MemeTrans: A Dataset for Detecting High-Risk Memecoin Launches on Solana

2026-02-13

AI Summary

This study addresses the growing problem of high-risk meme coins frequently launched on Solana via Launchpads, which have caused substantial investor losses. To tackle this issue, the authors construct MemeTrans, the first large-scale dataset comprising over 40,000 tokens and more than 200 million transactions. They introduce a risk labeling methodology that integrates statistical metrics with behavioral patterns, leveraging 122-dimensional launch features, bundled account identification, and manipulation pattern detection. Building upon this, they develop an end-to-end pipeline for detecting high-risk token launches. Experimental results demonstrate that models trained on MemeTrans effectively identify risky launches, reducing potential financial losses by 56.1%.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance

Abstract

Launchpads have become the dominant mechanism for issuing memecoins on blockchains due to their fully automated, no-code creation process. This new issuance paradigm has led to a surge in high-risk token launches, causing substantial financial losses for unsuspecting buyers. In this paper, we introduce MemeTrans, the first dataset for studying and detecting high-risk memecoin launches on Solana. MemeTrans covers over 40k memecoin launches that successfully migrated to the public Decentralized Exchange (DEX), with over 30 million transactions during the initial sale on launchpad and 180 million transactions after migration. To precisely capture launch patterns, we design 122 features spanning dimensions such as context, trading activity, holding concentration, and time-series dynamics, supplemented with bundle-level data that reveals multiple accounts controlled by the same entity. Finally, we introduce an annotation approach to label the risk level of memecoin launches, which combines statistical indicators with a manipulation-pattern detector. Experiments on the introduced high-risk launch detection task suggest that designed features are informative for capturing high-risk patterns and ML models trained on MemeTrans can effectively reduce financial loss by 56.1%. Our dataset, experimental code, and pipeline are publicly available at: https://github.com/git-disl/MemeTrans.

Problem

• memecoin
• high-risk launch
• Solana
• launchpad
• financial loss

Innovation

• MemeTrans
• high-risk memecoin detection
• launchpad transaction analysis
• bundle-level account clustering
• Solana blockchain