Identifying Topical Authorities in Microblogs Aditya Pal

Content in microblogging systems such as Twitter is pro-duced by tens to hundreds of millions of users. This diver-sity is a notable strength, but also presents the challenge of finding the most interesting and authoritative authors for any given topic. To address this, we first propose a set of features for characterizing social media authors, in-cluding both nodal and topical metrics. We then show how probabilistic clustering over this feature space, followed by a within-cluster ranking procedure, can yield a final list of top authors for a given topic. We present results across several topics, along with results from a user study confirming that our method finds authors who are significantly more inter-esting and authoritative than those resulting from several baseline conditions. Additionally our algorithm is compu-tationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dy-namics of microblogs