Identifying Fake Facebook Profiles Using Data Mining Techniques

Abstract

Facebook, the popular online social network, has changed our lives. Users can create a customized profile to share information about themselves with others that have agreed to be their 'friend'. However, this gigantic social network can be misused for carrying out malicious activities. Facebook faces the problem of fake accounts that enable scammers to violate users' privacy by creating fake profiles to infiltrate personal social networks. Many techniques have been proposed to address this issue. Most of them are based on detecting fake profiles/accounts, considering the characteristics of the user profile. However, the limited profile data made publicly available by Facebook makes it ineligible for applying the existing approaches in fake profile identification. Therefore, this research utilized data mining techniques to detect fake profiles. A set of supervised (ID3 decision tree, k-NN, and SVM) and unsupervised (k-Means and k-medoids) algorithms were applied to 12 behavioral and non-behavioral discriminative profile attributes from a dataset of 982 profiles. The results showed that ID3 had the highest accuracy in the detection process while k-medoids had the lowest accuracy.

Keywords

• Facebook
• fake profiles
• machine learning
• supervised algorithms
• unsupervised algorithms