Mining topical influencers based on the multi-relational network in micro-blogging sites

In micro-blogging contexts such as Twitter, the number of content producers can easily reach tens of thousands, and many users can participate in discussion of any given topic. While many users can introduce diversity, as not all users are equally influential, it makes it challenging to identify the true influencers, who are generally rated as being interesting and authoritative on a given topic. In this study, the influence of users is measured by performing random walks of the multi-relational data in micro-blogging: ret-weet, reply, reintroduce, and read. Due to the uncertainty of the reintroduce and read operations, a new method is proposed to determine the transition probabilities of uncertain relational networks. Moreover, we propose a method for performing the combined random walks for the multi-relational influence network, considering both the transition probabilities for intra- and inter-networking. Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7 million tweets, and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.