可视化网络新闻文章的时空关键词趋势

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI:10.1145/3397536.3422339

J. Kastner, H. Samet

{"title":"可视化网络新闻文章的时空关键词趋势","authors":"J. Kastner, H. Samet","doi":"10.1145/3397536.3422339","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"528 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Visualizing SpatioTemporal Keyword Trends in Online News Articles\",\"authors\":\"J. Kastner, H. Samet\",\"doi\":\"10.1145/3397536.3422339\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":233918,\"journal\":{\"name\":\"Proceedings of the 28th International Conference on Advances in Geographic Information Systems\",\"volume\":\"528 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 28th International Conference on Advances in Geographic Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3397536.3422339\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3397536.3422339","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

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。在线新闻的增长导致了在线发布的新闻文章文本形式的数据过剩。虽然大量的数据显然是可取的，但如果没有帮助，人类很难分析和发现数据中的趋势。本文演示的应用程序旨在通过在现有报摊架构的基础上构建时空数据可视化来帮助用户进行这种分析。该应用程序可用于跟踪一种疾病(例如COVID-19)随时间变化的地理流行情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Visualizing SpatioTemporal Keyword Trends in Online News Articles

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

自引率

0.00%

发文量