Analyzing and Predicting Privacy Settings in the Social Web

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)


Social networks provide a platform for people to connect and share information and moments of their lives. With the increasing engagement of users in such platforms, the volume of personal information that is exposed online grows accordingly. Due to carelessness, unawareness or difficulties in defining adequate privacy settings, private or sensitive information may be exposed to a wider audience than intended or advisable, potentially with serious problems in the private and professional life of a user. Although these causes usually receive public attention when it involves companies’ higher managing staff, athletes, politicians or artists, the general public is also subject to these issues. To address this problem, we envision a mechanism that can suggest users the appropriate privacy setting for their posts taking into account their profiles. In this paper, we present a thorough analysis of privacy settings in Facebook posts and evaluate prediction models that can anticipate the desired privacy settings with high accuracy, making use of the users’ previous posts and preferences.


Facebook Privacy Social networks 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.L3S Research CenterHannoverGermany
  2. 2.Middle East Technical UniversityAnkaraTurkey

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