Activity Prediction based on both Long Term and Current Activity on Twitter

• Item type: SIG Technical Reports(1)
• 公開日: 2013-11-07
• タイトル: Activity Prediction based on both Long Term and Current Activity on Twitter
• タイトル / 言語: en
• タイトル: Activity Prediction based on both Long Term and Current Activity on Twitter
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Faculty of Engineering, the University of Tokyo
• 著者所属: NTT DOCOMO, INC.
• 著者所属: Faculty of Engineering, the University of Tokyo
• 著者所属: Faculty of Engineering, the University of Tokyo
• 著者所属(英): en
• 著者所属(英): Faculty of Engineering, the University of Tokyo
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): Faculty of Engineering, the University of Tokyo
• 著者所属(英): en
• 著者所属(英): Faculty of Engineering, the University of Tokyo
• 著者名: Takuya, Shinmura; Yusuke, Fukazawa; Dandan, Zhu; Jun, Ota
• 著者名(英): Takuya, Shinmura; Yusuke, Fukazawa; Dandan, Zhu; Jun, Ota
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: we propose a method of predicting human's activity, including the location and purpose, by using Twitter posts with location information. The proposed method predicts target users' activities based on the location transition and tweet of users in the database. Concretely, we adopt both the similarity of current location and interest, and the similarity of long term interest and location to select the base user and tweet. And then, we can utilize these two baselines to predict target users' activities. We evaluate the proposed method by the following two points: one is the error range of the distance, and the other is the similarity of tweet contents. We used three months of Twitter data with location information (almost 40 mil.) as the database. The experiment results demonstrate that the prediction accuracy of the proposed method is superior to the two control groups which only consider one of the similarity of current location and interest and the similarity of long term interest and location.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: we propose a method of predicting human's activity, including the location and purpose, by using Twitter posts with location information. The proposed method predicts target users' activities based on the location transition and tweet of users in the database. Concretely, we adopt both the similarity of current location and interest, and the similarity of long term interest and location to select the base user and tweet. And then, we can utilize these two baselines to predict target users' activities. We evaluate the proposed method by the following two points: one is the error range of the distance, and the other is the similarity of tweet contents. We used three months of Twitter data with location information (almost 40 mil.) as the database. The experiment results demonstrate that the prediction accuracy of the proposed method is superior to the two control groups which only consider one of the similarity of current location and interest and the similarity of long term interest and location.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12628338
• 書誌情報: 研究報告デジタルコンテンツクリエーション（DCC）; 巻 2013-DCC-5, 号 1, p. 1-6, 発行日 2013-11-07
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会