Computer Science, Zhejiang University, 12377 Hangzhou, Beijing China 310058 (e-mail:
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, 58207 Kowloon, Hong Kong Hong Kong (e-mail:
IEEE Trans Vis Comput Graph. 2018 Oct;24(10):2758-2772. doi: 10.1109/TVCG.2017.2764459. Epub 2017 Oct 18.
Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.
分析社交流对于许多应用非常重要，例如危机管理。然而，大型事件的社交流具有相当大的多样性、不断增加的数量和高度动态性，这些仍然是必须克服的重大挑战，以确保有效的探索。我们提出了一个新的框架，以便在预算 PC 上处理复杂的社交流。该框架具有两个组成部分：1）一种在线方法来检测重要时间段（即子事件），以及 2）一种定制的 GPU 辅助自组织图（SOM）方法，该方法可以稳定有效地对子事件的推文进行聚类。基于该框架，我们提出了 StreamExplorer，以促进在三个层次上对社交流进行可视化分析、跟踪和比较。在宏观层面上，StreamExplorer 使用基于新字形的时间线可视化，快速提供社交流的多方面概述。在中观层面上，使用地图可视化从主题或地理方面对社交流进行可视化总结。在微观层面上，用户可以使用交互式镜头从不同角度可视化检查和探索社交流。通过两个案例研究和基于任务的评估来展示 StreamExplorer 的有效性和有用性。
