects such as "CryptoPunks" and since early 2021, has received an increasing interest by a community of people creating, buying, selling NFTs and by the media reporting to the general public. In this work it is shown how the landscape of one class of projects, specially those used as social media profile pictures, has become mainstream with leading projects such as "Bored Ape Yacht Club", "Cool Cats" and "Doodles". This work illustrates how heterogeneous data was collected from the Ethereum blockchain and Twitter and then analysed using algorithms and state-of-art metrics related to graphs. The initial results show that from a social network perspective, the collections of most popular NFTs can be considered as a single community around NFTs. Thus, while each project has its own value and volume of exchange, on a social level all of them are primarily influenced by the evolution of values and trades of "Bored Ape Yacht Club" collection. 1 INTRODUCTION Non Fungible Tokens (NFTs) are a booming technology, with a trading volume that - in just one year - has increased from $150 million to over $5 billion (recorded in January 2022 by considering only the transactions that took place on the Ethereum blockchain [2] through the main exchange platform OpenSea) [19]. Public attention towards NFTs has exploded since the beginning of 2021, when the NFT market experienced record sales and exchange volumes, up to the present day where individual pieces for some projects have reached prices of millions of dollars and have been purchased by a number of celebrities. However, little is known about the overall structure and evolution of the NFT ecosystem, or how this phenomenon has also grown thanks to the communities that have formed in social networks, such as Twitter. In a simplistic way, we can define an NFT as a unique digital asset whose information (such as features, traits and ownership) is certified and managed using blockchain technology. In other words, we can think of a NFT project as a set of football player cards. Each card of the set is di#erent from the others because of some specific traits (e.g., a player can be more or less wanted), the circulation of each card is fixed, and the owner of each card is known and traceable. The set of cards is managed on the blockchain by means of a smart contract, while each card represents an indivisible token. More specifically, NFTs are an example of a Distributed Ledger Technology (DLT) application that renders trustworthy intermediaries such as banks and notary firms obsolete [24]. In fact, cryptographic tokens based on DLTs, through guaranteed enforcement of encoded obligations (e.g., through smart contracts), can combine both concepts of (i) access rights to an underlying economic value (property) [5, 13], and (ii) a permission to access someone else’s property or services or a collective good. NFTs and their technology are already being used in a number of varied fields. Some examples include: the certification of digital generative artworks, the representation and management of land and objects ownership in the metaverse, and the certification of ownership of a profile image in social networks, such as Twitter. It is this latter use case of profile picture images (PFPs) that has been one of the largest contributors to the popularity of NFT technology during the past year. There have been digital figurine projects whose cards were previously trading for a few hundred dollars in early 2021 and whose average price today is in the hundreds of thousands of dollars. Leaving aside the purely economic aspect, this phenomenon of using NFTs as profile pictures has both social and technological interest that, in our opinion, are worth researching deeper. In fact, the use of profile pictures belonging to a digital collection with a limited circulation (typically 10,000 different figurines) and whose ownership is certified by a distributed ledger, introduces some entirely new and disruptive concepts. First of all, those who use a profile picture, with no apparent relation to their own photo, seek to eliminate stereotypes and preconceptions by hiding themselves in a sort of anonymity typical of the crypto world; moreover, it allows the user the ability to create a digital persona (that may or may not have any relation to their real identity from which their subconscious may wish to escape) [26]. Second, the fact of having a limited number of cards (a limit forced by construction by the smart contract written on blockchain) provides the possession of some of them (such as the CryptoPunks and Bored Ape Yacht Club) with real digital status symbols that allow the owner to highlight the fact that she or he has been a person in the crypto world for a long time or economically successful. Third, the owner of a figurine in a particular project can sometimes enjoy digital or real benefits: such as being able to access restricted discussion rooms in social networks like Discord or take part real world or metaverse special events and parties. This allows a union of real and digital worlds where the concept of ownership is easily transportable and usable, without requiring going through intermediaries. From the personal experience of the authors of this work, Twitter and Discord represent the two most representative and used social networks for people interested in NFTs of the PFPs type, where real communities have been created. This work, the first of its kind, aims to analyze the activity of the most PFPs in the Twitter ecosystem: which turns out to be both the main communication channel of these communities and the one most easily analyzed thanks to its content query API. In this paper we wanted to apply state of art social network analysis (SNA) techniques to analyze these communities and verify how they interact and drive the NFTs market and how they are interconnected. SNA techniques are in fact widely used in application areas such as community and topic detection [6]. In this paper we took 18 most significant NFT profile pictures projects and collected all their blockhain transactions and all their related tweets starting from the date of creation to April 15th, 2022. Next, we analyzed the data obtained from Twitter by creating two different kind of graphs: one related to tweets with commonalities between each project pair and a second related to tweets having common hashtags. We then analyzed which communities are the most influential in this ecosystem and how they interact with each other. The paper is structured as follows: Section 2 presents an overview of SNA techniques available in literature. Next, Section 3 discusses which projects have been selected for this work, the rationale for choosing them among others and how the data were collected from Twitter and the Ethereum blockchain. Next, Section 4 presents which SNA analyses have been conducted and the results obtained. Finally, Section Section 5 concludes this paper by providing future research insights. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv
