---
license: cc-by-4.0
---


# Dataset Card for *`epi-domains`* 0.1.1


`epi-domains` is a multi-class labelled set of web domains that contribute to informational exchanges and political discussions online. 

It is composed of 4 classes, totalling 23,257 web domains: 

<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/238YBB1CAB7tewbIHM61X.png" width="600">

- **Questionable Source** are domains whose integrity is questionable but haven't been flagged or verified as malicious or sharing misinformation.
- **Misinformation Spreaders** are websites that share misinformation online.
- **FIMI-related Domains** are websites that have been confirmed to be linked to foreign information manipulation interference operations, or coordinated / state-led campaigns. 
- **Reliable Sources** are organizations or institutions that safeguard information integrity online, e.g.,  fact-checker organisations, or other contributors to online informational spheres that have been assessed as reliable by experts (e.g. news outlets assessed by fact-checking organisations as highly reliable).


## Dataset Variants

The dataset is made available under two variants, both being `csv` formats: (1) a source-attributed dataset (`epidomains.csv`) where all datapoints, and each of their label, is sourced; 
and (2) a clean version (`epidomains_clean.csv`) with only label values. 
The sourced version is also made available in the `split` directory with a 60-20-20 train-val-test split, stratified by classes to have each split balanced. 


## FIMI-related Domains:
These datapoints mainly originate from primary sources (e.g. governmental or cybersecurity reports), or are supported by 2+ secondary sources (e.g. news articles).
The majority is attributed a perpetrator and/or target country: 



<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/AnumBFyi84wK0bm2g0gYG.png" width="900">


<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/8EoKCxDCQMpSB4wUPjyqX.png" width="900">

## Labels

Many of the domains in this dataset also have labels, usually expert-driven scores pertaining to their reliability. 


### Reliability

Reliability is mainly scored as a binary flag *(n = 17,977)*: 


<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/5C3Pvr2fvRXgk97H8SSgZ.png" width="600">

Though some domains also have a continuous reliability score *(n = 9,640)*: 

<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/vbovNHRwUrSby5HKKu05X.png" width="600">


And a smaller part of domains also have categorical reliability labels *(n = 3,889 for 3-cat, n = 1,750 for 6-cat)*: 


<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/MzEtG2sBRZB6Hby1dImGN.png" width="600">

<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/_E2F5Wf4uM_knRVJeJmS6.png" width="600">


### Factuality

Factuality is mainly scored as a categorical dimension *(n = 2,635)*: 

<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/99RwoHc7B7BoLVEGMZz0E.png" width="600">

Or a continuous one *(n = 9,640)*: 

<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/tgzxYfyd_jFTeXtrtjb6f.png" width="600">

### Conflict Resolution

When original data sources disagree on a domain's label (for categorical labels), either (1) the label is chosen through a majority vote, (2) the domain is discarded if there are not enough sources for a majority vote. 
<!-- Remaining agreements between datasets is thus 1.0 across the board for binary reliability. For categorical reliability, we observe similar rates: -->

<!-- <img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/Vvv6gt5pSt5TWr5FHFVIv.png" width="400"> -->


### Data sources

Some of the primary contributors to the dataset are: 
- Domains Quality Ratings (41.4%)
- Meta Threat Reports (27.0%)
- Wikipedia General (13.5%)

The full list:

| Source | Rows | % of Total |
|---|---:|---:|
| [Domains Quality Ratings](https://github.com/hauselin/domain-quality-ratings) | 9,640 | 41.4% |
| Meta Threat Reports (e.g, [[1](https://transparency.meta.com/sr/Q1-2025-Adversarial-threat-report/)]) | 6,283 | 27.0% |
| [Wikipedia General](https://github.com/kynoptic/wikipedia-reliable-sources) | 3,131 | 13.5% |
| [MBFC Ratings](https://mediabiasfactcheck.com) | 2,635 | 11.3% |
| [Manual](https://docs.google.com/spreadsheets/d/1zmQyorjaXswFNO4Vr-QpTPxt4B6EjCx6bmYF95X823A/edit?usp=sharing) | 1,892 | 8.1% |
| [Iffy Index](https://iffy.news) | 1,750 | 7.5% |
| [Misinfodomains](https://github.com/JanaLasser/misinformation_domains) | 1,690 | 7.3% |
| [Hosts Fake News](https://github.com/StevenBlack/hosts) | 1,148 | 4.9% |
| [MBFC's Questionable List](https://mediabiasfactcheck.com/fake-news/) | 1,138 | 4.9% |
| [CheckThat!](https://checkthat.gitlab.io/clef2025/) | 916 | 3.9% |
| [Wikipedia Campaigns](https://en.wikipedia.org/wiki/List_of_political_disinformation_website_campaigns) | 807 | 3.5% |
| [Wikipedia Miscellaneous](https://en.wikipedia.org/wiki/List_of_miscellaneous_fake_news_websites) | 769 | 3.3% |
| [Wikipedia Fake News](https://en.wikipedia.org/wiki/List_of_fake_news_websites) | 404 | 1.7% |
| [Zoznam](https://konspiratori.sk) | 335 | 1.4% |
| [Politifact's Almenac](https://infogram.com/politifacts-fake-news-almanac-1gew2vjdxl912nj) | 275 | 1.2% |
| [Paperwall](https://citizenlab.ca/research/paperwall-chinese-websites-posing-as-local-news-outlets-with-pro-beijing-content/) | 123 | 0.53% |
| [Ngo Report Israel Blacklist](https://ngoreport.org/blacklisted-ngo/pro-israel-ngos/) | 89 | 0.38% |
| [Ngo Report Uae Blacklist](https://ngoreport.org/blacklisted-ngo/pro-uae-ngos/) | 88 | 0.38% |
| [Ngo Report Saudi Blacklist](https://ngoreport.org/blacklisted-ngo/pro-saudi-arabia-ngos/) | 80 | 0.34% |
| [Wikipedia II Actors](https://en.wikipedia.org/wiki/List_of_fact-checking_websites) | 60 | 0.26% |
| [Nelež](https://www.nelez.cz/en/) | 51 | 0.22% |
| [FakeNewsNet](https://github.com/kaidmml/fakenewsnet) | 49 | 0.21% |
| [Hasbara Tracker](https://hasbaratracker.com) | 17 | 0.0731% |
| [EDMO Hubs](https://edmo.eu/about-us/edmo-hubs/) | 16 | 0.0688% |
| [Ngo Report Russia Blacklist](https://ngoreport.org/blacklisted-ngo/pro-russia-ngos/) | 4 | 0.0172% |


<!-- ## Splits:

``` json
[webgraph-ts] scanned=2025_oct_nov_dec matched_rows=15933
[webgraph-ts] scanned=2025_26_nov_dec_jan matched_rows=15937
[webgraph-ts] scanned=2025_26_dec_jan_feb matched_rows=16328
[webgraph-ts] scanned=2026_jan_feb_mar matched_rows=15993
[webgraph-ts] domains_with_timestamp=16781 total_seed_records=16781
{
  "snapshot_count": 4,
  "snapshots": [
    "2025_oct_nov_dec",
    "2025_26_nov_dec_jan",
    "2025_26_dec_jan_feb",
    "2026_jan_feb_mar"
  ],
  "timestamp_source": "webgraph",
  "class_counts": {
    "coordinated_inauthentic": {
      "train": 1269,
      "val": 423,
      "test": 423,
      "unsplittable": 0
    },
    "integrity_actor": {
      "train": 747,
      "val": 249,
      "test": 249,
      "unsplittable": 0
    },
    "misinfo_spreader": {
      "train": 2520,
      "val": 840,
      "test": 840,
      "unsplittable": 0
    },
    "questionable": {
      "train": 5532,
      "val": 1844,
      "test": 1845,
      "unsplittable": 0
    }
  },
  "unknown_class_rows": 0
}
``` -->


### Dataset Versions

| Version | Description | 
| ---- | ---- | 
| 0.1.0 | First public version | 
| 0.1.1 | Filtered out more extensively 'provider'-websites (e.g. media hosting services, search engines and social media, where content is attributable to a third-party, not to the domain itself).


- **Curated by** the CrediNet organisation, which consists of a team of collaborators from the Complex Data Lab @ Mila - Quebec AI Institute, the University of Oxford, McGill University, Concordia University, UC Berkeley, University of Montreal, and AITHYRA.
- **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant:
*Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI
Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada.
- **Dataset Contact:** emma.kondrup@mail.mcgill.ca