Blockchain Papers

Houji Chen, Peng Tao, Yuheng Zhang, Zhidong Xie · 5 authors

No abstract is available for this record.

Lejun Zhang, Xucan Zhang, Siyi Xiao, Zexin Li · 7 authors

Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.

Josua C. Barangay, Jose Marie E. Ocdenaria

The study focused on the discourses on cryptocurrency in national and international news articles. This study utilized a complementary research design to examine cryptocurrency's framing strategies, linguistic features, and ideologies. This study analyzed 50 news articles from leading National and International News outlets published from February 1, 2024, to June 15, 2024. Results revealed that economic consequence and conflict framing were dominant frames. Compounding and Derivation were common linguistic features, while clipping was evident in international news narratives. Moreover, no significant differences were found in economic consequence, conflict, and morality frames. However, there was a significant difference in the attribution of responsibility and human-interest frames. Moreover, there were no significant differences in compounding derivation, initialism, and acronym. However, there was a significant difference on clipping in linguistic features. The ideologies reflected habitus, field, capital, and doxa. Media and technology literacy and political preferences represent the Habitus. While the fields were government regulatory and cryptocurrency exchanges. Economic capital as Central Digital Currency and Bitcoin Etf. Social capital featured politically corrupt figures and influential individuals. Cultural capital represents the digital skills organization and educational background of individuals. The decentralized finance and Blockchain are represented as Doxa. KEYWORDS: Education, applied linguistics, framing strategies, linguistic features, cryptocurrency, Philippines

Xiaolin Wen, Tai D. Nguyen, Shaolun Ruan, Qiaomu Shen · 7 authors

With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive and reliable analysis of Ponzi-scheme-related features within these execution behaviors. PonziLens+ has three visualization modules that intuitively reveal all potential behaviors of a smart contract, highlighting fraudulent features across three levels of detail. It can help smart contract investors and auditors achieve confident identification of any smart Ponzi schemes. We conducted two case studies and in-depth user interviews with 12 domain experts and common investors to evaluate PonziLens+. The results demonstrate the effectiveness and usability of PonziLens+ in achieving an effective identification of smart Ponzi schemes.

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.

Rowena Slope

This chapter asserts that huge technological change is underway. The COVID-19 pandemic response offered opportunities for trialling out new technologies: RNA-based therapeutics, track and trace, digital propaganda, and technological surveillance. The greatest challenge to humanity is new technology and who has control of it. As Web 3.0 and ChatGPT rolls out, people must ensure that they support and use uncensored social media platforms and meta search engines. That should use cash as long as possible ( Giambruno, 2024 ), despite its declining value, establish offline bartering systems, and resist centralised cryptocurrencies for decentralised ones when they inevitably replace fiat currency, and they must understand how distributed ledgers and blockchain have the potential to end corruption.

Nwamaka A. Anaza, Bhaskar Upadhyaya, Cecilia Ruvalcaba, Ashok Bhattarai · 5 authors

A qualitative inquiry was undertaken to examine Generation Z’s level of cryptocurrency knowledge and its impact on their cryptocurrency consumption. The findings from 72 Generation Z respondents revealed that most people within this generational cohort lack knowledge about cryptocurrencies and hesitate to invest in them due to the uncertainty brought about by their lack of understanding. Even those with a basic to moderate understanding of cryptocurrencies remained sceptical of their knowledge and therefore of cryptocurrency. The few Generation Z respondents who acknowledged a greater understanding of cryptocurrencies were unwilling to allocate significant investment capital, again indicating signs of caution in their cryptocurrency consumption. The findings underscore the necessity for extensive and tailored educational resources about cryptocurrencies that cater to a wide range of Generation Z consumers, ranging from those requiring rudimentary introductions to cryptocurrencies as well as those desiring a more in-depth understanding of intricate market dynamics.

Arpit Jain, Aravind Sundeep Musunuri, Saketh Reddy Cheruku, Vijay Bhasker Reddy Bhimanapati · 6 authors

Information sharing on social media, especially about daily news and events, is a major focus area. Timely identification of urgent needs, sharing relevant posts, and delivering accurate information are crucial tasks. To combat the spread of fake news, a Reinforcement Learning (RL) technique is used alongside blockchain security to verify social media content. Twitter, a key platform with a major influence on public discourse, is particularly susceptible to false information due to its rapid news dissemination. The approach involves collecting news articles and their metadata, which are then pre-processed to clean and tokenize the data. An RL agent is trained on attributes like word frequency and readability, learning to distinguish between genuine and fake news through rewards and penalties. The trained RL agent classifies new news as true or false based on learned patterns. While blockchain's role in enhancing security is highlighted, further details are necessary to clarify its integration. This approach aims to reduce the spread of misinformation in digital news effectively.

Shane Littrell, Casey Klofstad, Joseph E. Uscinski

Cryptocurrency is a digital asset secured by cryptography that has become a popular medium of exchange and investment known for its anonymous transactions, unregulated markets, and volatile prices. Given the popular subculture of traders it has created, and its implications for financial markets and monetary policy, scholars have recently begun to examine the political, psychological, and social characteristics of cryptocurrency investors. A review of the existing literature suggests that cryptocurrency owners may possess higher-than-average levels of nonnormative psychological traits and exhibit a range of non-mainstream political identities. However, this extant literature typically employs small nonrepresentative samples of respondents and examines only a small number of independent variables in each given study. This presents the opportunity for both further testing of previous findings as well as broader exploratory analyses including more expansive descriptive investigations of cryptocurrency owners. To that end, we polled 2,001 American adults in 2022 to examine the associations between cryptocurrency ownership and individual level political, psychological, and social characteristics. Analyses revealed that 30% of our sample have owned some form of cryptocurrency and that these individuals exhibit a diversity of political allegiances and identities. We also found that crypto ownership was associated with belief in conspiracy theories, "dark" personality characteristics (e.g., the "Dark Tetrad" of narcissism, Machiavellianism, psychopathy, and sadism), and more frequent use of alternative and fringe social media platforms. When examining a more comprehensive multivariate model, the variables that most strongly predict cryptocurrency ownership are being male, relying on alternative/fringe social media as one's primary news source, argumentativeness, and an aversion to authoritarianism. These findings highlight numerous avenues for future research into the people who buy and trade cryptocurrencies and speak to broader global trends in anti-establishment attitudes and nonnormative behaviors.

Chuyi Yan, Xueying Han, Yan Zhu, Dan Du · 6 authors

Abstract Despite the growing attention on blockchain, phishing activities have surged, particularly on newly established chains. Acknowledging the challenge of limited intelligence in the early stages of new chains, we propose ADA-Spear-an automatic phishing detection model utilizing a dversarial d omain a daptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection. The model effectively identifies phishing behavior in new chains with limited reliable labels, addressing challenges such as significant distribution drift, low attribute overlap, and limited inter-chain connections. Our approach includes a subgraph construction strategy to align heterogeneous chains, a layered deep learning encoder capturing both temporal and spatial information, and integrated adversarial domain adaptive learning in end-to-end model training. Validation in Ethereum, Bitcoin, and EOSIO environments demonstrates ADA-Spear’s effectiveness, achieving an average F1 score of 77.41 on new chains after knowledge transfer, surpassing existing detection methods.

Nidhi Walia, Poonam Bandha, Naina Goyal

This paper investigates netizens’ viewpoints on Decentralized Finance (DeFi) using emotion theory and lexicon sentiment analysis via machine learning. The data of 15,000 tweets on DeFi is gathered through automated web-scraping. Emotion score is evaluated through sentiment lexicon analysis and includes anger, anticipation, disgust, fear, joy, sadness, surprise, trust, and primary sentiments. The supervised machine learning reveals a score of 47,054 sentiments from 15,000 tweets, showing predominantly positive and trust sentiments in the sample. The positive sentiment may describe potential of Decentralized market. Meanwhile, trust emotion was indicative of the market’s response to the transparency and security of the DeFi system. This study contributes to theoretically explaining the implications of the DeFi phenomenon under the lens of emotion theory.

Authors unavailable

No abstract is available for this record.

Poonam Rani, Jyoti Shokeen

No abstract is available for this record.

Federico Costantini, Francesco Crisci, Silvia Venier, Stefano Bistarelli · 5 authors

Tackling disinformation is crucial for the development of the Information Society. To do this, it is necessary to empower journalists in the production of trustworthy information, and to nurture an economic ecosystem centred on the secure circulation of content. In this contribution we present an interdisciplinary approach that aims (1) to find a balance between freedom of expression and other fundamental rights (e.g., privacy and data protection), (2) to develop business models driven by the production of genuine content, and (3) to exploit the potential of distributed ledger systems to provide media certification.

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

No abstract is available for this record.

Moein Shahiki Tash, Olga Kolesnikova, Zahra Ahani, Grigori Sidorov

This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our goal was to conduct a psycholinguistic and emotional analysis of social media content associated with these cryptocurrencies. Such analysis can enable researchers and experts dealing with cryptocurrencies to make more informed decisions. Our work involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns emerging in each coin's community. To achieve this, we utilized advanced text analysis techniques. Additionally, this work unveiled an understanding of the interplay between these digital assets. By examining which coin pairs are mentioned together most frequently in the dataset, we established co-mentions among different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from X. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable perception into the linguistic nuances of various digital coins' online communities and provides a deeper understanding of their interactions in the cryptocurrency space.

Stefano Ferretti, Marco Furini

This study sought to explore whether Twitter, as a passive sensor, could have foreseen the collapse of the Unified Stablecoin (USTC). In May 2022, in just a few days, the cryptocurrency went to near-zero valuation. Analyzing 244,312 tweets from 89,449 distinct accounts between April and June 2022, this study delved into the correlation between personal sentiments in tweets and the USTC market value, revealing a moderate correlation with polarity. While sentiment analysis has often been used to predict market prices, the results suggest the challenge of foreseeing sudden catastrophic events like the USTC collapse solely through sentiment analysis. The analysis uncovered unexpected global interest and noted positive sentiments during the collapse. Additionally, it identified events such as the launch of the new Terra blockchain (referred to as “Terra 2.0”) that triggered positive surges. Leveraging machine learning clustering techniques, this study also identified distinct user behaviors, providing valuable insights into influential figures in the cryptocurrency space. This comprehensive analysis marks an initial step toward understanding sudden and catastrophic phenomena in the cryptocurrency market.

Xi Luo, Hepeng Jia, Jingjie Qian

The COVID-19 pandemic reveals an obvious ideological influence on people’s health decisions worldwide. However, little research has examined such an effect on China’s public attitude to health issues. This article tries to fill the gap by examining how conventional factors linked to health behaviours – e.g. perceived threats, scientific knowledge, and trust in science—and ideological beliefs jointly influence the Chinese public’s attitude to the zero-COVID policies. Utilizing a national quota sample (n = 1,021) collected in April 2022 when Shanghai’s lockdown resulted in numerous online protests, this study found that perceived severity of COVID-19 infection, trust in science, left-leaning ideologies, and nationalism scores were associated with supporting the zero-COVID policies. Objective scientific judgments did not influence their views. Moreover, people whose ideological stances were closer to the official lines (political left and nationalism) were less likely to associate a zero-COVID attitude with perceived threats but more likely to link their trust in science to their support for the zero-COVID policy. The findings here provided empirical proof from China to the booming research agenda on the politicization of science and health communication while expanding our understanding of how ideological beliefs may have distorted scientific cognitions and health decisions.

Meng Shen, Xiangyun Tang, Wei Wang, Liehuang Zhu

No abstract is available for this record.

Shobhit Navani, Giuseppe T. Cirella

Understanding public sentiment is vital in the dynamic realm of digital currencies. This study analyses sentiments surrounding cryptocurrencies by examining a large dataset of X posts using Python-based methodologies. The research explores correlations between public opinions and market dynamics, uncovering the emotions and attitudes influencing cryptocurrency trends. By identifying drivers of sentiment on social media platforms, the findings offer insights into how public discourse impacts market fluctuations. The study's outcomes are valuable for investors, analysts, and policymakers, supporting strategy development, informed investment decisions, and effective policy creation. This research advances the growing field of cryptocurrency studies, highlighting the critical role of sentiment analysis in understanding and navigating the complexities of digital currency markets.

Lukáš Zelieska, Radovan Vojtko, Cyril Dujava

No abstract is available for this record.

Gianfranco Tusset

The paper aims to retrace the academic discourse on cryptocurrencies from 2015 to 2022 by treating it as a lexical unicum that evolves over time. The purpose is to understand what themes have emerged and how they have changed the discourse on cryptocurrencies. We used a three-step methodology. The first consists of text mining that allows us to create, from 1057 academic articles on the subject, the matrix containing the frequencies of words/n-grams. In a second step, lexical analysis is enriched by correspondence analysis, a useful tool to measure the "distance" and evolution of academic discourse and to identify significant content discontinuity. Finally, the causal analysis addresses the ultimate goal of understanding whether it is possible to define future developments in the cryptocurrency discourse, whether it will absorb instances from outside or remain focused on the prevailing themes to date. The identification and application of a method to analyze the evolution of the cryptocurrency discourse allowed us to distinguish at least two distinct phases characterized by specific content and cryptocurrencies.

Hao Sui, Jiale Zhang, Bing Chen, Di Wu · 6 authors

No abstract is available for this record.

Massimo Terenzi

The rise of blockchain and cryptocurrencies come with the promise of decentralizing transactions and disrupting the power of market intermediaries. Despite these promises, scholars argue that the risks associated with cryptocurrencies are still unclear. Some preliminary works investigate the manipulative role played by the circulation of problematic content on platforms such as Reddit, Twitter, Discord, or Telegram. Nonetheless, the research still lacks a clear and comprehensive picture of how widespread the phenomenon is across the whole ecosystem. Despite its prominence, Facebook is understudied in this context. To fill this gap, this work focuses on the role played by Meta’s main platform, as a venue employed to reach a wide audience prone to potential manipulative practices or scams related to cryptocurrencies. As part of a broader investigation on coordinated disinformation in Africa, we have come across a cluster of Facebook groups sharing content related to cryptocurrencies. The links shared by these networks revealed a very prolific cluster of 152 groups dedicated to Airdrop and Bounty initiatives for new cryptocurrencies. We collected a list of recent posts created by these groups between November 2021 and January 2022 (378,513 URLs). A preliminary analysis of these URLs pointed out an overwhelming presence of links to Telegram (47%) that highlights the central role played by this platform in this specific ecosystem. This paper explores the overlap between the cryptocurrency community and social media, analyzing how crypto-related projects are disseminated as a new type of problematic content on Facebook and Telegram.