Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance

The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors

Quantifying and modelling online decentralised systems: a complex systems approach

Cryptocurrencies are unique examples of decentralised socioeconomic systems. All the transactions, trading, and development are traceable and publicly available. Bitcoin, the first cryptocurrency, was introduced in 2009 launching a market of more than 2500 cryptocurrencies and has a value of more than 200 billion dollars. In comparison to the rising importance of cryptocurrencies in the financial world, the research on cryptocurrencies is still limited. In this thesis, we analyse three novel datasets namely, cryptocurrencies’ market data, cryptocurrencies’ Wikipedia page views and edits, and illicit transactions on Bitcoin. We study the cryptocurrencies ecosystem, including the market dynamics, the social attention and the transaction network. We find that the ecological neutral model can capture the market dynamics, hinting at the extent to which technological differences between cryptocurrencies are considered in investment decisions. We also investigate the relationship between information production and consumption and cryptocurrency market dynamics. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Finally, we assess dark markets’ Bitcoin transactions showing the ability of the markets to adapt to multiple closures, including law enforcement raids. We expect that our contribution will be of interest to researchers working on either cryptocurrencies or complex systems. We anticipate that adopting a complex systems approach, will spark more research that interweaves both the technological and socioeconomic aspect of cryptocurrencies

Collective Dynamics of Dark Web Marketplaces

Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving drugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and several police raids and scams have caused large losses to both customers and vendors over the past years. However, this uncertainty has not prevented a steady growth of the dark market phenomenon and a proliferation of new markets. The origin of this resilience have remained unclear so far, also due to the difficulty of identifying relevant Bitcoin transaction data. Here, we investigate how the dark market ecosystem re-organises following the disappearance of a market, due to factors including raids and scams. To do so, we analyse 24 episodes of unexpected market closure through a novel datasets of 133 million Bitcoin transactions involving 31 dark markets and their users, totalling 4 billion USD. We show that coordinated user migration from the closed market to coexisting markets guarantees overall systemic resilience beyond the intrinsic fragility of individual markets. The migration is swift, efficient and common to all market closures. We find that migrants are on average more active users in comparison to non-migrants and move preferentially towards the coexisting market with the highest trading volume. Our findings shed light on the resilience of the dark market ecosystem and we anticipate that they may inform future research on the self-organisation of emerging online markets

A House of No Importance: The rise and fall of Nasr City’s middle class extended family houses

Since its urban boom around the mid 1980s, the Cairene residential district of Nasr City has been the hub for a unique housing phenomenon. It has seen middle class professionals appropriating its apartment building typologies into households for their extended families. Over the past ten years, however, many of those families have been aiming to relocate their households to the emerging suburban developments on Cairo’s periphery. This desire seems to be driven by nothing more than their aspiration for the simulacrums of luxury and social status associated with suburban living. Apart from superficial stylistic variations in architectural expression, the housing typologies in these suburbs offer the same functional arrangements as those in Nasr City; and as per their building bylaws they accommodate the co-existence of fewer extended family generations. These facts, coupled with the increased financial hardships involved in acquiring a new suburban dwelling, highlight the absurdity of the middle class professionals’ desire for such relocation. Not only does it deplete their monetary standing in an Egyptian society that now recognizes size of income and wealth as the only measures of social status, but it also debases the solidarity inherent in their characteristic intergenerational living. That is to say, it compromises the basis of the very social status they are aiming to preserve. This thesis tracks the history of 11 El-Insha Street, an apartment building–extended family household in Nasr City, as well as the history of the street it stands on, over the span of 30 years. That narrative serves as the basis for a discussion of the evolution of the Egyptian middle class, Nasr City, and the apartment building – extended family house typology. Through an extensive analytical framework of demographic and urban data, the discourse of this thesis tracks the link between middle class professionals and that particular housing typology; its particular prevalence in Nasr City once upon time; and the current trend of its extinction as its inhabitants relocate to the suburbs

Machine Learning the Cryptocurrency Market

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market

Diabetic Ketoacidosis Following Bariatric Surgery in Patients With Type 2 Diabetes

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Inferring short-term volatility indicators from Bitcoin blockchain

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume

Associations between zinc deficiency, taste changes and salivary flow rate following gastric bypass and sleeve gastrectomy surgeries

Background. The prevalence of taste change (hypogeusia) and its association with zinc deficiency is unclear due to differences in methods of assessment. We investigate the prevalence of hypogeusia using mixed methods and link it with changes in zinc levels following mini gastric bypass (MGB) and sleeve gastrectomy (SG). Methods. This was a prospective observational study of MGB (N = 18) and SG (N = 25). Hypogeusia was evaluated by using a validated questionnaire and by taste strips procedure along with serum zinc levels and salivary flow rate measurements. Results. The mean age was 40.0 ± 9.7 years; 60.5% were female. By using a questionnaire, MGB patients experienced greater hypogeusia than SG at 3 months (72.0% vs 36.0%; ()), but not at 6 months (56.0% vs 45.0%; ()), respectively. Using taste strips, at 6 months, more MGB patients experienced hypogeusia compared with SG (44.0% vs 11.0%; ). Zinc level was reduced following MGB at 6 months (85.6 ± 16.9 μgm/dl vs 67.5 ± 9.2 μgm/dl; ()) but was increased at 6 months following SG (76.9 ± 11.4 vs 84.9 ± 21.7 μgm/dl). Reduction in the rate of salivary flow was observed in 66.0% and 72.0% of MGB and SG patients, respectively, at 3 months and in 53.0% and 70.0% at 6 months. Conclusion. Taste change is more prevalent following MGB compared with SG, especially at 6 months postoperation which parallel with changes in zinc levels. More than half of all patients who had undergone bariatric surgery (BS) had low to very low salivary flow rates during the follow-up. This study suggests an association between low zinc levels and reduced salivary flow with hypogeusia following BS

Emergence and structure of decentralised trade networks around dark web marketplaces

Dark web marketplaces (DWMs) are online platforms that facilitate illicit trade among millions of users generating billions of dollars in annual revenue. Recently, two interview-based studies have suggested that DWMs may also promote the emergence of direct user-to-user (U2U) trading relationships. Here, we carefully investigate and quantify the scale of U2U trading around DWMs by analysing 31 million Bitcoin transactions among users of 40 DWMs between June 2011 and Jan 2021. We find that half of the DWM users trade through U2U pairs generating a total trading volume greater than DWMs themselves. We then show that hundreds of thousands of DWM users form stable trading pairs that are persistent over time. Users in such stable pairs turn out to be the ones with the largest trading volume on DWMs. Then, we show that new U2U pairs often form while both users are active on the same DWM, suggesting the marketplace may serve as a catalyst for new direct trading relationships. Finally, we reveal that stable U2U pairs tend to survive DWM closures and that they were not affected by COVID-19, indicating that their trading activity is resilient to external shocks. Our work unveils sophisticated patterns of trade emerging in the dark web and highlights the importance of investigating user behaviour beyond the immediate buyer-seller network on a single marketplace

Macroscopic properties of buyer–seller networks in online marketplaces

Online marketplaces are the main engines of legal and illegal e-commerce, yet their empirical properties are poorly understood due to the absence of large-scale data. We analyze two comprehensive datasets containing 245M transactions (16B USD) that took place on online marketplaces between 2010 and 2021, covering 28 dark web marketplaces, i.e. unregulated markets whose main currency is Bitcoin, and 144 product markets of one popular regulated e-commerce platform. We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology. Specifically, we find remarkable regularities in the distributions of transaction amounts, number of transactions, interevent times, and time between first and last transactions. We show that buyer behavior is affected by the memory of past interactions and use this insight to propose a model of network formation reproducing our main empirical observations. Our findings have implications for understanding market power on online marketplaces as well as intermarketplace competition, and provide empirical foundation for theoretical economic models of online marketplaces