"Dr. Michael Fire Pioneers Method to Track Groups of Anomalous Users"
Malicious or fake online users have become a significant nuisance on Internet networks. While there has been much concern about their rising frequency, few have created ways to track and expose them. A researcher at Ben-Gurion University (BGU) of the Negev has developed a novel method for detecting groups of anomalous users. This study has the advantage of being able to detect anomalous groups, such as groups of fake profiles, rather than single users. Dr. Michael Fire, head of the Data4Good Lab and a member of BGU's Department of Software and Information Systems Engineering, explains that detecting groups of fake profiles is a difficult and understudied problem. Among the benefits of Fire's Co-Membership-based Generic Anomalous Communities Detection Algorithm (CMMAC) is that it is not limited to a single network type. The method is generic, so it can be applied to other social media platforms. It was tested on various types of social media networks, including Reddit and Wikipedia. After testing the method on randomly generated networks and real-world networks, it was found to outperform many other methods in a range of different settings. This article continues to discuss the method developed to track groups of malicious or fictitious users on Internet networks.