User communities and contents co‐ranking for user‐generated content quality evaluation in social networks

Autor: Jia-Yin Qi, Xin Lin, Li Lei, Yanquan. Zhou, Yue Zhai, Caixia Yuan
Rok vydání: 2014
Předmět:
Zdroj: International Journal of Communication Systems. 29:2147-2168
ISSN: 1099-1131
1074-5351
DOI: 10.1002/dac.2908
Popis: SUMMARY With the massive popularity of social networks, more and more users can produce millions of user-generated contents (UGCs) daily. However, UGC quality is uneven, which has posed challenges to finding superior contents in such a large data set. In this paper, we present a new idea of UGC quality evaluation exploiting user communities, which are formed by users either in a friend circle or with similar interests in social networks. The intuition is that a user community can help evaluate the UGC quality better than a single user. Hence, we propose a new graph-theoretic user communities and contents co-ranking (UCCC) algorithm for UGC quality evaluation. UCCC evaluates UGCs and their related user communities simultaneously based on three different relationship networks: the network connecting UGCs, the network connecting user communities, and a third network that ties the two together. Contents and user communities are ranked following a co-ranking algorithm based on the assumption that there is a mutually reinforcing relationship between them. Experiments using real-world data have shown that UCCC outperforms competitive algorithms by a good margin in most cases and a user community is more useful than a single user for UGC quality evaluation. Copyright © 2014 John Wiley & Sons, Ltd.
Databáze: OpenAIRE
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