Abstract
With the development of Web 2.0, people would like to share opinions on the Web, which are very helpful for other users to make decisions. Especially, some users have more powerful influence to other members of a community, group, or society, and their advice, opinions, and views are more valuable. We call these people opinion leaders. The study of opinion leader discovery from the social media is meaningful because it could help users to understand influential user behavior, and trace vital information diffusion of an e-society, even on-line ecology. However, existing approaches focus on linkage-based methods without considering the pests who have relationship with the potential opinion leader but carrying opposite opinions. In an extreme case, an opinion leader might be mistakenly identified according to his richer relationships with the pests. In this paper, we start from explaining the definition of opinion leader, and take into consideration of the user profile and posts’ opinions instead of using structural information (linkage) only. As such, those pests carrying opposite opinions could be gotten rid of from the social network, which could further improve the effectiveness of discovering opinion leaders. To evaluate the performance of our approach, we made experiments based on the Tweets data, and the results showed that our proposed approach could achieve 8% improvement compared with the linkage-based approach.
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Li, B., Wong, Kf., Zhou, L., Wei, Z., Xu, J. (2013). Pests Hidden in Your Fans: An Effective Approach for Opinion Leader Discovery. In: Sun, M., Zhang, M., Lin, D., Wang, H. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2013 2013. Lecture Notes in Computer Science(), vol 8202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41491-6_21
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DOI: https://doi.org/10.1007/978-3-642-41491-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41490-9
Online ISBN: 978-3-642-41491-6
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