@article{oai:repo.lib.tut.ac.jp:00001969,
 author = {廣中, 詩織 and 吉田, 光男 and Yoshida, Mitsuo and 岡部, 正幸 and 梅村, 恭司 and Hironaka, Shiori and Okabe, Masayuki and Umemura , Kyoji},
 issue = {1},
 journal = {人工知能学会論文誌, Transactions of the Japanese Society for Artificial Intelligence},
 month = {},
 note = {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.},
 pages = {WII-M_1--11},
 title = {日本における居住地推定に利用するためのフォロー関係の調査},
 volume = {32},
 year = {2017}
}