Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve, emerge and fade, making it more difficult for the journal- ist – or the press consumer – to decrypt the news. For in- stance, the current Syrian chemical crisis has been the start- ing point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contex- tualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependen- cies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past ac- cumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News arti- cles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics).