Abstract
Profiling users in Online Social Networks (OSNs) is of great benefit in multiple domains (e.g., marketing, sociology, and forensics). In this paper, we propose a new model for rating user’s profile (i.e., low, medium, high, and advanced) in an OSN community by embedding it into clusters located at predefined range of radius in a low-dimensional Cartesian space. The orthogonal coordinates of the profile are estimated using Principle Component Analysis (PCA) applied on a vector of metrics formulated as a set of attributes of interest (i.e., qualitative and quantitative) mined from the user’s profile to characterize his/her level of participation and behavior in the community. The experimentations are conducted on 3000 simulated profiles of three OSNs (Facebook, Twitter and Instagram) by embedding them in three cartesian spaces of three corresponding communities (Religion, Political and Lifestyle). The results show that we are able to estimate accurately the profile rates by reducing the vector of metrics to a low-dimensional space whittle down to 3-D space.
Highlights
Online Social Networks (OSNs) have gained a large popularity and became an integral part of our daily activity
Profiling OSN users has been frequently practiced for different purposes in various fields despite the numerous concerns that have been raised
In order to test the accuracy of the proposed model, we experimented the clustering of 3000 profiles in religion, political and lifestyle communities (Note 1) of three social networks (i.e., Facebook, Twitter and Instagram)
Summary
Online Social Networks (OSNs) have gained a large popularity and became an integral part of our daily activity. We define a new model for rating user’s profile in a community (e.g., political, religion, lifestyle) using a computerized algorithm in order to deal with huge and complex amount of profile’s data. The estimated rates are used for positioning the profiles in the clusters of each community, which are classified as low, medium, high, and advanced. In order to test the accuracy of the proposed model, we experimented the clustering of 3000 profiles in religion, political and lifestyle communities (Note 1) of three social networks (i.e., Facebook, Twitter and Instagram). Each case study consists in embedding the profiles of an OSN community in an independent Cartesian space to observe their distribution in the clusters.
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