Securing Social Media: Analyzing User Behaviors for Threat Detection Using Ensemble Random Forest Algorithm

The popularity of online social networks (OSNs) has exponentially increased over the past few years as a direct result of the rapid development of technology. The ability of online social networks (OSNs) to facilitate communication between users and their loved ones, coworkers, and other contacts is one of the primary causes behind this phenomenon. Attackers are tempted to gather information through social media and other means of rapid, near-instantaneous content dissemination because of the ease with which they can obtain this information. A variety of approaches ought to be taken in order to evaluate the safety and privacy of online social networks (OSNs). In this paper, we develop an ensemble random forest algorithm to classify the threats from the input dataset. The results show that the proposed method achieves higher rate of accuracy, precision, recall and f-measure than the existing methods.