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.

翻译：分析相互关联的思想如何在多个社会群体内部及之间流动，有助于理解社交媒体中信息、观点和思想的传播。现有关于思想流分析的动态文本研究工作大多基于主题模型。因此，在分析思想流动背后的原因时，人们不得不查看思想的文本数据，而这些文本数据数量庞大且结构复杂，令人烦恼。为解决这一问题，我们提出一种基于概念的动态文本可视分析方法，该方法展示了思想内容如何变化，并帮助用户分析思想流动的根本原因。我们使用概念来总结思想的内容，并通过流线展示概念的流动。为确保流线的稳定性，采用受约束的t-SNE投影算法来显示概念随时间的变化及其相关性。为了更好地传达概念的异常变化，我们提出一种基于异常检测的方法来检测概念异常变化的时间段，并对其进行高亮显示。定性评估和在真实Twitter数据集上的案例研究证明了我们可视分析方法的正确性和有效性。