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A content-linking-context model and automatic copyright verification in the notice-and-take-down procedures

A content-linking-context model and automatic copyright verification in the notice-and-take-down procedures
A content-linking-context model and automatic copyright verification in the 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. European Directive 2000/31/EC (e-Commerce Directive) introduced a set of liability exemptions similar to the one found in the DMCA, but did not specify any take-down procedure. Many intermediary (hosts and online search engines) 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, whether and how they verify the lawfulness of allegedly infringing content, and what criteria they use for these actions. Google’s Transparency Report is used as the benchmark to investigate the information content of take-down notices and to assess the accuracy of the resulting take-downs of allegedly infringing Web resources. Based on the investigation, a Content-Linking-Context (CLC) Model which identified the criteria to be considered by intermediary service providers to achieve more accurate take-down is proposed. The technical issues by applying the CLC Model to an automation system to automatically assess Web resources and produce a series of analytic results and, eventually, a ‘likelihood of infringement’ score are investigated. The CLC Model is validated by experienced copyright experts, all of whom have a good level of agreement regarding the usage of the criterion and the infringement score generated in the CLC Model. The automation system is evaluated by users and the results confirm that, for specific types of Web resources, the system helps to bring users’ decisions closer to those of the experts.
University of Southampton
Zhang, Pei
a881c1a8-503d-4429-be9b-6895a69be4f0
Zhang, Pei
a881c1a8-503d-4429-be9b-6895a69be4f0
Gilbert, Lester
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Zhang, Pei (2017) A content-linking-context model and automatic copyright verification in the notice-and-take-down procedures. University of Southampton, Doctoral Thesis, 136pp.

Record type: Thesis (Doctoral)

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. European Directive 2000/31/EC (e-Commerce Directive) introduced a set of liability exemptions similar to the one found in the DMCA, but did not specify any take-down procedure. Many intermediary (hosts and online search engines) 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, whether and how they verify the lawfulness of allegedly infringing content, and what criteria they use for these actions. Google’s Transparency Report is used as the benchmark to investigate the information content of take-down notices and to assess the accuracy of the resulting take-downs of allegedly infringing Web resources. Based on the investigation, a Content-Linking-Context (CLC) Model which identified the criteria to be considered by intermediary service providers to achieve more accurate take-down is proposed. The technical issues by applying the CLC Model to an automation system to automatically assess Web resources and produce a series of analytic results and, eventually, a ‘likelihood of infringement’ score are investigated. The CLC Model is validated by experienced copyright experts, all of whom have a good level of agreement regarding the usage of the criterion and the infringement score generated in the CLC Model. The automation system is evaluated by users and the results confirm that, for specific types of Web resources, the system helps to bring users’ decisions closer to those of the experts.

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Published date: November 2017

Identifiers

Local EPrints ID: 416475
URI: http://eprints.soton.ac.uk/id/eprint/416475
PURE UUID: 667e1e2a-5ffd-4793-a709-6e69892a6ac6

Catalogue record

Date deposited: 20 Dec 2017 17:30
Last modified: 15 Mar 2024 17:36

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Contributors

Author: Pei Zhang
Thesis advisor: Lester Gilbert

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