{"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}
引用次数: 3
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
