  Society is experimenting changes in information consumption, as new
information channels such as social networks let people share news that do not
necessarily be trust worthy. Sometimes, these sources of information produce
fake news deliberately with doubtful purposes and the consumers of that
information share it to other users thinking that the information is accurate.
This transmission of information represents an issue in our society, as can
influence negatively the opinion of people about certain figures, groups or
ideas. Hence, it is desirable to design a system that is able to detect and
classify information as fake and categorize a source of information as trust
worthy or not. Current systems experiment difficulties performing this task, as
it is complicated to design an automatic procedure that can classify this
information independent on the context. In this work, we propose a mechanism to
detect fake news through a classifier based on weighted causal graphs. These
graphs are specific hybrid models that are built through causal relations
retrieved from texts and consider the uncertainty of causal relations. We take
advantage of this representation to use the probability distributions of this
graph and built a fake news classifier based on the entropy and KL divergence
of learned and new information. We believe that the problem of fake news is
accurately tackled by this model due to its hybrid nature between a symbolic
and quantitative methodology. We describe the methodology of this classifier
and add empirical evidence of the usefulness of our proposed approach in the
form of synthetic experiments and a real experiment involving lung cancer.
