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Multi-source Multi-level Multi-token Ethereum Dataset and Benchmark Platform

  • Haoyuan Li*
  • , Mengxiao Zhang
  • , Maoyuan Li
  • , Jianzheng Li
  • , Zijian Zhang
  • , Junyi Yang
  • , Shuangyan Deng
  • , Jiamou Liu*
  • *Corresponding author for this work
  • The University of Auckland

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The rapid growth of the cryptocurrency ecosystem has intensified the need for multimodal data that captures the interplay among user behavior, market dynamics, and public sentiment. Existing public datasets remain fragmented and cannot support such integrated analysis. We present 3MEthTaskforce1, a large-scale multimodal Ethereum dataset that integrates 303 million transactions across 3,880 tokens, token histories, global market indicators, and LLM-annotated Reddit sentiment from 2014-2024, all aligned on a daily timeline. The dataset enables tasks such as user behavior prediction and market trend modeling. This paper reports benchmark results for user behavior prediction, while the project website provides additional evaluations. The dataset is publicly available via Figshare2, where it has been downloaded more than four hundred times, and the codebase is released at https://github.com/Haoyuan-Li-UoA/3MEthTaskforce.

Original languageEnglish
Title of host publicationWWW 2026 - Proceedings of the ACM Web Conference 2026
PublisherAssociation for Computing Machinery, Inc
Pages8517-8520
Number of pages4
ISBN (Electronic)9798400723070
DOIs
Publication statusPublished - 12 Apr 2026
Event35th ACM Web Conference, WWW 2026 - Dubai, United Arab Emirates
Duration: 29 Jun 20263 Jul 2026

Publication series

NameWWW 2026 - Proceedings of the ACM Web Conference 2026

Conference

Conference35th ACM Web Conference, WWW 2026
Country/TerritoryUnited Arab Emirates
CityDubai
Period29/06/263/07/26

Keywords

  • benchmark
  • ethereum dataset
  • user behavior modeling

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