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Loth","email":"alex@alexloth.com"},"license":"MIT","homepage":"https://github.com/aloth/cred-1#readme","keywords":["credibility","misinformation","fact-check","domain","dataset","browser-extension","news","media-literacy","fake-news","disinformation","journalism","reliability","cred1","cli"],"repository":{"url":"git+https://github.com/aloth/cred-1.git","type":"git"},"description":"CRED-1 domain credibility dataset — JavaScript library and CLI for checking news source reliability","maintainers":[{"name":"aloth","email":"home@alexloth.com"}],"readme":"# CRED-1: Open Domain Credibility Dataset\n\n<p align=\"center\">\n  <img src=\"figures/cred1-domain-credibility-dataset-banner.jpg\" alt=\"CRED-1 Domain Credibility Dataset Banner\" width=\"100%\">\n</p>\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18769459.svg)](https://doi.org/10.5281/zenodo.18769459)\n[![Publish](https://github.com/aloth/cred-1/actions/workflows/publish.yml/badge.svg)](https://github.com/aloth/cred-1/actions/workflows/publish.yml)\n[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)\n[![npm version](https://img.shields.io/npm/v/@aloth/cred1.svg)](https://www.npmjs.com/package/@aloth/cred1)\n[![npm downloads](https://img.shields.io/npm/dm/@aloth/cred1.svg)](https://www.npmjs.com/package/@aloth/cred1)\n[![CalVer](https://img.shields.io/badge/calver-YYYY.M.D-blue.svg)](https://calver.org/)\n[![Report Missing Domain](https://img.shields.io/badge/Report-Missing%20Domain-orange)](https://github.com/aloth/cred-1/issues/new?template=domain-report.yml)\n\n**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 **2,674 domains** 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.\n\n> 🎓 **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/). First production integration: [Trackless Links](https://github.com/aloth/trackless-links) for iOS and macOS.\n\n> 📰 **Independently reviewed in production use.** Mac & i 4/2026 (heise), the German Apple magazine, examined the CRED-1-powered credibility warnings in [Trackless Links](https://github.com/aloth/trackless-links) and confirmed the app checks URLs locally against the open CRED-1 list (2,673 entries at the time of testing). [Review](https://www.heise.de/select/mac-and-i/2026/4/2609012234834091131) (paywalled, German).\n\n> 🤖 **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)\n\n> **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)\n\n---\n\n## Install\n\n```bash\n# CLI (global)\nnpm install -g @aloth/cred1\n\n# Library (project dependency)\nnpm install @aloth/cred1\n\n# Or try without installing\nnpx @aloth/cred1 check infowars.com\n```\n\n## CLI Usage\n\n```bash\n# Single domain lookup\ncred1 check infowars.com\n# 🔴  infowars.com\n#    Score:    0.073 / 1.000\n#    Category: conspiracy\n#    Level:    low\n#    Sources:  2\n\n# Domain not in dataset\ncred1 check nytimes.com\n# ⚪  nytimes.com\n#    Not found in CRED-1 dataset — treat as unknown/neutral\n\n# Batch processing (stdin)\necho -e \"rt.com\\ninfowars.com\\nnytimes.com\" | cred1 batch\n\n# JSON output\ncred1 check breitbart.com --json\n\n# Search\ncred1 search \"news\"\ncred1 search \"\\.ru$\"\n\n# Statistics\ncred1 stats\ncred1 categories\n```\n\nDomain normalization is automatic — `https://www.infowars.com/politics/` resolves to `infowars.com`.\n\n## MCP Server (Claude Desktop / Cursor / Windsurf)\n\nCRED-1 ships an [MCP](https://modelcontextprotocol.io/) server so AI assistants can check domain credibility directly.\n\n### Tools exposed\n\n| Tool | Description |\n|------|-------------|\n| `check_domain` | Check a single domain (score, category, level, metadata) |\n| `batch_check` | Check up to 100 domains at once |\n| `search_domains` | Search domains by substring or regex pattern |\n| `get_stats` | Dataset statistics (total, per-category counts, version) |\n| `get_categories` | Category taxonomy with descriptions and score ranges |\n\n### Claude Desktop\n\nAdd to `~/Library/Application Support/Claude/claude_desktop_config.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"cred1\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@aloth/cred1\", \"--mcp\"]\n    }\n  }\n}\n```\n\n> **Note:** `--mcp` is handled by the CLI wrapper — or run the dedicated binary directly.\n\nAlternatively, if the package is installed globally:\n\n```json\n{\n  \"mcpServers\": {\n    \"cred1\": {\n      \"command\": \"cred1-mcp\"\n    }\n  }\n}\n```\n\n### Cursor\n\nAdd to `.cursor/mcp.json` (project) or `~/.cursor/mcp.json` (global):\n\n```json\n{\n  \"mcpServers\": {\n    \"cred1\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@aloth/cred1\", \"--mcp\"]\n    }\n  }\n}\n```\n\n### Windsurf\n\nAdd to `~/.codeium/windsurf/mcp_config.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"cred1\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@aloth/cred1\", \"--mcp\"]\n    }\n  }\n}\n```\n\n### OpenClaw / any MCP-compatible host\n\n```json\n{\n  \"command\": \"cred1-mcp\",\n  \"transport\": \"stdio\"\n}\n```\n\n---\n\n## Library Usage\n\n```typescript\nimport { checkDomain, searchDomains, getStats } from '@aloth/cred1';\n\n// Single lookup\nconst result = checkDomain('infowars.com');\n// { domain: 'infowars.com', score: 0.073, category: 'conspiracy', level: 'low', sources: 2, domainAge: 27.3, trancoRank: 15889 }\n\n// Not found → null\nconst unknown = checkDomain('nytimes.com'); // null\n\n// Search by pattern (substring or regex)\nconst russian = searchDomains('\\\\.ru$');\n\n// Dataset statistics\nconst stats = getStats();\n// { totalDomains: 2673, categories: { unreliable: 2001, fake: 233, ... }, version: '1.0.0' }\n```\n\n## Traffic-Light Scoring\n\n| Level   | Score     | Emoji | Meaning                          |\n|---------|-----------|-------|----------------------------------|\n| low     | ≤ 0.20   | 🔴    | High credibility risk            |\n| mixed   | 0.21–0.50 | 🟡    | Unreliable or mixed signals      |\n| ok      | > 0.50   | 🟢    | Generally considered reliable    |\n| neutral | not found | ⚪    | Unknown — absence ≠ trustworthy  |\n\n## Key Features\n\n- **2,674 domains** with credibility scores (0.0–1.0)\n- **Dual-mode** — works as CLI tool and JavaScript library\n- **Fully reproducible** — Python pipeline rebuilds the dataset from scratch\n- **Multi-signal scoring** combining source labels, domain age, web popularity, fact-check frequency, and threat intelligence\n- **Privacy-preserving** — designed for on-device client-side deployment (no server calls needed)\n- **Two openly-licensed sources** — no proprietary data dependencies\n- **Domain normalization** — handles www., protocols, paths automatically\n\n## Dataset Schema\n\n### Compact Format (`cred1_compact.json`)\n\n```json\n{\n  \"infowars.com\": { \"c\": \"c\", \"s\": 0.073, \"n\": 2, \"d\": \"1999-10-04\", \"r\": 15889 }\n}\n```\n\n| Field | Description |\n|-------|-------------|\n| `c`   | Category code: `f`=fake, `u`=unreliable, `m`=mixed, `c`=conspiracy, `s`=satire, `r`=rumor. Note that `reliable` has no single-letter code and is stored as the full string, because `r` is taken by `rumor`. |\n| `s`   | Credibility score (0.0–1.0, lower = less credible) |\n| `n`   | Number of independent source lists flagging this domain |\n| `d`   | Domain registration date (optional) |\n| `r`   | Tranco Top-1M rank (optional — lower rank = more popular) |\n\n### Full Format (`cred1_current.json`)\n\n```json\n{\n  \"infowars.com\": {\n    \"category\": \"fake\",\n    \"credibility_score\": 0.14,\n    \"domain_age_years\": 26.4,\n    \"domain_registered\": \"1999-10-04T04:00:00Z\",\n    \"iffy_factual\": \"VL\",\n    \"iffy_bias\": \"FN\",\n    \"iffy_score\": 0.1,\n    \"factcheck_claims\": 52,\n    \"safe_browsing_flagged\": false,\n    \"score_age\": 0.2,\n    \"score_cat\": 0.05,\n    \"score_factcheck\": 0.0,\n    \"score_iffy\": 0.1,\n    \"score_safebrowsing\": 0.05,\n    \"score_tranco\": 0.1,\n    \"sources\": 2,\n    \"tranco_rank\": 4382\n  }\n}\n```\n\nSee [CODEBOOK.md](CODEBOOK.md) for full field documentation.\n\n### What the score is, and what it is not\n\nThe 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.\n\n## Rebuilding the Dataset\n\n```bash\ncd pipeline/\npython3 build_dataset.py              # Full pipeline\npython3 build_dataset.py --step fetch # Download raw data only\npython3 build_dataset.py --step merge # Parse + merge (requires prior fetch)\npython3 enrich_dataset.py             # Add enrichment signals (API keys required)\n```\n\n## Versioning\n\nCRED-1 uses **calendar versioning** (CalVer) across all distribution channels:\n\n| Channel | Format | Example |\n|---------|--------|---------|\n| GitHub Release | `v2026-06-13` | Tag + Zenodo archive |\n| npm package | `2026.6.13` | Same date, dot-separated (valid semver) |\n\nA new version is released **weekly** with rescored domains. The npm package updates automatically with each GitHub release — no separate version scheme needed.\n\nTo pin a specific dataset version:\n```bash\nnpm install @aloth/cred1@2026.6.13\n```\n\n## Production Integrations\n\n- **[Trackless Links](https://github.com/aloth/trackless-links)** — Safari extension for iOS and macOS with real-time CRED-1 credibility warnings\n- **[RogueGPT](https://github.com/aloth/RogueGPT)** — Uses CRED-1 to identify low-credibility source domains for controlled fake-news stimulus generation\n- **[HuggingFace](https://huggingface.co/datasets/xlth/CRED-1)** — Dataset mirror for ML pipelines\n\n## Contributing\n\nKnow 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.\n\nWe accept community-reported domains that meet at least one of these criteria:\n\n- Listed by [Media Bias/Fact Check](https://mediabiasfactcheck.com/) (MBFC)\n- Flagged by an [IFCN](https://ifcncodeofprinciples.poynter.org/)-certified fact-checker\n- Subject to press council or regulatory action\n- Documented in Wikipedia with credibility concerns\n- Referenced in published academic research\n\nAccepted domains are added to the pipeline and appear in the next weekly dataset release.\n\n## Citation\n\nIf 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.\n\n**Paper:**\n\n```bibtex\n@misc{loth2026cred1,\n  author       = {Loth, Alexander and Kappes, Martin and Pahl, Marc-Oliver},\n  title        = {{CRED-1}: An Open Multi-Signal Domain Credibility Dataset for Automated Pre-Bunking of Online Misinformation},\n  year         = {2026},\n  howpublished = {Preprint},\n  doi          = {10.2139/ssrn.6448466},\n  url          = {https://ssrn.com/abstract=6448466},\n  urldate      = {2026-08-25}\n}\n```\n\n**Dataset:**\n\n```bibtex\n@dataset{loth2026cred1data,\n  author       = {Loth, Alexander},\n  title        = {{CRED-1}: An Open Multi-Signal Domain Credibility Dataset},\n  year         = {2026},\n  publisher    = {Zenodo},\n  version      = {v2026-03-17},\n  doi          = {10.5281/zenodo.18769459},\n  url          = {https://doi.org/10.5281/zenodo.18769459}\n}\n```\n\nThe 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.\n\n## Related Projects\n\nCRED-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:\n\n| Project | Description |\n|:---|:---|\n| [JudgeGPT](https://github.com/aloth/JudgeGPT) | Empirical platform for evaluating AI-generated news authenticity |\n| [RogueGPT](https://github.com/aloth/RogueGPT) | Controlled stimulus generator; draws fake-news sources from CRED-1 scores |\n| [verification-crisis](https://github.com/aloth/verification-crisis) | Expert survey on GenAI disinformation threats and countermeasures |\n| [Origin Lens](https://github.com/aloth/origin-lens) | iOS app for C2PA content credentials and EXIF verification |\n| [provenance-linkage](https://github.com/aloth/provenance-linkage) | Reproducibility bundle for a benchmark audit of AI-text detection |\n\n### Companion data deposits\n\nCRED-1 is archived alongside three companion datasets, which are versioned together so that they can be cited as one coherent set:\n\n| Deposit | Concept DOI | Current version |\n|:---|:---|:---|\n| 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) |\n| 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) |\n| Expert Survey | [10.5281/zenodo.18703600](https://doi.org/10.5281/zenodo.18703600) | [22226924](https://doi.org/10.5281/zenodo.22226924) (v2.1.0) |\n\n## Related Publications\n\n- **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)\n- **Blessing or Curse? A Survey on the Impact of Generative AI on Fake News** (2024). [arXiv:2404.03021](https://arxiv.org/abs/2404.03021)\n\n## License\n\n- **Dataset:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Copyright 2025-2026 Alexander Loth.\n- **Code & CLI:** [MIT](LICENSE)\n\nThe 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.\n\n## Author\n\nAlexander Loth — [alexloth.com](https://alexloth.com) · [@xlth](https://x.com/xlth) · [ORCID](https://orcid.org/0009-0003-9327-6865)\n","readmeFilename":"README.md"}