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Managing longitudinal exposure of socially shared data on the Twitter social media

  • Mainack MondalEmail author
  • Johnnatan Messias
  • Saptarshi Ghosh
  • Krishna P. Gummadi
  • Aniket Kate
Article

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 focused 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 6-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 The inaccessible tweets are either selectively deleted by users or withdrawn by users when they delete or make their accounts private.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 two exposure control mechanisms that eliminates information leakage via residual activities. One of our mechanisms optimize for allowing meaningful social interactions with user posts and another mechanism aims to control longitudinal exposure via anonymization . We discuss the merits and drawbacks of our proposed mechanisms compared to existing mechanisms.

Keywords

Longitudinal privacy Exposure Twitter User behavior 

Notes

Acknowledgements

This work is an extended version of the paper: Mondal et al. Forgetting in Social Media: Understanding and Controlling Longitudinal Exposure of Socially Shared Data, Proceedings of the 12th Symposium on Usable Privacy and Security (SOUPS’16), Denver, CO, USA, June 2016.

Compliance with ethical standards

Funding

Funding was provided by Max-Planck-Gesellschaft.

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Copyright information

© Indian Institute of Technology Madras 2017

Authors and Affiliations

  1. 1.MPI-SWSSaarbrücken/KaiserslauternGermany
  2. 2.Department of Computer Science and EngineeringIIT KharagpurKharagpurIndia
  3. 3.Department of Computer SciencePurdue UniversityWest LafayetteUSA

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