Identifying Domain-Dependent Influential Microblog Users: A Post-Feature Based Approach

Users of a social network like to follow the posts published by influential users. Such posts usually are delivered quickly and thus will produce a strong influence on public opinions. In this paper, we focus on the problem of identifying domaindependent influential users(or topic experts). Some of traditional approaches are based on the post contents of users users to identify influential users, which may be biased by spammers who try to make posts related to some topics through a simple copy and paste. Others make use of user authentication information given by a service platform or user self description (introduction or label) in finding influential users. However, what users have published is not necessarily related to what they have registed and described. In addition, if there is no comments from other users, its less objective to assess a users post quality. To improve effectiveness of recognizing influential users in a topic of microblogs, we propose a post-feature based approach which is supplementary to postcontent based approaches. Our experimental results show that the post-feature based approach produces relatively higher precision than that of the content based approach.

Authors

• Nian Liu Mohamed bin Zayed University of Artificial Intelligence
• Lin Li Shanxi University
• Guandong Xu University of Technology Sydney The Education University of Hong Kong
• Zhenglu Yang Nankai University, Tianjin, China

Keywords

No keywords are indexed for this paper.