Assessing User Privacy on Social Media: The Twitter Case Study

At the time of writing, nearly four billion people worldwide employ social media platforms such as Facebook, Instagram, WeChat, TikTok, etc. to share content of various kinds, which may also include personal data. In addition to this, users interact with members of the virtual community, leaving behind important behavioral traces. In most cases, people do not have a full understanding of who will be able to access and use such a body of information, and for what purposes. Although social platforms provide users with some tools to protect their privacy, the very nature of these technologies and the psychological characteristics of users often lead them to ignore such solutions. To address this issue, in this paper we aim to propose a model for assessing the privacy of users on social media by identifying the critical aspects associated with their content and interactions generated on such platforms. This model, in particular, considers distinct features, of different kinds, that capture the level of users' exposure with respect to privacy. These features, dropped into a vector space, are used to derive a score that expresses, in a measurable way, the privacy risk of users compared to the information available on social media about them. The proposed model is instantiated and tested on data collected from the microblogging platform Twitter, on which the results of the experimental evaluation are analyzed. Specifically, the model is tested by considering both a binary scenario, i.e., where users' privacy is evaluated as at risk or not, a multi-class scenario, i.e., where their privacy is evaluated against different risk ranges, and a ranking scenario, i.e., where the users are ranked according to their privacy assessment.