Visualization and analysis of user behaviour patterns for multimedia content view in social networks

Abstract

Online social networks have become the major source of information and entertainment for millions of users due to the tremendous increase of the accessibility options. Mobile internet has revolutionized the users to access social networking sites with ease and also allows to various social multimedia content anytime, anywhere and on behalf of any identity. This makes the analysis of user interactions and behaviours more complicated. This paper focuses on developing a visualization model named as Social Pattern Clustering WebClickviz (SPC-WebClickviz) for better analysis of the social user behaviour especially the multimedia access. This proposed model visualizes the social networking data based on user activities and then clusters them into specified groups. As the clustering is done for the multimedia content view, the Correlation clustering method has been introduced to group the user activities of multimedia access. Spearman's rank correlation coefficient is utilized as the correlation factor for the clustering. The clustering results are utilized by the organizations to produce the closely related products for the customer's intentions. It also aids in the multimedia developers to provide with content that serializes the users' intentions so that they can generate more traffic to their own websites through the common social networking sites.