Information Diffusion in Online Social Network

Recently, online social networks have become increasingly important media. From online social networks, people get all kinds of information, from popular restaurants in town, to breaking news from the other side of the world. Unlike traditional media that hold a one-to-all communication paradigm, social networks propagate information via word-of-mouth communication between users. The flourishing of social networks as new media have brought numerous applications for the researches on information diffusion. For example, viral marketing campaigns in social networks can benefit from an accurate model of information diffusion. Nevertheless, many research efforts still need to be made to fully understand the diffusion of information in online social networks. In this thesis, we focus on information diffusion in online social networks. We study real-world diffusion data from online social networks such as Twitter, Foursquare, and Slashdot to get data-driven observations. These observations lead us to rethink some key problems with regard to information diffusion: diffusion modeling, trend predicting, and influence maximization.