Visualizing SpatioTemporal Keyword Trends in Online News Articles
Visualizing SpatioTemporal Keyword Trends in Online News Articles
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DOI：
10.1145/3397536.3422339
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
J. Kastner;H. Samet
中科院分区：
文献类型：
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作者：
J. Kastner;H. Samet
随着互联网的发展，在线新闻来源已经稳步取代了纸质新闻来源。在线新闻的这种增长导致了在线发布的新闻文章文本形式的数据过剩。虽然丰富的数据显然是可取的，但它可能使人类难以在没有帮助的情况下分析和发现数据中的趋势。本文中所展示的应用程序旨在通过建立一个基于现有报摊架构的时空数据可视化来帮助用户进行此类分析。该应用被示出为适用于跟踪疾病的变化的地理流行率（例如，COVID-19）。
Online sources of news have steadily supplanted their paper counterparts alongside the growth of the internet. This growth in online news has led to a surplus of data in the form of the text of news articles published online. While an abundance of data is obviously desirable, it can make it difficult for a human to analyze and find trends in the data without assistance. The application demonstrated in the paper aims to aid users in such analysis by building a spatio-textual and spatiotemporal data visualization based on the existing NewsStand architecture. The application is shown to be applicable to tracking the changing geographic prevalence of a disease (e.g., COVID-19) over time.