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1 Twitter Twitter Twitter Twitter (1) (2) Use Trend Analysis of Twitter after the Great East Japan Earthquake Mai Miyabe, 1 Eiji Aramaki 1 and Asako Miura 2 After the Great Eastern Japan Earthquake in Japan 2011, numerous tweets were exchanged on Twitter. Several studies have already pointed out that micro-blogging systems have shown potential advantages in emergency situations, but it remains unclear how people use them. This paper presents a case study of how people used Twitter after the Great Eastern Japan Earthquake. First, we gathered tweets immediately after the earthquake and analyzed various factors, including locations. The results revealed two findings: (1) people in the disaster area tend to directly communicate with each other. On the other hand, people in the other area prefer tend to rely on re-tweet; (2) information posted from the disaster area tends to spread in the other area. 1 Center for Knowledge Structuring, The University of Tokyo 2 Department of Psychological Science, Kwansei Gakuin University 1. Facebook 1 Twitter 2 Twitter 140 1) 1 2) Twitter 1 3) Twitter Twitter 2 ( 1 ) BBS 4) 5) 4 ( 2 ) 4) 6) Twitter 2. Back Cohn ),8) c 2011 Information Processing Society of Japan
2 Mendoza 2010 Twitter 5) Twitter Longueville 2009 Twitter 6) URL Vieweg 2009 Oklahoma Grassfires Red River Floods Twitter 9) Qu BBS 4) Twitter 3. 10) Twitter 1 2 Twitter Table 1 Number of tweets in each data set. 1,612,074 A ,765,808 B C D B D 11) C 12) ,597 1,278,581 4,238, ,612, Twitter Twitter 1 API 2 c 2011 Information Processing Society of Japan
3 2.6% 3 ( 1 ) ( 2 ( 3 ) 4.2 ( 1 ) 1 ( 2 ) A Twitter 1 AREA5 2 Table 2 Area definition. 3 Fig. 1 3 Table 3 Number of users and tweets in each area. 1 Area map of our definition. % 1 18, , , , , , , , AREA5 346, , , ,612, D C B A A B C c 2011 Information Processing Society of Japan
4 5 Table 5 Number of re-tweets and replies. 4 Table 4 Example of re-tweet source. A B A A C B A A D C B RT A E A A (%) (%) 12, , , , , , , , AREA5 268, , , , A 7,620, ,554, B 81, , C 100, , D 554, , Fig. 2 Example of re-tweet flow. 70% 25% D C B A A 5. 60% 50% 40% 30% 20% 10% 20% 15% 10% 5% AREA % 7.6% 16.4% 7.9% 13.1% 16.8% 35.6% 27.5% 31.6% 22.1% % 9.1% Twitter 0% % (a) (b) 3 Fig. 3 Rate of re-tweet and rate of reply , Twitter 4 c 2011 Information Processing Society of Japan
5 情報処理学会研究報告 表 6 リツイート投稿者と被引用者の地域 Table 6 Relation between original tweet area and re-tweet area. 被引用者 12,470 1,430 26,239 6,047 46,186 計 3,136 10,517 45,042 10,778 69,473 リツイート投稿者 26,323 19, ,516 99, ,155 14,989 13, , , ,265 計 56,918 44, , ,676 1,207,079 表中の値は 各地域ペアに該当するリツイート数を示す 表 7 震災時の TTR 変化率 Table 7 Change rate of tweet-transfer rate. 被引用者の地域 低い 高い 低い 図 4 都道府県別リツイート率 Fig. 4 Map of re-tweet rate. 高い TTR 変化率 116.5% 88.9% 94.0% 95.6% 図 5 都道府県別リプライ率 Fig. 5 Map of reply rate. 図 4 図 5 においては 色が濃いほど割合が高いことを意味する 100% 90% 80% 70% イートされた情報が拡散される傾向があったと考えられる 5.3 仮説 2 被害の大きい地域から投稿された情報が他地域へと移動する 本節では 仮説 2 について検証する 60% 50% 40% 30% 20% まず ある地域から投稿された発言が どこでリツイートされたかを調査した 表 6 10% 次に各地域の発言が 他地域でどれだけリツイートされたかを計算した これを 本稿では 0% ツイート移動率 TTR と呼び 以下のように定義した TTR = 図 6 ツイート移動率の推移 Fig. 6 Time-line chart of tweet-transfer rate. 他地域での引用数 ある地域の被引用数 例えば 大災害地域 についての他地域は 3 4 となり これらの地 平常時 D の TTR を表 7 に 被引用者の地域別のツイート移動率の推移を図 6 にそれぞ 域でリツイートされた割合を測っている 震災時のツイート移動率を 平常時 (平常時 D 1 ) れ示す 2 特に被害の大きい地域 については 平常時と比較してツイート移動率が高い と比較し どのように変化したかを確認した 震災時の TTR 変化率 震災時の TTR 傾向が見られており 仮説 2 を支持する結果が得られ 震災時には被害の大きい地域の情報 1 発信地情報を抽出できた平常時 B C D のデータのうち 平常時 B C については リツイート投稿者と被引 用者のどちらの地域も取得できた数が少ないため 今回は比較対象として平常時 D のデータを用いることとする 2 ツイート移動率は 各地域の大きさに依存する可能性がある そこで 平常時と比較することにより震災時の特 性を明らかにする が拡散されたと考えられる ただし 表 6 を見ると 災害地域 が被引用者となっているリツイー トの合計は 101,152 件であり 全体の 10%程度であった そのため 被災地からの重要な 5 c 2011 Information Processing Society of Japan
6 8 URL Table 8 Number of tweets with URL. Table 9 9 Number of tweets with spreading phrases. (%) (%) (%) 6, , , , , , , , , , AREA5 168, , , , , , A 13,004, ,866, ,021, B 50, , , C 158, , , D 675, , , (%) (%) (%) , , , , , , AREA5 25, , , , A 40, , , B C 3, , D 12, , D URL 13) RT URL URL URL 9 URL Table 10 Number of re-tweets with comment. (%) 2, , , , AREA5 28, , A 6,194, B 25, C 43, D 163, Twitter URL 6. Twitter c 2011 Information Processing Society of Japan
7 ( 1 ) Twitter ( 2 ) JST A () on Location Based Social Networks (LBSN 09), pp (2009). 7) Back, M.D., Kufner, A.C.P. and Egloff, B.: The Emotional Timeline of September 11, 2001, Psychological Science, Vol.21, No.10, pp (2010). 8) Cohn, M.A., Mehl, M.R. and Pennebaker, J.W.: Linguistic markers of psychological change surrounding September 11, 2001, Psychological Science, Vol.15, No.10, pp (2004). 9) Vieweg, S., Hughes, A.L., Starbird, K., et al.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness, In Proceedings of the 28th international conference on Human factors in computing systems (CHI 10), pp (2010). 10) (2011). 11) Aramaki, E., Maskawa, S. and Morita, M.: Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP2011), pp (2011). 12) TYPO Writer:, 16, pp (2010). 13) Suh, B., Hong, L., Pirolli, P., et al.: Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network, Second IEEE International Conference on Social Computing (SocialCom), pp (2010). 1) : 4 Vol.51, No.7, pp (2010). 2) I : Twitter Vol.51, No.6, pp (2010). 3)? (2011). 4) Qu, Y., Huang, C., Zhang, P., et al.: Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake, In Proceedings of the ACM 2011 conference on Computer supported cooperative work (CSCW 11), pp (2011). 5) Mendoza, M., Poblete, B. and Castillo, C.: Twitter under crisis: can we trust what we RT?, In Proceedings of the First Workshop on Social Media Analytics (SOMA 10), pp (2010). 6) Longueville, B.D., Smith, R.S. and Luraschi,G.: OMG, from here, I can see the flames! : a use case of mining location based social networks to acquire spatiotemporal data on forest fires, In Proceedings of the 2009 International Workshop 7 c 2011 Information Processing Society of Japan
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