TY - GEN
T1 - Who drive people to forward information
T2 - 5th Workshop on Social Network Systems, SNS 2012
AU - Jiang, Jing
AU - Chen, Pei
AU - Wang, Xiao
AU - Dai, Yafei
PY - 2012/4/10
Y1 - 2012/4/10
N2 - The explosive growth in online social networks makes them major platforms of information diffusion. People receive large-scale information from friends, but hardly find what they really want. Understanding influential factors of forwarding behavior can be used to improve the ranking algorithm, and prevent the information overload. In this paper, we compare the influence of the publisher and the spreader in Renren, the largest and oldest online social network in China. We crawl a connected graph component of 42.1 million users, 1.6 billion social relationships, and 118.2 million unique URLs. We compare URLs received from friends, and URLs which are really adopted. We observe that people are more influenced by spreaders than publishers: the spreader's recommendation time is more important than the publisher's publication time. People prefer URLs which were forwarded by spreaders a short time ago. Moreover, the previous adoption from the spreader is useful to predict user's forwarding behavior, while the previous adoption from the publisher is useless. These findings are useful to improve the ranking algorithm and prevent the information overload.
AB - The explosive growth in online social networks makes them major platforms of information diffusion. People receive large-scale information from friends, but hardly find what they really want. Understanding influential factors of forwarding behavior can be used to improve the ranking algorithm, and prevent the information overload. In this paper, we compare the influence of the publisher and the spreader in Renren, the largest and oldest online social network in China. We crawl a connected graph component of 42.1 million users, 1.6 billion social relationships, and 118.2 million unique URLs. We compare URLs received from friends, and URLs which are really adopted. We observe that people are more influenced by spreaders than publishers: the spreader's recommendation time is more important than the publisher's publication time. People prefer URLs which were forwarded by spreaders a short time ago. Moreover, the previous adoption from the spreader is useful to predict user's forwarding behavior, while the previous adoption from the publisher is useless. These findings are useful to improve the ranking algorithm and prevent the information overload.
KW - forwarding behavior
KW - online social network
KW - publisher
KW - spreader
UR - https://www.scopus.com/pages/publications/84860732112
U2 - 10.1145/2181176.2181187
DO - 10.1145/2181176.2181187
M3 - 会议稿件
AN - SCOPUS:84860732112
SN - 9781450311649
T3 - Proceedings of the 5th Workshop on Social Network Systems, SNS'12
BT - Proceedings of the 5th Workshop on Social Network Systems, SNS'12
PB - Association for Computing Machinery
Y2 - 10 April 2012 through 10 April 2012
ER -