MDFP: A Machine Learning Model for Detecting Fake Facebook Profiles Using Supervised and Unsupervised Mining Techniques

Mohammed Albayati and Ahmad Altamimi (Applied Science Private University, Jordan)

This work presents a machine learning model that utilizes a set of supervised and unsupervised mining algorithms for detecting fake Facebook profiles. Specifically, three supervised algorithms (ID3 decision tree, k-NN, and SVM) and two unsupervised algorithms (k-Means and k-Medoids) are implemented using the RapidMiner© Studio with a set of 12 behavioral and non-behavioral attributes provided in the Facebook users' profiles. To collect the related data and due to the strict privacy settings of Facebook, a special tool (CRAWLER) is developed specifically for this purpose. This ends with a dataset of 982 profiles that are used to carried out two experiments, with and without removing the missing values profiles, to determine which technique has performed best. Results showed that the supervised algorithms have best accuracy rates over the unsupervised algorithms in both experiments. In particular, ID3 algorithm outperforms other classifiers, while k-Medoids registered the lowest accuracy rate in the detection process.

Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V20

Published: Feb 28, 2019

DOI: 10.5013/IJSSST.a.20.01.11