  Evaluating the reliability of news sources is a routine task for journalists
and organizations committed to acquiring and disseminating accurate
information. Recent research has shown that predicting sources' reliability
represents an important first-prior step in addressing additional challenges
such as fake news detection and fact-checking. In this paper, we introduce a
novel approach for source reliability estimation that leverages reinforcement
learning strategies for estimating the reliability degree of news sources.
Contrary to previous research, our proposed approach models the problem as the
estimation of a reliability degree, and not a reliability label, based on how
all the news media sources interact with each other on the Web. We validated
the effectiveness of our method on a news media reliability dataset that is an
order of magnitude larger than comparable existing datasets. Results show that
the estimated reliability degrees strongly correlates with journalists-provided
scores (Spearman=0.80) and can effectively predict reliability labels
(macro-avg. F$_1$ score=81.05). We release our implementation and dataset,
aiming to provide a valuable resource for the NLP community working on
information verification.
