While privacy preserving mechanisms, such as hiding one's friends list, may be available to withhold personal information on online social networking sites, it is not obvious whether to which degree a user's social behavior renders such an attempt futile. In this paper, we study the impact of additional interaction information on the inference of links between nodes in partially covert networks. This investigation is based on the assumption that interaction might be a proxy for connectivity patterns in online social networks. For this purpose, we use data collected from 586 Facebook profiles consisting of friendship ties (conceptualized as the network) and comments on wall posts (serving as interaction information) by a total of 64 000 users. The link-inference problem is formulated as a binary classification problem using a comprehensive set of features and multiple supervised learning algorithms. Our results suggest that interactions reiterate the information contained in friendship ties sufficiently well to serve as a proxy when the majority of a network is unobserved.
- Data privacy
- supervised learning