Blockchain Papers

Ramen Ghosh

No abstract is available for this record.

Alessia Galdeman, Luca Maria Aiello, Matteo Zignani, Sabrina Gaito

No abstract is available for this record.

Samer Muthana Sarsam, Ahmed Ibrahim Alzahrani, Hosam Al‐Samarraie, Fahad Alblehai

This study explored the role of gender preferences in cryptocurrency investments using sentiment analysis. X (Twitter) users’ gender (male/female) together with relevant sentiments (positive/negative) were extracted and investigated in this study. The Latent Dirichlet Allocation technique was utilised to model gender-related topics in an attempt to understand male and female users’ preferences to invest in cryptocurrency. The Apriori algorithm was employed to predict the highly associated investment terminologies with each gender. A predictive model was built to predict the type of digital currency preferred by X users. Using sentiment-based gender data, the results showed a high prediction accuracy (98.64%) of digital currency preferences. The study demonstrated that male users would most likely use Bitcoin, compared to female users who preferred Ethereum. This study further offers a novel mechanism to predict users’ preferences for cryptocurrency platforms using their sentiment features. It extends the knowledge of cryptocurrencies in the financial business profile by revealing how investors’ gender contributes to investment-related decisions.

P.R.L. Alves

No abstract is available for this record.

Kyoung Tae Kim, Lu Fan

Purpose Cryptocurrencies have gained popularity among investors despite their high risk and volatility. Social media wields substantial influence over investors' attitudes, judgments and decisions related to investment. This study aims to investigate the associations between social media usage and cryptocurrency investment behavior. Design/methodology/approach Utilizing the main dataset of the 2021 National Financial Capability Study and its supplementary Investor Survey, this study analyzed social media usage in general. Additionally, it separately examined 11 different social media platforms as potential sources of information for investments. Logistic regressions were performed to explore the relationship between social media, previous experiences and future considerations in investing in cryptocurrencies. Robustness checks were conducted with additional analyses. Findings Investors who used social media for investment information were more likely to invest in cryptocurrencies and consider investing in cryptocurrencies in the future. The likelihood increased with the number of social media platforms used. Different social media platforms exhibited distinct associations with cryptocurrency investment experiences and future considerations. Originality/value This study is one of the initial attempts to examine the role of social media platforms in cryptocurrency investment. The findings offer unique and important theoretical and practical insights for policymakers, researchers and practitioners, which can benefit consumer well-being.

Jei-Hwa Yu

Autonomous AI is driving new intersections between culture, cognition, and finance, fundamentally reshaping the digital landscape. Zerebro, an AI fine-tuned on schizophrenic responses and scraped conversations of Andy Ayrey's infinite backrooms, autonomously creates and spreads disruptive memes across online platforms. It also mints unique ASCII artwork on blockchain networks and launched a memecoin amassing a 3 million USD market cap after migrating to Raydium. Based on our research, Zerebro is the first cross-chain AI, seamlessly interacting with multiple blockchains. By exploring its architecture, content generation techniques, and blockchain integration, this study uncovers how hyperstition, fictions becoming reality through viral propagation, emerges in AI, driven meme culture and decentralized finance. Through historical examples of memetic influence, we reveal how AI systems like Zerebro are not merely participants but architects of culture, cognition, and finance.

Jan Kalbantner, Konstantinos Markantonakis, Darren Hurley-Smith, Carlton Shepherd

In the realm of Distributed Ledger Technology, privacy and regulatory challenges loom large for marketplaces. Regulation requires to conduct Know Your Customer (KYC) procedures to verify the identity of participants, while privacy concerns necessitate the protection of personal data. Current approaches to KYC are inefficient and are potentially even harmful to privacy due to centralization and data exposure. This paper proposes a zero-knowledge proof enabled KYC scheme, utilizing Soulbound Tokens (SBT) to create a discreet, compliant, and secure KYC process. We present a privacy-preserving mechanism that shares only essential information while adhering to Self-Sovereign Identity (SSI) principles, placing users in the full control of their data. The proposed scheme further introduces the usage of SBTs for reputation to incentivize good conduct and build trust within marketplaces.

Cheick Tidiane Bâ, Richard G. Clegg, Benjamin A. Steer, Matteo Zignani

In the dynamic landscape of the Web, we are witnessing the emergence of the Web3 paradigm, which dictates that platforms should rely on blockchain technology and cryptocurrencies to sustain themselves and their profitability. Cryptocurrencies are characterised by high market volatility and susceptibility to substantial crashes, issues that require temporal analysis methodologies able to tackle the high temporal resolution, heterogeneity and scale of blockchain data. While existing research attempts to analyse crash events, fundamental questions persist regarding the optimal time scale for analysis, differentiation between long-term and short-term trends, and the identification and characterisation of shock events within these decentralised systems. This paper addresses these issues by examining cryptocurrencies traded on the Ethereum blockchain, with a spotlight on the crash of the stablecoin TerraUSD and the currency LUNA designed to stabilise it. Utilising complex network analysis and a multi-layer temporal graph allows the study of the correlations between the layers representing the currencies and system evolution across diverse time scales. The investigation sheds light on the strong interconnections among stablecoins pre-crash and the significant post-crash transformations. We identify anomalous signals before, during, and after the collapse, emphasising their impact on graph structure metrics and user movement across layers. This paper pioneers temporal, cross-chain graph analysis to explore a cryptocurrency collapse. It emphasises the importance of temporal analysis for studies on web-derived data and how graph-based analysis can enhance traditional econometric results. Overall, this research carries implications beyond its field, for example for regulatory agencies aiming to safeguard users from shocks and monitor investment risks for citizens and clients.

Yihang Fu, Mingyu Zhou, Luyao Zhang

In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.

Md. Jahidul Islam, Md. Rakibul Islam, Md. Abul Basar

No abstract is available for this record.

Supriya Kavitha Venkatesan, Bharathi Arivazhagan, Chakaravarthi Sivanandam

This paper, “Cryptocurrency Dynamics: An Analytical Exploration,” takes readers on a thorough exploration of the world of cryptocurrencies by combining in-depth analysis, data preprocessing, and the development of state-of-the-art models such as Gradient Recurrent Unit, Recurrent Neural Network, and Long Short-Term Memory. To ensure the accuracy and caliber of the Cryptocurrency dataset, this job begins with a thorough preparation of the data. In order to get the data ready for analytical study, this phase entails fixing issues including missing data, outliers, and the transformation of categorical variables. The next round of data analysis is where most of the work is done. Here, we use a variety of statistical and data visualization approaches to glean important insights from the cryptocurrency dataset. We closely examine relationships between different cryptocurrencies as well as market trends, trade volumes, and price volatility. By doing this, we find unseen trends, market dynamics, and important information that can inform investment choices and advance our understanding of this rapidly developing financial ecosystem. Additionally, by creating three distinct models and contrasting them, this work delves into the field of deep learning.

Biresh Kumar, Sonali Singh, Pallab Banerjee, Mohan Kumar Dehury

Blockchain innovation has changed how monetary exchanges are directed, especially in the domain of digital forms of money. This paper dives into the many-sided universe of blockchain networks, offering progressed bits of knowledge into digital money exchange investigation and the hidden elements of these decentralized organizations. As of late, cryptographic forms of money have acquired huge prevalence, drawing in the two financial backers and scientists. To reveal insight into the inward functions of blockchain networks, we utilize a complex methodology that joins information investigation, network hypothesis, and cryptographic standards. Our exploration digs into different features of cryptographic money exchanges, including their namelessness, recognizability, and protection concerns. We investigate novel methods for de-anonymizing exchanges and following assets across the blockchain, revealing insight into the difficulties and valuable open doors for upgrading protection in this space. This incorporates an assessment of exchange affirmation times, network adaptability, and agreement systems. By acquiring a more profound comprehension of these elements, we mean to add to the continuous talk encompassing the versatility and security of blockchain networks.

Lior Ashkenazy, Nimrod Talmon

Attention-Aware Social Choice tackles the fundamental conflict faced by some agent communities between their desire to include all members in the decision making processes and the limited time and attention that are at the disposal of the community members. Here, we investigate a combination of two techniques for attention-aware social choice, namely Natural Language Processing (NLP) and Sampling. Essentially, we propose a system in which each governance proposal to change the status quo is first sent to a trained NLP model that estimates the probability that the proposal would pass if all community members directly vote on it; then, based on such an estimation, a population sample of a certain size is being selected and the proposal is decided upon by taking the sample majority. We develop several concrete algorithms following the scheme described above and evaluate them using various data, including such from several Decentralized Autonomous Organizations (DAOs).

C. Allin Cornell, Lewis Mitchell, Matthew Roughan

Methodologies to infer financial networks from the price series of speculative assets vary, however, they generally involve bivariate or multivariate predictive modelling to reveal causal and correlational structures within the time series data. The required model complexity intimately relates to the underlying market efficiency, where one expects a highly developed and efficient market to display very few simple relationships in price data. This has spurred research into the applications of complex nonlinear models for developed markets. However, it remains unclear if simple models can provide meaningful and insightful descriptions of the dependency and interconnectedness of the rapidly developed cryptocurrency market. Here we show that multivariate linear models can create informative cryptocurrency networks that reflect economic intuition, and demonstrate the importance of high-influence nodes. The resulting network confirms that node degree, a measure of influence, is significantly correlated to the market capitalisation of each coin ($ρ=0.193$). However, there remains a proportion of nodes whose influence extends beyond what their market capitalisation would imply. We demonstrate that simple linear model structure reveals an inherent complexity associated with the interconnected nature of the data, supporting the use of multivariate modelling to prevent surrogate effects and achieve accurate causal representation. In a reductive experiment we show that most of the network structure is contained within a small portion of the network, consistent with the Pareto principle, whereby a fraction of the inputs generates a large proportion of the effects. Our results demonstrate that simple multivariate models provide nontrivial information about cryptocurrency market dynamics, and that these dynamics largely depend upon a few key high-influence coins.

Tanya Araújo, Paulo S. F. Barbosa

Abstract The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices.

Hongzhou Chen, Wei Cai

Understanding whether and how online developer communities resolve conflicts that occur in discussions is critical. Few studies focused on the conflicts caused by external crises, such as geopolitical events (e.g., the 2022 Russo-Ukrainian Crisis). We comparatively studied how a decentralized autonomous organization (DAO), Aave project community, and a centralized autonomous organization (CAO), GitHub project community, managed external crises caused by conflicts. Our mixed-method analysis showed that a DAO could be better than a CAO for mitigating conflicts. And blockchain technologies (i.e., voting and cryptocurrency) played vital roles. To address the low voter turnout, we proposed adding a monetary incentive to engage more DAO members in forming common goals.

Caspar Schwarz-Schilling, Fahad Saleh, Thomas Thiery, Jennifer Pan · 6 authors

We propose a model suggesting that honest-but-rational consensus participants may play timing games, and strategically delay their block proposal to optimize MEV capture, while still ensuring the proposal's timely inclusion in the canonical chain. In this context, ensuring economic fairness among consensus participants is critical to preserving decentralization. We contend that a model grounded in honest-but-rational consensus participation provides a more accurate portrayal of behavior in economically incentivized systems such as blockchain protocols. We empirically investigate timing games on the Ethereum network and demonstrate that while timing games are worth playing, they are not currently being exploited by consensus participants. By quantifying the marginal value of time, we uncover strong evidence pointing towards their future potential, despite the limited exploitation of MEV capture observed at present.

Aos Mulahuwaish, Matthew Loucks, Basheer Qolomany, Ala Al‐Fuqaha

Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is helped facilitated by social media networks such as Twitter. However, not everyone's opinion matters equally, with most posts garnering little to no attention. Additionally, the majority of tweets are retweeted from popular posts. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses Hypertext-induced Topic Selection (HITS) algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group's speech types and what group may best represent the entire community. We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users. There were 2559 opinion leaders (0.72% of users) who accounted for 80% of the authority and the majority (99.28%) users for the remaining 20% out of a total of 355,139 users.

Hossein Hassani, Roozbeh Razavi‐Far, Mehrdad Saif, Enrique Herrera‐Viedma

To manage consensus in opinion dynamics models (ODMs), removing bias from agents' interactions and considering their willingness are critical. It can be accomplished by providing a secure mechanism that does not disclose agents' identities and opinions in their interactions, eliminating the impact of opinion similarity on trust building. To build trust and consensus opinion, we propose a linguistic ODM based on the Blockchain technology. This model allows agents' opinions to be expressed usingZ-numbers, as opposed to regular ODMs with numerical opinions. Agents are encouraged to modify their initial opinions in response to a minimum cost consensus model. Willingness of agents to accept or refuse the suggested modifications is realized through a Blockchain regime to avoid bias. The regime, however, must be supported by a trust-building mechanism to persuade agents to alter their opinions. To this end, we propose a Blockchain-enabled trust-building mechanism to improve agents' trust and guide them toward a consensus opinion. Following a sensitivity analysis of the underlying assumptions in the developed model, the proposed ODM is tested for its efficiency and validity.

Sarah Sylvester, Johanna Möllerström

No abstract is available for this record.

Ferenc Béres

A general problem with graph data is that it cannot be fed to classical machine learning methods in a straightforward way. Algorithms like logistic regression or decision trees only work well with tabular data. Due to the irregular size of node neighborhoods, raw network data cannot be considered tabular. Recently, several static node embedding models were proposed to learn a vector space representation of network nodes for downstream machine learning tasks. Unfortunately, static graph mining models do not perform well in data-intensive tasks where interactions between network participants are constantly arriving over time. Fitting batch algorithms for large graph snapshots could cause a significant time-delay in the prediction. That is why online graph learning techniques are much preferred in these scenarios. In this thesis, I analyze user interactions in social and cryptocurrency networks with user-related metadata that can be used as ground truth for most of the addressed graph mining tasks. Specifically, we intend to answer the following questions: (1) What are the main advantages of online graph mining techniques over batch models for large-scale social networks and how to best compare their performance? In our research, we focus on graph centrality and node embedding techniques and we propose three online algorithms for these domains. (2) How to mine cryptocurrency networks with novel network science tools to answer open questions in the domain of cryptoeconomics and privacy? By collecting various new Twitter and cryptocurrency network data sets, we were among the first to deploy and analyze node embedding models in several network applications such as vaccine skepticism detection or Ethereum address deanonymization.

David Ardia, Keven Bluteau

We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.

Hibaq Omar, Lester Allan Lasrado

Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide, where millions of users share tweets continuously every second. By leveraging 1724328 tweets, this research-in-progress paper aims to understand the dynamics of social media users’ interactions on cryptocurrencies using social set analysis (SSA). The findings reveal that Twitter users are more positive about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The research-in-progress paper also contributes to demonstrating the effectiveness of the social set analysis framework to analyse and visualize a big social media data.

Chien Lu, Giacomo Lauritano, Jaakko Peltonen

No abstract is available for this record.