  Coordinated inauthentic behavior is used as a tool on social media to shape
public opinion by elevating or suppressing topics using systematic engagements
-- e.g. through *likes* or similar reactions. In an honest world, reactions may
be informative to users when selecting on what to spend their attention:
through the wisdom of crowds, summed reactions may help identifying relevant
and high-quality content. This is nullified by coordinated inauthentic liking.
To restore wisdom-of-crowds effects, it is therefore desirable to separate the
inauthentic agents from the wise crowd, and use only the latter as a voting
*jury* on the relevance of a post. To this end, we design two *jury selection
procedures* (JSPs) that discard agents classified as inauthentic. Using machine
learning techniques, both cluster on binary vote data -- one using a Gaussian
Mixture Model (GMM JSP), one the k-means algorithm (KM JSP) -- and label agents
by logistic regression. We evaluate the jury selection procedures with an
agent-based model, and show that the GMM JSP detects more inauthentic agents,
but both JSPs select juries with vastly increased correctness of vote by
majority. This proof of concept provides an argument for the release of
reactions data from social media platforms through a direct use-case in the
fight against online misinformation.
