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:
- 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:
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):
Though some domains also have a continuous reliability score (n = 9,640):
And a smaller part of domains also have categorical reliability labels (n = 3,889 for 3-cat, n = 1,750 for 6-cat):
Factuality
Factuality is mainly scored as a categorical dimension (n = 2,635):
Or a continuous one (n = 9,640):
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.
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 | 9,640 | 41.4% |
| Meta Threat Reports (e.g, [1]) | 6,283 | 27.0% |
| Wikipedia General | 3,131 | 13.5% |
| MBFC Ratings | 2,635 | 11.3% |
| Manual | 1,892 | 8.1% |
| Iffy Index | 1,750 | 7.5% |
| Misinfodomains | 1,690 | 7.3% |
| Hosts Fake News | 1,148 | 4.9% |
| MBFC's Questionable List | 1,138 | 4.9% |
| CheckThat! | 916 | 3.9% |
| Wikipedia Campaigns | 807 | 3.5% |
| Wikipedia Miscellaneous | 769 | 3.3% |
| Wikipedia Fake News | 404 | 1.7% |
| Zoznam | 335 | 1.4% |
| Politifact's Almenac | 275 | 1.2% |
| Paperwall | 123 | 0.53% |
| Ngo Report Israel Blacklist | 89 | 0.38% |
| Ngo Report Uae Blacklist | 88 | 0.38% |
| Ngo Report Saudi Blacklist | 80 | 0.34% |
| Wikipedia II Actors | 60 | 0.26% |
| Nelež | 51 | 0.22% |
| FakeNewsNet | 49 | 0.21% |
| Hasbara Tracker | 17 | 0.0731% |
| EDMO Hubs | 16 | 0.0688% |
| Ngo Report Russia Blacklist | 4 | 0.0172% |
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