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

arXiv: https://arxiv.org/abs/2212.06951 · HTML: https://mlsanity.com/paper/2212.06951/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges](https://mlsanity.com/paper/1904.05234/) (2019)
- [Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain](https://mlsanity.com/paper/2102.03347/) (2021)
- [Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security](https://mlsanity.com/paper/2201.05574/) (2022)
- [A Flash(bot) in the Pan: Measuring Maximal Extractable Value in Private Pools](https://mlsanity.com/paper/2206.04185/) (2022)
- [Extracting Godl [sic] from the Salt Mines: Ethereum Miners Extracting Value](https://mlsanity.com/paper/2203.15930/) (2022)
- [SoK: Blockchain Decentralization](https://mlsanity.com/paper/2205.04256/) (2022)
- [What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Model](https://mlsanity.com/paper/2205.14725/) (2022)
- [On Blockchain We Cooperate: An Evolutionary Game Perspective](https://mlsanity.com/paper/2212.05357/) (2022)
- [Blockchain Network Analysis: A Comparative Study of Decentralized Banks](https://mlsanity.com/paper/2212.05632/) (2022)
- [SoK: MEV Countermeasures: Theory and Practice](https://mlsanity.com/paper/2212.05111/) (2022)

## Built on by (most cited first, 1 of 1)

- [DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting](https://mlsanity.com/paper/2405.00522/) (2024)

## Abstract (arXiv)

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.