credi-net/DomainPool

Dataset Card for domain-pool 0.1.1

domain-pool is a fine grained and cross-domain aggregate labelled set of 15,999,167 web domains.

These web domains have up to 11 features, including 5 grading axes:

• Features:
  • year;
  • website category (e.g. news or adult);
  • country, or if applicable, perpetrator and / or target country (e.g. in the case of coordinated campaigns).
• Scoring axes:
  • reliability (may be a continous, categorical or binary score);
  • factuality (same);
  • bias (may be continous or categorical);
  • popularity (as a rank). All domains also have the original data source indicated per label, along with their year to enable temporal analyses. A large part of these data sources are open-sources academic datasets, as well as sourced from fact-checking organisations, governmental or cybersecurity investigations, and more.

The full composition is provided below.

Dataset Overview

Label composition

Domain Features

These characterize the domain, with:

• Year: of each dataset the domain was present in;
• Type: the broader category the website belongs to (e.g. phishing or adult);
• Country: the domain may have one country associated, or in certain cases (e.g. targeted campaigns), have a perpetrator and/or atarget country.

The prominent types are represented below:

Moreover, all datapoints and labels are timestamped. Most data (in terms of volume) is sourced from recent or regularly updated, as reflected in the 2026 prominence shown below:

Reliability

Reliability as a broad category encompasses three types of quantitative labels at different granularities:

1. Continuous score ( n = ): these are numerical (float) on [0.0,1.0] that explicitly relates to the domain's reliability as assessed by expert fact-checkers (independent or academic).
2. Binary ( n = ): a boolean flag ('(un)reliable') indicating broader reliability.
3. 3-class ( n = ): same type of source and meaning, these span three levels: [low, medium, high].
4. 6-class ( n = ): same, at a finer granularity.

More precisely,

Reliability (continuous)

Distribution:

Reliability (binary)

We have a third 'N/A'-like category, for 'providers', in the sense of domains that are not responsible for their content either becuase they are a social media platform, a media or file hosting service, or another platform of the likes.

Reliability (3-class)

Reliability (6-class)

Factuality (continuous)

Factuality (3-class)

Bias (continuous)

Bias (categorical)

Popularity metrics

The Pool has 3 types of popularity metric: iffy_rank, mbfc_rank and tranco_rank, from the datasets of the same name. Iffy and Tranco are relative ranking, while MBFC has traffic-relative categories:

Data sources

Some of the primary contributors to the dataset are:

• UT1 by the University of Toulouse Capitole (41.5%),
• The Tranco List (28.27%).
• Blacklists (30.8%);
• 50+ others with <10% each.

The full list:

Source	Rows	% of Total
UT1	6,644,316	41.5%
Tranco	4,944,640	30.9%
Blacklists	4,931,489	30.8%
Malicious and Benign Webpages (Train)	1,200,000	7.5%
Malicious URLs	651,191	4.1%
Benign & Malicious URLs	632,508	3.9%
Phish DB	496,442	3.1%
RADEK (Benign C)	436,811	2.7%
Malicious and Benign Webpages (Test)	361,934	2.3%
RADEK (Benign U)	360,708	2.3%
RADEK (Phishing)	164,425	1.0%
DNS Blocklists	142,877	0.89%
URL Phish	103,011	0.64%
RADEK (Malware)	100,809	0.63%
HOSTS (Adware & Malware)	82,622	0.52%
HOSTS (Porn)	76,721	0.48
URLHaus	75,180	0.47%
LegitPhish	63,984	0.40%
Phish Dataset	44,265	0.28%
domains-quality-ratings	11,520	0.07%
Meta Threat Reports	6,379	0.04%
HOSTS (Gambling)	6,027	0.04%
Redirection Domains	5,751	0.04%
MisinfoDomains	4,767	0.0298%
MBFC Ratings	4,497	0.0281%
Wikipedia (General)	3,935	0.0246%
Manual	2,323	0.0145%
HOSTS (Fake News)	2,186	0.0137%
Iffy Index	2,040	0.0128%
MBFC's Questionable List	1,883	0.0118%
FakeNewsNet	1,130	0.0071%
CheckThat!	1,067	0.0067%
Wikipedia (Campaigns)	844	0.0053%
Wikipedia (Miscellaneous)	832	0.0052%
Providers	708	0.0044%
Providers (Manual)	708	0.0044%
Wikipedia (Fake News)	461	0.0029%
Reliable Legal Resources	393	0.0025%
Zoznam	337	0.0021%
Politifact's Almenac	327	0.0020%
Reliable Health Resources	325	0.0020%
Dictionaries (Manual)	304	0.0019%
SD22 Approved Software List	263	0.0016%
Paperwall	123	0.0008%
NGO Report (UAE Blacklist)	100	0.0006%
NGO Report (Israel Blacklist)	99	0.0006%
NGO Report (Saudi Blacklist)	86	0.0005%
Wikipedia (II Actors)	65	0.0004%
Nelež	51	0.0003%
Tools (Manual)	48	0.0003%
C2 Domains	20	0.0001%
Hasbara Tracker	19	0.0001%
EDMO Hubs	16	0.0001%
NGO Report (Russia Blacklist)	4	0.0000%

• 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.

Data sources:

• UT1 by the University of Toulouse Capitole,
• DQR by Lin et al.),
• Wikipedia,
• Lasser et al..