  Social media users who report content are key allies in the management of
online misinformation, however, no research has been conducted yet to
understand their role and the different trends underlying their reporting
activity. We suggest an original approach to studying misinformation: examining
it from the reporting users perspective at the content-level and comparatively
across regions and platforms. We propose the first classification of reported
content pieces, resulting from a review of c. 9,000 items reported on Facebook
and Instagram in France, the UK, and the US in June 2020. This allows us to
observe meaningful distinctions regarding reporting content between countries
and platforms as it significantly varies in volume, type, topic, and
manipulation technique. Examining six of these techniques, we identify a novel
one that is specific to Instagram US and significantly more sophisticated than
others, potentially presenting a concrete challenge for algorithmic detection
and human moderation. We also identify four reporting behaviours, from which we
derive four types of noise capable of explaining half of the inaccuracy found
in content reported as misinformation. We finally show that breaking down the
user reporting signal into a plurality of behaviours allows to train a simple,
although competitive, classifier on a small dataset with a combination of basic
users-reports to classify the different types of reported content pieces.
