Browser Simulation-based Crawler for Online Social Network Profile Extraction

The rapid proliferation and extensive use of online social networks (OSNs) like Facebook, Twitter, Instagram, etc., has attracted the attention of academia and industry, since these networks store massive information in them. But, acquiring data from these OSNs, which is a prerequisite for conducting any research on them, is a daunting task, which can be because of privacy concerns on one hand and complexity of underlying technologies of these complex networks, on the other. This paper presents the design and implementation of a crawler based on browser simulation for extraction of Facebook users profile data while preserving the privacy. The breadth-first-search (BFS) algorithm approach was also adopted for sampling of around 0.235 million Facebook users. Though the main purpose of this work is the design of a crawler still, the results have been briefly presented in terms of various social network metrics and analysed from different aspects of privacy.