Fake Account Detection on Social Media Using Machine Learning and Deep Learning
1 PARUCHURI SRIVALLI , 2 PODAMEKALA LAHARI , 3 PULETIPALLI SAFEENA
4 MRS.VIDHYA , 5 MR J.JAYAPRAKASH , 6 MRS.CHINCHU NAIR
1 2 3 Students, 4 Assistant Professor, 5 Professor, 6 Assistant Professor Paruchurisrivalli29@gmail.com, laharipodamekala@gmail.com, psafeena16@gmail.com Dr. MGR Educational and Research Institute, Maduravoyal , Chennai 600095, TN
ABSTRACT:
Social networking websites have become an essential part of life, making it easy for individuals to stay connected and exchange information. They provide numerous features, including the ability to chat with others, share news, plan events, and many more. But with the increasing number of users and the volume of personal information, the bad guys have also seen opportunities to exploit these networks. They exploit security loopholes to pilfer personal data, propagate false information, and partake in other malicious activities.
As a result, researchers have been focusing on developing effective methods to detect suspicious activities and identify fake accounts. While some features of social media accounts can help with these efforts, they may sometimes have little to no effect, or even negatively impact the results. Additionally, relying on standalone classification algorithms doesn’t always yield optimal outcomes.
This paper suggests using the Decision Tree algorithm to effectively detect fake Instagram accounts by employing four feature selection and dimensionality reduction techniques. In previous research, algorithms such as Decision Trees, Random Forest, Logistic Regression, and Convolutional Neural Networks (CNN) were explored for classification. Among these, CNNs performed exceptionally well, accurately identifying fake accounts and producing satisfying results. Given their high performance, we applied CNNs for Instagram account classification in our study. With deep learning offering various types of neural networks, CNNs have proven to be the most effective for this type of task.
KEYWORDS: Decision Tree, Random Forest, Logistic Regression, and CNN (convolution neural network)
