Determining Influential Users with Supervised Random Walks

Abstract

The emergence of social media and the enormous growth of social networks have initiated a great amount of research in social influence analysis. In this regard, many approaches take into account only structural information while a few have also incorporated content. In this study we propose a new method to rank users according to their topic-sensitive influence which utilizes a priori information by employing supervised random walks. We explore the use of supervision in a PageRank-like random walk while also exploiting textual information from the available content. We perform a set of experiments on Twitter datasets and evaluate our findings.