  Fake news is a growing problem in the last years, especially during
elections. It's hard work to identify what is true and what is false among all
the user generated content that circulates every day. Technology can help with
that work and optimize the fact-checking process. In this work, we address the
challenge of finding similar content in order to be able to suggest to a
fact-checker articles that could have been verified before and thus avoid that
the same information is verified more than once. This is especially important
in collaborative approaches to fact-checking where members of large teams will
not know what content others have already fact-checked.
