Identifying domain-dependent influential microblog users: A post-feature based approach

Publication Type:
Conference Proceeding
Citation:
Proceedings of the National Conference on Artificial Intelligence, 2014, 4 pp. 3122 - 3123
Issue Date:
2014-01-01
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Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 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 domain- dependent 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 post- content based approaches. Our experimental results show that the post-feature based approach produces relatively higher precision than that of the content based approach.
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