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

2026-01-28

AI Summary

This study addresses the critical gap in existing datasets, which lack comprehensive coverage of cross-chain meme coins and their essential off-chain signals—such as websites, social media presence, and visual branding—thereby hindering effective risk modeling and scam detection. To bridge this gap, we present the first systematic integration of on-chain transaction data from Ethereum, BNB Smart Chain, Solana, and Base with multimodal off-chain information, including HTML content, logos, and social media accounts, resulting in an open-source dataset comprising 34,988 tokens. Through multi-source data collection, cross-chain alignment, and multimodal feature extraction, our analysis reveals that low-effort projects commonly lack functional websites or coherent brand elements and exhibit extreme short-term failure: 5.15% of tokens cease trading within 24 hours of launch.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Cryptocurrency and smart contracts

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.

Problem

• meme coin
• cross-chain
• multimodal
• financial forensics
• risk analysis

Innovation

• multimodal
• cross-chain
• meme coin forensics
• off-chain artifacts
• risk analysis