Discovering Ephemeral Associations among News Topics*

News reports are an important source of information about society. Their analysis allows to understand its current interests and to measure the social importance and influence of different events. In this paper, we focus on the study of a very common phenomenon of news: the influence of the peak news topics on other current news topics. We propose a text mining method to analyze such influences. We differentiate between the observable associations—those discovered from the newspapers—and the real-world associations, and propose a technique in which the real ones can be inferred from the observable ones. We argue that the discovery of the ephemeral associations can be translated into knowledge about interests of society and social behavior.