Copyright Law and Web Scraping: Can You Legally Scrape Content?

Copyright is one of the most frequently overlooked legal considerations in web scraping. While much of the public discussion around scraping legality focuses on computer fraud statutes and data protection laws, copyright can be the most significant risk for operations that collect textual content, images, or other creative works from the web.

This guide examines how copyright law applies to web scraping across major jurisdictions and provides practical guidance for staying compliant.

Copyright Basics for Scrapers

What Copyright Protects

Articles and blog posts (literary works)
Photographs and images (artistic works)
Videos and audio (audiovisual works)
Software code (literary works)
Database structures (compilations, in some cases)
Website design elements (artistic works)

What Copyright Does Not Protect

Facts and data: Raw factual information is not copyrightable. A product’s price, a company’s address, or a stock’s trading volume are facts.
Ideas and concepts: Copyright protects expression, not underlying ideas.
Short phrases and titles: Generally too short for copyright protection.
Government works: In many jurisdictions, government publications are not copyrighted.
Public domain works: Works whose copyright has expired.

The Critical Distinction

The line between protectable expression and unprotectable facts is where most scraping copyright analysis lives. Scraping a product price from an e-commerce site collects a fact. Scraping a product review collects copyrighted expression. Scraping a product description may collect either, depending on its originality.

Jurisdiction-Specific Analysis

United States: Fair Use

US copyright law provides the fair use doctrine (17 U.S.C. Section 107), which permits certain uses of copyrighted material without permission. Fair use is evaluated through four factors:

1. Purpose and character of the use

Transformative uses — those that add new meaning, expression, or purpose — are favored. Scraping content to analyze it (sentiment analysis, trend identification) is more likely transformative than scraping to republish.

Commercial use weighs against fair use but is not disqualifying.

2. Nature of the copyrighted work

Factual works receive less protection than creative works. Scraping a database of factual information is more likely fair use than scraping a collection of creative writing.

Published works are more amenable to fair use than unpublished works.

3. Amount and substantiality of the portion used

Using more of the copyrighted work weighs against fair use. However, even copying an entire work can be fair use if the purpose is sufficiently transformative (as with search engine indexing).

4. Effect on the market

If the scraping substitutes for the original work in its market, fair use is unlikely. If the scraping serves a different market or function, this factor favors fair use.

Fair Use in Scraping Case Law

Authors Guild v. Google (2015): The Second Circuit held that Google’s scanning of books for its search index was fair use. The copies were transformative because they served a fundamentally different purpose (search) than the originals (reading). This decision supports scraping for indexing and analytical purposes.

Thomson Reuters v. Ross Intelligence (2025): This case examined whether scraping legal headnotes to train an AI system was fair use. The outcome has significant implications for AI training data collection.

Various AI training lawsuits: Multiple ongoing cases (involving major AI companies) are testing whether large-scale scraping of copyrighted content for AI model training constitutes fair use. These cases will significantly shape the law.

European Union: Text and Data Mining Exceptions

The EU takes a different approach through statutory exceptions rather than a flexible fair use doctrine.

Article 3 of the Copyright Directive (Research Exception):

Applies to research organizations and cultural heritage institutions
Permits text and data mining for scientific research purposes
Cannot be overridden by contract
Requires lawful access to the content

Article 4 of the Copyright Directive (General TDM Exception):

Applies to anyone with lawful access
Permits text and data mining for any purpose
Rights holders can opt out through “appropriate means” (including robots.txt)
Can be overridden by contract for online content

Practical effect: In the EU, scraping copyrighted content for analysis is generally permitted unless the rights holder has opted out. This opt-out mechanism has driven the proliferation of AI-specific directives in robots.txt files.

United Kingdom

The UK has considered but not yet enacted a broad text-and-data mining exception. Currently:

Fair dealing exceptions exist for research, criticism, and news reporting, but they are narrower than US fair use
A proposed TDM exception has been debated but not finalized
The UK’s approach may diverge from the EU post-Brexit

Japan

Japan has one of the most permissive copyright regimes for data collection:

Article 30-4 of Japan’s Copyright Act allows use of copyrighted works for purposes that do not involve enjoying the work’s original expression
This effectively permits scraping for AI training, data analysis, and similar non-consumptive uses
The exception applies regardless of the rights holder’s wishes

Singapore

Singapore’s copyright framework includes:

Fair dealing provisions similar to (but narrower than) US fair use
A computational data analysis exception (Section 243 of the Copyright Act 2021) that permits copying for computational analysis
This exception cannot be overridden by contract
It requires lawful access and non-communication of the copies

Other Southeast Asian Jurisdictions

Thailand: Copyright law permits fair use for research, study, and news reporting. No specific TDM exception exists.

Malaysia: Fair dealing provisions exist for research, criticism, and education. No specific TDM exception.

Indonesia: Limited fair use provisions. Copyright protection is strong for creative works.

Philippines: Fair use doctrine similar to the US four-factor test.

Practical Compliance Framework

Step 1: Classify Your Content

Before scraping, categorize the content you intend to collect:

Facts and data (lowest copyright risk):

Product prices, specifications, availability
Business contact information
Stock prices, weather data
Public records

Functional content (moderate risk):

Product descriptions (may be factual or creative)
Technical documentation
Database compilations

Creative content (highest risk):

Articles, blog posts, news stories
Reviews and commentary
Photographs and images
Videos and multimedia

Step 2: Determine Your Use

How you use scraped content determines your copyright exposure:

Lower-risk uses:

Statistical analysis and trend identification
Search indexing (with snippets, not full reproduction)
Market research based on aggregated insights
Training machine learning models (jurisdiction-dependent)
Price comparison and monitoring

Higher-risk uses:

Republishing scraped content on your own platform
Creating competing products from scraped content
Reselling scraped content
Displaying substantial portions of copyrighted works

Step 3: Apply Jurisdictional Rules

Based on where the content originates and where you operate:

US operations: Apply the four-factor fair use test. Transformative, analytical uses of scraped content are more defensible.

EU operations: Check for TDM opt-outs. If the rights holder has not opted out, the Article 4 exception likely applies for analytical uses. If they have opted out, you need another basis.

Singapore operations: The computational data analysis exception may apply for analytical uses.

Multi-jurisdictional: Apply the most restrictive relevant standard.

Step 4: Respect Opt-Outs

In jurisdictions that recognize TDM opt-outs:

Check robots.txt for TDM-related directives
Review meta tags for rights reservations
Monitor terms of service for TDM restrictions
Document your opt-out compliance

Step 5: Minimize and Transform

Reduce copyright risk through:

Data minimization: Collect only the data elements you need. If you need pricing data, do not also collect product descriptions.

Transformation: Process scraped content into non-substitutable forms. Extract insights, statistics, and structured data rather than storing raw copyrighted text.

Temporal limitation: Do not maintain archives of copyrighted content beyond what is necessary for your analytical purpose.

Database Protection

Beyond individual works, databases themselves may receive protection:

US: Feist Publications v. Rural Telephone

The Supreme Court held that factual compilations receive copyright protection only if the selection, coordination, or arrangement of facts is sufficiently creative. A mere alphabetical listing of facts (like a phone book) is not copyrightable.

Implication: Scraping facts from a database generally does not infringe copyright, even if the database as a whole is copyrighted.

EU: Database Directive

The EU provides sui generis database protection for databases that required substantial investment to create. This right exists independently of copyright and protects against extraction or re-utilization of a substantial part of the database.

Implication: Even if individual data points are not copyrighted, extracting a substantial portion of a protected database may infringe the sui generis right. This is a significant risk for EU-targeted scraping operations.

Southeast Asia

Most ASEAN jurisdictions do not have EU-style sui generis database protection. However, databases may receive copyright protection as compilations if they meet originality thresholds.

Using Proxies for Copyright-Compliant Scraping

Proxy infrastructure supports copyright-compliant scraping by enabling:

Selective, targeted collection: Rather than bulk-downloading entire websites, proxy users can make targeted requests for specific data points, minimizing the copyrighted content collected.

Geographic compliance: Different jurisdictions have different copyright rules. DataResearchTools mobile proxies across Southeast Asian markets enable you to collect data in compliance with local copyright frameworks.

Rate-limited access: Respectful rate limiting demonstrates good faith and reduces the risk that scraping is viewed as systematic extraction of a protected database.

Documentation support: Request logs from proxy infrastructure provide evidence of what was accessed and when, supporting compliance documentation.

AI Training Data: The Current Frontier

The most active area of copyright law development in 2026 relates to AI training data. Key considerations:

The debate: Rights holders argue that scraping their content to train AI models infringes copyright. AI developers argue that training is transformative fair use (in the US) or covered by TDM exceptions (in the EU).

The EU position: The AI Act requires compliance with copyright law, including the TDM opt-out mechanism. If a rights holder has opted out, scraping their content for AI training is copyright infringement.

The US position: Multiple cases are pending. The outcome will depend on whether courts find AI training to be transformative use.

The practical impact: Organizations scraping web data for AI training should implement robust opt-out detection, maintain detailed provenance records, and prepare for the possibility that the legal landscape shifts.

Best Practices Summary

Focus on facts over expression: Scrape data points, not creative content
Transform rather than reproduce: Use scraped content for analysis, not republication
Respect opt-outs: Check robots.txt and meta tags for TDM reservations
Minimize collection: Collect only what you need for your stated purpose
Document everything: Maintain records of what you scraped, why, and how you used it
Monitor legal developments: Copyright law is evolving rapidly, especially regarding AI
Use compliant infrastructure: Providers like DataResearchTools support targeted, responsible data collection
Seek legal counsel: For high-value or high-risk scraping projects, get jurisdiction-specific legal advice

Conclusion

Copyright law adds a significant layer of complexity to web scraping compliance. The key insight is that not all data is created equal: facts are free, but expression is protected. By focusing on factual data, transforming content for analytical purposes, respecting opt-outs, and maintaining thorough documentation, organizations can build scraping operations that respect copyright while delivering business value.

The current evolution of AI training data law makes this an especially dynamic area. Organizations that invest in compliance infrastructure now — including both legal frameworks and technical tools like DataResearchTools — will be best positioned as the law continues to develop.