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5 Extraction of Rumor Corrective Information from Twitter posts in Disaster Hiroshi Takahashi Abstract Twitter, a micro-blogging service, is widely used in Japan. Because information on Twitter is updated in real time, people used Twitter as a means of emergency communication instead of phone or , during the Great Eastern Japan Earthquake in However, at that time, many rumors, which have no information source were posted and spread on Twitter. False rumors may adversely affect the transmission of important information. Therefore, it is important to construct the environment that prevents people from spreading rumors. In order to prevent the spread of rumors, an extraction task of rumor corrective information by using rumor markers rumor have been studied. However previous research has not investigated the influence of rumor markers on a variety of extracted corrective information. In this study, we use additional rumor markers misinformation and lie to make clear this influence. Keywords: Text mining, Twitter, Natural Language Processing Master s Thesis, Department of Information Processing, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-MT , March 15, iii
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20 TAG RT CC URL 3.5: Twitter URL Twitter 3.5 Twitter URL Twitter Twitter 140 URL Twitter SVM Support vector machine SVM libsvm 1 RBF grid.py 1 cjlin/libsvm/ 12
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39 31 [1] 24 3 [2] : 1997 [3] 4, pp. 1-4, [4] 18, pp , [5] Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. Information credibility on twitter. In WWW 2011, pp , [6] M. Mathioudakis and N. Koudas. TwitterMonitor: trend detection over the twitter stream. In Proceedings of the 2010 international conference on Management of data, pages ACM, [7] Twitter 26 CD-ROM, [8] Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, and Qiaozhu Mei. Rumor has it: Identifying misinformation in microblogs. In EMNLP, pp , [9] Yajuan Duan, Long Jiang, Tao Qin, Ming Zhou, and Heung-Yeung Shum. An emprical study on learning to rank of tweets. In COLING 2010, pp , 2010.
40 [10] Zhiyuan Cheng, James Caverlee, and Kyumin Lee. You are where you tweet: A content-based approach to geo-locating twitter users. In CIKM 10, pp ,
41 33 A Twitter Twitter Tweet Twitter 140 Time Line Twitter Twitter follow follower Retweet Quote
42 図 A.1: タイムラインの例 する際に再投稿するツイートの先頭に自身のコメントを付け加えることが できる リスト Lists 好みに合わせたアカウントを登録し 登録したアカウントのツイートだけ を表示するタイムラインを作成する機能 ハッシュタグ Hash tag #から始まるタグであり ツイートが言及するトピックを投稿者が明示する ことができる 非公開 Protect アカウントを非公開にすると自分のツイートをフォロワにのみ公開するこ とができる 34
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25 Feature Selection for Prediction of Stock Price Time Series 1140357 2014 2 28 ..,,,,. 2013 1 1 12 31, ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i Abstract Feature Selection for Prediction of Stock Price Time
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