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Generating Trust in Collaborative Annotation Environments

Published: 17 August 2016 Publication History

Abstract

The main goal of this work is to create a model of trust which can be considered as a reference for developing applications oriented on collaborative annotation. Such a model includes design parameters inferred from online communities operated on collaborative content. This study aims to create a static model, but it could be dynamic or more than one model depending on the context of an application. An analysis on Genius as a peer production community was done to understand user behaviors. This study characterizes user interactions based on the differentiation between Lightweight Peer Production (LWPP) and Heavyweight Peer Production (HWPP). It was found that more LWPP- interactions take place in the lower levels of this system. As the level in the role system increases, there will be more HWPP-interactions. This can be explained as LWPP-interacions are straightforward, while HWPP-interations demand more agility by the user. These provide more opportunities and therefore attract other users for further interactions.

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Cited By

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  • (2020)Wireless sensor network for AI-based flood disaster detectionAnnals of Operations Research10.1007/s10479-020-03754-x319:1(697-719)Online publication date: 7-Aug-2020
  • (2018)Investigating the Effect of Attributes on User Trust in Social MediaDatabase and Expert Systems Applications10.1007/978-3-319-99133-7_23(278-288)Online publication date: 7-Aug-2018

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cover image ACM Other conferences
OpenSym '16: Proceedings of the 12th International Symposium on Open Collaboration Companion
August 2016
50 pages
ISBN:9781450344814
DOI:10.1145/2962132
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 17 August 2016

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Author Tags

  1. Annotation
  2. Collaboration
  3. Genius
  4. Heavyweight Peer Production
  5. Lightweight Peer Production
  6. Trust
  7. User Generated Content

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Overall Acceptance Rate 108 of 195 submissions, 55%

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View all
  • (2020)Wireless sensor network for AI-based flood disaster detectionAnnals of Operations Research10.1007/s10479-020-03754-x319:1(697-719)Online publication date: 7-Aug-2020
  • (2018)Investigating the Effect of Attributes on User Trust in Social MediaDatabase and Expert Systems Applications10.1007/978-3-319-99133-7_23(278-288)Online publication date: 7-Aug-2018

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