Finding influentials based on the temporal order of information adoption in twitter

This work proposes a novel method to find influentials by considering both the link structure and the temporal order of information adoption in Twitter that finds distinct influentials who are not discovered by other methods.

Abstract

Twitter offers an explicit mechanism to facilitate information diffusion and has emerged as a new medium for communication. Many approaches to find influentials have been proposed, but they do not consider the temporal order of information adoption. In this work, we propose a novel method to find influentials by considering both the link structure and the temporal order of information adoption in Twitter. Our method finds distinct influentials who are not discovered by other methods.