Social media plays a fundamental role in the diffusion of information. There are two different ways of information diffusion in social media platforms such as Twitter and Weibo. Users can either re-share messages posted by their friends or re-create messages based on the information acquired from other non-local information sources such as the mass media. By analyzing around 60 million messages from a large micro-blog site, we find that about 69 % of the diffusion volume can be attributed to users' re-sharing behaviors, and the remaining 31 % are caused by user re-creating behaviors. The information diffusions caused by the two kinds of behaviors have different characteristics and variation trends, but most existing models of information diffusion do not distinguish them. The recent availability of massive online social streams allows us to study the process of information diffusion in much finer detail. In this paper, we introduce a novel model to capture and simulate the process of information diffusion in the micro-blog platforms, which distinguishes users' re-sharing behaviors from re-creating behaviors by introducing two different components. Thus, our model not only considers the effect of the underlying network structure, but also the influence of other non-local information sources. The empirical results show the superiority of our proposed model in the fitting and prediction tasks of information diffusion.
['Yiran Xie', 'Hongzhi Yin', 'Bin Cui', 'Junjie Yao', 'Quanqing Xu']
Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion