Machine learning is critical for innovation and efficiency in financial markets, offering predictive models and data-driven decision-making. However, challenges such as missing data, lack of transparency, untimely updates, insecurity, and incompatible data sources limit its effectiveness. Blockchain technology, with its transparency, immutability, and real-time updates, addresses these challenges. We present a framework for integrating high-frequency on-chain data with low-frequency off-chain data, providing a benchmark for addressing novel research questions in economic mechanism design. This framework generates modular, extensible datasets for analyzing economic mechanisms such as the Transaction Fee Mechanism, enabling multi-modal insights and fairness-driven evaluations. Using four machine learning techniques, including linear regression, deep neural networks, XGBoost, and LSTM models, we demonstrate the framework's ability to produce datasets that advance financial research and improve understanding of blockchain-driven systems. Our contributions include: (1) proposing a research scenario for the Transaction Fee Mechanism and demonstrating how the framework addresses previously unexplored questions in economic mechanism design; (2) providing a benchmark for financial machine learning by open-sourcing a sample dataset generated by the framework and the code for the pipeline, enabling continuous dataset expansion; and (3) promoting reproducibility, transparency, and collaboration by fully open-sourcing the framework and its outputs. This initiative supports researchers in extending our work and developing innovative financial machine-learning models, fostering advancements at the intersection of machine learning, blockchain, and economics.

Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natural experiment to bridge this gap. Through an interdisciplinary approach combining causal inference and computational text analysis, we find significant price declines and capital reallocation from centralized to decentralized exchanges following the event. While sentiment metrics showed no sharp discontinuities, topic modeling and network analysis of Discord communities reveal that seasonal holiday discourse obscured underlying trust concerns in centralized exchange forums. These findings underscore the fragility of institutional trust architectures and demonstrate how mixed methods can illuminate behavioral patterns during systemic crises, offering insights for exchange risk management and regulatory assessment.

Recent years have witnessed significant advancements in cross-chain technology. However, the field faces two pressing challenges. On the one hand, hacks on cross-chain bridges have led to monetary losses of around 3.1 billion USD, highlighting flaws in security models governing interoperability mechanisms and the ineffectiveness of incident response frameworks. On the other hand, users and bridge operators experience restricted privacy, which broadens the potential attack surface.In this paper, we present the most comprehensive study to date on the security and privacy of blockchain interoperability. We employ a systematic literature review, yielding a corpus of 212 relevant documents, including 58 academic papers and 154 gray literature documents, out of a pool of 531 results. We systematically categorize 57 interoperability solutions based on a novel security and privacy taxonomy. Our dataset, comprising academic research, disclosures from bug bounty programs, and audit reports, exposes 45 cross-chain vulnerabilities, 4 privacy leaks, and 92 mitigation strategies. Leveraging this data, we analyze 18 notable bridge hacks accounting for over 2.9 billion USD in losses, mapping them to the identified vulnerabilities.Our findings reveal that a substantial portion (65.8%) of stolen funds originates from projects secured by intermediary permissioned networks with unsecured cryptographic key operations. Privacy-wise, we demonstrate that achieving unlinkability in cross-chain transactions is contingent on the underlying ledgers providing some form of confidentiality. Our study offers 17 critical insights into the security and privacy of cross-chain systems. We pinpoint promising future research directions, underscoring the urgency of enhancing security and privacy efforts in cross-chain technology. The identified improvements have the potential to mitigate the financial risks associated with bridge hacks, fostering user trust in the blockchain ecosystem and, consequently, wider adoption.

Non-fungible tokens (NFTs) are decentralized digital tokens to represent the unique ownership of items. Recently, NFTs have been gaining popularity and at the same time bringing up issues, such as scams, racism, and sexism. Decentralization, a key attribute of NFT, contributes to some of the issues that are easier to regulate under centralized schemes, which are intentionally left out of the NFT marketplace. In this work, we delved into this centralization-decentralization dilemma in the NFT space through mixed quantitative and qualitative methods. Centralization-decentralization dilemma is the dilemma caused by the conflict between the slogan of decentralization and the interests of stakeholders. We first analyzed over 30,000 NFT-related tweets to obtain a high-level understanding of stakeholders' concerns in the NFT space. We then interviewed 15 NFT stakeholders (both creators and collectors) to obtain their in-depth insights into these concerns and potential solutions. Our findings identify concerning issues among users: financial scams, counterfeit NFTs, hacking, and unethical NFTs. We further reflected on the centralization-decentralization dilemma drawing upon the perspectives of the stakeholders in the interviews. Finally, we gave some inferences to solve the centralization-decentralization dilemma in the NFT market and thought about the future of NFT and decentralization.

Resource optimization in blockchain systems is a critical aspect of their architectural design. Despite frequent network congestion in Ethereum, a notable proportion of block space is underutilized, with occurrences of completely unused blocks exacerbating resource inefficiency in the network. This study investigates the motivations behind miners’ production of empty blocks. It is found that the immediate gains from mining empty blocks often outweigh the potential benefits derived from including transactions. Furthermore, our analysis indicates a substantial decrease in the frequency of empty blocks following Ethereum’s transition at the Merge, underscoring the effectiveness of the Proof-of-Stake (PoS) consensus mechanism in improving block space utilization in blockchain environments.

Minority groups have been using social media to organize social movements that create profound social impacts. Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter that promote protests and activities against racism and increase the public's awareness of other social challenges that minority groups face. However, previous studies have mostly conducted qualitative analyses of tweets or interviews with users, which may not comprehensively and validly represent all tweets. Very few studies have explored the Twitter topics within BLM and SAH dialogs in a rigorous, quantified and data-centered approach. Therefore, in this research, we adopted a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics. We implemented (1) the latent Dirichlet allocation model to understand the top high-level words and topics and (2) open-coding analysis to identify specific themes across the tweets. We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics. Our findings revealed that the tweets discussed a variety of influential topics in depth, and social justice, social movements, and emotional sentiments were common topics in both movements, though with unique subtopics for each movement. Our study contributes to the topic analysis of social movements on social media platforms in particular and the literature on the interplay of AI, ethics, and society in general.

A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs.