Scalable topic-specific influence analysis on microblogs

This work proposes a novel Followship-LDA (FLDA) model, which integrates both content topic discovery and social influence analysis in the same generative process, and demonstrates that FLDA produces results with significantly better precision than existing approaches.

Abstract

Social influence analysis on microblog networks, such as Twitter, has been playing a crucial role in online advertising and brand management. While most previous influence analysis schemes rely only on the links between users to find key influencers, they omit the important text content created by the users. As a result, there is no way to differentiate the social influence in different aspects of life (topics). Although a few prior works do support topic-specific influence analysis, they either separate the analysis of content from the analysis of network structure, or assume that content is the only cause of links, which is clearly an inappropriate assumption for microblog networks.