| --- |
| license: cc-by-4.0 |
| --- |
| |
| # Dataset Card for *`domain-pool`* |
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| `domain-pool` is a fine grained and cross-domain aggregate labelled set of web domains. Its default form has majority categories downsampled to present a more balanced set of |
| 148,830 domains, while the full imbalanced set has 5,671,355 datapoints. |
| These web domains are mainly labelled across three axes: |
| - reliability labels form the principal category (spans all datapoints). Reliability scores can be numerical, then normalized to [0.0,1.0] where higher is better; or categorical. Categories are listed below. |
| - factuality labels are sparser, spanning around 12k domains. They are categorical: a domain can have low, medium or high factuality, as assessed by sources like fact-checking organisations. |
| - bias labels can, like the reliability ones, either be a continuous score on [0.0,1.0], or a category (can either be quantitative or across left/right axis). |
| All domains also have the original data source indicated, and the dataset's domain scope (e.g., misinformation, or malware; domain-pool spans Y domains). |
| A large part of these data sources are open-sources academic datasets, and the labels for Y domains were also collected manually |
| from online sources (governmental, journalistic or academic) that gathered domain lists in non-machine readable format. |
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| The full composition is provided below for both dataset versions, the downsampled one and the full variant: |
| - `domain-pool`: 5,671,880 domains, labelled across the three axes, all with at least one categorical label that can pertain to its reliability (e.g., 'fake news' or 'adult content'). |
| Categories are listed below. |
| - `domain-pool-downsampled`: 149,086 domains, where the dominating categories (ones with more than 20,000 datapoints) are downsampled to 15,000 or less. |
| Due to overlaps between datasets, this brings some of the dominating categories to counts between 10 and 15,000; the processing includes an iterative optimizer that tries to minimize such loss. |
| Downsampling here is beneficial because the entire domain pool is predominantly composed of a few large categories (e.g., adult content accounts for more than 4 million domains). |
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| ## Dataset Overview |
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| Here we present an overview of `domain-pool-downsampled`, a curated and normalized fine-grained dataset, |
| where we downsample the top few data domains that otherwise overwhelm the dataset's composition. |
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| ### Label composition |
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| #### Reliability |
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| Reliability as a broad category encompasses three types of labels; two quantitative, and one qualitative: |
| 1. **Continuous score ( n = 11,980 ):** these are academic-sourced float on [0.0,1.0] that explicitly relates to the domain's reliability as assessed by expert fact-checkers. |
| 2. **3-class ( n = 12,053 ):** same type of source and meaning, these span three levels: [low, medium, high]. |
| 3. **Categorical ( n = 149,086 ):** these are broader categories that describe the nature of the website. Most are directly related to the website's reliability (e.g. 'malware'), while some are more neutral (e.g. 'sports'). |
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| More precisely, |
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| ##### Reliability (continuous) |
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| - Count: 11,980 |
| <!-- - Min score: 0.00, --> |
| <!-- - 25th perc.: 0.44, --> |
| <!-- - median: 0.64, --> |
| - mean: 0.59, |
| <!-- - 75th perc. = 0.75, - max = 1.00 --> |
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| Distribution: |
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| | Range | [0.0, 0.1) | [0.1, 0.2) | [0.2, 0.3) | [0.3, 0.4) | [0.4, 0.5) | [0.5, 0.6) | [0.6, 0.7) | [0.7, 0.8) | [0.8, 0.9) | [0.9, 1.0] | |
| |--------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|------------| |
| | Domains | 49 | 161 | 809 | 1252 | 1751 | 1133 | 2415 | 2969 | 1356 | 84 | |
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| ##### Reliability (3-class) |
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| | Value | low | high | medium | |
| |---------|------|------|--------| |
| | Domains | 6440 | 5426 | 309 | |
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| ##### Reliability (categorical) |
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| These are the 5 largest categories; the full list can be found at the bottom of the dataset card. |
| Within each category, the labels usually contain a flag related to reliability (e.g., within 'political', a domain can for example be labelled 'fake news' |
| or 'fact-checker'; within 'phishing', either 'pishing' or 'not'.) |
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| | Domain / area | Count | |
| |---------------------------|--------------| |
| | political | 17,180 | |
| | adult | 15,000 | |
| | phishing | 15,000 | |
| | gambling | 14,993 | |
| | shopping | 14,939 | |
| | cryptojacking, games, jobsearch, malware... | 90k+ | |
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| ##### Factuality |
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| | Factuality | very low | low | medium | high | very high | |
| |-------------|--------|------|------|----------|-----------| |
| | Count | 230 | 2089 | 5889 | 3962 | 103 | |
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| ##### Bias (continuous) |
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| - Count: 11,477 |
| - Mean: 0.65 |
| <!-- count=11477 min=0.2625 p25=0.5043 median=0.6553 mean=0.6454 p75=0.7696 max=0.9988 --> |
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| | Range | [0.2, 0.3) | [0.3, 0.4) | [0.4, 0.5) | [0.5, 0.6) | [0.6, 0.7) | [0.7, 0.8) | [0.8, 0.9) | [0.9, 1.0] | |
| |--------------|------------|------------|------------|------------|------------|------------|------------|------------| |
| | Domains | 4 | 504 | 2327 | 1592 | 2384 | 2549 | 1867 | 250 | |
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| ##### Bias (categorical) |
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| | Bias Category | Far-Left | Left | Left-Center | Least Biased | Right-Center | Right | Far-Right | Pro-Science | Pseudoscience | Conspiracy | |
| |---------------|----------|------|-------------|--------------|--------------|-------|-----------|-------------|---------------|------------| |
| | Domains | 23 | 305 | 757 | 966 | 969 | 483 | 270 | 118 | 256 | 202 | |
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| ### Data sources |
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| Some of the primary contributors to the dataset are: |
| - [UT1](http://dsi.ut-capitole.fr/blacklists/index_en.php) by the University of Toulouse Capitole (88.6%), |
| - [DQR](https://academic.oup.com/pnasnexus/article/2/9/pgad286/7258994?login=false) by Lin et al. (7.6%), |
| - Wikipedia (3.6%), |
| - [Lasser et al.]()'s data (3.1%). |
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| The full list: |
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| ``` |
| ── SAMPLED POOL: 148,830 domains ── |
| ut1 131,795 ( 88.6%) |
| DQR 11,380 ( 7.6%) |
| wikipedia 5,421 ( 3.6%) |
| lasser 4,682 ( 3.1%) |
| mbfc_raw 4,365 ( 2.9%) |
| manual 2,128 ( 1.4%) |
| iffy 2,001 ( 1.3%) |
| mbfc_questionnable 1,881 ( 1.3%) |
| checkthat 1,053 ( 0.7%) |
| legit-phish 756 ( 0.5%) |
| url-phish 564 ( 0.4%) |
| zoznam 337 ( 0.2%) |
| politifact 325 ( 0.2%) |
| dicts 266 ( 0.2%) |
| phish-dataset 170 ( 0.1%) |
| sd22_approved_software 157 ( 0.1%) |
| legal_all 154 ( 0.1%) |
| urlhaus 150 ( 0.1%) |
| health_all 148 ( 0.1%) |
| ngoreport 135 ( 0.1%) |
| paperwall 123 ( 0.1%) |
| nelez 51 ( 0.0%) |
| tools 43 ( 0.0%) |
| hasbara 19 ( 0.0%) |
| edmo_hubs 16 ( 0.0%) |
| ``` |
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| ### Geographical Distribution |
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| Political-scoped data sources largely have country attribution. In most cases, it's the country or region that the misinformation / propaganda |
| targets. In the case of coordinated disinformation campaigns, the perpetrators may also be attributed: |
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| | Country | Target | Perp | |
| |----------------------------|--------|------| |
| | USA | 3640 | 1 | |
| | Czech Republic | 360 | 0 | |
| | India | 348 | 52 | |
| | Europe | 317 | 3 | |
| | China | 241 | 206 | |
| | Global | 221 | 0 | |
| | United Kingdom | 190 | 0 | |
| | Canada | 183 | 0 | |
| | North Macedonia | 178 | 0 | |
| | Myanmar | 97 | 0 | |
| | Iran | 94 | 18 | |
| | Ghana | 82 | 0 | |
| | Ukraine | 58 | 0 | |
| | Australia | 46 | 0 | |
| | Georgia | 45 | 0 | |
| | France | 38 | 0 | |
| | Hong Kong | 36 | 34 | |
| | Israel | 34 | 143 | |
| | Russia | 34 | 551 | |
| | Germany | 28 | 0 | |
| | Africa | 27 | 0 | |
| | Japan | 26 | 0 | |
| | Italy | 24 | 0 | |
| | South Korea | 24 | 0 | |
| | Turkey | 21 | 21 | |
| | South Africa | 20 | 0 | |
| | Cambodia | 17 | 0 | |
| | Taiwan | 17 | 0 | |
| | Central African Republic | 16 | 0 | |
| | Netherlands | 16 | 0 | |
| | Pakistan | 14 | 0 | |
| | Spain | 14 | 0 | |
| | Sweden | 14 | 0 | |
| | United Arab Emirates | 14 | 0 | |
| | Switzerland | 13 | 0 | |
| | Brazil | 12 | 0 | |
| | Ireland | 12 | 0 | |
| | Egypt | 10 | 0 | |
| | Mexico | 10 | 0 | |
| | Romania | 10 | 0 | |
| | Kosovo | 9 | 0 | |
| | Philippines | 9 | 0 | |
| | Tunisia | 9 | 0 | |
| | Argentina | 8 | 0 | |
| | Austria | 8 | 0 | |
| | Belgium | 8 | 0 | |
| | Nigeria | 8 | 0 | |
| | Poland | 7 | 0 | |
| | Bangladesh | 6 | 0 | |
| | Ecuador | 6 | 0 | |
| | Greece | 6 | 0 | |
| | South Asia | 6 | 0 | |
| | Cyprus | 5 | 0 | |
| | Denmark | 5 | 0 | |
| | Finland | 5 | 0 | |
| | Indonesia | 5 | 0 | |
| | Malaysia | 5 | 0 | |
| | Venezuela | 5 | 0 | |
| | Bulgaria | 4 | 0 | |
| | Kenya | 4 | 0 | |
| | Norway | 4 | 0 | |
| | Oceania | 4 | 0 | |
| | Saudi Arabia | 4 | 0 | |
| | Thailand | 4 | 2 | |
| | Algeria | 3 | 0 | |
| | New Zealand | 3 | 0 | |
| | Serbia | 3 | 0 | |
| | Singapore | 3 | 0 | |
| | Tanzania | 3 | 0 | |
| | Albania | 2 | 0 | |
| | Armenia | 2 | 0 | |
| | Chile | 2 | 0 | |
| | Colombia | 2 | 0 | |
| | Croatia | 2 | 0 | |
| | Iceland | 2 | 0 | |
| | Iraq | 2 | 0 | |
| | Jordan | 2 | 0 | |
| | Lebanon | 2 | 2 | |
| | Lithuania | 2 | 0 | |
| | North Korea | 2 | 0 | |
| | Portugal | 2 | 0 | |
| | Slovenia | 2 | 0 | |
| | Sri Lanka | 2 | 0 | |
| | Andorra | 1 | 0 | |
| | Belarus | 1 | 0 | |
| | Beligium | 1 | 0 | |
| | Bosnia and Herzegovina | 1 | 0 | |
| | Cameroon | 1 | 0 | |
| | Costa Rica | 1 | 0 | |
| | Cuba | 1 | 0 | |
| | Estonia | 1 | 0 | |
| | Guam | 1 | 0 | |
| | Guinea | 1 | 0 | |
| | Hungary | 1 | 0 | |
| | Latvia | 1 | 0 | |
| | Luxembourg | 1 | 0 | |
| | Morocco | 1 | 0 | |
| | Puerto Rico | 1 | 0 | |
| | Qatar | 1 | 0 | |
| | Syria | 1 | 0 | |
| | Uruguay | 1 | 0 | |
| | Zimbabwe | 1 | 0 | |
| | Benin | 0 | 1 | |
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| ## `domain-pool` (full) |
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| The full dataset, with no downsampling, has a majority of datapoints sourced from ut1 (96.1%) and phishing datasets (1.8%, 0.8% and 0.7% for `url-phish`, `phish-dataset` and `legit-phish` |
| respectively). It is overwhelmingly composed of adult websites (4.6 mio), phishing ones (865k) and malware (713k). |
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| ### Domain Composition |
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| | dataset_domain | domain_count | |
| |---------------------------|-------------:| |
| | adult | 4,592,820 | |
| | phishing | 865,220 | |
| | malware | 713,376 | |
| | jobsearch | 60,732 | |
| | shopping | 36,960 | |
| | games | 34,560 | |
| | gambling | 32,233 | |
| | political | 17,025 | |
| | cryptojacking | 16,289 | |
| | bank | 6,638 | |
| | dating | 6,518 | |
| | vpn | 6,038 | |
| | press | 4,605 | |
| | publicite | 4,502 | |
| | audio-video | 3,673 | |
| | sports | 2,361 | |
| | coordinated campaigns | 2,329 | |
| | blog & forums | 1,707 | |
| | bitcoin | 1,400 | |
| | filehosting | 946 | |
| | manga | 735 | |
| | social_networks | 713 | |
| | celebrity | 666 | |
| | drogue | 615 | |
| | radio | 566 | |
| | stalkerware | 525 | |
| | educational | 510 | |
| | financial | 470 | |
| | webmail | 412 | |
| | agressif | 291 | |
| | health | 215 | |
| | chat | 209 | |
| | lingerie | 200 | |
| | translation | 173 | |
| | legal | 167 | |
| | cult | 144 | |
| | marketingware | 79 | |
| | ai | 77 | |
| | child | 75 | |
| | cleaning | 70 | |
| | mobile-phone | 52 | |
| | dangerous_material | 48 | |
| | cooking | 37 | |
| | astrology | 29 | |
| | sexual_education | 22 | |
| | educational_games | 9 | |
| | religious associations | 1 | |
| | special | 1 | |
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| ### Data Sources |
| ``` |
| |
| |
| ── FULL POOL: 5,671,355 domains ── |
| ut1 5,448,682 ( 96.1%) |
| url-phish 102,468 ( 1.8%) |
| phish-dataset 43,975 ( 0.8%) |
| legit-phish 39,164 ( 0.7%) |
| urlhaus 29,723 ( 0.5%) |
| DQR 11,380 ( 0.2%) |
| wikipedia 5,423 ( 0.1%) |
| lasser 4,682 ( 0.1%) |
| mbfc_raw 4,365 ( 0.1%) |
| manual 2,195 ( 0.0%) |
| iffy 2,001 ( 0.0%) |
| mbfc_questionnable 1,881 ( 0.0%) |
| checkthat 1,053 ( 0.0%) |
| zoznam 337 ( 0.0%) |
| politifact 325 ( 0.0%) |
| dicts 282 ( 0.0%) |
| health_all 215 ( 0.0%) |
| sd22_approved_software 191 ( 0.0%) |
| legal_all 167 ( 0.0%) |
| ngoreport 145 ( 0.0%) |
| paperwall 123 ( 0.0%) |
| nelez 51 ( 0.0%) |
| tools 46 ( 0.0%) |
| hasbara 19 ( 0.0%) |
| edmo_hubs 16 ( 0.0%) |
| ``` |
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| ### Full category counts: |
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| #### Downsampled: |
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| | dataset_domain | domain_count | |
| |----------------------------|--------------| |
| | political misinformation | 17180 | |
| | adult | 15000 | |
| | phishing | 15000 | |
| | gambling | 14993 | |
| | shopping | 14939 | |
| | cryptojacking | 14918 | |
| | games | 14905 | |
| | jobsearch | 13752 | |
| | malware | 13680 | |
| | bank | 6316 | |
| | dating | 6268 | |
| | vpn | 6030 | |
| | press | 4524 | |
| | publicite | 4424 | |
| | audio-video | 3475 | |
| | sports | 2295 | |
| | coordinated campaigns | 2248 | |
| | blog & forums | 1654 | |
| | bitcoin | 1280 | |
| | filehosting | 823 | |
| | manga | 652 | |
| | social_networks | 651 | |
| | drugs | 583 | |
| | celebrity | 565 | |
| | radio | 549 | |
| | stalkerware | 517 | |
| | educational | 477 | |
| | financial | 454 | |
| | webmail | 406 | |
| | agressif | 278 | |
| | chat | 193 | |
| | translation | 176 | |
| | legal | 171 | |
| | lingerie | 162 | |
| | health | 155 | |
| | cult | 141 | |
| | marketingware | 77 | |
| | ai | 72 | |
| | child | 70 | |
| | cleaning | 67 | |
| | mobile-phone | 47 | |
| | dangerous_material | 41 | |
| | cooking | 37 | |
| | astrology | 27 | |
| | sexual_education | 17 | |
| | educational_games | 8 | |
| | religious associations | 1 | |
| | special | 1 | |
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| <!-- pool.csv: 5671880 |
| downsampled.csv: 149086 |
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| Reliability continuous |
| ---------------------- |
| count=11980 min=0.0000 p25=0.4337 median=0.6420 mean=0.5911 p75=0.7491 max=1.0000 |
| range domains |
| [0.0, 0.1) 49 |
| [0.1, 0.2) 161 |
| [0.2, 0.3) 809 |
| [0.3, 0.4) 1252 |
| [0.4, 0.5) 1751 |
| [0.5, 0.6) 1133 |
| [0.6, 0.7) 2415 |
| [0.7, 0.8) 2969 |
| [0.8, 0.9) 1356 |
| [0.9, 1.0] 84 |
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| Reliability 3-class |
| ------------------- |
| value domains |
| low 6440 |
| high 5426 |
| medium 309 --> |
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| - **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. |
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| Data sources: |
| - [UT1](http://dsi.ut-capitole.fr/blacklists/index_en.php) by the University of Toulouse Capitole, |
| - [DQR](https://academic.oup.com/pnasnexus/article/2/9/pgad286/7258994?login=false) by Lin et al.), |
| - Wikipedia, |
| - [Lasser et al.](). |
