2016 IEEE Conference on Visual Analytics Science and Technology (VAST) (2016)
Baltimore, MD, USA
Oct. 23, 2016 to Oct. 28, 2016
Xiting Wang , School of Software, Tsinghua University, China
Shixia Liu , School of Software, Tsinghua University, China
Yang Chen , School of Software, Tsinghua University, China
Tai-Quan Peng , Michigan State University, United States of America
Jing Su , Tsinghua University, China
Jing Yang , UNCC, United States of America
Baining Guo , Microsoft Research, United States of America
Tracking how correlated ideas flow within and across multiple social groups facilitates the understanding of the transfer of information, opinions, and thoughts on social media. In this paper, we present IdeaFlow, a visual analytics system for analyzing the lead-lag changes within and across pre-defined social groups regarding a specific set of correlated ideas, each of which is described by a set of words. To model idea flows accurately, we develop a random-walk-based correlation model and integrate it with Bayesian conditional cointegration and a tensor-based technique. To convey complex lead-lag relationships over time, IdeaFlow combines the strengths of a bubble tree, a flow map, and a timeline. In particular, we develop a Voronoi-treemap-based bubble tree to help users get an overview of a set of ideas quickly. A correlated-clustering-based layout algorithm is used to simultaneously generate multiple flow maps with less ambiguity. We also introduce a focus+context timeline to explore huge amounts of temporal data at different levels of time granularity. Quantitative evaluation and case studies demonstrate the accuracy and effectiveness of IdeaFlow.
Lead, Correlation, Time series analysis, Social groups, Social network services, Visual analytics
X. Wang et al., "How ideas flow across multiple social groups," 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), Baltimore, MD, USA, 2016, pp. 51-60.