Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Identifying opinion leaders in the blogosphere
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Algorithms of BBS opinion leader mining based on sentiment analysis
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Detecting opinion leader dynamically in chinese news comments
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
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Opinion leaders play a very important role in information diffusion; they are found in all fields of society and influence the opinions of the masses in their fields. Most proposed algorithms on identifying opinion leaders in internet social network are global measure algorithms and usually omit the fact that opinion leaders are field-limited. We propose and test several algorithms, including interest-field based algorithms and global measure algorithms, to identify opinion leaders in BBS. Our experiments show that different algorithms are sensitive to different indicators; the interest-field based algorithms which not only take into account of the social networks’ structure but also the users’ interest space are more reasonable and effective in identifying opinion leaders in BBS. The interest-field based algorithms are sensitive to the high status nodes in the social network, and their performance relies on the quality of field discovery.