Spammer Identification Using Novel Machine Learning Algorithm in Industrial Mobile Cloud Computing

Abstract

Social media networks are gaining more traction in a variety of industries throughout the world and have emerged as one of the most used and well-liked digital marketing platforms for monitoring societal trends and better understanding consumer preferences. False social profiles are multiplying quickly and disseminating false information and news through this expanding channel. Online impersonation and fraudulent accounts are a major problem on the social media network, which is a vital part of our lives. Intruders frequently utilize false personas to engage in illegal activity on Online Social Networks (OSN), including injuring others, identity theft, and invasions of privacy. Consequently, determining whether an account is real or false is one of the major issues with OSN. Many classification algorithms, including the Advanced Support Vector Machine Learning algorithm and deep machine learning networks, are proposed in this paper. This research offers a Spammer Identification strategy based on Gaussian Mixture Model (SIGMM) for industrial mobile networks in order to solve this issue. It delivers accurate spammer identification without relying on fluid and erratic interactions. SIGMM integrates the data presentation with the model generation process, which assigns a class to each user node.