Detecting Fake Accounts on Social Media

In the present generation, on-Line social networks (OSNs) have become increasingly popular, people's social lives have become more associated with these sites. They use on-Line social networks (OSNs) to keep in touch with each others, share news, organize events, and even run their own e-business. The rapid growth of OSNs and the massive amount of personal data of its subscribers have attracted attackers, and imposters to steal personal data, share false news, and spread malicious activities. On the other hand, researchers have started to investigate efficient techniques to detect abnormal activities and fake accounts relying on accounts features, and classification algorithms. However, some of the account's exploited features have negative contribution in the final results or have no impact, also using standalone classification algorithms does not always achieve satisfactory results. In this paper, a new algorithm, SVM-NN, is proposed to provide efficient detection for fake Twitter accounts and bots, feature selection and dimension reduction techniques were applied. Machine learning classification algorithms were used to decide the target accounts identity real or fake, those algorithms were support vector machine (SVM), neural Network (NN), and our newly developed algorithm, SVM-NN. The proposed algorithm (SVM-NN) uses less number of features, while still being able to correctly classify about 98% of the accounts of our training dataset.