Prediction of information diffusion based on influence spreader identification in an online social network

Abstract

A lot of people today spend much of their time on online social networks such as Facebook Twitter and Google plus all of which have become immensely popular Given the massive quantum of user generated data in social media businesses are increasingly using them to promote their products services or campaigns for effective information diffusion Information diffusion generally refers to a concept a topic piece of information or an idea to be shared and transferred from one to another Information diffusion has applications in preventing terrorist attacks predicting natural disasters assessing grassland biological risks optimizing social media campaigns online marketing and epidemiology In recent times Twitter has grown to become a mainstream medium for the dissemination of messages and the public discussion of news and events on its vast network Consequently the increased use of Twitter for information diffusion has necessitated intensive research in the Prediction of Information Diffusion PID alongside an effective influence spreader selection strategy Selecting influential users for information diffusion and resolving influence maximization problems are key challenges This is because in most cases it is assumed that the selection of influential users is based on the network centrality measures rather than on user-generated content in the network newline