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An architecture for ethical and privacy-sensitive social network experiments

Published: 29 April 2013 Publication History

Abstract

Social Network Sites (SNSs) are used for sharing personal data and delivering personalised services to hundreds of millions of users, and thus represent an important sector of the Digital Economy. Measuring and collecting data from SNSs is crucial for research and development of new services, but the sensitive and personal nature of these data means that great care must be taken by researchers when conducting SNS studies.
This paper presents a work-in-progress architecture for conducting experiments across multiple SNSs while acknowledging and preserving participant privacy. We evaluate the architecture by conducting an experiment using live SNS data, exploring willingness to share sensitive data with researchers. We also outline some outstanding challenges as we finalise the implementation of the architecture.

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review
ACM SIGMETRICS Performance Evaluation Review  Volume 40, Issue 4
March 2013
105 pages
ISSN:0163-5999
DOI:10.1145/2479942
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 April 2013
Published in SIGMETRICS Volume 40, Issue 4

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  • (2021)Data Curation Implications of Qualitative Data Reuse and Big Social ResearchJournal of eScience Librarianship10.7191/jeslib.2021.121810:4Online publication date: 10-Nov-2021
  • (2019)Automating Dynamic Consent Decisions for the Processing of Social Media Data in Health ResearchJournal of Empirical Research on Human Research Ethics10.1177/155626461988371515:3(187-201)Online publication date: 6-Nov-2019
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