Identifying Fake Profiles across Online Social Networks Using Neural Networks

The rapid growth of online social networks has increased communication and information sharing among users. However, it has also led to the creation of many fake profiles used for spam, fraud, and spreading misinformation. Detecting these fake accounts is important to maintain the security and reliability of social networking platforms. This project proposes a neural network-based approach to identify fake profiles across online social networks. The system analyzes various user profile features such as account activity, number of friends, posting behavior, and profile information. These features are given as input to a neural network model that learns patterns to distinguish between genuine and fake profiles. The trained model can automatically classify new profiles with good accuracy. The proposed method helps improve the detection of fake accounts and supports social networking platforms in enhancing user security and trust.