Modeling and evaluating information propagation in a microblogging social network

Microblogging platforms, such as Twitter and Plurk, allow users to express feelings, discuss ideas, and share interesting things with their friends or even strangers with similar interests. With the popularity of microblogs, there are growing data and opportunities in understanding information propagation behaviors in online social networks. Though some influence models had been proposed based on certain assumptions, most of them are based on the simulation approach (not data driven). This paper aims at designing a framework to model, measure, evaluate, and visualize influence propagation in a microblogging social network. Considering how information contents are spread in a social network, we devise two influence propagation models from the views of messages posted and responded. Based on the proposed models, we are able to measure the influence capability of an individual with respect to a user-given topic. Our design of influence measures consider (a) the number of people influenced, (b) the speed of propagation, and (c) the geographic distance of the propagation. To test the effectiveness of our influence model, we further propose a novel evaluation framework that predicts the propagation links and influential nodes in a real-world microblogging social network. Finally, we develop an online visualization system allowing users to explore the information propagation with the functions of displaying propagation structures, influence scores of individuals, timelines, and the geographical information for any user-query terms.