震災時におけるTwitterのリツイート分析(「Webインテリジェンス」及び一般)  [in Japanese] Analysis of Retweet on Twitter under the Disaster Situation  [in Japanese]

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Author(s)

Abstract

本論文では,東日本大震災時の前後にTwitterに投稿された約4億のTweetを用いて,震災がTwitterを用いたリツイート行動に与えた影響を分析した.リツイートの時系列を混合正規分布を用いてモデル化し,震災直後にはリツイートが行われるタイミングが短くなり多くの情報が素早く大勢のユーザに共有されたことを明らかにした.また,得られたモデルをクラスタリングすることで,リツイートの時系列変化を5パターンに分類しそれぞれの特徴を分析し,各パターンの出現数が震災前後でどのように変化したかを確認した.

In this paper, we analyzed the 400 millions of Tweet data which posted around the Great East Japan Earthquake to find how the twitter used and how the Twitter was influenced by the disaster. We modeled the time series data of Retweet by Gaussian Mixture Model. By analyzing the model, we found that the peak times of the retweets are become shorter, and there are few long range retweets after the disaster. As a result, we can say that the role of the Twitter was changed from communication tools to information sharing tools since the Great East Japan Earthquake were occurred.

Journal

  • IEICE technical report. Artificial intelligence and knowledge-based processing   [List of Volumes]

    IEICE technical report. Artificial intelligence and knowledge-based processing 112(94), 19-24, 2012-06-14  [Table of Contents]

    The Institute of Electronics, Information and Communication Engineers

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Codes

  • NII Article ID (NAID)
    110009588523
  • NII NACSIS-CAT ID (NCID)
    AN10013061
  • Text Lang
    JPN
  • Article Type
    REV
  • ISSN
    0913-5685
  • NDL Article ID
    023810961
  • NDL Call No.
    Z16-940
  • Data Source
    CJP  NDL  NII-ELS 
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