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

Rafiq, Yasmin and Dickens, Luke and Russo, Alessandra and Bandara, Arosha K. and Yang, Mu and Stuart, Avelie and Levine, Mark and Calikli, Gul and Price, Blaine A. and Nuseibeh, Bashar (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. Institute of Electrical and Electronics Engineers Inc., USA, pp. 280-285. ISBN 9781538626849

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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.

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22 Jun 2019 00:55
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20 Sep 2023 02:26