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A Content-Linking-Context Model for automatic assessment of web resources in “Notice-and-take-down” Procedures

A Content-Linking-Context Model for automatic assessment of web resources in “Notice-and-take-down” Procedures
A Content-Linking-Context Model for automatic assessment of web resources in “Notice-and-take-down” Procedures
The US Digital Millennium Copyright Act (DMCA) of 1998 adopted a notice-and-take-down procedure to help tackle alleged online infringements through online service providers’ actions. The European Directive 2000/31/EC (e-Commerce Directive) introduced similar liability exemptions, but did not specify any take-down procedure. Many intermediary (host, and online search engine) service providers even in Europe have followed this notice-and-take-down procedure to enable copyright owners to issue notices to take down allegedly infringing Web resources. However, the accuracy of take-down is not known, and notice receivers do not reveal clear information about how they check the legitimacy of these requests, about whether and how they check the lawfulness of allegedly infringing content, or what criteria they use for these actions. In this paper, we use Google’s Transparency Report as the benchmark to investigate the information content of take-down notices and the accuracy of the resulting take-downs of allegedly infringing Web resources. The analysis of copyright infringement is limited to the five scenarios most frequently encountered in our study of Web resources. Based on our investigation, we propose a Content-Linking-Context (CLC) model of the criteria to be considered by intermediary service providers to achieve more accurate take-down, and investigate technical issues applying the CLC Model to automatically assess web resources and output a ‘likelihood of infringement’ score.
Zhang, Pei
a881c1a8-503d-4429-be9b-6895a69be4f0
Stalla-Bourdillon, Sophie
c189651b-9ed3-49f6-bf37-25a47c487164
Gilbert, Lester
ae0a0f4e-ed80-45a8-ac1b-3d571e86f586
Zhang, Pei
a881c1a8-503d-4429-be9b-6895a69be4f0
Stalla-Bourdillon, Sophie
c189651b-9ed3-49f6-bf37-25a47c487164
Gilbert, Lester
ae0a0f4e-ed80-45a8-ac1b-3d571e86f586

Zhang, Pei, Stalla-Bourdillon, Sophie and Gilbert, Lester (2017) A Content-Linking-Context Model for automatic assessment of web resources in “Notice-and-take-down” Procedures. The Journal of Web Science, 3 (1).

Record type: Article

Abstract

The US Digital Millennium Copyright Act (DMCA) of 1998 adopted a notice-and-take-down procedure to help tackle alleged online infringements through online service providers’ actions. The European Directive 2000/31/EC (e-Commerce Directive) introduced similar liability exemptions, but did not specify any take-down procedure. Many intermediary (host, and online search engine) service providers even in Europe have followed this notice-and-take-down procedure to enable copyright owners to issue notices to take down allegedly infringing Web resources. However, the accuracy of take-down is not known, and notice receivers do not reveal clear information about how they check the legitimacy of these requests, about whether and how they check the lawfulness of allegedly infringing content, or what criteria they use for these actions. In this paper, we use Google’s Transparency Report as the benchmark to investigate the information content of take-down notices and the accuracy of the resulting take-downs of allegedly infringing Web resources. The analysis of copyright infringement is limited to the five scenarios most frequently encountered in our study of Web resources. Based on our investigation, we propose a Content-Linking-Context (CLC) model of the criteria to be considered by intermediary service providers to achieve more accurate take-down, and investigate technical issues applying the CLC Model to automatically assess web resources and output a ‘likelihood of infringement’ score.

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More information

e-pub ahead of print date: 30 June 2017
Published date: 16 October 2017

Identifiers

Local EPrints ID: 416165
URI: http://eprints.soton.ac.uk/id/eprint/416165
PURE UUID: 655089aa-0094-4dd1-9b14-484797a9f24f

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Date deposited: 06 Dec 2017 17:30
Last modified: 13 Mar 2019 19:09

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