# An Empirical Study on Snapshot DAOs

arXiv: https://arxiv.org/abs/2211.15993 · HTML: https://mlsanity.com/paper/2211.15993/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study](https://mlsanity.com/paper/2501.18158/) (2025)
- [New Online Communities: Graph Deep Learning on Anonymous Voting Networks to Identify Sybils in Polycentric Governance](https://mlsanity.com/paper/2311.17929/) (2023)
- [Distributed Trust Through the Lens of Software Architecture](https://mlsanity.com/paper/2306.08056/) (2023)

## Abstract (arXiv)

Decentralized Autonomous Organization (DAO) is an organization constructed by automatically executed rules such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by stakeholders. As of Nov 2022, DAO has impacted over 11.2B market caps. However, there are no substantial studies focused on this emerging field. To fill the gap, we start from the ground truth by empirically studying the breadth and depth of the DAO markets in mainstream public chain ecosystems in this paper. We dive into the most widely adoptable DAO launchpad, textitSnapshot, which covers 95% in the wild DAO projects for data collection and analysis. By integrating extensive enrolled DAOs and corresponding data measurements, we explore statistical data from Snapshot and try to demystify its undiscovered truths by delivering a series of summarised insights. We also present DAO status, patterns, distribution, and trends. To our knowledge, this is the first empirical study putting concentration on DAO spaces.