The legal boundary of data scraping*

The standard for determining the legitimacy of data scraping is vague, and the existing literature is insufficient. Jurisdictions around the world have come up with two predominant enforcement patterns, namely, the behaviour pattern and the entitlement pattern. The co-existence of the two incompatible patterns has led to conflicting judgments in China and compromised the first-mover advantage of data innovation. The behaviour pattern, despite its flaws, is more suitable for China than the entitlement pattern and should be set as the basis for future improvement. In particular, such a pattern can be modified based on the lessons learned from the entitlement pattern. A normative reconstruction of the analytical framework suggests that the standards for evaluating data scraping shall incorporate the value of datasets, the technical measures of datasets and the substantial substitution effect of the acquisition.

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Publication Details

Journal
Queen Mary Journal of Intellectual Property
Published
2025-04-29
DOI
https://doi.org/10.4337/qmjip.2025.02.04
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
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article

The legal boundary of data scraping*

Wei Liu, Liyang Hou
Queen Mary Journal of Intellectual Property
Privacy-Preserving Technologies in Data
article

The legal boundary of data scraping*

Wei Liu, Liyang Hou
article en

Abstract

The standard for determining the legitimacy of data scraping is vague, and the existing literature is insufficient. Jurisdictions around the world have come up with two predominant enforcement patterns, namely, the behaviour pattern and the entitlement pattern. The co-existence of the two incompatible patterns has led to conflicting judgments in China and compromised the first-mover advantage of data innovation. The behaviour pattern, despite its flaws, is more suitable for China than the entitlement pattern and should be set as the basis for future improvement. In particular, such a pattern can be modified based on the lessons learned from the entitlement pattern. A normative reconstruction of the analytical framework suggests that the standards for evaluating data scraping shall incorporate the value of datasets, the technical measures of datasets and the substantial substitution effect of the acquisition.

Queen Mary Journal of Intellectual PropertyVol. 15(2)
Shanghai University of Political Science and Law (CN), Tongji University (CN), Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 96%
Privacy-Preserving Technologies in Data
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