  The proliferation of misleading information in everyday access media outlets
such as social media feeds, news blogs, and online newspapers have made it
challenging to identify trustworthy news sources, thus increasing the need for
computational tools able to provide insights into the reliability of online
content. In this paper, we focus on the automatic identification of fake
content in online news. Our contribution is twofold. First, we introduce two
novel datasets for the task of fake news detection, covering seven different
news domains. We describe the collection, annotation, and validation process in
detail and present several exploratory analysis on the identification of
linguistic differences in fake and legitimate news content. Second, we conduct
a set of learning experiments to build accurate fake news detectors. In
addition, we provide comparative analyses of the automatic and manual
identification of fake news.
