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Remixers' understandings of fair use online

Published: 15 February 2014 Publication History

Abstract

How do online content creators make decisions about copyright law? In the course of day-to-day online activities, Internet users are forced to make subtle judgments about one of the most confusing and nuanced areas of law, copyright and fair use. In this study, we conducted semi-structured interviews with eleven content creators who participate in remix and fan creation activities online, to try to probe their legal understandings and attitudes. We found that social norms that emerge among these content creators do not always track to what the law actually says, but are often guided more by ethical concerns. Our participants showed surprisingly similar patterns of understandings and confusions, impacting technology use and interaction online.

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    cover image ACM Conferences
    CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
    February 2014
    1600 pages
    ISBN:9781450325400
    DOI:10.1145/2531602
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 15 February 2014

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    Author Tags

    1. copyright
    2. creativity
    3. fair use
    4. fandom
    5. intellectual property
    6. internet
    7. law
    8. remix
    9. social norms
    10. user-generated content

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    CSCW'14: Computer Supported Cooperative Work
    February 15 - 19, 2014
    Maryland, Baltimore, USA

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    CSCW '14 Paper Acceptance Rate 134 of 497 submissions, 27%;
    Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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    • (2023)Probing Respiratory Care With Generative Deep LearningProceedings of the ACM on Human-Computer Interaction10.1145/36100997:CSCW2(1-34)Online publication date: 4-Oct-2023
    • (2023)Conflicts of Control: Continuous Blood Glucose Monitoring and Coordinated Caregiving for Teenagers with Type 1 DiabetesProceedings of the ACM on Human-Computer Interaction10.1145/36100977:CSCW2(1-32)Online publication date: 4-Oct-2023
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