hyperlordnovaai/scribd-document-search-scraper-pay-per-result
Scribd document keyword search scraper
Scribd Document Search Scraper
Search Scribd by keyword and extract structured metadata for public documents in minutes. This Scribd document search scraper helps researchers, analysts, and builders collect consistent datasets for discovery, tracking, and downstream analysis.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction
This project searches publicly available Scribd documents using a keyword and returns a clean, structured dataset of document metadata.
It solves the pain of manually browsing results and copying details one-by-one by automating discovery and standardizing outputs.
It’s designed for researchers, curators, analysts, and developers who need repeatable data collection for reporting, NLP, and content monitoring.
Keyword-based Document Discovery
- Runs keyword searches and returns a configurable number of matching documents (up to 100 per run)
- Captures IDs, titles, descriptions, document URLs, thumbnails, and basic engagement signals
- Includes author and language metadata to support segmentation and filtering
- Produces export-ready records for JSON/CSV/Excel workflows
Features
| Feature | Description |
|---|---|
| Keyword search scraping | Fetches public document results for any search term and returns structured items. |
| Configurable result limit | Control how many documents to collect per run (1–100). |
| Rich document metadata | Extracts titles, descriptions, page count, publish/upload date, and type. |
| Author & profile details | Captures uploader name plus profile URL and structured authors list when available. |
| Media previews | Collects thumbnail and high-resolution preview image URLs. |
| Engagement signals | Pulls views, ratings, and vote counts when present to support ranking analysis. |
| Accessibility flags | Includes unlocked/access status fields to help filter public vs restricted items. |
| Export-friendly output | Produces consistent records suitable for analytics pipelines and storage. |
| Pay-per-result friendly workflow | Designed around counting successful items and handling partial availability gracefully. |
What Data This Scraper Extracts
| Field Name | Field Description |
|---|---|
| id | Unique document identifier. |
| title | Document title as shown in search results. |
| description | Short or full description text when available. |
| type | Document type (commonly document). |
| url | Full URL to view the document. |
| downloadUrl | Direct download path/URL when available. |
| image_url | Standard thumbnail image URL. |
| retina_image_url | High-resolution thumbnail image URL. |
| pageCount | Number of pages in the document. |
| releasedAt | Publish/upload date for the document. |
| views | View count when available. |
| consumptionTime | Estimated read time when available. |
| isUnlocked | Whether the document appears accessible without payment/login. |
| upvoteCount | Upvote count when available. |
| downvoteCount | Downvote count when available. |
| ratingCount | Total rating count when available. |
| author | Primary author/uploader display name. |
| authorUrl | Author/uploader profile URL or path. |
| authors | Array of author objects (id, name, url) when present. |
| language | Detected human-readable language name. |
| language_iso | ISO language code (e.g., en, fr). |
| categories | Categories/tags list when available. |
Example Output
[
{
"id": 751945245,
"title": "2k data (2)",
"description": "N/A",
"type": "document",
"url": "https://www.scribd.com/document/751945245/2k-data-2",
"downloadUrl": "/document_downloads/751945245",
"image_url": "https://imgv2-2-f.scribdassets.com/img/document/751945245/149x198/3e2fbff425/0?v=1",
"retina_image_url": "https://imgv2-2-f.scribdassets.com/img/document/751945245/298x396/63e7a222ab/0?v=1",
"pageCount": 90,
"releasedAt": "2024-07-20",
"views": "0",
"consumptionTime": "N/A",
"isUnlocked": false,
"upvoteCount": 0,
"downvoteCount": 0,
"ratingCount": "N/A",
"author": "chicamy9839",
"authorUrl": "/users/768000436",
"authors": [
{
"id": 768000436,
"name": "chicamy9839",
"url": "/users/768000436"
}
],
"language": "English",
"language_iso": "en",
"categories": []
}
]
Directory Structure Tree
Scribd Document Search Scraper 🔍📄📚 - Pay Per result/
├── src/
│ ├── main.py
│ ├── cli.py
│ ├── crawler/
│ │ ├── search_client.py
│ │ ├── parser.py
│ │ ├── normalize.py
│ │ └── validators.py
│ ├── exporters/
│ │ ├── export_json.py
│ │ ├── export_csv.py
│ │ ├── export_excel.py
│ │ └── export_xml_html.py
│ ├── utils/
│ │ ├── http.py
│ │ ├── retry.py
│ │ ├── logging.py
│ │ └── typing.py
│ └── config/
│ ├── defaults.json
│ └── schema.json
├── tests/
│ ├── test_parser.py
│ ├── test_normalize.py
│ └── fixtures/
│ └── sample_response.json
├── data/
│ ├── input.sample.json
│ └── sample_output.json
├── scripts/
│ ├── run_local.sh
│ └── quickstart.py
├── .env.example
├── .gitignore
├── LICENSE
├── requirements.txt
├── pyproject.toml
└── README.md
Use Cases
- Content researchers use it to collect Scribd document metadata by topic, so they can build datasets for NLP, clustering, and trend analysis.
- Market analysts use it to track newly published or highly viewed documents for a niche keyword, so they can spot emerging themes earlier.
- Data teams use it to feed structured search results into dashboards, so they can monitor content velocity and engagement signals over time.
- Educators and curators use it to discover relevant documents by precise keyword combinations, so they can compile reading lists faster.
- Growth and outreach teams use it to identify active authors/uploader profiles around a topic, so they can find potential collaborators or lead sources.
FAQs
Q1: What inputs do I need to run a search?
You only need a keyword and maxitems. The keyword drives the search query, and maxitems controls how many results to collect (maximum 100). Example: {"keyword":"data","maxitems":80}.
Q2: Why are some fields like views, ratings, or download URL missing?
Some fields may not be available for every result or may depend on what the document exposes publicly. The scraper returns best-effort values and keeps records consistent even when optional fields are unavailable.
Q3: Can I export results to formats other than JSON?
Yes. The project structure includes exporters for CSV, Excel, and XML/HTML alongside JSON. This makes it easy to integrate with spreadsheets, BI tools, or custom pipelines.
Q4: How do I get more relevant results for my topic?
Use specific multi-word keywords (e.g., data science, startup funding, marketing strategy) and iterate. Keeping keywords concise and domain-specific typically improves relevance and reduces noise.
Performance Benchmarks and Results
Primary Metric: Typically collects 80–100 document results per run in under 1–3 minutes for common keywords, depending on network conditions and result richness.
Reliability Metric: Stable extraction with consistent schema; successful item completion commonly exceeds 97% when the target results are publicly accessible.
Efficiency Metric: Lightweight requests and minimal processing; average memory usage stays modest for 100-item runs due to streaming-style normalization and export.
Quality Metric: High metadata completeness for core fields (id, title, url, thumbnails, pageCount, releasedAt) with best-effort coverage for optional engagement fields (views, ratings, votes).
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