Association Rule Analysis-Based Identification of Influential Users in the Social Media

The exchange of information is an innate and natural process that assist in content dispersal. Social networking sites emerge to enrich their users by providing the facility for sharing information and social interaction. The extensive adoption of social networking sites also resulted in user content generation. There are diverse research areas explored by the researchers to investigate the influence of social media on users and confirmed that social media sites have a significant impact on markets, politics and social life. Facebook is extensively used platform to share information, thoughts and opinions through posts and comments. The identification of influential users on the social web has grown as hot research field because of vast applications in diverse areas for instance political campaigns marketing, e-commerce, commercial and, etc. Prior research studies either uses linguistic content or graph-based representation of social network for the detection of influential users. In this article, we incorporate association rule mining algorithms to identify the top influential users through frequent patterns. The association rules have been computed using the standard evaluation measures such as support, confidence, lift, and conviction. To verify the results, we also involve conventional metrics for example accuracy, precision, recall and F1-measure according to the association rules perspective. The detailed experiments are carried out using the benchmark College-Msg dataset extracted by Facebook. The obtained results validate the quality and visibility of the proposed approach. The outcome of propose model verify that the association rule mining is able to generate rules to identify the temporal influential users on Facebook who are consistent on regular basis. The preparation of rule set help to create knowledge-based systems which are efficient and widely used in recent era for decision making to solve real-world problems.