AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security

Paper: arXiv 2212.06951

Authors: Yihang Fu, Zesen Zhuang, Luyao Zhang

Abstract

Blockchain has empowered computer systems to be more secure using a distributed network. However, the current blockchain design suffers from fairness issues in transaction ordering. Miners are able to reorder transactions to generate profits, the so-called miner extractable value (MEV). Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. However, previous studies have mostly analyzed blockchain data, which might not capture the impacts of MEV in a much broader AI society. Thus, in this research, we applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags and analyzed their topics. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV. We also identify the co-movements of MEV activities on blockchain and social media platforms. Our study contributes to the literature at the interface of blockchain security, MEV solutions, and AI ethics.

Complexity vs Empirical Score

• Math Complexity: 1.5/10
• Empirical Rigor: 4.5/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper provides a descriptive NLP and topic analysis on social media sentiment surrounding MEV and Flashbots, featuring negligible mathematical formulation. While it offers novel qualitative insights into community ethical concerns, the empirical framework lacks rigorous quantitative modeling, statistical testing, or direct market microstructure backtests.

Research Flowchart

  flowchart TD
    A[Goal: Analyze broader AI Ethics & Social Impact of MEV] --> B[Data Collection: 20,000+ Twitter Posts with #MEV & #Flashbots]
    B --> C[NLP Topic Analysis & Sentiment Processing]
    C --> D[Correlate Social Media Topics with On-Chain Blockchain Data]
    D --> E[Outcomes: Ethical Concerns Identified & Co-movement Between MEV Activity & Social Trends]