ClueWeb22: 10 Billion Web Documents with Rich Information
Abstract
ClueWeb22, the newest iteration of the ClueWeb line of datasets, is the result of more than a year of collaboration between industry and academia. Its design is influenced by the research needs of the academic community and the real-world needs of large-scale industry systems. Compared with earlier ClueWeb datasets, the ClueWeb22 corpus is larger, more varied, and has higher-quality documents. Its core is raw HTML, but it includes clean text versions of documents to lower the barrier to entry. Several aspects of ClueWeb22 are available to the research community for the first time at this scale, for example, visual representations of rendered web pages, parsed structured information from the HTML document, and the alignment of document distributions (domains, languages, and topics) to commercial web search.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ClueWeb22 was collected in 2022 and comprises approximately 10 billion web pages.
“ClueWeb22 was collected in 2022 and comprises approximately 10 billion web pages.”
From DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research (2025) · cites this paper as Overwijk et al., 2022
ClueWeb22-B includes around 200 million web pages, with approximately 87 million in English.
“It includes around 200 million web pages, with approximately 87 million in English.”
From DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research (2025) · cites this paper as Overwijk et al., 2022
ClueWeb22-A encompasses approximately 1 billion English pages from the mostly head of the web.
“This larger subset encompasses approximately 1 billion English pages from the mostly head of the web, offering a more diverse mix of frequently visited websites.”
From DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research (2025) · cites this paper as Overwijk et al., 2022
ClueWeb22-B approximates the super head of the web, encompassing the most frequently visited pages.
“Category B, known as ClueWeb22-B, approximates the super head of the web, encompassing the most frequently visited pages (e.g., pages from Wikipedia, major news outlets, and other top domains).”
From DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research (2025) · cites this paper as Overwijk et al., 2022
Cites
Open Domain Web Keyphrase Extraction Beyond Language Modeling
Xiong, L., Hu, C., Xiong, C., Campos, D., & Overwijk, A. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1521 · Cited by 62 · see citing works
Self-citationLess is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder
Lu, S., He, D., Xiong, C., Ke, G., Malik, W., Dou, Z., Bennett, P., Liu, T.-Y., & Overwijk, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.220 · Cited by 49 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Instruction Tuning for Large Language Models: A Survey
Zhang, S., Dong, L., Li, X., Zhang, S., Sun, X., Wang, S., Li, J., Hu, R., Zhang, T., Wang, G., et al. (2025) · ACM Computing Surveys · doi:10.1145/3777411 · Cited by 640 · see citing works
(2024) · ACM eBooks · doi:10.1145/3674127 · Cited by 82 · see citing works
Sabiá: Portuguese Large Language Models
Pires, R., Abonizio, H., Almeida, T. S., & Nogueira, R. F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45392-2_15 · Cited by 85 · see citing works
Hashemi, H., Eisner, J., Rosset, C., Van Durme, B., & Kedzie, C. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.745 · Cited by 47 · see citing works
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation
Abootorabi, M. M., Zobeiri, A., Dehghani, M., Mohammadkhani, M., Mohammadi, B., Ghahroodi, O., Baghshah, M. S., & Asgari, E. (2025) · Findings of the Association for Computational Linguistics: ACL 2025 · doi:10.18653/v1/2025.findings-acl.861 · Cited by 76 · see citing works
Chuweb21D: A Deduped English Document Collection for Web Search Tasks
Chu, Z., Sakai, T., Ai, Q., & Liu, Y. (2023) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3624918.3625317 · Cited by 21 · see citing works
TeenyTinyLlama: Open-source tiny language models trained in Brazilian Portuguese
Corrêa, N. K., Falk, S., Fatimah, S., Sen, A., & Oliveira, N. F. D. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100558 · Cited by 22 · see citing works
TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
Aliannejadi, M., Abbasiantaeb, Z., Chatterjee, S., Dalton, J., & Azzopardi, L. A. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657860 · Cited by 19 · see citing works
Zhou, X., Jia, Q., Hu, Y., Xie, R., Huang, T., & Yu, F. R. (2024) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps61880.2024.10620716 · Cited by 14 · see citing works
MS MARCO Web Search: A Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Chen, Q., Geng, X., Rosset, C., Buractaon, C., Lu, J., Shen, T., Zhou, K., Xiong, C., Gong, Y., Bennett, P., et al. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3648327 · Cited by 12 · see citing works
Self-citationRagnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track
Pradeep, R., Thakur, N., Sharifymoghaddam, S., Zhang, E., Nguyen, R., Campos, D., Craswell, N., & Lin, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88708-6_9 · Cited by 24 · see citing works
Conversational Gold: Evaluating Personalized Conversational Search System Using Gold Nuggets
Abbasiantaeb, Z., Lupart, S., Azzopardi, L. A., Dalton, J., & Aliannejadi, M. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730316 · Cited by 15 · see citing works
Zhou, X., Hu, Y., Jia, Q., & Xie, R. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3480932 · Cited by 10 · see citing works
Generative Colorization of Structured Mobile Web Pages
Kikuchi, K., Inoue, N., Otani, M., Simo‐Serra, E., & Yamaguchi, K. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00364 · Cited by 10 · see citing works
Castells, P., & Jannach, D. (2024) · ACM eBooks · doi:10.1145/3674127.3674131 · Cited by 9 · see citing works
How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
Mo, F., Zhao, L., Huang, K., Dong, Y., Huang, D., & Nie, J. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679939 · Cited by 8 · see citing works
Overview of Touché 2023: Argument and Causal Retrieval
Bondarenko, A., Fröbe, M., Kiesel, J., Schlatt, F., Barrière, V., Ravenet, B., Hemamou, L., Luck, S., Reimer, J. H., Stein, B., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42448-9_31 · Cited by 8 · see citing works
Hendriksen, G., Dinzinger, M., Farzana, S. M., Fathima, N. A., Fröbe, M., Schmidt, S., Zerhoudi, S., Granitzer, M., Hagen, M., Hiemstra, D., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56069-9_10 · Cited by 9 · see citing works
Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora
Fröbe, M., Parry, A., Scells, H., Wang, S., Zhuang, S., Zuccon, G., Potthast, M., & Hagen, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88708-6_29 · Cited by 7 · see citing works
Towards Adaptive Personalized Conversational Information Retrieval
Mo, F., Hui, Y., Tian, Y., Tan, Z., Meng, C., Su, Z., Huang, K., & Nie, J. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761255 · Cited by 7 · see citing works
Advancing General Sensor Data Synthesis by Integrating LLMs and Domain-Specific Generative Models
Zhou, X., Jia, Q., & Hu, Y. (2024) · IEEE Sensors Letters · doi:10.1109/lsens.2024.3470748 · Cited by 6 · see citing works
Making Changes in Webpages Discoverable: A Change-Text Search Interface for Web Archives
Frew, L., Nelson, M. L., & Weigle, M. C. (2023) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl57899.2023.00021 · Cited by 5 · see citing works
Investigating LLM Variability in Personalized Conversational Information Retrieval
Lupart, S., van Dijk, D., Langezaal, E., van Dort, I., & Aliannejadi, M. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3767695.3769502 · Cited by 5 · see citing works
Overview of Touché 2023: Argument and Causal Retrieval - Extended Abstract
Bondarenko, A., Fröbe, M., Kiesel, J., Schlatt, F., Barrière, V., Ravenet, B., Hemamou, L., Luck, S., Merker, J. H., Stein, B., et al. (2023) · European Conference on Information Retrieval · doi:10.1007/978-3-031-28241-6_61 · Cited by 9 · see citing works
Quati: A Brazilian Portuguese Information Retrieval Dataset from Native Speakers
Bueno, M. C., de Oliveira, E. S., Nogueira, R. F., Lotufo, R., & Pereira, J. (2024) · Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL) · doi:10.5753/stil.2024.245426 · Cited by 5 · see citing works
Assis, G., Freitas, C., & Paes, A. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5814 · Cited by 5 · see citing works
HC3: A Suite of Test Collections for CLIR Evaluation over Informal Text
Lawrie, D., Mayfield, J., Oard, D. W., Yang, E., Nair, S., & Galuščáková, P. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591893 · Cited by 4 · see citing works
Trippas, J. R. (2024) · ACM eBooks · doi:10.1145/3674127.3674135 · Cited by 4 · see citing works
Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for Deep Research
Rosset, C., Chung, H.-L., Qin, G., Chau, E. C., Feng, Z., Awadallah, A. H., Neville, J., & Rao, N. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730275 · Cited by 3 · see citing works
Variations in Relevance Judgments and the Shelf Life of Test Collections
Parry, A., Fröbe, M., Scells, H., Schlatt, F., Faggioli, G., Zerhoudi, S., MacAvaney, S., & Yang, E. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730308 · Cited by 4 · see citing works
Less LLM, More Documents: Searching for Improved RAG
Ning, J., Kong, Y., Long, Y., & Callan, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21289-4_38 · Cited by 5 · see citing works
Self-citationWeb-Scale Retrieval Experimentation with chatnoir-pyterrier
Reimer, J. H., Bevendorff, J., Fröbe, M., Hagen, T., Scells, H., Wiegmann, M., Stein, B., Hagen, M., & Potthast, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88720-8_17 · Cited by 3 · see citing works
Neural Prioritisation for Web Crawling
Pezzuti, F., MacAvaney, S., & Tonellotto, N. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744597 · Cited by 3 · see citing works
Temporal Extraction and Retrieval
Alonso, O., & Spaniol, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674137 · Cited by 2 · see citing works
Tonellotto, N. (2024) · ACM eBooks · doi:10.1145/3674127.3674130 · Cited by 3 · see citing works
Schuck, A. D. F., Garcia, G. L., Manesco, J. R. R., Paiola, P. H., & Papa, J. P. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5793 · Cited by 2 · see citing works
Weighted AUReC: Handling Skew in Shard Map Quality Estimation for Selective Search
Hendriksen, G., Hiemstra, D., & de Vries, A. P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56066-8_10 · Cited by 2 · see citing works
Junqueira, J. D. R., Lopes, É. P., M., C. L. D. S., Silva, F. L. V., Carvalho, E. A., de Freitas, L. A., & Corrêa, U. B. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650878 · Cited by 2 · see citing works
Verberne, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674141 · Cited by 3 · see citing works
Ferro, N., & Maistro, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674132 · Cited by 2 · see citing works
Privacy in Information Retrieval
Soboroff, I. M. (2024) · ACM eBooks · doi:10.1145/3674127.3674139 · Cited by 2 · see citing works
Almeida, T. S., Nogueira, R. F., & Pedrini, H. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5788 · Cited by 4 · see citing works
DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research
Coelho, J., Ning, J., He, J., Mao, K., Paladugu, A., Setlur, P., Jin, J., Callan, J., Magalhães, J., Martins, B., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.19253 · Cited by 1 · see citing works
“At the core of our framework is a free and open-source search API built upon public web snapshots comprising millions of documents, such as ClueWeb22 [22] and FineWeb [23].”
Self-citationeCommerce Search and Discovery
Kallumadi, S. T., & King, T. H. (2024) · ACM eBooks · doi:10.1145/3674127.3674140 · Cited by 1 · see citing works
The Second International Workshop on Open Web Search (WOWS)
Farzana, S. M., Fröbe, M., Granitzer, M., Hendriksen, G., Hiemstra, D., Potthast, M., de Vries, A. P., & Zerhoudi, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88720-8_55 · Cited by 1 · see citing works
JurisTCU: a Brazilian Portuguese information retrieval dataset with query relevance judgments
Fernandes, L. C., Ribeiro, L. D. S., de Castro, M. V. B., Pacheco, L. A. D. S., & Sandes, E. F. O. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-025-09881-w · Cited by 4 · see citing works
Data Preparation for Large Language Models
Liang, H., Wong, Z. H., Liu, R., Wang, Y.-H., Qiang, M.-Y., Zhao, Z.-Y., Shen, C.-Y., He, C.-H., Zhang, W.-T., & Cui, B. (2026) · Journal of Computer Science and Technology · doi:10.1007/s11390-026-5948-8 · Cited by 2 · see citing works
Galuščáková, P., Oard, D. W., & Nair, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674136 · Cited by 2 · see citing works
Gómez, E., Cuesta, H., Gkiokas, A., Gómez-Cañón, J. S., Porcaro, L., & Yesiler, F. (2024) · ACM eBooks · doi:10.1145/3674127.3702959 · Cited by 1 · see citing works
Baeza-Yates, R. A., Murgai, L., & Dudy, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674138 · Cited by 1 · see citing works
SearchLab: Exploring Conversational and Traditional Search Interfaces in Information Retrieval
Zerhoudi, S., & Granitzer, M. (2025) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3698204.3716475 · Cited by 1 · see citing works
BRoverbs - Measuring how much LLMs understand Portuguese proverbs
Almeida, T. S., Bonás, G. K., & Santos, J. G. A. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5797 · Cited by 1 · see citing works
DeepResearchGym: A Free, Transparent, and Reproducible Sandbox for Deep Research
Coelho, J., Ning, J., He, J., Mao, K., Paladugu, A. S., Setlur, P., Jin, J., Callan, J., Magalhães, J., Martins, B., et al. (2026) · Proceedings of the 2026 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR) · doi:10.1145/3805713.3820409
Self-citationAlonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3674128
Searching Personal Collections
Bendersky, M., Metzler, D., Najork, M., & Wang, X. (2024) · ACM eBooks · doi:10.1145/3674127.3674142
Query-driven Search Functionality
Li, L. (2024) · ACM eBooks · doi:10.1145/3674127.3674133
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3702962
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3674129
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3702961
Hersh, W., & Roberts, K. E. (2024) · ACM eBooks · doi:10.1145/3674127.3702960
Bast, H., Kalmbach, J., Klumpp, T., & Korzen, C. (2024) · ACM eBooks · doi:10.1145/3674127.3674134
Aroeira: A Curated Corpus for the Portuguese Language with a Large Number of Tokens
Lira, T., Cação, F. N., de Souza, C. A., Valentini, J. P. H., Bollis, E., Oliveira, O., Almeida, R., Magalhães, M. L. D., Poloni, K. M., Oliveira, A., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79029-4_13
Wang, B., Zhang, Z., Chen, L., & Li, H. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228563
Silveira, M. R., Lima, G. D., Júnior, C. A. C. S., de Freitas, L. A., & Corrêa, U. B. (2025) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia.2025.16048
Tucano em Question Answering: Exploração das capacidades do modelo no dataset FairytaleQA
Ehlert, A. D., de Freitas, L. A., & Corrêa, U. B. (2025) · Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS) · doi:10.5753/eramiars.2025.16756
Abbasiantaeb, Z., Lupart, S., Gohsen, M., Mirzakhmedova, N., Kiesel, J., Dalton, J., & Aliannejadi, M. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808591
Bridging the Gap between Subsampled and Full-Corpus Evaluation
Dinzinger, M., Dastidar, K. G., Caspari, L., Mitrović, J., & Granitzer, M. (2026) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3805713.3820404
Mirzakhmedova, N., Gohsen, M., Kiesel, J., Hagen, M., & Stein, B. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809556
Domain-specific information retrieval
Verberne, S., Piroi, F., & Hanbury, A. (2026) · Foundations and Trends® in Information Retrieval · doi:10.1108/ftinr-09-2025-0088
- Authors
- Overwijk, A., Xiong, C., & Callan, J.
- Affiliations
- Microsoft (United States) (US)
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2022-07-06
- Pages
- 3360-3362
- OpenAlex
- W4284691825
- Cited by
- 64
- References
- 2
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 31 · 2023: 7
- FWCI
- 12.97
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- Funders
- NSF (National Science Foundation) (NS-1822975 and CNS-182298)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- web corpus, HTML documents, structured data extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review