TY - GEN
T1 - Characterizing information diffusion in online social networks with linear diffusive model
AU - Wang, Feng
AU - Wang, Haiyan
AU - Xu, Kuai
AU - Wu, Jianhong
AU - Jia, Xiaohua
PY - 2013
Y1 - 2013
N2 - Mathematical modeling is an important approach to study information diffusion in online social networks. Prior studies have focused on the modeling of the temporal aspect of information diffusion. A recent effort introduced the spatiotemporal diffusion problem and addressed the problem with a theoretical framework built on the similarity between information propagation in online social networks and biological invasion in ecology [1]. This paper examines the spatio-temporal characteristics in further depth and reveals that there exist regularities in information diffusion in temporal and spatial dimensions. Furthermore, we propose a simpler linear partial differential equation that takes account of the influence of spatial population density and temporal decay of user interests in the information. We validate the proposed linear model with Digg news stories which received more than 3000 votes during June 2009, and show that the model can describe nearly 60% of the news stories with over 80% accuracy. We also use the most popular news story as a case study and find that the linear diffusive model can achieve an accuracy as high as 97:41% for this news story. Finally, we discuss the potential applications of this model towards finding super spreaders and classifying news story into groups.
AB - Mathematical modeling is an important approach to study information diffusion in online social networks. Prior studies have focused on the modeling of the temporal aspect of information diffusion. A recent effort introduced the spatiotemporal diffusion problem and addressed the problem with a theoretical framework built on the similarity between information propagation in online social networks and biological invasion in ecology [1]. This paper examines the spatio-temporal characteristics in further depth and reveals that there exist regularities in information diffusion in temporal and spatial dimensions. Furthermore, we propose a simpler linear partial differential equation that takes account of the influence of spatial population density and temporal decay of user interests in the information. We validate the proposed linear model with Digg news stories which received more than 3000 votes during June 2009, and show that the model can describe nearly 60% of the news stories with over 80% accuracy. We also use the most popular news story as a case study and find that the linear diffusive model can achieve an accuracy as high as 97:41% for this news story. Finally, we discuss the potential applications of this model towards finding super spreaders and classifying news story into groups.
KW - PDE
KW - information diffusion
KW - mathematical modeling
KW - online social network
KW - spatio-temporal
UR - http://www.scopus.com/inward/record.url?scp=84893238276&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893238276&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2013.14
DO - 10.1109/ICDCS.2013.14
M3 - Conference contribution
AN - SCOPUS:84893238276
SN - 9780769550008
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 307
EP - 316
BT - Proceedings - 2013 IEEE 33rd International Conference on Distributed Computing Systems, ICDCS 2013
T2 - 2013 IEEE 33rd International Conference on Distributed Computing Systems, ICDCS 2013
Y2 - 8 July 2013 through 11 July 2013
ER -