Identifying Fake Accounts on Social Networks Based on Graph Analysis and Classification Algorithms

Social networks have become popular due to the ability to connect people around the world and share videos, photos, and communications. One of the security challenges in these networks, which have become a major concern for users, is creating fake accounts. In this paper, a new model which is based on similarity between the users' friends' networks was proposed in order to discover fake accounts in social networks. Similarity measures such as common friends, cosine, Jaccard, L1-measure, and weight similarity were calculated from the adjacency matrix of the corresponding graph of the social network. To evaluate the proposed model, all steps were implemented on the Twitter dataset. It was found that the Medium Gaussian SVM algorithm predicts fake accounts with high area under the curve=1 and low false positive rate=0.02.