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S. Abe, M. Shirakawa, T. Nakamura, T. Hara, K. Ikeda, and K. Hoashi, "Predicting the Occurrence of Life Events from User’s Tweet History," Proc. of IEEE International Conference on Semantic Computing (ICSC 2018), February 2018.
ID 913
分類 国際会議
タグ Twitter,ライフイベント,イベント抽出,イベント予測
表題 (title) Predicting the Occurrence of Life Events from User’s Tweet History
表題 (英文) Predicting the Occurrence of Life Events from User’s Tweet History
著者名 (author) Shun Abe,Masumi Shirakawa,Tatsuya Nakamura,Takahiro Hara,Kazushi Ikeda,Keiichiro Hoashi
英文著者名 (author) ,Masumi Shirakawa,Tatsuya Nakamura,Takahiro Hara,Kazushi Ikeda,Keiichiro Hoashi
キー (key) ,Masumi Shirakawa,Tatsuya Nakamura,Takahiro Hara,Kazushi Ikeda,Keiichiro Hoashi
定期刊行物名等 Proc. of IEEE International Conference on Semantic Computing (ICSC 2018)
定期刊行物名等
巻数 (volume)
号数 (number)
ページ範囲 (pages)
刊行月 (month) 2
出版年 (year) 2018
Impact Factor (JCR)
URL
付加情報 (note) 助成:文部科学省科学研究費補助金・基盤研究(A)(26240013),JST 国際科学技術共同研究推進事業(戦略的国際共同研究プログラム),開催地 : Laguna Hills, California, USA,開催日程 : 1/31~2/2, 発表日 : 2/2
発表年度 2017年度
内容梗概 (abstract) This paper addresses the new problem of predicting the occurrence of Twitter users life events using word occurrence tendencies in users tweet histories. Many previous studies have addressed public event prediction and life event extraction on Twitter. Most of the methods for these two problems use tweets that explicitly refer to event occurrence. However, users who will
experience a life event are unlikely to post tweets that reference the occurrence of the life event explicitly; thus, existing methods to find tweets that refer to event occurrence explicitly
are not applicable to life event prediction.
Therefore, we assume that users who will experience a life event tend to post tweets that refer to life event occurrence implicitly and propose a method to identify such a tendency to predict life events. First, we extract users who experienced a specific life event and collect their past tweets to identify features that implicitly indicate the life event occurrence. We
use the word occurrence tendency in such tweets as training data to construct a life event prediction model. We chose five life events, that is, ``Giving birth, ``Getting a job offer, ``Leaving the hospital, ``Pregnancy, and ``Marriage, and assessed the prediction performance of the proposed method for each event. Experimental results demonstrate that the proposed method outperformed a baseline method for all selected life events except ``Leaving the hospital and achieved the highest prediction accuracy for ``Giving birth. We suppose that this event resulted in the highest prediction accuracy because all users who gave birth experienced pregnancy and common features appeared in their tweets over a long period.
論文電子ファイル ICSC_abe_cameraready.pdf (application/pdf) [一般閲覧可]
BiBTeXエントリ
@article{id913,
         title = {Predicting the Occurrence of Life Events from User’s Tweet History},
        author = {Shun Abe and Masumi Shirakawa and Tatsuya Nakamura and Takahiro Hara and Kazushi Ikeda and Keiichiro Hoashi},
       journal = {Proc. of IEEE International Conference on Semantic Computing (ICSC 2018)},
         month = {2},
          year = {2018},
          note = {助成:文部科学省科学研究費補助金・基盤研究(A)(26240013),JST 国際科学技術共同研究推進事業(戦略的国際共同研究プログラム),開催地 : Laguna Hills, California, USA,開催日程 : 1/31~2/2, 発表日 : 2/2},
        annote = {2017年度},
}