Scribd Document Search Scraper

Search Scribd by keyword and pull clean, structured metadata for every matching document — title, author, page count, view counts, ratings, language, categories, and direct reader and download links. Run many keywords in a single pass and get one tidy row per document. Built for market researchers, competitive analysts, and content and lead-gen teams who need Scribd document data at scale without paging through search results by hand.

Why This Scraper?

• Multi-keyword batch in one run — pass a list of search terms and each keyword runs as its own search, so you cover an entire topic map in a single pass instead of one query at a time.
• Up to 10,000 results per keyword — lifts the typical 100-result search ceiling 100× so you can sweep a whole topic, not just the first page.
• 22 structured fields per document — id, title, author, type, description, page count, release date, views, ratings, language, categories, and direct links — every row consumption-ready, no raw markup.
• Vote counts on 100% of rows, star scores wherever the votes exist — ratingCount, upvoteCount, and downvoteCount arrive as plain numbers on every single row, 0 included, so totals and averages never break. The derived 0–5 star score is computed from those votes, and because most Scribd uploads have never been voted on it fills on about a third of rows (629 of 1,905 across an 8-topic sample) and swings sharply by topic — 59% on service manuals, under 6% on accounting templates.
• Author profile URLs for outreach — the primary author's name lands on every row and their absolute Scribd profile link on roughly 97 rows in 100, alongside an authors array giving each contributor's id, name, and profile URL. The remainder are uploads by accounts that have no public profile page, so the name ships without a link rather than with a broken one.
• Engagement signals built in — real view counts, parsed from Scribd's "15K"/"1.2M" shorthand into plain integers you can sort and sum, so you can rank a topic by actual readership instead of search relevance.
• Reader and download-page links on 100% of rows — both absolute and ready to open: the canonical Scribd reader URL, plus a direct link that lands on Scribd's own download control for that exact document. Scribd asks you to be signed in before it hands over the file, so the link is the shortest route to it rather than the file itself. No path reconstruction, no ID guesswork.
• Result-language preference across 21 languages — bias results toward English, Spanish, Portuguese, French, German, Arabic, Hindi, Japanese, and more so you collect documents in the language your audience reads.
• Query provenance on every row — each document carries the exact keyword that surfaced it, so a single mixed dataset stays attributable per search term.

Use Cases

Market & Content Research

• Map how much Scribd content exists around a topic, product, or industry
• Surface the most-viewed and highest-rated documents in a niche
• Track templates, whitepapers, and guides circulating in your space
• Build topic libraries spanning dozens of keywords in one run

Competitive Analysis

• See which authors and brands publish the most in your category
• Benchmark engagement (views, ratings) against competing documents
• Monitor new uploads tied to a brand or product name
• Compare document depth by page count across competitors

Lead Generation & Outreach

• Collect author profile URLs and names for creator outreach
• Identify prolific publishers in a target vertical
• Build prospect lists from documents matching buyer-intent keywords
• Prioritize outreach by author reach using view counts and ratings

Academic & Reference

• Gather reference document metadata across many search terms at once
• Filter your reading list by page count and publication date before opening anything
• Prefer results in a specific language for non-English literature reviews
• Catalog community ratings to triage which documents are worth reading

Content Curation

• Power recommendation feeds and resource roundups with fresh metadata
• Enrich an existing content database with views, ratings, and categories
• Curate by the category labels Scribd has filed a document under, where it has filed one
• Feed a newsletter or knowledge base with structured document records

Getting Started

Simple — one keyword

{
    "queries": ["business plan template"]
}

Several keywords, more results each

{
    "queries": ["machine learning", "data science", "neural networks"],
    "maxResultsPerQuery": 250
}

Advanced — language preference and a deep sweep

{
    "queries": ["recetas de cocina", "plan de negocios"],
    "maxResultsPerQuery": 1000,
    "language": "4"
}

Input Reference

All fields are optional — run with just a keyword and sensible defaults handle the rest.

Parameter	Type	Default	Description
queries	string[]	["business plan template"]	One or more keywords to search on Scribd. Each keyword runs its own search — add several to cover a whole topic in one run.
maxResultsPerQuery	integer	100	How many documents to return per keyword. Set to 0 to fetch every available match. Results arrive in pages of 40, so the final page may slightly overshoot rather than cut off mid-page.
language	select	Any language	Prefer results written in a chosen language — English, Spanish, Portuguese, French, German, Italian, Dutch, Russian, Japanese, Korean, Chinese, Arabic, Hindi, Indonesian, Turkish, Polish, Danish, Romanian, Thai, Swedish, or Czech. Leave on "Any language" for no preference. Coverage depends on how much content Scribd has in that language for your keyword.

Output

Each matching document becomes one flat row. Here's a representative result:

{
    "id": "238702049",
    "title": "Sample Business Plan Template",
    "author": "Jane Author",
    "authorUrl": "https://www.scribd.com/users/12345678",
    "authors": [
        { "id": 12345678, "name": "Jane Author", "url": "https://www.scribd.com/users/12345678" }
    ],
    "type": "document",
    "description": "A complete business plan template covering executive summary, market analysis, and financials...",
    "url": "https://www.scribd.com/document/238702049/Sample-Business-Plan-Template",
    "downloadUrl": "https://www.scribd.com/document_downloads/238702049?extension=pdf",
    "imageUrl": "https://imgv2-1-f.scribdassets.com/img/document/238702049/original.jpg",
    "pageCount": 32,
    "releasedAt": "2018-04-12",
    "views": 15000,
    "isUnlocked": false,
    "rating": 4.5,
    "upvoteCount": 90,
    "downvoteCount": 10,
    "ratingCount": 100,
    "language": "English",
    "languageIso": "en",
    "categories": ["Business", "Templates"],
    "query": "business plan template"
}

Document Fields

Field	Type	Description
id	string	Scribd document identifier
title	string	Document title
type	string	Document type label as classified by Scribd
description	string	Scribd's document description, when the upload has one (around 9 rows in 10)
pageCount	integer	Number of pages, filled on 100% of rows
releasedAt	string	Publication or upload date
isUnlocked	boolean	Scribd's own unlocked-for-this-viewer flag. Results are collected without a Scribd account, so it reads false on every row
categories	string[]	Category labels Scribd files the document under, on roughly 4 rows in 10. Scribd categorises only part of its library, so this is an empty list on documents it has never filed
query	string	The search keyword that surfaced this row

Author Fields

Field	Type	Description
author	string	Primary author name (may be null)
authorUrl	string	Primary author's Scribd profile URL, filled on roughly 97% of rows; null where the uploading account has no public profile page
authors	object[]	All contributors, each with id, name, and profile url. Filled on about 98.5% of rows; empty on the older uploads Scribd no longer attributes to a profile

Engagement & Ratings

Field	Type	Description
views	integer	View count, parsed to a plain integer
rating	number	Derived 0–5 star rating from community votes. Null on a document nobody has rated yet, which is the majority: 33% of rows carried a score across a 1,905-row sample, ranging from 6% to 59% depending on the topic
upvoteCount	integer	Number of upvotes (0 when there are none)
downvoteCount	integer	Number of downvotes (0 when there are none)
ratingCount	integer	Total ratings cast, on 100% of rows (0 when there are none)
language	string	Language name
languageIso	string	ISO language code

Links & Media

Field	Type	Description
url	string	Canonical Scribd reader URL. Scribd publishes non-English documents on its matching language site, so a Spanish document reads es.scribd.com and a Portuguese one pt.scribd.com
downloadUrl	string	Direct link to this document's download page on Scribd, on 100% of rows. It opens Scribd's own download control for that file; Scribd requires a signed-in account to complete the download, so this is the entry point rather than the file itself
imageUrl	string	Cover thumbnail image URL

Tips for Best Results

• Use specific multi-word phrases to narrow large topics — a broad single word like "business" returns tens of thousands of loosely related documents, while "small business marketing plan" returns a focused, usable set.
• Batch related keywords in one run — the query field tags every row with its source keyword, so you can split one mixed dataset back out per term afterward, and the numeric id stays the same wherever a document lives on Scribd, so id is the key to dedupe and join on when you merge runs.
• Start with a small maxResultsPerQuery (40–100) to confirm the results match your intent, then raise it once you're happy with the keywords.
• Set maxResultsPerQuery to 0 only when you genuinely want the full match set — it sweeps deep and is best paired with tight, specific phrases.
• Treat language as a preference, not a hard filter — for keywords with little Scribd content in a given language, results fall back to the most available language; pair a language with a keyword written in that language for the best hit rate.
• Rank by views first, then break ties with rating — views land on 100% of rows while star scores exist on about a third, so views make the better primary sort and ratings the tie-breaker. ratingCount is on every row too, so use it to weigh how much a given star score is actually worth.
• Use pageCount to pre-screen depth before opening anything — filter out one-page stubs or zero in on long-form references in seconds, and pair it with releasedAt to drop dated material.

Pricing

From $3.50 per 1,000 results — undercuts comparable Scribd search scrapers while lifting the result ceiling 100×. No compute or time-based charges — you pay per result, plus a small fixed per-run start fee. Bronze, Silver, and Gold subscribers pay progressively less; the table below shows total cost at each discount tier.

Results	No discount	Bronze	Silver	Gold
100	$0.42	$0.40	$0.38	$0.35
1,000	$4.20	$3.95	$3.75	$3.50
10,000	$42.00	$39.50	$37.50	$35.00
100,000	$420.00	$395.00	$375.00	$350.00

A "result" is any document row in the output dataset. The fixed per-run start fee and any platform usage (storage) are additional and depend on your Apify plan.

Integrations

Export data in JSON, CSV, Excel, XML, or RSS. Connect to 1,500+ apps via:

• Zapier / Make / n8n — Workflow automation
• Google Sheets — Direct spreadsheet export
• Slack / Email — Notifications on new results
• Webhooks — Trigger custom APIs on run completion
• Apify API — Full programmatic access

Legal & Ethical Use

This actor is designed for legitimate research, market analysis, content curation, and lead generation. Users are responsible for complying with applicable laws and Scribd's Terms of Service. Only collect publicly available document metadata, respect copyright and authors' rights, and do not use extracted data for spam, harassment, or any unlawful purpose.