Uncovering Malicious Accounts in Open Mobile Social Networks Using a Graph- and Text-Based Attention Fusion Algorithm

In recent years, open mobile social networks focused on socializing and dating purposes have gained widespread popularity, such as Soul, Tinder, Momo, and Tantan, among several others. These applications permit users to post, comment, and send private messages to other users without their consent, making communication accessible. However, this low-entry communication approach has also increased malicious user attacks. We delve into a comprehensive analysis of malicious accounts in open socializing and dating applications, revealing that the existing methods overlook hidden malicious signals within the user text-related information, thus resulting in poor detection performance. For such, we propose GraphTAM, a novel graph- and text-based multihead attention fusion network model for detecting such malicious accounts, consisting of modules that effectively combine nontext-related and text-related information, enhancing the accuracy and performance of detecting malicious accounts. We employ graph convolutional networks (GCNs) for nontext-related information to extract advanced representations of users, incorporating their attribute and social relationship features. Regarding text-related information, we employ a multihead attention model to identify suspicious patterns in users’ posted articles, comments, and relevant behavioral statistics, so finally, we merge the advanced representations of nontext-related and text-related information using a multilayer perceptron to determine the maliciousness of an account. Data sets collected from SLink are utilized for the experimental evaluation and to compare the performance of the proposed model with the several state of the art algorithms. Experimental results show significant advantages in malicious account detection, where the F1 score achieves over 0.9, outperforming the existing methods that range between 0.6 and 0.85. Furthermore, the comparative experiments substantiate the critical role of text-related information in detecting malicious accounts in open socializing and dating applications.