TY - JOUR
T1 - Understanding User Behavior in Sina Weibo Online Social Network
T2 - A Community Approach
AU - Lei, Kai
AU - Liu, Ying
AU - Zhong, Shangru
AU - Liu, Yongbin
AU - Xu, Kuai
AU - Shen, Ying
AU - Yang, Min
N1 - Funding Information:
This work was supported by the Shenzhen Key Fundamental Research Projects under Grant JCYJ20170412150946024 and Grant JCYJ20170412151008290.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/2/19
Y1 - 2018/2/19
N2 - Sina Weibo, a Twitter-like microblogging Website in China, has become the main source of different kinds of information, such as breaking news, social events, and products. There is great value to exploiting the actual interests and behaviors of users, which creates opportunity for better understanding of the information dissemination mechanisms on social network sites. In this paper, we focus our attention to characterizing user behaviors in tweeting, retweeting, and commenting on Sina Weibo. In particular, we built a Shenzhen Weibo community graph to analyze user behaviors, clustering the coefficients of the community graph and exploring the impact of user popularity on social network sites. Bipartite graphs and one-mode projections are used to analyze the similarity of retweeting and commenting activities, which reveal the weak correlations between these two behaviors. In addition, to characterize the user retweeting behaviors deeply, we also study the tweeting and retweeting behaviors in terms of the gender of users. We observe that females are more likely to retweet than males. This discovery is useful for improving the efficiency of message transmission. What is more, we introduce an information-theoretical measure based on the concept of entropy to analyze the temporal tweeting behaviors of users. Finally, we apply a clustering algorithm to divide users into different groups based on their tweeting behaviors, which can improve the design of plenty of applications, such as recommendation systems.
AB - Sina Weibo, a Twitter-like microblogging Website in China, has become the main source of different kinds of information, such as breaking news, social events, and products. There is great value to exploiting the actual interests and behaviors of users, which creates opportunity for better understanding of the information dissemination mechanisms on social network sites. In this paper, we focus our attention to characterizing user behaviors in tweeting, retweeting, and commenting on Sina Weibo. In particular, we built a Shenzhen Weibo community graph to analyze user behaviors, clustering the coefficients of the community graph and exploring the impact of user popularity on social network sites. Bipartite graphs and one-mode projections are used to analyze the similarity of retweeting and commenting activities, which reveal the weak correlations between these two behaviors. In addition, to characterize the user retweeting behaviors deeply, we also study the tweeting and retweeting behaviors in terms of the gender of users. We observe that females are more likely to retweet than males. This discovery is useful for improving the efficiency of message transmission. What is more, we introduce an information-theoretical measure based on the concept of entropy to analyze the temporal tweeting behaviors of users. Finally, we apply a clustering algorithm to divide users into different groups based on their tweeting behaviors, which can improve the design of plenty of applications, such as recommendation systems.
KW - Sina Weibo
KW - bipartite graphs
KW - clustering
KW - entropy
KW - online social network
KW - user behavior
UR - http://www.scopus.com/inward/record.url?scp=85042367568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042367568&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2808158
DO - 10.1109/ACCESS.2018.2808158
M3 - Article
AN - SCOPUS:85042367568
SN - 2169-3536
VL - 6
SP - 13302
EP - 13316
JO - IEEE Access
JF - IEEE Access
ER -