SNEWPapers

What is SNEWPapers?

SNEWPapers is an AI-powered newspaper archive and research platform focused on American history, offering access to millions of historical newspaper stories drawn from the 1730s through the 1960s. Its core purpose is to help users search and explore newspapers in ways that go beyond keyword matching.

The platform uses AI to extract and organize stories from a large collection of newspaper titles, and it provides tools to discover articles by meaning, filter by categories, and support research workflows with an assistant that can answer questions with citations.

Key Features

• AI-powered semantic search: Search by meaning (concepts, events, and themes) rather than relying only on exact keyword matches.
• Category and sub-category browsing: Use 24 categories and 1,000+ sub-categories to narrow results, including state and date filters.
• Large historical archive coverage: Explore 6M+ stories spanning 250 years of American history, sourced from 3,000+ newspaper titles.
• Collections and discovery: Build curated collections, browse public collections from other researchers, and find connections across different time periods.
• The Sleuth research assistant: Ask research questions and receive answers with citations, with the assistant able to dig through the archive.
• Today in History timeline: View a daily, curated timeline of what happened on that date, sourced directly from the newspapers.

How to Use SNEWPapers

1. Start with search: Enter a question or topic to find relevant articles using semantic (meaning-based) search.
2. Refine results: Narrow what you see using category/sub-category filters, plus the available state and date filters.
3. Explore and save: Open articles and organize your findings into collections. You can also browse public collections created by other researchers.
4. Ask The Sleuth: If you want help synthesizing findings, ask The Sleuth a research question and review the cited answers.
5. Use daily context: Check Today in History for a quick, newspaper-sourced timeline tied to the current date.

Use Cases

• Researching a historical event without perfect keywords: When you don’t know the exact phrasing used in old newspapers, search by the event or theme and then filter to the relevant time and place.
• Tracing a concept across decades: Use categories and sub-categories to follow how a topic appears across different newspaper coverage over a longer period.
• Building an evidence-backed historical narrative: Create a collection of articles, then ask The Sleuth to help summarize and connect sources with citations.
• Comparing regional coverage: Use state and date filters to focus on how newspapers in different locations reported the same or related events.
• Curating starting points for study: Use curated collections (including public collections from other researchers) to quickly identify promising articles for a specific research angle.

FAQ

• What time period does SNEWPapers cover? The platform is described as covering the 1730s to the 1960s.

• How is SNEWPapers different from standard keyword search? It uses AI-powered search by meaning, helping find articles about concepts and themes even when the exact words don’t appear.

• What kinds of filters can I use? The site describes filters based on 24 categories, 1,000+ sub-categories, plus state and date.

• Does SNEWPapers include tools to help summarize findings? Yes. It includes The Sleuth, an AI research assistant that provides answers with citations after digging through the archive.

• What is “Today in History” on SNEWPapers? It’s a daily curated timeline of what happened on that date, sourced directly from the newspapers.

Alternatives

• General web search (e.g., Google): Useful for broad discovery, but it is not an AI-organized, newspaper-specific archive designed for semantic searching across a structured historical collection.
• Chat-based AI tools: Can help draft answers, but the source content described for SNEWPapers (organized, extracted newspaper stories) is intended to be searched within the archive rather than relying on chat output.
• Digital newspaper databases and archive platforms: These typically focus on search within newspaper collections; compare whether they support semantic/meaning-based search, strong category filtering, and research workflows like cited Q&A.
• Library and historical research tools: Useful for academic research, but may not offer the same AI semantic search and archive-specific organization described for SNEWPapers.