What dataset should I use for web crawl analysis?

It depends on what you mean by “web crawl analysis,” because different datasets fit different goals. Here are the most common choices:

1) General large-scale web crawl analysis

• Common Crawl
  • Best for: broad analysis of the web, language distribution, link structures, content trends, retrieval experiments.
  • Pros: huge, public, regularly updated.
  • Cons: noisy, deduplication and filtering are usually needed.

2) Search/relevance or ranking experiments

• Common Crawl + curated subsets
• ClueWeb / ClueWeb12 / ClueWeb22
  • Best for: IR research, ranking, document retrieval.
  • Pros: cleaned and widely used in academic search tasks.
  • Cons: access may be restricted or require agreements.

3) Measuring site structure / hyperlink graphs

• Common Crawl
• UC Web graph datasets / WebGraph subsets
  • Best for: link analysis, graph modeling, crawl frontier research.
  • Pros: can be used to study connectivity and page graphs.
  • Cons: graph extraction can be expensive.

4) Focused analysis on archived web pages over time

• Internet Archive / Wayback Machine datasets
  • Best for: temporal analysis, page change tracking, historical web studies.
  • Pros: captures web evolution.
  • Cons: access and completeness vary.

5) Language or domain-specific web text

• CCNet / OSCAR / multilingual Common Crawl-derived corpora
  • Best for: NLP, multilingual analysis, language filtering.
  • Pros: already filtered/processed.
  • Cons: less raw and less suitable if you need crawl-level metadata.

If you want one default recommendation:

Use Common Crawl unless you specifically need a cleaner academic benchmark dataset like ClueWeb.

Quick rule of thumb

• Raw web-scale analysis: Common Crawl
• Search engine research: ClueWeb
• Historical changes: Wayback/Internet Archive
• Text-only multilingual analysis: OSCAR or CCNet-derived corpora

If you tell me your exact goal — e.g. topic modeling, deduplication, link graph analysis, page quality classification, or crawl coverage — I can recommend the best dataset more precisely.