Local Attention Analysis and prediction of Online News Articles in Twitter

被引：2

作者：

Nagaki, Saki ^{[1
]}

Yamaguchi, Yuto ^{[2
]}

Amagasa, Toshiyuki ^{[1
]}

Kitagawa, Hiroyuki ^{[1
]}

机构：

[1] Univ Tsukuba, Tsukuba, Ibaraki, Japan

[2] AIST, Tokyo, Japan

来源：

ADJUNCT PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING NETWORKING AND SERVICES (MOBIQUITOUS 2016) | 2016年

关键词：

On line news; Twitter; microblog; social media analysis;

D O I：

10.1145/3004010.3004042

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：136 / 141

页数：6

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