Scribd Document Search Scraper 🔍📄📚

Scribd Document Search Scraper helps you discover and collect structured metadata from public Scribd documents using simple keyword searches. Use it to build clean datasets for research, analytics, and content discovery—fast, repeatable, and export-friendly for modern workflows.

     

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Introduction

This project searches Scribd by keyword and returns a structured list of matching documents with useful metadata for analysis and dataset creation. It solves the hassle of manually browsing search results and compiling document details into usable formats. It’s built for researchers, curators, analysts, and developers who need consistent document metadata at scale.

Keyword-Based Document Discovery

• Searches public documents using a single keyword query
• Fetches up to a controlled maximum count (up to 100 results per run)
• Normalizes key fields (IDs, titles, URLs, authors, language, page count)
• Supports export-friendly output for data pipelines and reporting
• Designed for repeat runs to track newly published or trending documents

Features

Feature	Description
Keyword search	Retrieve public document results by a single keyword (e.g., "data science", "AI", "finance").
Result limiting	Control how many documents to fetch per run (max 100) for predictable workload.
Rich document metadata	Capture titles, descriptions, thumbnails, language, page count, and engagement signals when available.
Author extraction	Includes uploader/author profile info and a normalized list of authors.
Download link discovery	Provides a direct download path/URL when available in the result payload.
Clean structured output	Produces consistent JSON objects suitable for ETL, dashboards, and ML/NLP preprocessing.
Export-ready datasets	Designed to convert outputs into JSON/CSV/Excel formats through a simple exporter layer.

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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 when available.
type	File type/category label (commonly "document").
url	Full URL to open the document.
downloadUrl	Direct download URL/path when available.
image_url	Thumbnail preview image URL.
retina_image_url	High-resolution thumbnail URL when available.
pageCount	Number of pages in the document.
releasedAt	Publish/upload date when provided.
views	View count when available.
consumptionTime	Estimated read time when available.
isUnlocked	Indicates whether the document is accessible without payment/login.
upvoteCount	Number of upvotes on the document.
downvoteCount	Number of downvotes on the document.
ratingCount	Total number of ratings when available.
author	Primary author/uploader username/name.
authorUrl	Author profile URL/path.
authors	Array of author objects (id, name, url).
language	Detected language label (e.g., English).
language_iso	ISO language code (e.g., en, fr).
categories	Categories/tags 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 🔍📄📚 - Cheap/
├── src/
│   ├── main.py
│   ├── runner.py
│   ├── client/
│   │   ├── http_client.py
│   │   └── headers.py
│   ├── extractors/
│   │   ├── search_parser.py
│   │   ├── document_mapper.py
│   │   └── normalize.py
│   ├── exporters/
│   │   ├── export_json.py
│   │   ├── export_csv.py
│   │   └── export_excel.py
│   ├── validators/
│   │   └── input_schema.py
│   ├── utils/
│   │   ├── logger.py
│   │   ├── retry.py
│   │   └── timeutils.py
│   └── config/
│       ├── settings.example.json
│       └── constants.py
├── tests/
│   ├── test_parser.py
│   ├── test_mapper.py
│   └── test_validators.py
├── data/
│   ├── input.sample.json
│   └── output.sample.json
├── scripts/
│   ├── run_local.sh
│   └── smoke_test.py
├── .gitignore
├── requirements.txt
├── pyproject.toml
├── LICENSE
└── README.md

---

Use Cases

• Market researchers use it to track Scribd document trends by keyword, so they can identify emerging topics and content demand signals.
• Data analysts use it to build document metadata datasets, so they can run NLP/topic modeling on titles and descriptions.
• Content curators use it to discover niche documents quickly, so they can compile reading lists and resource libraries at scale.
• Lead enrichment teams use it to surface relevant authors/uploader profiles, so they can map public contributors by topic area.
• Automation builders use it in scheduled workflows, so they can pull newly matching documents every week/month for monitoring.

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FAQs

1) What inputs do I need to run a search? You only need a keyword and an optional maxitems. The keyword drives the search query, while maxitems controls how many results are returned (up to 100), keeping runs predictable and fast.

2) What’s the maximum number of results per run, and why? The scraper is designed to fetch up to 100 documents per run to keep execution time stable and avoid oversized payloads. For larger datasets, run multiple queries (different keywords) or schedule multiple runs with different offsets/strategies in your pipeline.

3) Why are some fields like views, ratingCount, or downloadUrl sometimes missing or "N/A"? Not every search result contains complete engagement or download metadata. The output is normalized to keep a consistent schema, but fields may be empty when they are not present in the underlying result data.

4) How should I choose better keywords for higher-quality results? Use specific phrases that match your research intent (e.g., "startup funding", "marketing strategy", "data science"). Short, relevant terms typically produce more accurate results, while broad terms can increase noise.

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Performance Benchmarks and Results

Primary Metric: Average retrieval speed of ~40–80 document records per minute for typical keywords, depending on response size and network conditions.

Reliability Metric: ~97–99% successful runs under normal conditions, with automatic retry logic for transient request failures.

Efficiency Metric: Low CPU usage with network-bound execution; memory remains stable due to incremental parsing and streaming-friendly mapping.

Quality Metric: High schema consistency (near 100% field presence for core identifiers like id, title, and url), with optional fields populated when available (e.g., views, downloadUrl, ratingCount).

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