A Survey on Machine Learning Methodologies in Social Network Analysis

Social Network is an emerging area of research today. The amount of information carried by Online Social Networks in the form of text and images is of immense value for data mining and knowledge extraction. There are many approaches to social network analysis including machine learning. Machine learning algorithms work on a set of observable features extracted from user information. Application of machine learning in the field of online social network analysis includes spammer detection, user classification, link prediction, troll pages detection, friend suggestions, community or cluster identification, trend analysis, sentiment analysis of political blogging etc. This paper surveys on the existing work on a) fake profile detection b) personality trait recognition c) depression detection based on using machine learning algorithms in social network analysis and presents a comparative study of the different approaches.