---
license: mit
task_categories:
- tabular-classification
language:
- en
tags:
- ethereum
- fraud-detection
- blockchain
- multimodal
pretty_name: FuseChain Multimodal Ethereum Fraud Dataset
size_categories:
- 10K<n<100K
---

# Dataset Card for FuseChain Multimodal Ethereum Fraud Dataset

## Dataset Summary

The FuseChain Multimodal Ethereum Fraud Dataset is a novel, address-level dataset designed for Ethereum fraud detection research. Unlike traditional blockchain datasets that rely solely on transactional heuristics, this dataset is constructed using a multimodal data fusion approach that integrates on-chain transactional behaviour with off-chain contextual signals from three external modalities: market data, Reddit community signals, and Twitter sentiment metrics.

The dataset consists of **35,272 unique Ethereum Externally Owned Accounts (EOAs)**, reflecting a real-world class distribution of approximately **16.3% fraudulent addresses** and **83.7% normal addresses**.

---

## Supported Tasks

- **Tabular Classification:** Train models to classify Ethereum addresses as either Normal (0) or Scam (1)
- **Fraud Detection Research:** Evaluate the efficacy of multimodal data fusion versus traditional on-chain only approaches
- **Modality Contribution Analysis:** Assess the independent and combined contribution of each off-chain data source through ablation studies

---

## Languages

Features are numerical representations of behaviour, market conditions, and text sentiment. The underlying social media data from which sentiment features were derived was in English.

---

## Dataset Structure

### Data Instances

Each instance represents a single fused behavioural profile for a distinct Ethereum EOA address stored in `address_level_fused.parquet`. Features represent aggregated temporal behaviours across the address's entire active period.

### Data Fields

The dataset contains **31 features** alongside the address identifier and target label, spanning four modalities.

**Identifiers and Labels:**
- `address` (string): The 42-character Ethereum EOA address
- `is_scam` (int): Binary target variable — 1 = Scam, 0 = Normal

**On-Chain Features (14):** Transactional behavioural heuristics including ETH flows, burst activity, session patterns, and wallet lifecycle metrics.
> "active_days", "normal_sent_cnt_mean", "normal_sent_cnt_max", "normal_recv_cnt_mean", "normal_recv_cnt_max", "normal_total_cnt_mean", "normal_total_cnt_max", "eth_sent_sum_mean", "eth_net_flow_mean", "eth_net_flow_max", "sessions_cnt_mean", "burst_max_tx_5m_mean", "burst_max_tx_5m_max", "days_since_last_activity_mean"

**Market Features (5):** Macroeconomic indicators from CoinMarketCap capturing daily ETH price dynamics and trading conditions during the address's active period.
> "market_daily_return_mean", "market_volatility_7d_mean", "market_volume_spike_mean", "market_momentum_7d_mean", "market_intraday_volatility_mean"

**Reddit Features (3):** Community discussion signals and fraud-related discourse metrics from Ethereum-related Reddit communities, temporally aligned to the address's active period.
> "reddit_fraud_mention_ratio_mean", "reddit_total_fraud_mentions_mean", "reddit_avg_sentiment_mean"

**Twitter Features (9):** Social engagement and sentiment metrics from Ethereum-related Twitter activity during the address's active period.
> "twitter_total_activity_mean", "twitter_avg_sentiment_mean", "twitter_avg_negative_mean", "twitter_avg_positive_mean", "twitter_negative_ratio_mean", "twitter_fraud_mention_ratio_mean", "twitter_avg_likes_mean", "twitter_avg_retweets_mean", "twitter_avg_replies_mean"

> For the full feature schema, refer to `address_features_metadata.json` in the accompanying model repository.

---

## Data Splits

The full dataset of 35,272 records was split using **80/20 stratified sampling** to preserve the 16.3% minority class distribution:

| Split | Addresses | Normal | Scam |
|---|---|---|---|
| Full Dataset | 35,272 | 29,530 (83.7%) | 5,742 (16.3%) |
| Training Set | 28,217 | 23,623 | 4,594 |
| Test Set | 7,055 | 5,907 | 1,148 |

---

## Dataset Creation

### Curation Rationale

Modern Ethereum fraud detection requires more than analysing token flows. Scammers exploit social media platforms and market conditions to coordinate phishing campaigns, rug pulls, and pump-and-dump schemes. This dataset was created to demonstrate that fusing on-chain transaction features with off-chain contextual signals significantly improves fraud detection performance, providing a reusable research artifact for the wider blockchain security community.

### Source Data

This fused dataset is built upon raw data sourced from the following publicly available datasets:

- **On-Chain Data:** [Ethereum Fraud Dataset by Activity](https://huggingface.co/datasets/fesevu/ethereum_fraud_dataset_by_activity) (Ishmukhamedov, 2025) hosted on Hugging Face
- **Market Data:** Historical ETH OHLCV pricing and trading volume data from [CoinMarketCap](https://coinmarketcap.com/currencies/ethereum/historical-data/)
- **Reddit Data:** [The Reddit Ethereum Dataset](https://www.kaggle.com/datasets/pavellexyr/the-reddit-ethereum-dataset) (pavellexyr) on Kaggle
- **Twitter Data:** [Cryptocurrency Tweets with Sentiment Analysis](https://www.kaggle.com/datasets/fabioturazzi/cryptocurrency-tweets-with-sentiment-analysis) (fabioturazzi) on Kaggle

### Analysis Window

All datasets were temporally aligned within the shared research window of **2017-01-01 to 2021-02-10**, constrained by the coverage of the Twitter dataset.

### Preprocessing and Feature Engineering

- **EOA Filtering:** Dataset restricted to Externally Owned Accounts, excluding smart contract addresses
- **Zero-Activity Removal:** Days with no meaningful transaction activity were removed
- **Temporal Alignment:** All data sources aligned at daily granularity using a shared date key
- **Feature Engineering:** Five market features, four Reddit features, and eleven Twitter features derived from daily aggregated signals
- **Address-Level Aggregation:** Daily records aggregated into static address profiles using mean, maximum, and sum statistics for on-chain features, and mean only for off-chain features
- **Feature Reduction:** Initial feature space reduced to 31 final features using two-stage Spearman rank correlation filtering applied at both the daily and address levels

---

## Considerations for Using the Data

### Social Impact

This dataset contributes to securing Decentralised Finance ecosystems by demonstrating the value of off-chain contextual signals for fraud detection. It provides a foundation for future research into more robust Web3 security tools capable of identifying malicious addresses before significant financial damage occurs.

### Known Limitations

- **Temporal Coverage:** The dataset covers 2017-2021 and may not reflect contemporary fraud patterns such as NFT-related scams or cross-chain bridge exploits
- **Global Social Signals:** Reddit and Twitter features represent global daily Ethereum community activity rather than signals directly attributable to individual addresses
- **Label Noise:** Address labels are sourced from community-reported databases which may contain mislabelled or unreported addresses
- **Static Dataset:** The dataset represents a fixed historical snapshot and requires retraining on updated data for contemporary applicability

---

## Citation

If you use this dataset in your research, please cite the FuseChain project:
```bibtex
@misc{fusechain2026,
  title={FuseChain: Ethereum Fraud Detection via Multimodal Signal Fusion},
  author={Fernando, Nileshka},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/Nileshka/fusechain-data}}
}
```

---

## Related Resources

- **Code Repository & Pipeline:** [FuseChain GitHub](https://github.com/NileshFdo/FuseChain-FYP)
- **Model:** [Model Repository](https://huggingface.co/Nileshka/fusechain-model)