Improving Malicious Accounts Discrimination through a New Feature Engineering Approach Using Relaxed Functional Dependencies

Social network platforms include several tasks such as advertisements, political communications, and so on, producing vast amounts of data spread over the network. Consequently, verifying the truthfulness of such data and the accounts generating them becomes necessary. In particular, malicious users often create fake accounts and followers for harmful activities, potentially producing negative societal implications. In order to increase the capability to identify fake accounts correctly, this discussion paper presents a new feature engineering strategy that exploits relaxed functional dependencies (RFDS) to enhance the capability of existing machine learning strategies in discriminating fake accounts. In particular, experimental results conducted using several machine learning models on account datasets of both the Twitter and Instagram platforms emphasize the effectiveness of the proposed approach in fake account discrimination activities.