Analyzing internet topics by visualizing microblog retweeting. (June 2015)

• Record Type: Journal Article
• Authors: Wang, Changbo; Liu, Yuhua; Xiao, Zhao; Zhou, Aoying; Zhang, Kang
• Is Part Of: Journal of visual languages & computing. Volume 28(2015)
• Page Start: 122
• Page End: 133
• Publication Date: 2015-06
• Subjects: Microblog retweeting -- Internet topics -- Visualization -- Analyzing; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2014.11.007
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Microblog is a large-scale information sharing platform where retweeting plays an important role in information diffusion. Analyzing retweeting evolutions can help reasoning about the trend of public opinions. Information visualization techniques are used to demonstrate the retweeting behavior in order to understand how Internet topics diffuse on Microblogs. First, a graph clustering method is used to analyze the retweeting relationships among people of different occupations. Then a new algorithm based on electric field is proposed to visualize the layout of the relationship links. A prediction method based on three diffusion models is presented to predict the number of retweets over time. Finally, three real world case studies show the validity of our methods. Highlights: We build a multi-view Microblog Visualization system for analyzing internet topics. We propose a novel graph clustering method to analyze the retweeting relationships. The graph layout based on electric field can show the relationship links more clear. We predict the trend of retweeting through three diffusion models with case studies.