Twitter在线新闻文章的局部关注分析与预测

Saki Nagaki, Yuto Yamaguchi, T. Amagasa, H. Kitagawa
{"title":"Twitter在线新闻文章的局部关注分析与预测","authors":"Saki Nagaki, Yuto Yamaguchi, T. Amagasa, H. Kitagawa","doi":"10.1145/3004010.3004042","DOIUrl":null,"url":null,"abstract":"What is the difference between the local news and the general news? Due to a huge number of online news articles published on the Web, it has become quite important to recommend appropriate articles to users. Locality of an article is one of the important features to judge whether it gets interests from users in a specific area. In this work, we give a precise definition of local news article using the geographical distributions of the Twitter users. Using this definition, we analyze the characteristics of Japanese online news articles shared on Twitter with respect to their locality, and also propose a method for predicting the locality. Our method contributes to improving the performance of news recommendation because it becomes easy to identify the target area from local news we predict. In our experiments on Twitter and Yahoo! Japan News dataset, the results reveal that the prediction accuracy is 73%, which is 9 points higher than the naive method based on the news titles.","PeriodicalId":406787,"journal":{"name":"Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services","volume":"14 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Local Attention Analysis and Prediction of Online News Articles in Twitter\",\"authors\":\"Saki Nagaki, Yuto Yamaguchi, T. Amagasa, H. Kitagawa\",\"doi\":\"10.1145/3004010.3004042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"What is the difference between the local news and the general news? Due to a huge number of online news articles published on the Web, it has become quite important to recommend appropriate articles to users. Locality of an article is one of the important features to judge whether it gets interests from users in a specific area. In this work, we give a precise definition of local news article using the geographical distributions of the Twitter users. Using this definition, we analyze the characteristics of Japanese online news articles shared on Twitter with respect to their locality, and also propose a method for predicting the locality. Our method contributes to improving the performance of news recommendation because it becomes easy to identify the target area from local news we predict. In our experiments on Twitter and Yahoo! Japan News dataset, the results reveal that the prediction accuracy is 73%, which is 9 points higher than the naive method based on the news titles.\",\"PeriodicalId\":406787,\"journal\":{\"name\":\"Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services\",\"volume\":\"14 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3004010.3004042\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3004010.3004042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

本地新闻和一般新闻有什么不同?由于网络上发布了大量的在线新闻文章,向用户推荐合适的文章变得非常重要。文章的地域性是判断文章是否获得特定区域用户兴趣的重要特征之一。在这项工作中,我们使用Twitter用户的地理分布给出了本地新闻文章的精确定义。利用这一定义,我们分析了Twitter上分享的日本网络新闻文章的地方性特征,并提出了一种地方性预测方法。我们的方法有助于提高新闻推荐的性能,因为它变得容易从我们预测的本地新闻中识别目标区域。在Twitter和Yahoo!结果表明,该方法的预测准确率为73%,比基于新闻标题的朴素方法提高了9个百分点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local Attention Analysis and Prediction of Online News Articles in Twitter
What is the difference between the local news and the general news? Due to a huge number of online news articles published on the Web, it has become quite important to recommend appropriate articles to users. Locality of an article is one of the important features to judge whether it gets interests from users in a specific area. In this work, we give a precise definition of local news article using the geographical distributions of the Twitter users. Using this definition, we analyze the characteristics of Japanese online news articles shared on Twitter with respect to their locality, and also propose a method for predicting the locality. Our method contributes to improving the performance of news recommendation because it becomes easy to identify the target area from local news we predict. In our experiments on Twitter and Yahoo! Japan News dataset, the results reveal that the prediction accuracy is 73%, which is 9 points higher than the naive method based on the news titles.
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