Supervised Machine Learning Based Anomaly Detection In Online Social Networks

With the rapid development of online social networks (OSNs), a huge number of information provided by some entities around the world are well dispersed in OSNs every day. Most of those are useful but not all as anomalous entities utilize anomaly users to spread malicious content (like spam or rumors to achieve their pecuniary or political aims. In this paper, we propose a mechanism to detect such anomaly users according to the user profile and tweet content of each user. We design several features related to near-duplicate content (including lexical similarity and semantic similarity) to enhance the precision of detecting anomaly users. Utilizing the data by public honeypot dataset, the proposed approach deals with supervised learning approach to carry out the detection task.