Hierarchical content group detection from different social media platforms using Web link structure
Daichi Takehara, Ryosuke Harakawa, Takahiro Ogawa, Miki Haseyama · 2016
This paper presents a method for hierarchical content group detection from different social media platforms, which can reveal hierarchical structure of content groups. In this paper, content groups are defined as sets of contents with similar topics. Based on the revealed hierarchical structure, our method enables users to efficiently find the desired contents from large amount of contents placed in diversified social media platforms. The main contributions of this paper are twofold. First, effective latent features for comparing the contents placed in different social media platforms can be extracted by the combination use of the correlation between features obtained from different social media platform and the Web link structure. Second, the hierarchical structure of the content groups, which captures their various abstraction levels, can be revealed by hierarchically detecting their content groups. Experimental results on the real-world dataset containing YouTube videos and Wikipedia articles show the effectiveness of our method.