Paper Key : IRJ************239
Author: Akilesh A,Kaushik N,Aravind R,Dinesh Reddy M,Vinoth Kumar S
Date Published: 04 Apr 2025
Abstract
With the increasing popularity of social networking platforms, there has been a concurrent rise in the number of spammers and fake user accounts. These activities significantly degrade the user experience and compromise the integrity of online platforms. The System is a machine learning model for identifying spammers and fake users in social networks, utilizing Natural Language Processing (NLP) techniques and the Random Forest algorithm. The system focuses on analyzing various account-related features, such as followers, engagement rate, post activities, and identifying commonly used spam messages. Text preprocessing techniques such as stemming and cleaning are employed to prepare the input data for classification. The Random Forest classifier is trained to detect and classify accounts as legitimate, spam, or fake based on their behavioural and textual characteristics. Additionally, the project includes a Flask-based web interface, enabling users to interact with the system in real-time. The system processes live user data, providing classifications and feedback on potentially fraudulent activities. The objective is to enhance the overall security and reliability of social networking applications by mitigating the impact of spammers and fake users.