Narratives: A visualization to track narrative events as they develop

Danyel Fisher, Aaron Hoff, G. Robertson, Matthew F. Hurst
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引用次数: 63

Abstract

Analyzing unstructured text streams can be challenging. One popular approach is to isolate specific themes in the text, and to visualize the connections between them. Some existing systems, like ThemeRiver, provide a temporal view of changes in themes; other systems, like In-Spire, use clustering techniques to help an analyst identify the themes at a single point in time. Narratives combines both of these techniques; it uses a temporal axis to visualize ways that concepts have changed over time, and introduces several methods to explore how those concepts relate to each other. Narratives is designed to help the user place news stories in their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as weblogs), a user discovers both important events and measures the social relevance of these stories.
叙事:跟踪叙事事件发展的可视化
分析非结构化文本流可能很有挑战性。一种流行的方法是在文本中分离出特定的主题,并将它们之间的联系可视化。一些现有的系统,如themerriver,提供了主题变化的时间视图;其他系统,如in - spire,使用聚类技术帮助分析师在单个时间点识别主题。叙事结合了这两种技巧;它使用时间轴来可视化概念随时间变化的方式,并介绍了几种方法来探索这些概念如何相互关联。叙事的目的是通过理解与新闻相关的主要话题如何随着时间的推移而变化,帮助用户将新闻故事置于历史和社会背景中。用户可以通过检查总结特定新闻事件的主题关键词来将文章联系起来。通过在社交媒体(如博客)中以引用的形式跟踪对新闻文章的关注,用户可以发现重要事件并衡量这些故事的社会相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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