Whom to follow: Efficient followee selection for cascading outbreak detection on online social networks
Online social networks (OSNs), such as Twitter and Sina Weibo, have become important platforms for generating and spreading information on the Internet. On these OSNs, the follow model has become a popular way to discover information; i.e., a user subscribes to content generated by others by following them as information sources. The content producers are called followees. Due to human beings limited attention capacity and the constraints imposed by OSNs, a user can only follow a few followees. The question then arises: which subset of followees shall we follow so that we can discover the most information in an OSN in a timely fashion? To solve this problem, we present a randomized method that does not require complete OSN data and is well suited for third parties who do not own OSN data. Our method is based on the birthday paradox and is mathematically tractable for analysing its solution quality and computational efficiency. Moreover, we find that the power-law structure of real-world OSNs can further improve the solution quality of our method. Experiments conducted on two real datasets demonstrate that our method can create a good trade-off between solution quality and computational efficiency.
followee selection outbreak detection follow model submodularity