著者

廣中 詩織 吉田 光男 岡部 正幸 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.32, no.1, pp.WII-M_1-11, 2017-01-06 (Released:2017-01-20)

参考文献数

24

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The home locations of Twitter users can be estimated using a social network, which is generated by various relationships between users. There are many network-based location estimation methods with user relationships. However, the estimation accuracy of various methods and relationships is unclear. In this study, we estimate the users’home locations using four network-based location estimation methods on four types of social networks in Japan. We have obtained two results. (1) In the location estimation methods, the method that selects the most frequent location among the friends of the user shows the highest precision and recall. (2) In the four types of social networks, the relationship of follower has the highest precision and recall.

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

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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.

著者

廣中 詩織 佃 洸摂 濱崎 雅弘 後藤 真孝

出版者

ARG Webインテリジェンスとインタラクション研究会

雑誌

ARG 第11回Webインテリジェンスとインタラクション研究会 予稿集 = Proceedings of the 11th ARG Web Intelligence and Interaction

巻号頁・発行日

pp.17-22, 2017

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オリジナルコンテンツから次々と新しい派生コンテンツが制作されるN 次創作活動では,複数人のクリエータがコラボレーションをしてひとつのコンテンツを制作することが盛んに行われている.本稿では,動画共有サービスに投稿された,音楽に関するN 次創作動画を対象として,コラボレーションがもたらす効果について分析する.具体的には,以下の3 つの観点から分析を行う:(1)コラボレーションが動画の視聴のされ方に与える影響,(2)コラボレーションがクリエータのアクティビティに与える影響,(3)コラボレーション関係に基づくクリエータの特性.分析の結果,コラボレーションによって制作された動画は再生数がより多くなること,コラボレーション動画を制作したクリエータはより長い期間N 次創作活動を行うこと,コラボレーションをしたクリエータのペアの25%以上は複数回のコラボレーションをしており,コラボレーションには一定の継続性があることなどが明らかになった.

著者

廣中 詩織 吉田 光男 岡部 正幸 梅村 恭司

出版者

人工知能学会

雑誌

人工知能学会論文誌 = Transactions of the Japanese Society for Artificial Intelligence (ISSN:13460714)

巻号頁・発行日

vol.32, no.1, pp.WII-M_1-11, 2017

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The home locations of Twitter users can be estimated using a social network, which is generated by various relationships between users. There are many network-based location estimation methods with user relationships. However, the estimation accuracy of various methods and relationships is unclear. In this study, we estimate the users’home locations using four network-based location estimation methods on four types of social networks in Japan. We have obtained two results. (1) In the location estimation methods, the method that selects the most frequent location among the friends of the user shows the highest precision and recall. (2) In the four types of social networks, the relationship of follower has the highest precision and recall.