Towards Inferring Communication Patterns in Online Social Networks

Abstract

The separation between the public and private spheres on online social networks is known to be at best blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the OSN provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this paper, we study the relationship between private communication patterns and publicly available OSN data. Such relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key towards developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision.

A Computational Complexity‐Based Method for Predicting Scholars’ Ages through Articles’ Information

“…Most of the current research work on predicting the scholars' true ages originates from the study of user attribute mining in social networks [7]. With the growing popularity of social media, more and more people are joining and using social media networks to share their daily lives and browse the news they are interested in.…”