000000291 001__ 291
000000291 005__ 20140218161527.0
000000291 037__ $$aLERSSE-RefConfPaper-2014-001
000000291 100__ $$aHyoungshick Kim
000000291 245__ $$aFinding Influential Neighbors to Maximize Information Diffusion in Twitter
000000291 260__ $$c2014-02-17
000000291 300__ $$a6
000000291 520__ $$aThe problem of spreading information is a topic of considerable recent interest, but the traditional influence maximization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbitrary k nodes in a network, we have developed a decentralized version of the influential maximization problem by influencing k neighbours rather than arbitrary users in the entire network. We present several reasonable neighbour selection schemes and evaluate their performance with a real dataset collected from Twitter. Unlike previous studies using net- work topology alone or synthetic parameters, we use real propagation rate for each node calculated from the Twitter messages during the 2010 UK election campaign. Our experimental results show that information can be efficiently propagated in online social networks using neighbours with a high propagation rate rather than those with a high number of neighbours.
000000291 6531_ $$aInformation diffusion
000000291 6531_ $$aInformation dissemination
000000291 6531_ $$aOnline social networks
000000291 6531_ $$aViral marketing
000000291 700__ $$aKonstantin Beznosov
000000291 700__ $$aEiko Yoneki
000000291 8560_ $$flersse-it@ece.ubc.ca
000000291 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/291/files/w10scn13-kim.pdf
000000291 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/291/files/w10scn13-kim.pdf?subformat=pdfa$$xpdfa
000000291 909C4 $$pFinding Influential Neighbors to Maximize Information Diffusion in Twitter, Hyoungshick Kim, Konstantin Beznosov, and Eiko Yoneki, WWW’14 Companion, April 7–11, 2014, Seoul, Korea.
000000291 980__ $$aRefConfPaper