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

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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 The inaccessible tweets are either selectively deleted by users or withdrawn by users when they delete or make their accounts private.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.

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Notes

  1. This study was conducted respecting the guidelines set by our institute’s ethics board and with their explicit knowledge and permission.

  2. Facebook’s longitudinal exposure control mechanisms are more granular as observed by previous studies [6, 21]. Facebook users can choose to make their content available to only themselves, to their friends, subsets of friends, friends of friends or to general public.

  3. https://dev.twitter.com/overview/api/response-codes.

  4. We observed that Twitter provides a tweet in their random sample nearly instantaneously (within seconds) after a user posts the tweet. Consequently, there is at most a minimal chance that a user deleted a tweet even before it could appear in our random sample.

  5. We only considered original tweets (and not retweets) during sampling since our goal is to understand how much of the tweets originally posted by users are withdrawn today.

  6. We obtained the country of our users by leveraging location data of Twitter users gathered by Kulshrestha et al. [23]. They used the location and timezone field of the Twitter profile for inferring location of users.

  7. We use a list of English stopwords and a list of Twitter-specific stopwords from [27].

  8. For an interested reader to check the resemblance in meaning between the guessed and original tweets, we put our complete AMT evaluation result at http://twitter-app.mpi-sws.org/soups2016/amt_guess.html.

  9. For example, Twitter today automatically deletes re-tweets of a deleted tweet, but not replies or mentions generated by other users.

  10. https://support.twitter.com/articles/20171990.

    https://www.facebook.com/help/437430672945092.

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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.

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Authors and Affiliations

  1. MPI-SWS, Saarbrücken/Kaiserslautern, Germany

    Mainack Mondal, Johnnatan Messias & Krishna P. Gummadi

  2. Department of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India

    Saptarshi Ghosh

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Aniket Kate

Authors
  1. Mainack Mondal
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  2. Johnnatan Messias
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  3. Saptarshi Ghosh
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  4. Krishna P. Gummadi
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  5. Aniket Kate
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Corresponding author

Correspondence to Mainack Mondal.

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Funding was provided by Max-Planck-Gesellschaft.

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Mondal, M., Messias, J., Ghosh, S. et al. Managing longitudinal exposure of socially shared data on the Twitter social media. Int J Adv Eng Sci Appl Math 9, 238–257 (2017). https://doi.org/10.1007/s12572-017-0196-3

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  • DOI: https://doi.org/10.1007/s12572-017-0196-3

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