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Construct a Bipartite Signed Network in YouTube

Construct a Bipartite Signed Network in YouTube

Tianyuan Yu (National University of Defense Technology, Changsha, China), Liang Bai (National University of Defense Technology, Changsha, China), Jinlin Guo (National University of Defense Technology, Changsha, China), and Zheng Yang (National University of Defense Technology, Changsha, China)
Copyright: © 2015 |Volume: 6 |Issue: 4 |Pages: 22
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781466677166|DOI: 10.4018/IJMDEM.2015100104
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MLA

Yu, Tianyuan, et al. "Construct a Bipartite Signed Network in YouTube." IJMDEM vol.6, no.4 2015: pp.56-77. https://doi.org/10.4018/IJMDEM.2015100104

APA

Yu, T., Bai, L., Guo, J., & Yang, Z. (2015). Construct a Bipartite Signed Network in YouTube. International Journal of Multimedia Data Engineering and Management (IJMDEM), 6(4), 56-77. https://doi.org/10.4018/IJMDEM.2015100104

Chicago

Yu, Tianyuan, et al. "Construct a Bipartite Signed Network in YouTube," International Journal of Multimedia Data Engineering and Management (IJMDEM) 6, no.4: 56-77. https://doi.org/10.4018/IJMDEM.2015100104

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Abstract

Nowadays, the video-sharing websites are becoming more and more popular, which leads to latent social networks among videos and users. In this work, results are integrated with the data collected from YouTube, one of the largest user-driven online video repositories, and are supported by Chinese sentiment analysis which excels the state of art. Along with it, the authors construct two types of bipartite signed networks, video network (VN) and topic participant network (TPN), where nodes denote videos or users while weights of edges represent the correlation between the nodes. Several indices are defined to quantitatively evaluate the importance of the nodes in the networks. Experiments are conducted by using YouTube videos and corresponding metadata related to two specific events. Experimental results show that both the analysis of social networks and indices correspond very closely with the events' evolution and the roles that topic participants play in spreading Internet videos. Finally, the authors extend the networks to summarization of a video set related to an event.

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