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This paper aims to show how some popular topics on social networks can beused to predict online newspaper views, related to the topics. Newspapers siteand many social networks, become a good source of data to analyse and explaincomplex phenomena. Understanding the entropy of a topic, could help allorganizations that need to share information like government, institution,newspaper or company, to expect an higher activity over their channels, and insome cases predict what the receiver expect from the senders or what is wrongabout the communication. For some organization such political party, leaders,company and many others, the reputation and the communication are (for most ofthem) the key part of a more and complex huge system. To reach our goal, we usegathering tools and information theory to detect and analyse trends topic onsocial networks, with the purpose of proved a method that helps organization,newspapers to predict how many articles or communication they will have to doon a topic, and how much flow of views they will have in a given period,starting with the entropy-article ratio. Our work address the issue to explorein which entropy-rate, and through which dynamics, a suitable informationdiffusion performance is expected on social network and then on newspaper. Wehave identified some cross-cutting dynamics that, associated with the contexts,might explain how people discuss about a topic, can move on to argue andinforms on newspapers sites.

Entropy-rate as prediction method for newspapers and information diffusion