Abstract: In microblogs contexts like Twitter, the number of content producers can easily reach tens of thousands and a large number of users participate in the discussion of the topic, for any given topic. While this large number can generate notable diversity and not all users are equally influential, it also makes finding the true influencers, those generally rated as interesting and authoritative on a given topic, challenging. In this paper, the influence of users is measured by random walks of multi-relational data in microblogs: repost, reply, copy, and read. As the uncertainty of copy and read, a new method is proposed to determine transition probabilities of uncertain relational networks. Moreover, the combined random walk is proposed for multi-relational influence network, considering both of the transition probabilities between the intra and inter of the network. Finally, influencers are classified into two types: multi-topical influencers and single-topical influencers. Experiments are conducted on a real dataset from Twitter containing about 0.26 million users and 2.7 million posts, and the results showed that the method in this paper is more effective than TwitterRank and other methods of discovering influencers. Also, the results show that the number of multi-topical influencers is far less than that of single-topical influencers, but the effect of influence is much stronger than that of single-topical influencers.