Applying the multiclass classification methods for the classification of online social network friends

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...

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Bibliographic Details
Published in:	2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM) pp. 1 - 6
Main Authors:	Sever, Nikolina, Humski, Luka, Ilic, Juraj, Skocir, Zoran, Pintar, Damir, Vranic, Mihaela
Format:	Conference Proceeding
Language:	English
Published:	University of Split, FESB 01-09-2017
Subjects:	Algorithm design and analysis Buildings Classification algorithms Data mining educational data mining Facebook Linear regression multiclass classification online social networks
Online Access:	Get full text
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Summary:	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.
ISSN:	1847-358X
DOI:	10.23919/SOFTCOM.2017.8115508