著者
廣中 詩織 吉田 光男 梅村 恭司
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.35, no.1, pp.E-J71_1-10, 2020-01-01 (Released:2020-01-01)
参考文献数
26

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.

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今日の論文。https://t.co/a9Gglw05u6 Twitterのプロフィール欄から既存の居住地推定の精度を上げようって趣旨の報告書。結論としては、 ・位置情報付きの写真が無い ・名前が長い ・自己紹介が長い ・アカウント作成から1400日以上 ・フォロワー数に対してフォロー数が多い だと推定しにくいとのこと
2020年最初の論文。ユーザ名や自己紹介文が長いようなユーザは、居住地を推定しづらい(実社会ソーシャルグラフと異なる?)ことなどを明らかにしました。 / 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 https://t.co/hw07Q9bmtr

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