Article
Spammer detection and fake user identification on social networks explaining details
Online Social Networks (OSNs) provide powerful platforms for sharing information, following current events, promoting products, and connecting with people across the world. As a result, millions of users actively participate in social media platforms such as Instagram, Twitter, and Facebook. Despite these advantages, it is often challenging to determine whether a particular social media account genuinely belongs to a real individual or an organization. The creation of fake or malicious accounts allows harmful or misleading content to spread quickly across these networks. Consequently, detecting and predicting fake accounts has become an important research problem in the field of social network security. In this study, machine learning techniques are applied to identify fake accounts on social media platforms. The research also evaluates the performance of different activation functions within Artificial Neural Network (ANN) models. Experimental findings indicate that machine learning approaches are effective in detecting suspicious or fraudulent accounts with satisfactory accuracy. Additionally, the selection of different activation functions in various layers of the neural network significantly influences the classification results. Previous studies in the literature have explored several classification techniques for identifying fake profiles and spammers in online social networks. However, to the best of our knowledge, only limited research has specifically examined the classification of fake accounts using ANN models with multiple activation functions.
Full Text Attachment
