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Home » Forgetting in Social Media: Understanding and Controlling Longitudinal Exposure of Socially Shared Data
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Forgetting in Social Media: Understanding and Controlling Longitudinal Exposure of Socially Shared Data

Authors: 

Mainack Mondal and Johnnatan Messias, Max Planck Institute for Software Systems (MPI-SWS); Saptarshi Ghosh, Indian Institute of Engineering Science and Technology, Shibpur; Krishna P. Gummadi, Max Planck Institute for Software Systems (MPI-SWS); Aniket Kate, Purdue University

Abstract: 

On most online social media sites today, user-generated data remains accessible to allowed viewers unless and until the data owner changes her privacy preferences. In this paper, we present a large-scale measurement study focussed on understanding how users control the longitudinal exposure of their publicly shared data on social media sites. Our study, using data from Twitter, finds that a significant fraction of users withdraw a surprisingly large percentage of old publicly shared data—more than 28% of six-year old public posts (tweets) on Twitter are not accessible today. The inaccessible tweets are either selectively deleted by users or withdrawn by users when they delete or make their accounts private. We also found a significant problem with the current exposure control mechanisms—even when a user deletes her tweets or her account, the current mechanisms leave traces of residual activity, i.e., tweets from other users sent as replies to those deleted tweets or accounts still remain accessible. We show that using this residual information one can recover significant information about the deleted tweets or even characteristics of the deleted accounts. To the best of our knowledge, we are the first to study the information leakage resulting from residual activities of deleted tweets and accounts. Finally, we propose an exposure control mechanism that eliminates information leakage via residual activities, while still allowing meaningful social interactions with user posts. We discuss its merits and drawbacks compared to existing mechanisms.

Mainack Mondal, Max Planck Institute for Software Systems (MPI-SWS)

Johnnatan Messias, Max Planck Institute for Software Systems (MPI-SWS)

Saptarshi Ghosh, Indian Institute of Engineering Science and Technology, Shibpur

Krishna P. Gummadi, Max Planck Institute for Software Systems (MPI-SWS)

Aniket Kate, Purdue University

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