Utilizing Social Relationships for Blog Popularity Mining

  • Authors:

  • Chih-Lu Lin;Hao-Lun Tang;Hung-Yu Kao

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.

  • Venue:
  • AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Due to the ease of use in blogs, this new form of web content has become a popular online media. Detecting the popularity of blogs in the massive blogosphere is a critical issue. General search engines that ignore the social interconnection between bloggers have less discrimination of blogs. This study extracts real-world blog data and analyzes the interconnection in these blog communities for blog popularity mining. The interconnections reveal the consciousness of bloggers and the popularity of blogs which may refer to blog qualities. In this paper, we propose a blog network model based on the interconnection structure between blogs and a popularity ranking method, called BRank, on the constructed model. Several experiments are conducted to analyze the various explicit and implicit interconnection structures and discover variances of the impact of interactions in different communities. Experiments on several real blog communities show that the proposed method could detect blogs with great popularity in the blogosphere.