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Story Tracker: Incremental visual text analytics of news story development

机译：故事跟踪器：新闻故事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. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories 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. Text clustering algorithms extract stories from online news streams in consecutive time windows and identify similar stories from the past. The stories are displayed in a visualization, which (1) sorts the stories by minimizing clutter and overlap from edge crossings, (2) shows their temporal characteristics in different time frames with different levels of detail, and (3) allows incremental updates of the display without recalculating the past data. Stories can be interactively filtered by their duration and connectivity in order to be explored in full detail. To demonstrate the system's capabilities for detailed dynamic text stream exploration, we present a use case with real news data about the Arabic Uprising in 2011.
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。有关这些事件的故事具有复杂的关系和特征，很难建模：它们之间的关联性可能很弱，也可能随着时间的流逝而合并或分裂。在本文中，我们提供了一个可视化分析系统，用于对动态信息流中的新闻故事进行时态分析，该系统将交互式可视化和文本挖掘技术相结合，以方便分析随时间分裂和合并的相似主题。文本聚类算法从连续时间窗口的在线新闻流中提取故事，并识别过去的相似故事。故事以可视化方式显示，该故事（1）通过最大程度地减少边缘交叉处的混乱和重叠来对故事进行分类；（2）在不同时间范围内以不同的详细程度显示其时间特征；（3）允许对故事进行增量更新显示而不重新计算过去的数据。故事可以通过其持续时间和连接性进行交互过滤，以便进行详细探讨。为了演示该系统进行详细的动态文本流浏览的功能，我们提供了一个用例，其中包含有关2011年阿拉伯起义的真实新闻数据。

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