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Volumn , Issue , 2012, Pages 2335-2338

Prediction of retweet cascade size over time

Author keywords

influence; information diffusion; retweet cascade

Indexed keywords

CASCADE SIZES; FIXED TIME; INFLUENCE; INFORMATION DIFFUSION; INITIAL MOMENTS; ONLINE MEDIA; PAGERANK; SOCIAL MEDIA; VIRAL MARKETING;

EID: 84871058841     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2398634     Document Type: Conference Paper
Times cited : (148)

References (14)
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    • Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, Krishna P. Gummadi Measuring User Inuence in Twitter: The Million Follower Fallacy, ICWSM 11
    • ICWSM 11
    • Cha, M.1    Haddadi, H.2    Benevenuto, F.3    Gummadi, K.P.4
  • 6
    • 79955141054 scopus 로고    scopus 로고
    • Predicting Popular Messages in Twitter
    • Liangjie Hong, Ovidiu Dan, Brian D. Davison Predicting Popular Messages in Twitter, WWW 11
    • WWW 11
    • Hong, L.1    Dan, O.2    Davison, B.D.3
  • 7
    • 84871101824 scopus 로고    scopus 로고
    • Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter
    • Nasir Naveed, Thomas Gottron, Jerome Kunegis, Arifah Che Alhadi, Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter, WebSci'11
    • WebSci'11
    • Naveed, N.1    Gottron, T.2    Kunegis, J.3    Alhadi, A.C.4
  • 8
    • 77954619566 scopus 로고    scopus 로고
    • What is Twitter, a Social Network or a News Media?
    • H Kwak, C Lee, H Park, S Moon, What is Twitter, a Social Network or a News Media?, WWW 10
    • WWW 10
    • Kwak, H.1    Lee, C.2    Park, H.3    Moon, S.4
  • 9
    • 84871037949 scopus 로고    scopus 로고
    • RT to Win! Predicting Message Propagation in Twitter
    • Sasa Petrovic, Miles Osborne, Victor Lavrenko, RT to Win! Predicting Message Propagation in Twitter, ICWSM 11
    • ICWSM 11
    • Petrovic, S.1    Osborne, M.2    Lavrenko, V.3
  • 10
    • 80052653625 scopus 로고    scopus 로고
    • Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex contagion on twitter
    • Daniel M. Romero, Brendan Meeder, Jon Kleinberg Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex contagion on twitter, WWW 11
    • WWW 11
    • Romero, D.M.1    Meeder, B.2    Kleinberg, J.3
  • 11
    • 35348842925 scopus 로고    scopus 로고
    • Information Flow Modeling based on Diffusion Rate for Prediction and Ranking
    • Xiaodan Song, Yun Chi, Koji Hino, Belle L. Tseng Information Flow Modeling based on Diffusion Rate for Prediction and Ranking, WWW 07
    • WWW 07
    • Song, X.1    Chi, Y.2    Hino, K.3    Tseng, B.L.4
  • 13
    • 77950897279 scopus 로고    scopus 로고
    • TwitterRank: Finding topic-sensitive inuential twitterers
    • J. Weng, E.-P. Lim, J. Jiang, and Q. He TwitterRank: Finding topic-sensitive inuential twitterers, WSDM 10
    • WSDM 10
    • Weng, J.1    Lim, E.-P.2    Jiang, J.3    He, Q.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.