M. SHIVARANJANI,DONGALA SWATHI,CHAVABATHINA SHREYA,KANKANALA VENUVARDHAN REDDY,DANDU SAI BHAGEERATH REDDY 03 03 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 Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026