ekmpa/epidomains

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