A Novel Approach for Studying Privacy Behavior in Social Media

Social Networking Sites achieved a breakthrough over the past few years since they allow individuals to be in touch with family and friends, but also raised privacy concerns when it comes to disclosing personal information with others. This study investigates users' behavior in terms of how they choose their privacy settings on Facebook. It examines the impact of faces and tags existence on user's privacy. Moreover, we extend the work on YourPrivacyProtector, a system for monitoring and recommending privacy settings in Facebook, and enhance the recommendation system by examining photos with faces and tags. We use machine learning techniques to understand privacy settings of different users and recommend them a stronger setting. We also evaluate our algorithms with a case study.