Understanding User Behavior in Sina Weibo Online Social Network: A Community Approach

• Published in: IEEE access Vol. 6; pp. 13302 - 13316
• Main Authors: Lei, Kai, Liu, Ying, Zhong, Shangru, Liu, Yongbin, Xu, Kuai, Shen, Ying, Yang, Min
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; bipartite graphs; Clustering; Clustering algorithms; Correlation; Data collection; Entropy; Information dissemination; News; online social network; Recommender systems; Sina Weibo; Social network services; Social networks; Sociology; User behavior; Websites
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2808158

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.