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MemeChain: A Multimodal Cross-Chain Dataset for Meme Coin Forensics and Risk Analysis

Alberto Maria Mongardini, Alessandro Mei

TL;DR

MemeChain tackles the challenge of observing highly volatile meme coins by constructing the first large-scale cross-chain, multimodal dataset that links on-chain activity with off-chain web artifacts. The authors implement a multi-stage data collection pipeline across Ethereum, BSC, Solana, and Base, collecting 34,988 meme coins with archived website HTML, token logos, and social endpoints. Key findings include frequent web abandonment and the One-Day Meme Coin phenomenon, demonstrating the value of web-area signals for early risk prediction and cross-chain analyses. The release supports multimodal forensics, early-survival modeling, and automated scam prevention, with a FAIR-compliant open resource to foster future research and defense mechanisms in the meme-coin ecosystem.

Abstract

The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market, characterized by extreme churn, minimal project commitment, and widespread fraudulent behavior. While countless meme coins are deployed across multiple blockchains, they rely heavily on off-chain web and social infrastructure to signal legitimacy. These very signals are largely absent from existing datasets, which are often limited to single-chain data or lack the multimodal artifacts required for comprehensive risk modeling. To address this gap, we introduce MemeChain, a large-scale, open-source, cross-chain dataset comprising 34,988 meme coins across Ethereum, BNB Smart Chain, Solana, and Base. MemeChain integrates on-chain data with off-chain artifacts, including website HTML source code, token logos, and linked social media accounts, enabling multimodal and forensic study of meme coin projects. Analysis of the dataset shows that visual branding is frequently omitted in low-effort deployments, and many projects lack a functional website. Moreover, we quantify the ecosystem's extreme volatility, identifying 1,801 tokens (5.15%) that cease all trading activity within just 24 hours of launch. By providing unified cross-chain coverage and rich off-chain context, MemeChain serves as a foundational resource for research in financial forensics, multimodal anomaly detection, and automated scam prevention in the meme coin ecosystem.

MemeChain: A Multimodal Cross-Chain Dataset for Meme Coin Forensics and Risk Analysis

TL;DR

MemeChain tackles the challenge of observing highly volatile meme coins by constructing the first large-scale cross-chain, multimodal dataset that links on-chain activity with off-chain web artifacts. The authors implement a multi-stage data collection pipeline across Ethereum, BSC, Solana, and Base, collecting 34,988 meme coins with archived website HTML, token logos, and social endpoints. Key findings include frequent web abandonment and the One-Day Meme Coin phenomenon, demonstrating the value of web-area signals for early risk prediction and cross-chain analyses. The release supports multimodal forensics, early-survival modeling, and automated scam prevention, with a FAIR-compliant open resource to foster future research and defense mechanisms in the meme-coin ecosystem.

Abstract

The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market, characterized by extreme churn, minimal project commitment, and widespread fraudulent behavior. While countless meme coins are deployed across multiple blockchains, they rely heavily on off-chain web and social infrastructure to signal legitimacy. These very signals are largely absent from existing datasets, which are often limited to single-chain data or lack the multimodal artifacts required for comprehensive risk modeling. To address this gap, we introduce MemeChain, a large-scale, open-source, cross-chain dataset comprising 34,988 meme coins across Ethereum, BNB Smart Chain, Solana, and Base. MemeChain integrates on-chain data with off-chain artifacts, including website HTML source code, token logos, and linked social media accounts, enabling multimodal and forensic study of meme coin projects. Analysis of the dataset shows that visual branding is frequently omitted in low-effort deployments, and many projects lack a functional website. Moreover, we quantify the ecosystem's extreme volatility, identifying 1,801 tokens (5.15%) that cease all trading activity within just 24 hours of launch. By providing unified cross-chain coverage and rich off-chain context, MemeChain serves as a foundational resource for research in financial forensics, multimodal anomaly detection, and automated scam prevention in the meme coin ecosystem.
Paper Structure (27 sections, 6 figures, 5 tables)

This paper contains 27 sections, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Collection
  4. Data Collection
  5. Established Meme Coins
  6. Emerging Tokens
  7. Classification Methodology
  8. Dataset Refinement
  9. Dataset Overview
  10. Blockchain Distribution
  11. Addresses Validation
  12. Dataset Schema
  13. Comparison with Other Datasets
  14. Dataset Analysis
  15. Temporal Aspects
  16. ...and 12 more sections

Figures (6)

  • Figure 1: The Data Collection Pipeline. Negative numbers indicate the count of tokens filtered out during the refinement.
  • Figure 2: TF-IDF scores of keywords associated with meme coin names.
  • Figure 3: CDF of the market cap. of meme coins collected.
  • Figure 4: Creation date of meme coins over time and their distribution on different blockchains.
  • Figure 5: Percentages of projects with social media presence.
  • ...and 1 more figures