Automated Twitter Author Clustering with Unsupervised Learning for Social Media Forensics

Twitter is one of the key social media platforms, which is also used for cyber-crimes. Hence, monitoring and detecting the malicious activities of Twitter users is critically important for cybersecurity concerns around the globe since cybercriminals are heavily using Twitter for illegal purpose. It is increasingly common for cybercriminals signing up many accounts while masquerading different users for malicious behaviors. This fact has brought forward the issue of identifying the authors of Twitter accounts. In this paper, we propose a novel approach through a combination of feature extraction methods and then convert high dimensional data to kernel matrix for Twitter author clustering. The experimental results show that our approach can be used to effectively identify the groups among more than one hundred Twitter aliases even without knowing the number of authors.