Unmasking Deceptive Profile in Social Network using Machine Learning

Abstract

Social media platforms have become a regular aspect of our lives, acting as mediums for networking, information sharing, and communication. However, the proliferation of deceptive profiles poses a significant challenge to the authenticity and integrity of these platforms. Deceptive profiles can be utilized for various malicious purposes such as spreading misinformation, engaging in cyberbullying, or conducting fraudulent schemes. In this research paper, we propose a comprehensive approach for distinguishing between genuine and fake anonymous profiles on social media platforms using machine learning (ML) and natural language processing (NLP). We investigate a range of features and methodologies, including behavioral patterns, sentiment analysis, and profile completeness, also use ML algorithms to effectively discern between genuine and fake profiles. Experimental results obtained from real-world datasets demonstrate the efficacy and scalability of our proposed approach. Moreover, we discuss the implications of fake profiles on social media and propose strategies for mitigation and prevention. Key Words: Deceptive Profile Identification, Machine Learning, Natural Language Processing, Behavioral Patterns, Sentiment Analysis, Profile Completeness.