Uncovering information flow among users by time-series retweet data: Who is a friend of whom on Twitter?

Although it is crucial to transmit important information to those who require it during disasters, neither of the following questions have been answered: who contributes to information diffusion? How do users construct helpful relationships in social media? Unfortunately, most previous research has focused on the scale of information diffusion, instead of the flow of information and the paths traveled by the transmitted information. In this study, we calculate the information flow probability between pairs of users and experimentally verified its validity. Our results showed a maximum precision of around 75% when we consider pairs as friends whose probability scores equal 1.0. Since our method is simple, easy to compute, and needs only time-series post-shared data in social media, we believe that it can be applied to various kinds of shared information data. By applying it to disaster data, we can identify the core information diffusers and contribute to disaster mitigation in the future.