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Incremental Visual Text Analytics of News Story Development

机译：新闻报道开发的增量可视文本分析

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摘要

Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. Additionally, the stories have very complex relationships and characteristics that are difficult to model: they can be weakly or strongly connected, or they can merge or split over time. In this paper, we present a visual analytics system for exploration of news topics in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. We employ text clustering techniques to automatically extract stories from online news streams and present a visualization that: 1) shows temporal characteristics of stories in different time frames with different level of detail; 2) allows incremental updates of the display without recalculating the visual features of the past data; 3) sorts the stories by minimizing clutter and overlap from edge crossings. By using interaction, stories can be filtered based on their duration and characteristics in order to be explored in full detail with details on demand. To demonstrate the usefulness of our system, case studies with real news data are presented and show the capabilities for detailed dynamic text stream exploration.
机译：在线新闻源每天都会产生数千篇新闻文章，报道本地和全球现实事件。新信息迅速取代了旧信息，使读者很难将当前事件与过去联系起来。此外，这些故事具有非常复杂的关系和特征，很难建模：它们之间的联系薄弱或联系紧密，或者随着时间的流逝它们可以合并或分裂。在本文中，我们提供了一种可视化分析系统，用于探索动态信息流中的新闻主题，该系统结合了交互式可视化和文本挖掘技术，可以方便地分析随时间分裂和合并的相似主题。我们采用文本聚类技术从在线新闻流中自动提取故事，并提供一种可视化效果：1）在不同时间范围内以不同的详细程度显示故事的时间特征； 2）允许显示的增量更新，而无需重新计算过去数据的视觉特征； 3）通过最小化边缘交叉的混乱和重叠来对故事进行分类。通过使用交互，可以根据故事的持续时间和特征来过滤故事，以便按需详细了解细节。为了展示我们系统的实用性，我们提供了带有真实新闻数据的案例研究，并展示了详细的动态文本流探索功能。

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