pfaha/goodreads-books

Goodreads Books Metadata

Dataset Description

Goodreads Books Metadata is a structured dataset of book records scraped directly from Goodreads, a social platform for book readers and recommendations. The dataset was collected in July 2026 and contains rich metadata per book: bibliographic information, crowd-sourced ratings, reader engagement signals (currently reading, want to read), author-level statistics, genre tags, and descriptive text.

The primary research objectives behind this dataset are:

• Understanding how users rate books on Goodreads — what drives average ratings, what genres score highest, how reviewer volume correlates with ratings.
• Identifying the most preferred books — top-rated titles, best-sellers by engagement, most discussed books.
• Profiling the type of readers on the platform — inferred from engagement metrics such as num_want_to_read, num_currently_reading, and num_reviews.

This dataset is part of a personal data science portfolio project, demonstrating end-to-end data collection, transformation, and analysis on a real-world social platform.

• Homepage: https://www.goodreads.com
• Repository (source code): goodreads_books
• Point of Contact: pfaha (Hugging Face profile)

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Dataset Summary

Property	Value
Source	Goodreads Books (scraped)
Collection date	July 2026
Format	Parquet (partitioned)
Language	English (primary)
Storage size	~28 MB
Number of files	~100 part files

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Supported Tasks

This dataset can support various tasks, among which we identify the following:

• Book rating prediction — Predict a book's average_rating from other features in the dataset.
• Genre classification — Classify books into genre categories using description text and other metadata.
• Popularity analysis — Rank and compare books by reader engagement (num_want_to_read, num_currently_reading, num_reviews).
• Author influence analysis — Study how author follower counts and book counts correlate with book ratings.
• Reading behavior profiling — Use num_currently_reading and num_want_to_read as proxies for user interest distribution.

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Data Collection

Scraping pipeline

The dataset was collected using a custom asynchronous Python ETL pipeline. The collection process works as follows:

1. Sampling: The scraper randomly samples book IDs within a valid range to build a target list of books to fetch.
2. Fetching: For each sampled book ID, the crawler fetches the corresponding Goodreads page (https://www.goodreads.com/book/show/{id}) using session pooling and automatic rotation to avoid rate limits and blocks.
3. Parsing: Each page is validated against a hard JSON-LD gate:
   • The scraper first attempts to extract application/ld+json structured data from the page.
   • If JSON-LD is missing or empty, the page is discarded entirely — no record is created, and the crawler immediately draws a fresh random book ID as a replacement.
   • Only once JSON-LD is confirmed present does the scraper additionally pull the __NEXT_DATA__ hydration payload and raw HTML elements (via CSS selectors) to enrich fields like description, genres, format, series, num_pages, publisher, and reading-engagement counts. These are supplementary sources, never substitutes for JSON-LD.
4. Enrichment: After extracting book metadata, the crawler additionally fetches the first author's Goodreads profile page to enrich the record with their total number of published books and follower count.
5. Transformation & Load: Records are buffered in memory, schema-validated, and cleaned. Missing numeric counts are filled with zeros. Finally, records are checkpointed to the Hugging Face Hub periodically (e.g., every 1,000 books) as numbered Parquet files (books-partN.parquet).
6. Tracking: A manifest of already-scraped IDs is maintained locally and on the Hub to allow resumable, fault-tolerant runs across multiple sessions.

Politeness and rate limiting

The scraper handles rate limits smoothly through active session management. It manages concurrency and limits execution to a default of tasks per minute. It automatically triggers session rotation when encountering HTTP 429 (Too Many Requests) or HTTP 403 blocks to ensure polite and sustained extraction.

Text cleaning

All text fields undergo a standard cleaning pipeline: HTML entity unescaping, NFKC Unicode normalization, non-breaking space replacement, BOM stripping, control character removal, parenthesized segment removal, whitespace collapsing, and lowercasing.

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Dataset Structure

Data Fields

The output strictly follows the schema defined in the project:

Column	Type	Description
book_id	string	Numeric Goodreads book ID, extracted from the page URL (kept as string).
url	string	Full Goodreads URL for the book page (e.g. https://www.goodreads.com/book/show/92).
title	string	Book title as displayed on Goodreads, lowercased.
first_author	string	Name of the first (primary) author listed on the page.
first_author_url	string	Goodreads author profile URL for the first author.
first_author_num_books	int64	Number of distinct works listed on the author's Goodreads page. Proxy for author prolificacy. (0 if missing)
first_author_num_followers	int64	Number of Goodreads followers for the first author at the time of scraping. Proxy for author popularity. (0 if missing)
average_rating	float64	Crowd-sourced average star rating (1.0–5.0) aggregated across all Goodreads user ratings. (0.0 if missing)
num_reviews	int64	Total number of text reviews submitted by Goodreads users. (0 if missing)
first_published	string	First publication date as a string (e.g. "october 1, 2020"). Not normalized to a date type to preserve original granularity.
publisher	string	Publisher name for the edition referenced on the Goodreads page. null if not available.
language_code	string	Language of the edition (e.g. "eng", "en-US").
num_pages	int64	Page count for the edition. (0 if not available or bad scraping).
description	string	Full book description text from the Goodreads page, lowercased and cleaned.
genres	string	Comma-separated list of genres tags (e.g. "fiction, historical fiction, classics"). null if not available.
format	string	Publication format type (e.g. "hardcover", "paperback", "ebook"). null if not available.
series	string	Name of the literary series, if applicable. null if standalone.
num_currently_reading	int64	Number of Goodreads users who had this book marked as "currently reading" at scrape time. Engagement/opularity/demand signal. (0 if missing)
num_want_to_read	int64	Number of Goodreads users who had this book on their "want to read" list at scrape time. Engagement/opularity/demand signal. (0 if missing)

Data Splits

This dataset has no predefined train/validation/test split. All records are provided as a single collection partitioned across numbered Parquet files (books-part1.parquet, books-part2.parquet, etc.). Users should define their own splits depending on the downstream task.

Example Row

book_id                     : "1"
url                         : "https://www.goodreads.com/book/show/1"
title                       : "harry potter and the half-blood prince"
first_author                : "j.k. rowling"
first_author_url            : "https://www.goodreads.com/author/show/1077326.J_K_Rowling"
first_author_num_books      : 743
first_author_num_followers  : 235611
average_rating              : 4.58
num_reviews                 : 73768
first_published             : "july 16, 2005"
publisher                   : "scholastic"
language_code               : "eng"
num_pages                   : 652
description                 : "it is the middle of the summer, ... "
genres                      : "fantasy, fiction, young adult, harry potter, magic, audiobook, childrens"
format                      : "paperback"
series                      : "harry potter"
num_want_to_read            : 57250
num_currently_reading       : 607895

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Dataset Creation

Curation Rationale

Goodreads is a social reading platform, hosting over a million user ratings and reviews. This dataset was created to enable research into collective reading preferences, rating dynamics, and book popularity patterns as expressed by a large and diverse online community. It provides a real-world, richly annotated corpus suitable for social science research.

Source Data

All data originates from the public Goodreads web page. No authentication or account was required for access. Only publicly visible metadata was collected, and no user-identifiable information is present in this dataset.

Annotations

This dataset contains no manual annotations. All labels and values are scraped directly from Goodreads pages and reflect the community-aggregated state of the platform at the time of collection (July 2026).

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Considerations for Using this Dataset

Limitations

• Sampling bias from JSON-LD gating

Some valid Goodreads book IDs are systematically excluded from this dataset simply because their pages lack JSON-LD, independent of genre or content quality. This may introduce subtle selection bias toward more "standard" or actively maintained Goodreads listings.

• Snapshot in time

Data was scraped in July 2026. Books which are added after this date aren't taken into account. Additionally, metrics like first_author_num_books, first_author_num_followers, num_reviews, num_want_to_read, and num_currently_reading change continuously on the live platform.

• Parsing coverage

Only pages with valid JSON-LD structured data are included in this dataset; pages lacking JSON-LD are skipped and replaced with a different randomly sampled book ID, so they never appear as rows here. For pages that pass this JSON-LD gate, secondary fields sourced from __NEXT_DATA__ or raw HTML (description, genres, format, series, publisher, num_pages) may still be null or 0 or 1 if those specific selectors fail to match.

• Author enrichment

Only the first listed author is enriched with follower and book count data. Co-authors, illustrators, and secondary authors are not captured.

• No ratings distribution

Individual star ratings (1★ to 5★ breakdown counts) are not included - only the aggregated average_rating is captured.

• Social Impact

Findings derived from this data may not be representative of global reading preferences.

Licensing & Terms

This dataset was collected from publicly accessible Goodreads pages for research and educational purposes. Users of this dataset are responsible for complying with Goodreads' Terms of Service.

Do not use this dataset for commercial purposes without verifying applicable terms.

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Citation

If you use this dataset in your research or projects, please cite it as:

@dataset{pfaha_goodreads_books_2026,
  author    = {pfaha},
  title     = {Goodreads Books},
  year      = {2026},
  month     = {July},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/pfaha/goodreads-books}
}