Attribute inference over real-world online social networks: a comprehensive privacy analysis

Today, people can make interactions through online social networks (OSNs) that allow users to reveal and see their personal information, make connections with other users, and view the public information of other users through the connections. As such, OSNs are extremely popular and inevitable parts of people's daily lives today. As a result, OSNs have become attractive data sources for researchers from many disciplines including computer science to investigate privacy, security, and user behavior issues. However, studies that aim to make privacy analysis of Turkish OSN users are quite limited. In this thesis, a comprehensive privacy analysis of Turkish Facebook users was carried out due to the popularity of Facebook OSN. Different methods were used to infer users' private attributes including gender, kinship, and so on. Then the inferred attributes were used in the privacy risk analysis to show that users are at higher risk than they believe. Additionally, two natural language processing (NLP) tasks including sentiment analysis and named entity recognition have been performed as they can be utilized in privacy risk analysis. NLP and feature inference tasks were handled as sub-problems. In these tasks, various contributions were made to the literature, and some of them provided state-of-the-art results.