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  • 标题:ソーシャルグラフによる居住地推定のためのユーザプロフィール分析
  • 本地全文:下载
  • 作者:廣中 詩織 ; 吉田 光男 ; 梅村 恭司
  • 期刊名称:人工知能学会論文誌
  • 印刷版ISSN:1346-0714
  • 电子版ISSN:1346-8030
  • 出版年度:2020
  • 卷号:35
  • 期号:1
  • 页码:1-10
  • DOI:10.1527/tjsai.E-J71
  • 出版社:The Japanese Society for Artificial Intelligence
  • 摘要:

    Users’ attributes, such as home location, are necessary for various applications, such as news recommendations and event detections. However, most real user attributes (e.g., home location) are not open to the public. Therefore, their attributes are estimated by relationships between users. A social graph constructed from relationships between users can help estimate home locations, but it is difficult to collect many relationships, such as followers’ relationships. We focus on users whose home locations are difficult to estimate, so that we can select users whose locations can be accurately estimated before collecting relationships. In this paper, we use their profiles which can be collected before collecting relationships. Then, we analyze difficult users with their profiles. As a result, we found that users whose home locations incorrectly estimated had a longer duration since the date their account was created, longer name, and longer description. In addition, the results indicated that the users whose home locations were incorrectly estimated differed from those whose home locations could not be estimated.

  • 关键词:social graph;home location estimation;user profile;Twitter
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