Fake Profile Detection Using Machine Learning Techniques

Abstract

Our lives are significantly impacted by social media platforms such as Facebook, Twitter, Instagram, LinkedIn, and others. People are actively participating in it the world over. However, it also has to deal with the issue of bogus profiles. False accounts are frequently created by humans, bots, or computers. They are used to disseminate rumors and engage in illicit activities like identity theft and phishing. So, in this project, the author’ll talk about a detection model that uses a variety of machine learning techniques to distinguish between fake and real Twitter profiles based on attributes like follower and friend counts, status updates, and more. The author used the dataset of Twitter profiles, separating real accounts into TFP and E13 and false accounts into INT, TWT, and FSF. Here, the author discusses LSTM, XG Boost, Random Forest, and Neural Networks. The key characteristics are chosen to assess a social media profile’s authenticity. Hyperparameters and the architecture are also covered. Finally, results are produced after training the models. The output is therefore 0 for genuine profiles and 1 for false profiles. When a phony profile is discovered, it can be disabled or destroyed so that cyber security problems can be prevented. Python and the necessary libraries, such as Sklearn, Numpy, and Pandas, are used for implementation. At the end of this study, the author will come to the conclusion that XG Boost is the best machine learning technique for finding fake profiles.