# CRED-1: Open Domain Credibility Dataset

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18769459.svg)](https://doi.org/10.5281/zenodo.18769459) [![Publish](https://github.com/aloth/cred-1/actions/workflows/publish.yml/badge.svg)](https://github.com/aloth/cred-1/actions/workflows/publish.yml) [![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) [![npm version](https://img.shields.io/npm/v/@aloth/cred1.svg)](https://www.npmjs.com/package/@aloth/cred1) [![npm downloads](https://img.shields.io/npm/dm/@aloth/cred1.svg)](https://www.npmjs.com/package/@aloth/cred1) [![CalVer](https://img.shields.io/badge/calver-YYYY.M.D-blue.svg)](https://calver.org/) [![Report Missing Domain](https://img.shields.io/badge/Report-Missing%20Domain-orange)](https://github.com/aloth/cred-1/issues/new?template=domain-report.yml) **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 **2,600+ domains** (exact count in the [latest release](https://github.com/aloth/cred-1/releases/latest)) known to publish mis/disinformation, conspiracy theories, or other unreliable content. Community members can [report missing domains](https://github.com/aloth/cred-1/issues/new?template=domain-report.yml) for inclusion. > 🎓 **Presented at ACM WebSci 2026 (Braunschweig).** Landing page: [aloth.github.io/agentic-ai-information-integrity/cred-1](https://aloth.github.io/agentic-ai-information-integrity/cred-1/). > 🤖 **MCP-ready** — query domain credibility directly from Claude Desktop, Cursor & Windsurf, or any MCP-compatible agent. [Jump to setup ↓](#mcp-server-claude-desktop--cursor--windsurf) > **Paper:** A. Loth, M. Kappes, and M.-O. Pahl, "CRED-1: An Open Multi-Signal Domain Credibility Dataset for Automated Pre-Bunking of Online Misinformation," *Preprint*, 2026. [doi:10.2139/ssrn.6448466](https://doi.org/10.2139/ssrn.6448466) --- ## Install ```bash # CLI (global) npm install -g @aloth/cred1 # Library (project dependency) npm install @aloth/cred1 # Or try without installing npx @aloth/cred1 check infowars.com ``` ## CLI Usage ```bash # Single domain lookup cred1 check infowars.com # 🔴 infowars.com # Score: 0.073 / 1.000 # Category: conspiracy # Level: low # Sources: 2 # Domain not in dataset cred1 check nytimes.com # ⚪ nytimes.com # Not found in CRED-1 dataset — treat as unknown/neutral # Batch processing (stdin) echo -e "rt.com\ninfowars.com\nnytimes.com" | cred1 batch # JSON output cred1 check breitbart.com --json # Search cred1 search "news" cred1 search "\.ru$" # Statistics cred1 stats cred1 categories ``` Domain normalization is automatic — `https://www.infowars.com/politics/` resolves to `infowars.com`. ## MCP Server (Claude Desktop / Cursor / Windsurf) CRED-1 ships an [MCP](https://modelcontextprotocol.io/) server so AI assistants can check domain credibility directly. ### Tools exposed | Tool | Description | |------|-------------| | `check_domain` | Check a single domain (score, category, level, metadata) | | `batch_check` | Check up to 100 domains at once | | `search_domains` | Search domains by substring or regex pattern | | `get_stats` | Dataset statistics (total, per-category counts, version) | | `get_categories` | Category taxonomy with descriptions and score ranges | ### Claude Desktop Add to `~/Library/Application Support/Claude/claude_desktop_config.json`: ```json { "mcpServers": { "cred1": { "command": "npx", "args": ["-y", "@aloth/cred1", "--mcp"] } } } ``` > **Note:** `--mcp` is handled by the CLI wrapper — or run the dedicated binary directly. Alternatively, if the package is installed globally: ```json { "mcpServers": { "cred1": { "command": "cred1-mcp" } } } ``` ### Cursor Add to `.cursor/mcp.json` (project) or `~/.cursor/mcp.json` (global): ```json { "mcpServers": { "cred1": { "command": "npx", "args": ["-y", "@aloth/cred1", "--mcp"] } } } ``` ### Windsurf Add to `~/.codeium/windsurf/mcp_config.json`: ```json { "mcpServers": { "cred1": { "command": "npx", "args": ["-y", "@aloth/cred1", "--mcp"] } } } ``` ### OpenClaw / any MCP-compatible host ```json { "command": "cred1-mcp", "transport": "stdio" } ``` --- ## Library Usage ```typescript import { checkDomain, searchDomains, getStats } from '@aloth/cred1'; // Single lookup const result = checkDomain('infowars.com'); // { domain: 'infowars.com', score: 0.073, category: 'conspiracy', level: 'low', sources: 2, domainAge: 27.3, trancoRank: 15889 } // Not found → null const unknown = checkDomain('nytimes.com'); // null // Search by pattern (substring or regex) const russian = searchDomains('\\.ru$'); // Dataset statistics const stats = getStats(); // { totalDomains: 2673, categories: { unreliable: 2001, fake: 233, ... }, version: '1.0.0' } ``` ## Traffic-Light Scoring | Level | Score | Emoji | Meaning | |---------|-----------|-------|----------------------------------| | low | ≤ 0.20 | 🔴 | High credibility risk | | mixed | 0.21–0.50 | 🟡 | Unreliable or mixed signals | | ok | > 0.50 | 🟢 | Generally considered reliable | | neutral | not found | ⚪ | Unknown — absence ≠ trustworthy | ## Key Features - **2,674 domains** with credibility scores (0.0–1.0) - **Dual-mode** — works as CLI tool and JavaScript library - **Fully reproducible** — Python pipeline rebuilds the dataset from scratch - **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 - **Domain normalization** — handles www., protocols, paths automatically ## Dataset Schema ### Compact Format (`cred1_compact.json`) ```json { "infowars.com": { "c": "c", "s": 0.073, "n": 2, "d": "1999-10-04", "r": 15889 } } ``` | Field | Description | |-------|-------------| | `c` | Category code: `f`=fake, `u`=unreliable, `m`=mixed, `c`=conspiracy, `s`=satire, `r`=rumor. | | `s` | Credibility score (0.0–1.0, lower = less credible) | | `n` | Number of independent source lists flagging this domain | | `d` | Domain registration date (optional) | | `r` | Tranco Top-1M rank (optional — lower rank = more popular) | ### Full Format (`cred1_current.json`) ```json { "infowars.com": { "category": "fake", "credibility_score": 0.14, "domain_age_years": 26.4, "domain_registered": "1999-10-04T04:00:00Z", "iffy_factual": "VL", "iffy_bias": "FN", "iffy_score": 0.1, "factcheck_claims": 52, "safe_browsing_flagged": false, "score_age": 0.2, "score_cat": 0.05, "score_factcheck": 0.0, "score_iffy": 0.1, "score_safebrowsing": 0.05, "score_tranco": 0.1, "sources": 2, "tranco_rank": 4382 } } ``` See [CODEBOOK.md](CODEBOOK.md) for full field documentation. ### What the score is, and what it is not The credibility score is a **domain-level** signal, not an item-level verdict. It says how a source has been rated by public source-rating initiatives; it does not say whether any individual article is true. Research that uses it as a veracity label is therefore working with a **provenance-implied rather than item-verified** signal: material drawn from domains scoring below 0.25 can be labelled low-credibility, material from established outlets legitimate, but no single item has been fact-checked. Outlet reputation is a good but imperfect proxy, and that bound should be carried into any reuse. ## Rebuilding the Dataset ```bash cd pipeline/ python3 build_dataset.py # Full pipeline python3 build_dataset.py --step fetch # Download raw data only python3 build_dataset.py --step merge # Parse + merge (requires prior fetch) python3 enrich_dataset.py # Add enrichment signals (API keys required) ``` ## Versioning CRED-1 uses **calendar versioning** (CalVer) across all distribution channels: | Channel | Format | Example | |---------|--------|---------| | GitHub Release | `v2026-06-13` | Tag + Zenodo archive | | npm package | `2026.6.13` | Same date, dot-separated (valid semver) | A new version is released **weekly** with rescored domains. The npm package updates automatically with each GitHub release — no separate version scheme needed. To pin a specific dataset version: ```bash npm install @aloth/cred1@2026.6.13 ``` ## Production Integrations - **[Trackless Links](https://github.com/aloth/trackless-links)** — Safari extension for iOS and macOS that checks links locally against CRED-1 (reviewed in Mac & i 4/2026) - **[RogueGPT](https://github.com/aloth/RogueGPT)** — Uses CRED-1 to identify low-credibility source domains for controlled fake-news stimulus generation - **[HuggingFace](https://huggingface.co/datasets/xlth/CRED-1)** — Dataset mirror for ML pipelines ## Contributing Know a domain that should be in the dataset? [Report a missing domain](https://github.com/aloth/cred-1/issues/new?template=domain-report.yml) with evidence. We accept community-reported domains that meet at least one of these criteria: - Listed by [Media Bias/Fact Check](https://mediabiasfactcheck.com/) (MBFC) - Flagged by an [IFCN](https://ifcncodeofprinciples.poynter.org/)-certified fact-checker - Subject to press council or regulatory action - Documented in Wikipedia with credibility concerns - Referenced in published academic research Accepted domains are added to the pipeline and appear in the next weekly dataset release. ## Citation If you use CRED-1 in your work, please cite **both** the paper and the dataset version you used. The paper describes the method; the Zenodo record pins the exact data, which is what makes your results reproducible. **Paper:** ```bibtex @misc{loth2026cred1, 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}, howpublished = {Preprint}, doi = {10.2139/ssrn.6448466}, url = {https://ssrn.com/abstract=6448466}, urldate = {2026-08-25} } ``` **Dataset:** ```bibtex @dataset{loth2026cred1data, author = {Loth, Alexander}, title = {{CRED-1}: An Open Multi-Signal Domain Credibility Dataset}, year = {2026}, publisher = {Zenodo}, version = {v2026-03-17}, doi = {10.5281/zenodo.18769459}, url = {https://doi.org/10.5281/zenodo.18769459} } ``` The DOI above always resolves to the latest release. To cite a specific weekly snapshot, use the version DOI shown on that release's Zenodo page. ## Related Projects CRED-1 is one strand of a wider research program on generated disinformation. The domain scores are used to select low-credibility sources for stimulus generation in RogueGPT: | Project | Description | |:---|:---| | [JudgeGPT](https://github.com/aloth/JudgeGPT) | Empirical platform for evaluating AI-generated news authenticity | | [RogueGPT](https://github.com/aloth/RogueGPT) | Controlled stimulus generator; draws fake-news sources from CRED-1 scores | | [verification-crisis](https://github.com/aloth/verification-crisis) | Expert survey on GenAI disinformation threats and countermeasures | | [Origin Lens](https://github.com/aloth/origin-lens) | iOS app for C2PA content credentials and EXIF verification | | [provenance-linkage](https://github.com/aloth/provenance-linkage) | Reproducibility bundle for a benchmark audit of AI-text detection | ### Companion data deposits CRED-1 is archived alongside three companion datasets, which are versioned together so that they can be cited as one coherent set: | Deposit | Concept DOI | Current version | |:---|:---|:---| | RogueGPT Stimulus Corpus | [10.5281/zenodo.18703137](https://doi.org/10.5281/zenodo.18703137) | [22225536](https://doi.org/10.5281/zenodo.22225536) (v1.2.0) | | JudgeGPT Human Perception Data | [10.5281/zenodo.18703384](https://doi.org/10.5281/zenodo.18703384) | [22226580](https://doi.org/10.5281/zenodo.22226580) (v1.2.0) | | Expert Survey | [10.5281/zenodo.18703600](https://doi.org/10.5281/zenodo.18703600) | [22226924](https://doi.org/10.5281/zenodo.22226924) (v2.1.0) | ## Related Publications - **Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens** (INFORMATIK 2026, LNI). Positions domain-level credibility as a Delivery-stage signal. [arXiv:2608.21389](https://arxiv.org/abs/2608.21389) - **Blessing or Curse? A Survey on the Impact of Generative AI on Fake News** (2024). [arXiv:2404.03021](https://arxiv.org/abs/2404.03021) ## License - **Dataset:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Copyright 2025-2026 Alexander Loth. - **Code & CLI:** [MIT](LICENSE) The dataset is an aggregation of domain-level ratings from public source-rating initiatives. The aggregation, scoring and documentation are this project's own work and are released under CC BY 4.0; the underlying ratings remain attributable to their respective initiatives, which are named per record and listed in [ATTRIBUTION.md](ATTRIBUTION.md). No access restriction applies to the dataset. ## Author Alexander Loth — [alexloth.com](https://alexloth.com) · [@xlth](https://x.com/xlth) · [ORCID](https://orcid.org/0009-0003-9327-6865)