| --- |
| 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> |
|
|
| [](https://doi.org/10.5281/zenodo.18769460) |
| [](https://creativecommons.org/licenses/by/4.0/) |
| [](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). |
|
|
