A real-time hostile activities analyses and detection system

Abstract

Over recent years, the development of online social media has dramatically changed the way people connect and share information. It is undeniable that social platform has promoted the quickest type of spread for fake stories. Almost all the current online fact-checking sources and researches are concentrating on the validating political content and context. The proposed system in this paper provides a complete visual data analytics methods to assist users in achieving a comprehensive understanding of malicious activities at multiple levels such as adversary’s behavior, victim’s behavior, content, and context level. In this paper, we investigate a variety of datasets from different aspects such as role, vulnerabilities, influential level, and distribution pattern. The proposed method in this paper focuses on automatic fake/hostile activity detection by utilizing a variety of machine learning (ML) techniques, deep learning models, natural language processes (NLP), and social network analysis (SNA) techniques. Different auxiliary models, such as bot detection, user credibility, and text readability, are deployed to generate additional influential features. The classification performance of ten different machine learning algorithms using a variety of well-known datasets is evaluated by utilizing 10-fold cross-validation.