TY - GEN
T1 - Confluence
T2 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
AU - Tang, Jie
AU - Wu, Sen
AU - Sun, Jimeng
N1 - Publisher Copyright:
Copyright © 2013 ACM.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2013/8/11
Y1 - 2013/8/11
N2 - Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods.
AB - Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods.
KW - Conformity
KW - Social influence
KW - Social network
UR - http://www.scopus.com/inward/record.url?scp=84924247519&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84924247519&partnerID=8YFLogxK
U2 - 10.1145/2487575.2487691
DO - 10.1145/2487575.2487691
M3 - Conference contribution
AN - SCOPUS:84924247519
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 347
EP - 355
BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
A2 - Parekh, Rajesh
A2 - He, Jingrui
A2 - Inderjit, Dhillon S.
A2 - Bradley, Paul
A2 - Koren, Yehuda
A2 - Ghani, Rayid
A2 - Senator, Ted E.
A2 - Grossman, Robert L.
A2 - Uthurusamy, Ramasamy
PB - Association for Computing Machinery
Y2 - 11 August 2013 through 14 August 2013
ER -