Analysis and Comparison of Interaction Patterns in Online Social Network and Social Media

• Published in: 2012 21st International Conference on Computer Communications and Networks (ICCCN) pp. 1 - 7
• Main Authors: Jiali Lin, Zhenyu Li, Dong Wang, Salamatian, Kave, Gaogang Xie
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2012
• Subjects: Correlation; Data models; Facebook; Hidden Markov models; Media; Twitter
• ISBN: 9781467315432; 1467315435
• ISSN: 1095-2055; 2637-9430
• DOI: 10.1109/ICCCN.2012.6289250

In this work, we aim to analyze and compare interaction patterns in different types of social platforms. To this end, we measured Renren, the largest online social network in China, and Sina Weibo, the most popular microblog service in China. We model the interaction networks as unidirectional weighted graphs in light of the asymmetry of user interactions. Following this model, we first study the basic interaction patterns. Then, we examine whether weak ties hypothesis holds in these interaction graphs and analyze the impacts on information diffusion. Furthermore, we model the temporal patterns of user interactions and cluster users based on the temporal patterns. Our findings demonstrate that although users in the two platforms share some common interaction patterns, users in Sina Weibo are more popular and diverse. Moreover, analysis and simulation results show that Sina Weibo is a more efficient platform for information diffusion. These findings provide an in-depth understanding of interaction patterns in different social platforms and can be used for the design of efficient information diffusion.