Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729750
Title: A content-linking-context model and automatic copyright verification in the notice-and-take-down procedures
Author: Zhang, Pei
ISNI:       0000 0004 6497 2351
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2017
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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.
Supervisor: Gilbert, Lester Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.729750  DOI: Not available
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