TY - GEN
T1 - Evaluating event credibility on twitter
AU - Gupta, Manish
AU - Zhao, Peixiang
AU - Han, Jiawei
PY - 2012
Y1 - 2012
N2 - Though Twitter acts as a realtime news source with people acting as sensors and sending event updates from all over the world, rumors spread via Twitter have been noted to cause considerable damage. Given a set of popular Twitter events along with related users and tweets, we study the problem of automatically assessing the credibility of such events. We propose a credibility analysis approach enhanced with event graph-based optimization to solve the problem. First we experiment by performing PageRanklike credibility propagation on a multi-typed network consisting of events, tweets, and users. Further, within each iteration, we enhance the basic trust analysis by updating event credibility scores using regularization on a new graph of events. Our experiments using events extracted from two tweet feed datasets, each with millions of tweets show that our event graph optimization approach outperforms the basic credibility analysis approach. Also, our methods are significantly more accurate (∼86%) than the decision tree classifier approach (∼72%).
AB - Though Twitter acts as a realtime news source with people acting as sensors and sending event updates from all over the world, rumors spread via Twitter have been noted to cause considerable damage. Given a set of popular Twitter events along with related users and tweets, we study the problem of automatically assessing the credibility of such events. We propose a credibility analysis approach enhanced with event graph-based optimization to solve the problem. First we experiment by performing PageRanklike credibility propagation on a multi-typed network consisting of events, tweets, and users. Further, within each iteration, we enhance the basic trust analysis by updating event credibility scores using regularization on a new graph of events. Our experiments using events extracted from two tweet feed datasets, each with millions of tweets show that our event graph optimization approach outperforms the basic credibility analysis approach. Also, our methods are significantly more accurate (∼86%) than the decision tree classifier approach (∼72%).
UR - http://www.scopus.com/inward/record.url?scp=84876796382&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876796382&partnerID=8YFLogxK
U2 - 10.1137/1.9781611972825.14
DO - 10.1137/1.9781611972825.14
M3 - Conference contribution
AN - SCOPUS:84876796382
SN - 9781611972320
T3 - Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
SP - 153
EP - 164
BT - Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PB - Society for Industrial and Applied Mathematics Publications
T2 - 12th SIAM International Conference on Data Mining, SDM 2012
Y2 - 26 April 2012 through 28 April 2012
ER -