title: OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media.
abstract: It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow.
authors.forename: Yingcai
authors.initials: Y
authors.lastname: Wu
authors.forename: Shixia
authors.initials: S
authors.lastname: Liu
authors.forename: Kai
authors.initials: K
authors.lastname: Yan
authors.forename: Mengchen
authors.initials: M
authors.lastname: Liu
authors.forename: Fangzhao
authors.initials: F
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D003142:Communication; D003196:Computer Graphics; D006801:Humans; D048088:Informatics / Q000379:methods*; D011639:Public Opinion; D061108:Social Media