Online Social Networks in Business Frameworks

13Design and Development of Techniques for Fake Profile Detection in Online Social Networks

R. Anto Arockia Rosaline^*, D. Vinod, P. Nancy, K. Anitha and A. Devipriya

Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu, India

Abstract

Today, Facebook and Twitter are among the top 10 trafficked websites. Most social networking sites provide mobile applications or services. Unwary users using SNs on websites may push spammers to behave destructively. Extra security measures protect users’ personal information on social media. People are quite eager to provide personal information on most sites. Adjust this data privacy setting to private or public. Only trustworthy network users may read sensitive data. SNSs’ unreliable identity identification is a major problem. So, the user may see someone else’s genuine identity. Accepting friend requests and sharing personal information usually makes persons more popular. Even if they don’t know the individual, most social media users click on links. This behavior may indicate spamming. Spam detection systems must prevent account access by spammers. This article presents a system using machine learning to identify spam profiles in online social networks. The particle swarm optimization approach is used to choose features from the social network data set. The classification model is trained using a dataset, using both the Support Vector Machine (SVM) and Artificial Neural Network ...