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Learning to share: Engineering adaptive decision-support for online social networks

Rafiq, Y., Dickens, L., Russo, A., Bandara, A.K., Yang, M., Stuart, A., Levine, M., Calikli, G., Price, B.A., Nuseibeh, B. and others. (2017) Learning to share: Engineering adaptive decision-support for online social networks. In: ASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering. 32nd IEEE/ACM International Conference on Automated Software Engineering. . pp. 280-285. IEEE, USA (doi:10.1109/ASE.2017.8115641) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:89575)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
http://dx.doi.org/10.1109/ASE.2017.8115641

Abstract

Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook. © 2017 IEEE.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ASE.2017.8115641
Uncontrolled keywords: Computer software reusability; Data privacy; Decision support systems; E-learning, Decision supports; Informed decision; Multiple contacts; On-line social networks; Online social networks (OSNs); Run-time analysis; Sharing information; Social interactions, Social networking (online)
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Marketing, Entrepreneurship and International Business
Depositing User: Mu Yang
Date Deposited: 03 Aug 2021 13:42 UTC
Last Modified: 04 Aug 2021 10:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/89575 (The current URI for this page, for reference purposes)

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