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1 SIR SIR-Extended Information Diffusion Model of alse Rumor and its Prevention Strategy for Twitter Okada Yoshiyuki 1, Takeshi Sakaki 2, ujio Toriumi 2, Kosuke Shinoda 3, Kazuhiro Kazama 4, Itsuki Noda 5, Masayuki Numao 6 and Satoshi Kurihara Division of Applied Science, Gradate School of Engineering, Osaka University 2 2 Tokyo University 3 3 RIKEN 4 4 Wakayama University 5 5 National Institute of Advanced Industrial Science and Technology 6 6 The Institute of Scientific and Industrial Research, Osaka University Abstract: Twitter is a famous social networking service and has received attention recently. Twitter user have increased rapidly, and many users exchange information. When 2011 Tohoku earthquake and tsunami happened, people were able to obtain information from social networking service. Though Twitter played the important role, one of the problem of Twitter, a false rumor diffusion, was pointed out. In this research, we focus on a false rumor diffusion. We propose a information diffusion model based on SIR model, classify the way of diffusion in four categories, and reapper the real diffussion by using this new model. 1 acebook 1 Twitter 2 SNS [9] SNS okada@ai.sanken.osaka-u.ac.jp Twitter Twitter SIR 4 4

2 1 2 SNS Twitter Twitter Twitter Huberman Twitter 2 [5] Akioka Twitter [1] Akioka Twitter Twitter Twitter Kawk Twitter [7] Kawak Twitter [10] Weng Twitter PageRank Twitter TwitterRank [12] Balshy URL URL [3] Twitter Castillo [4] [11] Marcelo [14] Castillo SIR SIR Kermack [6][8] SIR S IR 3 S I ρ (S I) I I ρ (I R) R t S S(t) I I(t) R R(t) S I R 1 ds(t) = ρ (S I) I(t)S(t) di(t) = ρ (S I) I(t)S(t) ρ (I R) I(t) dr(t) = ρ (I R) I(t) (1) N N = S(t) + I(t) + R(t) 3.2 SIR R I R SIR

3 S I R S I get I SNS S I get I R get R R get R S I get I ρ (S I) S I ρ (Iget I) ρ (S R) ρ (Iget R) ρ (I R) ρ (Rget R) N t S I get I R get R S(t) I get (t) I(t) R get (t) R(t) SNS SIR S SNS I 2 ds(t) = N I(t)S(t) di get(t) = (1 ρ (S I) ) N I(t)S(t) ρ (Iget I) N I get(t)i(t) di(t) = ρ (S I) N I(t)S(t) +ρ (Iget I) N Iget(t)I(t) (2) 3 ds(t) = N I(t)S(t) N R(t)S(t) di get (t) = (1 ρ (S I) ) N I(t)S(t) ρ (Iget I) N Iget(t)I(t) di(t) dr get (t) dr(t) N I get(t)r(t) = ρ (S I) N I(t)S(t) +ρ (Iget I) N Iget(t)I(t) N I(t)R(t) = (1 ρ (S R) ) N R(t)S(t) +(1 ρ (Iget R)) N I get(t)r(t) +(1 ρ (I R) ) N I(t)R(t) ρ (Rget R) N R get(t)r(t) = ρ (S R) N R(t)S(t) +ρ (Iget R) N Iget(t)R(t) +ρ (I R) N I(t)R(t) +ρ (Rget R) N R get(t)r(t) (3) SNS Twitter Twitter [10]

4 1: Step1 Step2 Step3 Step4 RT Step5 Step6 Step7 1 2: ( ) 9,652 25,883 1: ( ) ( ) 4.3 3: ( ) 15,373 29,819 Hz HP : ( ) ( )

5 4.4 4: () 6,300 20,050 4: ( ) ( ) 4.6 Twitter URL 3: ( ) ( ) : () 1,174 1, : ( ) 1,746 1,147? 5: ( ) ( ) 4.7 Twitter

7: ( ) 305 1,344 5 4 2 4 ( 4.2), ( 4.5), ( 4.6) ( 4.7), ( 4.8) ( 4.4) ( 4.

6 7: ( ) 305 1, ( 4.2), ( 4.5), ( 4.6) ( 4.7), ( 4.8) ( 4.4) ( 4.3) 6: ( ) ( ) : ( ) 1, : ( ) ( ) 8: Twitter 1 9 Twitter

7 S 2 [12] i j i j ρ (S I) ρ (Iget I) ρ (S R) ρ (Iget R) ρ (I R) ρ (Rget R) t 9: 1 Process1 Process2 Process1 I R ?? 10: 50,000 =30,00 =10 =0.5 =15.0 =0.05 = 数ド20000 ーノリンク ( フォロー ) している数 9: 1 I get I 4 1 Iget I 57.6% 1 I get R get 6.3 () Twitter

8 11: Step1 6.2 Step2 t = 1 1 I Step3 t = 11 1 R Step4 t = : ρ (S I) ρ (Iget I) ρ (S R) ρ (Iget R) ρ (I R) ρ (Rget R) ( ) Twitter I R 11 Step3 R I t = : ρ (S I) ρ (Iget I) 0 0 ρ (S R) ρ (Iget R) ρ (I R) ρ (Rget R) : : ( ) ( ) ( ) Twitter 11: : ( ) ( )

9 7 Twitter SIR Twitter Twitter SNS Twitter NTT [3] Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts Everyone s an Influencer: Quantifying Influence on Twitter, Proceedings of the fourth ACM international conference on Web search and data mining, pp.65-74, [4] Carlos Castillo, Marcelo Mendoza, Barbara Poblete Information Credibility on Twitter, Proceedings of the 20th international conference on World wide web, pp [5] Bernardo A. Huberman, Daniel M. Romero, ang Wu Social networks that matter: Twitter under the microscope, irst Monday, Vol.14, No.1, [6] W. O. Kermack, A. G. McKendrick A Contribution to the Mathematical Theory of Epidemics, Proceedings of the Royal Society 115A, pp , [7] Haewoon Kwak, Changhyun Lee, Hosung Park, Sue Moon What is Twitter, a Social Network or a News Media?, Proceedings of the 19th international conference on World wide web, pp , [8],,, [9] 23, go.jp/johotsusintokei/whitepaper/ja/h23/pdf/index. html, [10],,,,,, JWEIN11, pp.41-46, [11],,, Twitter RT,, Vol.2011-IAT-103, No.4, pp.1-6, [12] Jianshu Weng, Ee-Peng Lim, Jing Jiang, Qi He TwitterRank: inding Topic-sensitive Influential Twitterers, Proceedings of the third ACM international conference on Web search and data mining, pp , [13],,, 27 1, pp.35-42, [14] Marcelo Mendoza Barbara Poblete Carlos Castillo Twitter Under Crisis Can we trust what we RT? 1st Workshop on Social Media Analytics (SOMA 10) 2010 [1] Sayaka Akioka, Norikazu Kato, Yoichi Muraoka, Hayato Yamana Cross-media Impact on Twitter in Japan, Proceedings of the 2nd international workshop on Search and mining user-generated contents, pp , [2] Yasuyoshi Aosaki, Taro Sugihara, Katsuhiro Umemoto Examining the Trend toward a Service Economy in Information Media through Changes to Technology: Influence of Twitter on Media Companies, Proceedings of Technology Management for Global Economic Growth (PICMET) 10, pp.1-5, 2010.

[9]SNS SNS SNS Twitter SNS SIR Twitter SNS Twitter Twitter Twitter Huberman [5] Akioka Twitter []Akioka Twitter Twitter Twitter Kawk Twitter

[9]SNS SNS SNS Twitter SNS SIR Twitter SNS Twitter Twitter Twitter Huberman [5] Akioka Twitter []Akioka Twitter Twitter Twitter Kawk Twitter Twitter Estimation of alse Rumor Diffusion Model and Prevention Model of alse Rumor Diffusion on Twitter 5 6 7 7,8 Takashi Shirai, Takeshi Sakaki, ujio Toriumi, Kosuke Shinoda, Kazuhiro Kazama 5, Itsuki