Datasets:

xlth
/

CRED-1

---
license: cc-by-4.0
language:
  - en
tags:
  - arxiv:2604.20856
  - credibility
  - misinformation
  - disinformation
  - fact-checking
  - news-credibility
  - domain-credibility
  - media-bias
  - information-integrity
  - ai-safety
  - content-moderation
task_categories:
  - tabular-classification
  - text-classification
size_categories:
  - 1K<n<10K
pretty_name: "CRED-1: Open Multi-Signal Domain Credibility Dataset"
configs:
  - config_name: default
    data_files:
      - split: train
        path: cred1_current.csv
---

# CRED-1: Open Multi-Signal Domain Credibility Dataset

<p align="center">
  <img src="figures/cred1-domain-credibility-dataset-banner.jpg" alt="CRED-1 Domain Credibility Dataset Banner" width="100%">
</p>

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18769460.svg)](https://doi.org/10.5281/zenodo.18769460)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![GitHub](https://img.shields.io/badge/GitHub-aloth%2Fcred--1-181717?logo=github)](https://github.com/aloth/cred-1)

**CRED-1** is an open, reproducible domain-level credibility dataset combining multiple openly-licensed source lists with computed enrichment signals. It provides credibility scores for **2,672 domains** known to publish mis/disinformation, conspiracy theories, or other unreliable content.

## Key Features

- **2,672 domains** with credibility scores (0.0 to 1.0)
- **Fully reproducible** Python pipeline rebuilds the dataset from scratch (see [GitHub repo](https://github.com/aloth/cred-1))
- **Multi-signal scoring** combining source labels, domain age, web popularity, fact-check frequency, and threat intelligence
- **Privacy-preserving** designed for on-device client-side deployment (no server calls needed)
- **Two openly-licensed sources**, no proprietary data dependencies

## Quick Start

### Load with `datasets`

```python
from datasets import load_dataset

ds = load_dataset("xlth/CRED-1")
print(ds["train"][0])
# {'domain': '100percentfedup.com', 'category': 'mixed', 'credibility_score': 0.173, ...}
```

### Lookup a domain

```python
from datasets import load_dataset

ds = load_dataset("xlth/CRED-1", split="train")
lookup = {row["domain"]: row for row in ds}

domain = "infowars.com"
if domain in lookup:
    print(f"{domain}: credibility = {lookup[domain]['credibility_score']}")
else:
    print(f"{domain}: not in dataset (treat as neutral)")
```

## Dataset Schema

| Field | Type | Description |
|---|---|---|
| `domain` | string | Domain name (lowercase, no scheme) |
| `category` | string | Category from source lists (e.g. `fake`, `conspiracy`, `unreliable`, `mixed`, `clickbait`) |
| `credibility_score` | float | Aggregated credibility score (0.0 = least credible, 1.0 = most credible) |
| `sources` | int | Number of source lists the domain appears in |
| `iffy_factual` | string | Factual reporting rating from Iffy.news (VL, L, M, H, VH) |
| `iffy_bias` | string | Bias rating from Iffy.news (e.g. FN = fake news, CP = conspiracy) |
| `iffy_score` | float | Normalized Iffy score |
| `tranco_rank` | int | Tranco web popularity rank (1 = most popular) |
| `domain_age_years` | float | Age of the domain in years |
| `domain_registered` | string | ISO 8601 registration date |
| `factcheck_claims` | int | Count of fact-check claims targeting this domain |
| `safe_browsing_flagged` | bool | Whether Google Safe Browsing flagged this domain |
| `score_*` | float | Individual signal contributions to the aggregate score |

Full schema in [`CODEBOOK.md`](CODEBOOK.md).

## Files

- `cred1_current.csv` (~250 KB): Full dataset, recommended for `load_dataset` and the dataset viewer
- `cred1_current.json` (~1 MB): Same data as nested JSON with domain as key
- `cred1_compact.json` (~170 KB): Minimal `{domain: score}` mapping for lightweight on-device lookups
- `CODEBOOK.md`: Field definitions, scoring methodology, source provenance
- `figures/`: Project banner

## Intended Use

CRED-1 is designed for:

- **Browser extensions and on-device clients** that need fast, offline domain credibility lookups
- **Research on misinformation detection, news verification, and platform governance**
- **Pre-bunking pipelines** that flag suspicious sources before users engage
- **Educational and digital-literacy tooling**

## Limitations

- Coverage is concentrated on English-language misinformation sources
- The dataset captures domains, not individual articles; legitimate journalism on otherwise unreliable domains is not distinguished
- Source lists carry their own biases; consult [`CODEBOOK.md`](CODEBOOK.md) for provenance

## Citation

Canonical citation (Zenodo DOI for this dataset):

```bibtex
@dataset{loth_cred1_2026,
  author       = {Loth, Alexander and Kappes, Martin and Pahl, Marc-Oliver},
  title        = {{CRED-1}: An Open Multi-Signal Domain Credibility Dataset},
  year         = 2026,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.18769460},
  url          = {https://doi.org/10.5281/zenodo.18769460}
}
```

Accompanying preprint:

```bibtex
@article{loth_cred1_preprint_2026,
  author  = {Loth, Alexander and Kappes, Martin and Pahl, Marc-Oliver},
  title   = {{CRED-1}: An Open Multi-Signal Domain Credibility Dataset for Automated Pre-Bunking of Online Misinformation},
  year    = 2026,
  journal = {SSRN Preprint},
  doi     = {10.2139/ssrn.6448466},
  url     = {https://ssrn.com/abstract=6448466}
}
```

## Links

- **GitHub (pipeline + raw sources):** https://github.com/aloth/cred-1
- **Zenodo archive (canonical):** https://doi.org/10.5281/zenodo.18769460
- **SSRN preprint:** https://doi.org/10.2139/ssrn.6448466
- **Blog post:** https://alexloth.com/cred-1-open-domain-credibility-dataset-preprint/

## License

CC BY 4.0. Free to use with attribution. See [`LICENSE`](LICENSE).