Association rule mining in social network data

Abstract

The use of social networks has significantly altered the way of life of online community since last decade. The user-generated contents help to investigate various aspects of the online communities. This paper presents an approach of extracting associations between contents and contextual features of social network data. The aim is to discover the hidden correlations among the contents posted on social networking website, and detect trends of online users. The proposed approach uses association rule mining technique to uncover correlations and build taxonomy based on their corresponding relationships to deeply analyse the social network data contents. The obtained results show the efficiency of the proposed framework in mining association rules and analysing behaviours and trends of online users.