通过文章对比分析多个新闻网站

Masaharu Yoshioka
{"title":"通过文章对比分析多个新闻网站","authors":"Masaharu Yoshioka","doi":"10.1109/SITIS.2008.42","DOIUrl":null,"url":null,"abstract":"Today, there is access to large numbers of news sites in different countries, and there are several experimental systems, such as Newsblaster and NewsExplorer, that integrate news articles about a particular event from multiple news sites. These systems enable a good understanding of particular events by using multiple news sites, but they ignore the characteristics of the various news sites. To characterize the differences between news sites, the News Site Contrast (NSContrast) system has been proposed. This system analyzes multiple news sites using the concept of contrast- set mining. However, NSContrast has only limited analysis functions and is not mature enough for evaluation via user experiments. Therefore, in this paper, we analyze contrast set mining results for NSContrast, aiming to understand the requirements for extracting useful information that also reflects the interests of different countries. Based on this analysis, a new NSContrast is introduced and applied to a news article database constructed from multiple news sites in different countries for user experimentation.","PeriodicalId":202698,"journal":{"name":"2008 IEEE International Conference on Signal Image Technology and Internet Based Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Analyzing Multiple News Sites by Contrasting Articles\",\"authors\":\"Masaharu Yoshioka\",\"doi\":\"10.1109/SITIS.2008.42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today, there is access to large numbers of news sites in different countries, and there are several experimental systems, such as Newsblaster and NewsExplorer, that integrate news articles about a particular event from multiple news sites. These systems enable a good understanding of particular events by using multiple news sites, but they ignore the characteristics of the various news sites. To characterize the differences between news sites, the News Site Contrast (NSContrast) system has been proposed. This system analyzes multiple news sites using the concept of contrast- set mining. However, NSContrast has only limited analysis functions and is not mature enough for evaluation via user experiments. Therefore, in this paper, we analyze contrast set mining results for NSContrast, aiming to understand the requirements for extracting useful information that also reflects the interests of different countries. Based on this analysis, a new NSContrast is introduced and applied to a news article database constructed from multiple news sites in different countries for user experimentation.\",\"PeriodicalId\":202698,\"journal\":{\"name\":\"2008 IEEE International Conference on Signal Image Technology and Internet Based Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Signal Image Technology and Internet Based Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITIS.2008.42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Signal Image Technology and Internet Based Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2008.42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

今天,人们可以访问不同国家的大量新闻网站,并且有几个实验系统,如Newsblaster和newsexexplorer,可以整合来自多个新闻网站的关于特定事件的新闻文章。这些系统通过使用多个新闻站点来很好地理解特定事件,但它们忽略了各种新闻站点的特征。为了描述新闻网站之间的差异,新闻网站对比(NSContrast)系统被提出。该系统利用对比集挖掘的概念对多个新闻站点进行分析。然而,NSContrast的分析功能有限,还不够成熟,无法通过用户实验进行评估。因此,在本文中,我们对NSContrast的对比集挖掘结果进行分析,旨在了解提取有用信息的需求,同时也反映了不同国家的利益。在此基础上,本文引入了一种新的NSContrast方法,并将其应用于一个由多个不同国家的新闻网站构建的新闻文章数据库中进行用户实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Multiple News Sites by Contrasting Articles
Today, there is access to large numbers of news sites in different countries, and there are several experimental systems, such as Newsblaster and NewsExplorer, that integrate news articles about a particular event from multiple news sites. These systems enable a good understanding of particular events by using multiple news sites, but they ignore the characteristics of the various news sites. To characterize the differences between news sites, the News Site Contrast (NSContrast) system has been proposed. This system analyzes multiple news sites using the concept of contrast- set mining. However, NSContrast has only limited analysis functions and is not mature enough for evaluation via user experiments. Therefore, in this paper, we analyze contrast set mining results for NSContrast, aiming to understand the requirements for extracting useful information that also reflects the interests of different countries. Based on this analysis, a new NSContrast is introduced and applied to a news article database constructed from multiple news sites in different countries for user experimentation.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信