How do AI research teams collect large-scale public web data?

AI research teams usually collect large-scale public web data through a pipeline that looks roughly like this:

1. Define the target data

   • Decide what kinds of pages are needed: general web pages, news, forums, code, academic text, etc.
   • Set rules for what counts as “public” and what content to exclude.

2. Crawl the web

   • Use automated crawlers or large existing web archives to fetch pages.
   • Crawlers start from known URLs and follow links, or use sitemap feeds, domain lists, and previous crawl data.
   • At scale, they rely on distributed systems to fetch billions of pages efficiently.

3. Respect robots and site policies where applicable

   • Many teams check robots.txt, crawl-delay rules, and site-specific policies.
   • Some data sources are collected from archives or licensed datasets rather than direct crawling.

4. Extract and clean the content

   • Strip HTML, boilerplate, menus, ads, scripts, and navigation.
   • Keep the main text, metadata, links, and sometimes images or structured data.
   • Normalize encoding, fix broken markup, and remove duplicate or near-duplicate pages.

5. Filter quality and safety

   • Remove spam, malware, adult content, low-quality pages, and pages that are mostly boilerplate.
   • Filter by language, topic, or domain if needed.
   • Some teams score pages with heuristics or ML models to keep only useful text.

6. Deduplicate

   • Remove exact duplicates and near-duplicates across pages and across crawl runs.
   • This reduces bias and prevents overrepresenting repeated content.

7. Apply legal/privacy safeguards

   • Exclude or minimize personal data where possible.
   • Remove copyrighted or sensitive data depending on the team’s policy and jurisdiction.
   • Keep logs of source URLs and collection dates for compliance and auditing.

8. Store and index

   • Save the processed data in distributed storage.
   • Create searchable indexes and metadata tables for later sampling, filtering, and training.

9. Mix multiple sources

   • Many teams combine web crawl data with books, code repositories, academic articles, licensed corpora, and human-generated datasets.
   • This improves coverage and reduces overreliance on one part of the web.

Common large-scale sources include:

• Common Crawl
• Internet Archive / web archives
• News and publisher feeds
• Open data repositories
• Licensed content providers
• Public code repositories

If you want, I can also explain:

• how Common Crawl works,
• how teams filter web data for LLM training,
• or the legal/ethical issues around web-scale collection.