TY - GEN
T1 - PSEISMIC
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
AU - Chen, Hsin Yu
AU - Li, Cheng Te
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Social networking websites allow users to create and share a variety of items. Big information cascades of post resharing can be generated because users of these sites reshare each other's posts with their friends and followers. In this work, we aim at predicting the final number of reshares for any given post. We build on the theory of self-exciting point processes to develop a statistical model, PSEISMIC, which leads to accurate predictions of popularity. Moreover, we perform cluster analysis to group all tweets so that the coefficient of memory kernel in PSEISMIC can be estimated for every cluster, rather than using the same memory kernel. Experiments conducted on a large-scale retweet dataset show that the proposed PSEISMIC model outperforms the state-of-the-art approach, SEISMIC in predicting the popularity of a given post.
AB - Social networking websites allow users to create and share a variety of items. Big information cascades of post resharing can be generated because users of these sites reshare each other's posts with their friends and followers. In this work, we aim at predicting the final number of reshares for any given post. We build on the theory of self-exciting point processes to develop a statistical model, PSEISMIC, which leads to accurate predictions of popularity. Moreover, we perform cluster analysis to group all tweets so that the coefficient of memory kernel in PSEISMIC can be estimated for every cluster, rather than using the same memory kernel. Experiments conducted on a large-scale retweet dataset show that the proposed PSEISMIC model outperforms the state-of-the-art approach, SEISMIC in predicting the popularity of a given post.
UR - http://www.scopus.com/inward/record.url?scp=85047747783&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047747783&partnerID=8YFLogxK
U2 - 10.1109/BigData.2017.8258234
DO - 10.1109/BigData.2017.8258234
M3 - Conference contribution
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 2710
EP - 2713
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2017 through 14 December 2017
ER -