TY - JOUR
T1 - A model-free scheme for meme ranking in social media
AU - He, Saike
AU - Zheng, Xiaolong
AU - Zeng, Daniel
N1 - Funding Information:
We would like to thank each member of the SMILES group in the Institute of Automation, Chinese Academy of Sciences, especially Kainan Cui, Zhu Zhang, and Chuan Luo for their useful discussions. We also appreciate the kind help from Yufang Wu and Kainan Cui for the data collection. This work was supported in part by the following grants: the National Natural Science Foundation of China under Grant Nos. 71402177 , 71472175 , and 71103180 ; the National Institutes of Health (NIH) of USA under Grant No. 1R01DA037378-01 ; and the Ministry of Health under Grant No. 2013ZX10004218 .
Publisher Copyright:
© 2015 The Authors.
PY - 2016/1
Y1 - 2016/1
N2 - The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, and tags). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence.
AB - The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, and tags). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence.
KW - Meme ranking
KW - Model-free scheme
KW - Transfer entropy
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U2 - 10.1016/j.dss.2015.10.002
DO - 10.1016/j.dss.2015.10.002
M3 - Article
AN - SCOPUS:84952861196
VL - 81
SP - 1
EP - 11
JO - Decision Support Systems
JF - Decision Support Systems
SN - 0167-9236
ER -