  Analyzing how interrelated ideas flow within and between multiple social
groups helps understand the propagation of information, ideas, and thoughts on
social media. The existing dynamic text analysis work on idea flow analysis is
mostly based on the topic model. Therefore, when analyzing the reasons behind
the flow of ideas, people have to check the textual data of the ideas, which is
annoying because of the huge amount and complex structures of these texts. To
solve this problem, we propose a concept-based dynamic visual text analytics
method, which illustrates how the content of the ideas change and helps users
analyze the root cause of the idea flow. We use concepts to summarize the
content of the ideas and show the flow of concepts with the flow lines. To
ensure the stability of the flow lines, a constrained t-SNE projection
algorithm is used to display the change of concepts over time and the
correlation between them. In order to better convey the anomalous change of the
concepts, we propose a method to detect the time periods with anomalous change
of concepts based on anomaly detection and highlight them. A qualitative
evaluation and a case study on real-world Twitter datasets demonstrate the
correctness and effectiveness of our visual analytics method.
