Online social networks (OSNs) are platforms which facilitate social interactions between their users through message exchange, photo and video sharing, status updates, etc. One of the most popular OSNs is Facebook. Connections between users on Facebook are modeled through concept of friendship. Each connection between users is binary - two users either are or aren't "friends". Information about of the actual intensity or nature of their connection is not available although in real life it can vary significantly. A majority of observed network friends are acquaintances in real-life while close friends are in the minority. The goal of this paper is to demonstrate and evaluate how user interaction statistics can be utilized for effective assessment of the nature of users' real-life relationship. Using an ensemble of popular classification algorithms, we will classify ego-user's network friends into 3 groups: close friends, friends and acquaintances. As our main contribution, we will compare the efficiency of chosen algorithms and suggest the best approach for conducting this type of analysis on similar OSN communication data.