City Research Online

Abstract

Since the development of Bitcoin in 2008, blockchain-based technologies have flourished and several applications have been created, such as smart contracts, NFTs or cryptocurrencies. The blockchain allows for different kinds of transactions to be verified in a decentralized way, without the need for an intermediary, allowing new socio-technical ecosystems to be born and grow. Transactions are stored publicly, anonymously and openly on the blockchain, granting unprecedented access to data on human (collective) behaviour and socio-technical systems. The question thus arises: can we use this data to characterize these systems, and possibly to further our understanding of human behaviour? In this thesis, we address this question by studying different blockchain-based ecosystems through a combination of different datasets. Firstly, we study Dark Web Marketplaces (DWMs), online illicit markets on the dark web using cryptocurrencies for payments, and we characterize how they first reacted and then adapted to the COVID-19 pandemic using web scraping data. Secondly, we exploit a unique dataset of Bitcoin and proprietary transactions to characterize the buyer-seller network on DWMs and regulated e-commerce platforms. Thirdly, we study a comprehensive dataset of scientific publications to investigate the evolution of the concept of decentralization, pillar of blockchain-based ecosystems, in time. Then, we extend the literature on DWMs by studying the wider ecosystem of direct interactions between users, a network we can study only thanks to blockchain data. Finally, we analyse the trade of NFT collectibles on the largest open marketplace available, characterizing the role of rarity in determining market trends. Overall, this thesis presents a series of pioneering studies improving our understanding of blockchain-based socio-technical systems, thanks to unique comprehensive large scale datasets giving us unprecedented access to the history and behaviour of these ecosystems. We hope researchers will extend this work to improve our understanding of these systems and more generally human behaviour.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > QA Mathematics
Departments:	School of Science & Technology > Mathematics School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

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