Improving Accuracy of Twitter Fake Profile Detection Using Deep Learning

Outline the issue of fake accounts on popular social media platforms like Twitter, which spread false information, malicious content, and spam. Online social networks have grown rapidly, with billions of users worldwide. This growth has led to many fake ac- counts, causing problems like spam, fake news, and political manipulation. Fake accounts can also harm businesses financially and damage their reputation. Therefore, detecting these fraudulent accounts is crucial. Recently, researchers have been using neural network algorithms to identify fake accounts more effectively. Our system uses several types of neural networks, including feedforward and recurrent neural networks, as well as deep learning models, to address this issue. Specifically, we combine artificial neural networks (ANN) with principal component analysis (PCA) to create a reliable system for spotting fake accounts on social media. By collecting and processing data thoroughly, extracting important features, and training the ANN, we show that our method is better than traditional ones at detecting fake accounts. Our results highlight the potential for greater accuracy and efficiency in protecting the integrity of online social networks.