SPAMMER DETECTION AND FAKE USER IDENTIFICATION ON SOCIAL NETWORKS

Abstract

The rapid growth of social networking platforms has transformed the way people communicate, share information, and interact online. However, the increasing popularity of these platforms has also led to the emergence of spammers and fake user accounts that spread misinformation, distribute malicious content, perform fraudulent activities, and compromise user trust. Traditional detection methods often struggle to identify sophisticated spam behaviors and fake profiles due to the dynamic and evolving nature of social network activities. This paper presents a machine learning-based framework for spammer detection and fake user identification on social networks. The proposed system analyzes user profiles, posting behavior, network interactions, content characteristics, and activity patterns to distinguish legitimate users from malicious entities. Machine learning algorithms are employed to classify accounts based on extracted behavioral and content-related features. The framework incorporates data preprocessing, feature selection, and predictive modeling techniques to enhance detection accuracy and minimize false classifications. Experimental analysis demonstrates that the proposed approach effectively identifies spam accounts and fake users, improves platform security, and supports the creation of a trustworthy social networking environment. The developed system offers an intelligent and scalable solution for combating online spam and fraudulent activities.