ABSTRACT
Web scraping, a method frequently employed in data analysis, artificial intelligence, and big data projects, refers to the automated collection of data from the internet. However, the legal framework surrounding this practice raises various legal debates, particularly in the fields of intellectual property rights and unfair competition.
I. INTRODUCTION
Websites host a vast pool of data stored in various formats and accessible to the public on the internet. Publicly available data can be accessed by any user with an internet connection. Although it is possible to collect data from the internet using traditional methods, such as manually copying and pasting information piece by piece, this approach becomes impractical when dealing with large datasets due to significant disadvantages in terms of time, cost, and error rates. Therefore, techniques and technologies known as web scraping have been developed to systematically and automatically retrieve large-scale data from the internet1. Through this method, data can be collected rapidly, accurately, and efficiently from numerous websites and stored for future analysis.
In recent years, the rapid advancement and widespread adoption of big data analytics, machine learning, and artificial intelligence technologies across various sectors have significantly expanded the application areas of web scraping. Web scraping has become an indispensable tool for institutions, businesses, and researchers seeking to analyze the vast amounts of data available on the internet and extract meaningful insights from it. The ability to easily collect different types of data from multiple sources has enabled data analysis processes to be conducted more quickly, comprehensively, and efficiently.
Although there is no specific legal regulation regarding web scraping under Turkish law, this legal gap is addressed through the interpretation and application of existing legal provisions. The content and structure of websites may, under certain conditions, benefit from protection as a work or database under the Law on Intellectual and Artistic Works. On the other hand, unauthorized use of data produced or compiled by a business may be evaluated within the framework of unfair competition provisions regulated under the Turkish Commercial Code. In this context, the legal assessment of web scraping processes is crucial to ensuring the protection of data owners’ rights and compliance with relevant legal requirements.
II. WEB SCRAPING METHOD
A. Definition
Web scraping is a method that enables the extraction of data available on the internet using automated tools and software, allowing the collected information to be stored in digital environments such as files or databases2. As a fundamental stage of text and data mining (“TDM”) processes, this technique systematically aggregates scattered information from web pages and makes it analyzable, thereby facilitating big data research3. It is one of the most critical technologies used to derive meaningful insights from large scale datasets. This technique employs bots or programs that analyze the HTML structure of a website and extract the desired data4. Web scraping methods range from simple scripts to fully integrated embedded browser engines. Artificial intelligence models require extensive and high-quality datasets to function effectively. At this point, web scraping serves as a primary method for collecting raw data in both TDM and artificial intelligence processes. Through web scraping, large-scale datasets can be automatically gathered, processed, and analyzed from different websites.
Fundamentally, web scraping refers to the process of collecting data from websites and typically aims to automatically extract, organize, and convert the information available on web pages into a usable format5. Compared to manual data entry, which is time-consuming and prone to errors, web scraping enables the rapid and efficient collection and processing of large volumes of data.
From a technical and methodological perspective, the web scraping process consists of two levels. The first level, known as “semantic scraping,” focuses on interpreting the meaning of the collected data and analyzing its content. This method seeks to determine what the extracted data signifies and relate it to broader, meaningful information6. The second level, known as “syntactic scraping,” defines how the data will be obtained and what techniques will be used for extraction, concentrating on where the data is located and how it should be retrieved7. When evaluated together, these two approaches reveal that the web scraping process is a multidimensional procedure encompassing both the interpretation of the content’s meaning and the technical aspects of data extraction. The first approach addresses the what question, while the second focuses on the how question. The web scraping process primarily consists of two stages: first, accessing web resources and retrieving the relevant data, and second, processing the collected data to extract the desired information8. In particular, unauthorized or automated large-scale data extraction may infringe upon the rights of the data owner and could be considered within the scope of unfair competition. However, in cases where the data is publicly available or the data owner has given consent, web scraping activities may be deemed legally permissible.
Websites serve as platforms that provide users with access to text, images, and other content via the Internet, while databases are structured systems designed to store and manage data. Since databases are often encrypted and protected, direct access to them through web scraping is generally not possible. However, as websites reflect information retrieved from their underlying databases, web scraping extracts this content through web pages. This situation gives rise to various legal issues that should be examined within the framework of the Law on Intellectual and Artistic Works No. 5846 (“LIAW”) in terms of the legal nature of the collected content. Particularly, since digital databases, serving as the primary data source for websites, are subject to various protection mechanisms under intellectual property law, the legal dimensions of web scraping activities must be evaluated within this context.
B. Database Protection
LIAW provides a dual protection regime for databases, comprising copyright protection and sui generis rights. According to Article 6/I-11 of LIAW, databases that reflect the originality of the author due to the selection or arrangement of their content are protected under copyright law. If a database does not possess such a level of originality, that is, if the data has been compiled arbitrarily or merely constitutes a simple list, it is not entirely without protection. LIAW, influenced by the EU Database Directive (96/9/EC)9, also grants sui generis protection for databases. Under supplementary article 8 of LIAW, databases are protected under sui generis rights if a substantial amount of labor, time, and financial resources has been invested in their compilation, verification, or presentation, regardless of whether they exhibit creative originality. In particular, the systematic largescale extraction of data from databases protected by copyright or sui generis rights mayconstitute an infringement of intellectual property rights. Therefore, when conducting web scraping activities, it is crucial to determine the type of protection applicable to the targeted databases.
1. Protection Under Copyright Law
Whether the content collected through web scraping constitutes copyright infringement depends on whether the extracted content or compilations qualify as a protected work. Pursuant to Article 1/B-a of the LIAW, a work must bear the originality of its author and fall within one of the work categories specified in the law. Texts, images, graphic designs, data compilations, and other elements found on websites may benefit from copyright protection either individually or as a whole, provided they meet these criteria. Therefore, copying such copyright-protected content from a website via web scraping without the author’s permission may constitute an infringement of process, reproduction, or distribution rights. However, not all website content qualifies as a copyright-protected work under intellectual property law. Since web scraping is generally used for collecting large volumes of data, the extracted information often consists of raw data, such as user reviews, square meter or price information in real estate listings, or other factual data. In such cases, direct copyright protection is not applicable.
Nevertheless, when considering database protection as a whole, copyright infringement may arise. Article 6/I-11 of LIAW states that “databases that result from the selection and compilation of data and materials according to a specific purpose and a particular plan, which are readable by a device or in other forms,” may be protected as compilation works. This protection applies not to the data itself but rather to the database as a derivative work10. Under this provision, whether copyright protection is granted depends on the way data and materials are selected and compiled, there must be a distinction between random compilation and compilation based on a specific purpose and plan. To qualify for copyright protection, a database must also include an element of intellectual creativity, as required by Article 1/B-I-d of LIAW, which defines compilations as works resulting from creative thought. Therefore, Article 6 of LIAW protects only those databases that bear the originality of their author11.
In this context, when extracting content from a website via web scraping, it is essential to assess not only individual data points but also whether the database as a whole fall under copyright protection. If a database reflects the originality of its owner and consists of carefully selected and organized content for a specific purpose, web scraping activities may constitute copyright infringement. Conversely, if the extracted content consists solely of raw data without originality, or if the database lacks distinctiveness, scraping activities may not directly infringe copyright protection.
2. Sui Generis Protection
In 2004, an additional provision, supplementary article 8, was incorporated into the LIAW to protect databases that, while not inherently original, involve substantial labor and financial investment in their creation. This provision grants a sui generis right to the database producer, defined as: “A person who has made a substantial qualitative and quantitative investment in the creation, verification, or presentation of a database shall be granted sui generis rights and protection over such a database.” Accordingly, the unauthorized reproduction, distribution, or communication to the public of all or a substantial part of a database’s content is prohibited. To safeguard the efforts and financial investment of database producers, supplementary article 8 of LIAW provides a special legal framework granting sui generis protection12.
To qualify as a sui generis database, a substantial investment must be made, with priority given to investments in content creation. The legal doctrine defines the concept of content creation as: “The obtaining of the content refers to the act of collecting works, data, information, or other materials for publication within the database”13. In the Fixtures Marketing v. Svenska Spel AB case, the Court of Justice of the European Union (CJEU) ruled that failing to meet the substantial investment criterion disqualifies a database from sui generis protection. The court emphasized that: “…investment in ... the obtaining ... of the contents’… resources used to seek out existing independent materials and collect them in the database, and not to the resources used for the creation as such of independent material”14. This ruling highlights that resources used for generating data (e.g., software, technical infrastructure, etc.) do not fall within the scope of sui generis protection. As a result, not all website content qualifies for sui generis database protection.
Within this framework, sui generis protection aims to safeguard the investment made by the database producer in content creation, verification, and presentation. Consequently, web scraping activities should also be assessed under this protection framework.
However, collecting only raw data, using publicly available information, or engaging in activities that do not require significant economic investment may not always be considered a violation of sui generis rights. Thus, web scraping practices must be carefully examined within the scope of both copyright protection and sui generis database protection to ensure compliance with legal regulations.
III. EVALUATION OF WEB SCRAPING UNDER UNFAIR COMPETITION PROVISIONS
The legal assessment of web scraping activities is not limited to intellectual property law but must also be considered within the framework of unfair competition regulations. Under Turkish law, the provisions on unfair competition are regulated in Article 57 of the Turkish Code of Obligations Law No. 6098 (“TCO”) and Articles 54-63 of the Turkish Commercial Code Law No. 6102 (“TCC”). Article 57 of the TCO governs non-commercial acts of unfair competition and is relatively narrow in scope. In contrast, the provisions in the TTK are drafted in great detail and apply to all cases of unfair competition, regardless of whether the activity is of a commercial nature15.
A. Unfair Competition Within the Scope of Article 54/2 of the TCC
Article 54 of the TCC provides a general framework for the provisions concerning unfair competition. In its first paragraph, the fundamental objective of the provisions on unfair competition is stated as ensuring fair and undistorted competition for the benefit of all participants in the market.
Article 54/2 of the TCC further elaborates on this framework. According to this provision, for an act to be considered within the scope of unfair competition, the following conditions must be met: (i) There must be a behavior or practice that affects commercial life, (ii) This behavior or practice must be misleading or otherwise contrary to the principle of good faith, and (iii) The relationships between competitors or between suppliers and customers must be affected16. While Article 54/2 serves as a general provision outlining the principle of unfair competition, Article 55. of the TCC constitutes a special provision, enumerating specific behaviors and practices that violate the principle of good faith17. Consequently, the lawfulness of an act will be assessed in light of the principle of good faith. Within this framework, web scraping may be evaluated under unfair competition provisions if it meets the necessary conditions.
Web scraping, particularly when conducted on a large scale, can have a direct impact on commercial life. The collection and use of another business’s data by competitors or third parties may alter market dynamics. If the use of data obtained through web scraping results in the loss of a data-owning business’s marketing advantage, reputational damage, or weakened customer relationships, such an act may constitute unfair competition. However, each instance of web scraping must be assessed individually, taking into account the specific context and the manner in which the data is utilized. Therefore, determining whether unfair competition has occurred will critically depend on factors such as the method of data collection, how the data is used, whether its use has an impact on commercial life, and how it affects the balance between competitors, suppliers, and customers.
B. Unauthorized Exploitation of Others’ Work Products
If the act of web scraping meets the conditions set forth in TCC Article 54, it may constitute unfair competition. Additionally, it may specifically qualify as an act of unfair competition under TCC Article 55. Indeed, Article 55/1-c explicitly classifies the “unauthorized exploitation of others’ work products” as an instance of unfair competition. The unauthorized acquisition of third parties’ digital content through web scraping may, if the necessary conditions are met, be deemed an act of unfair competition.
The rationale of the law clarifies that intellectual property rights, which are specifically protected under legal provisions, do not fall within the scope of this provision. Independently of legal protection, this provision seeks to safeguard elements that are critical to business, operational, and production processes, such as offers, calculations, and plans, against unauthorized use. Furthermore, according to the rationale of the law, this provision represents an extended application of the “labor principle,” which forms the foundation of unfair competition law and is intended to prevent unjust gains derived from the labor, knowledge, and experience of others. In this context, the concept of “exploitation” refers to the unauthorized use of data obtained through the efforts of another party to gain an economic advantage. Indeed, the primary objective of unfair competition regulations, and specifically TCC Article 55, is to uphold the principle of protecting the labor of individuals and businesses. In this regard, the Competition Authority, in a decision concerning a similar case related to online second-hand book sales, emphasized that the data entered by users were “voluntarily provided and publicly accessible.” Moreover, it was noted that the platform hosting these data did not hold any proprietary rights over them, and therefore lacked the authority to restrict access18.
In legal doctrine, a “work product” is defined as “a product that is not specifically protected under intellectual or industrial property law but is nonetheless created through labor and holds significance in business, operational, and production contexts, such as offers, calculations, and plans”19. TCC Article 55/1-c enumerates various cases of unauthorized exploitation. In the context of web scraping, unauthorized exploitation may be associated with the provision stating that: “3. Without making an appropriate contribution, acquiring and utilizing another’s market-ready work products through technical reproduction methods”. For unfair competition to be established under subparagraph c-3, three conditions must be met: (i) The work product must be market-ready. (ii) The exploitation must occur through the acquisition of the work product via technical reproduction methods. (iii) The party benefiting from the work product must not have made an appropriate contribution to it20.
When analyzed under TCC Article 55/1-c, web scraping activities may pose legal risks, particularly concerning the unauthorized exploitation of others’ work products. If data that have been compiled and developed through significant investment by a website, data that are market-ready and possess economic value, are collected and used without permission via scraping methods, this may constitute unfair competition. In this context, critical factors to consider include the methods by which the scraping party acquires the data, whether the scraping party has made any contribution to the data, and whether the data qualifies as market-ready work products. Additionally, whether the data are publicly accessible and the extent of the effort involved in their creation must be assessed on a case-by-case basis to conduct a thorough legal analysis.
C. Cases Where Web Scraping May Not Constitute Unfair Competition
Although web scraping, particularly when used for commercial purposes, may be considered an act of unfair competition, it may be deemed lawful in certain exceptional circumstances. To determine whether web scraping constitutes unfair competition, factors such as the nature of the data, the consent of the data owner, and the intended use of the collected data must be taken into account. In this context, web scraping may not be considered unfair competition in specific situations where these factors justify its legality.
1. User-Generated Content and Hosting Providers
Law No. 5651 on the Regulation of Publications on the Internet and Combating Crimes Committed Through Such Publications (“Law No. 5651”) outlines the responsibilities related to content shared online. According to Article 2 of this law, a hosting provider is defined as: “A natural or legal person who provides or operates systems that host services and content.” A review of Law No. 5651 reveals that hosting providers do not supply content themselves and have no ownership or direct responsibility over the user-generated content hosted on their platforms. The legal doctrine further clarifies the role of hosting providers as follows: “Hosting providers do not generate content themselves and are therefore distinct from content providers. Similarly, hosting providers differ significantly from access providers, as access providers merely facilitate the transmission of data in real time without the ability to control its content, whereas hosting providers offer a continuous data/information storage service. As a result, data and information stored on the hosting provider’s server are made accessible to Internet users (Feral-Schuhl, 2018: 1313)”21.
Since the original rights holders of the data published on a given website are the users themselves, it cannot be said that the hosting provider has directly contributed labor to the creation or dissemination of such content. Hosting providers merely offer technical infrastructure for users to create and share content; they do not play an active role in content creation, modification, or direct supervision. Thus, the ownership of data stored on such platforms remains with the users who produce the content, and as a result, the legality of web scraping in such cases may not always be considered unlawful.
2. Publicly Available Data and Open Data Initiatives
In the context of web scraping, another important issue is publicly available data and the concept of open data. Open data is defined as data that (i) is accessible via the internet, (ii) is open for reuse and redistribution, and (iii) can be used by anyone, regardless of identity or affiliation22. In recent years, there has been an increasing trend of making data produced by public institutions or public interest projects freely accessible to benefit society. For example, legislative texts published in the Official Gazette, Supreme Court of Appeals decisions23, and Turkish Grand National Assembly (TBMM) records are not subject to copyright or database protection. Therefore, collecting, organizing, and utilizing such information through software tools is legally permissible. Publicly available data has been released for the benefit of society to support innovation and increase transparency. As a result, web scraping is not always a negative act but can also be considered a natural component of the data-sharing ecosystem. Indeed, in a case brought before the U.S. Ninth Circuit Court of Appeals, it was ruled that data collection from publicly accessible sources does not constitute an illegal activity. In the lawsuit filed by LinkedIn against HiQ Labs, the court determined that terms of service prohibiting data collection or unauthorized data extraction do not apply to publicly accessible data. The court held that a bot crawling a publicly available website does not constitute “unauthorized access” since there was no password protection or restricted access24.
However, it is important to note that not all “publicly available” data is legally unrestricted. Data may be accessible on the internet, but this does not necessarily mean it qualifies as open data. For example, research articles published by a university may be accessible to everyone on its website; however, these articles are protected by copyright and cannot be collected and distributed in bulk unless explicitly licensed for open access. Similarly, social media posts may be visible on public profiles, but they remain the intellectual property of the users and are subject to the platform’s terms of service. Therefore, it is essential to distinguish between data that is intentionally released for public benefit and data that is merely publicly accessible but still legally restricted.
3. Consent of the Rights Holder
Another situation where web scraping may be considered legitimate use is when it is carried out with the consent of the rights holder. Some websites may partially or fully permit the use of their data. For instance, platforms that provide an Application Programming Interface (“API”) for developers may allow data extraction under certain conditions25. An API is an interface that enables one software to communicate with another26. If a website owner provides an alternative method for accessing data, such as an API, it is best practice to retrieve data through this method instead of direct scraping. However, API access still implies permission for data use, albeit under specific limitations. In internet culture, certain “gentlemen’s agreements between robots and humans”27 exist, but legal gaps must be addressed to prevent unfair competition. Some websites indicate which bots are allowed or restricted through robots.txt files or terms of service. While search engine bots are generally permitted, other data scraping bots may be restricted. Many websites use a robots.txt file to specify which sections can or cannot be crawled by bots. This is a technical protocol and does not carry legal enforcement. Although ignoring robots.txt does not have directly legal consequences, the presence of such restrictions indicates the website owner’s intent. Similarly, if a website’s terms of service explicitly prohibit the automated extraction of content, this signals that the site owner does not consent to data collection. If a website owner is explicitly opposed to sharing data and clearly states this, scraping their content may not constitute a direct legal violation but could be seen as bad faith. In the event of a legal dispute, a court may consider this a negative factor against the scraper.
4. Abuse of Dominant Position
The Law on the Protection of Competition (Law No. 4054) defines the dominant position as: “The power of one or more undertakings in a particular market to determine economic parameters such as price, supply, production, and distribution quantities independently of their competitors and customers.” The law prohibits the abuse of this dominant position. If companies holding a dominant position use web scraping to restrict competitors’ access to data or suppress competition, this may give rise to legal issues. A dominant firm may strengthen its market power by preventing its competitors from collecting data from its systems. Such actions could make it more difficult for competitors to enter the market and limit consumer choices. Given that data is a fundamental element of the digital ecosystem, preventing competitors from collecting data through web scraping could lead to market imbalance and an increased risk of monopolization.
Indeed, in a Competition Authority decision, an online second-hand book sales platform was found to have restricted sellers’ access to their own book data and denied data portability requests without valid justification28. Additionally, sellers who transferred their data to competing platforms were suspended from the platform, and their accounts were only reactivated after removing the data from rival platforms. The Competition Authority ruled that such restrictions on data portability constituted an abuse of the dominant position under competition law, as they created market exclusivity, hindered competitors’ operations, and acted as a barrier to market entry. Thus, digital data restrictions will be deemed lawful only as long as they do not violate competition rules or constitute an abuse of dominant position. Therefore, it is crucial to define the legal boundaries of web scraping practices to ensure compliance with competition law.
IV. ARTIFICIAL INTELLIGENCE AND WEB SCRAPING
Advancements in artificial intelligence (“AI”) have introduced a new dimension to web scraping discussions. Modern AI models, particularly those based on machine learning and deep learning algorithms, are predominantly developed using large-scale datasets. As a result, the vast pool of data available on the internet has become a natural resource for AI researchers and corporations. However, while web scraping provides technical convenience for AI-driven data collection, it also raises complex legal questions.
AI models require enormous volumes of data to enhance their performance. For instance, a language model in natural language processing must be trained on billions of words to generate human-like responses. Similarly, an image recognition system must analyze millions of images to achieve high accuracy in object detection. Such data is typically scattered across the internet and compiled from multiple sources. Web scraping plays a crucial role in structuring and collecting this vast, unorganized data. AI developers often scrape publicly available data sources, such as news websites, encyclopedia entries, social media posts, forums, and academic articles, to create training datasets. This approach has significantly accelerated AI research. For example, it is well known that OpenAI’s GPT-series models were trained using massive text corpora collected from the internet. Similarly, in the field of image processing, the widely recognized ImageNet dataset was compiled through large-scale aggregation of online images. However, the inherently data-hungry nature of AI models raises a critical question: How can such vast amounts of data be obtained lawfully? Not all data sources can be freely used due to potential legal restrictions, such as copyright laws, privacy regulations, and terms of use agreements. As competition in AI development intensifies, companies often push the boundaries of data collection practices, leading to legal disputes over data usage.
The increasing prevalence of web scraping reinforces an environment of unfair competition. Large technology companies extensively scrape data to train their AI models, while smaller firms and independent researchers may struggle to access data at the same scale. Additionally, content creators may suffer financial losses due to the unauthorized use of their original content. This situation can lead to data-driven monopolization, as companies that build large-scale datasets through web scraping may distort the competitive market by hoarding their data for exclusive use, rather than making it openly accessible. Such a scenario would provide them with an unfair competitive advantage. If legal loopholes are not addressed and market balance is not maintained, innovation could stagnate, and new startups may lose their ability to compete effectively. Therefore, legal regulations must be established to preserve fair competition. The European Union
(EU) has introduced text and data mining (TDM) exemptions that support AI research while also safeguarding the rights of data owners. Similarly, in the United Kingdom, legislative efforts are underway to balance the commercial use of data mining technologies. However, it is crucial that such regula tions extend beyond academic research to commercial AI developers as well. The imple - mentation of licensing mechanisms, usage permissions, and transparency obligations is essential to clearly define the boundaries of web scraping. These measures will not only protect content creators’ rights but also en - sure fair access to data within a competitive market. To foster a digital ecosystem that supports innovation, the introduction of legal regulations is inevitable.
V. CONCLUSION
Web scraping is a crucial technique that supports the datadriven structure of the digital age; however, it also raises numerous legal debates. In particular, when evaluated within the framework of unfair competition and intellectual property rights, it becomes evident that defining the legal boundaries of scraping activities is a pressing necessity. Although there is no specific legal regulation regarding web scraping under Turkish law, it is assessed within the existing legal framework, particularly in relation to intellectual property rights and unfair competition regulations. Depending on the data owner’s consent, scraping activities may be deemed lawful. However, the unauthorized collection of copyrightprotected content, the violation of sui generis database protection, or the misuse of scraped data in a way that harms competitors’ business models may render such activities legally impermissible. Additionally, under the TCC, unfair competition provisions require an evaluation of how scraping practices impact market competition. Nonetheless, in exceptional cases, such as the collection of publicly available data, the explicit consent of the data owner, or the prevention of the abuse of dominant market positions, the lawfulness of web scraping will be assessed based on the specific circumstances of each case. AI and big data technologies continue to expand, the scope of web scraping applications is also increasing. Consequently, legal regulations must adapt to this digital transformation. In this context, the development of Turkish legislation and the establishment of clearer legal frameworks for web scraping will not only enhance data security but also support the sustainable growth of the digital economy.
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