  Over the past few years, there is a heated debate and serious public concerns
regarding online content moderation, censorship, and the principle of free
speech on the Web. To ease these concerns, social media platforms like Twitter
and Facebook refined their content moderation systems to support soft
moderation interventions. Soft moderation interventions refer to warning labels
attached to potentially questionable or harmful content to inform other users
about the content and its nature while the content remains accessible, hence
alleviating concerns related to censorship and free speech. In this work, we
perform one of the first empirical studies on soft moderation interventions on
Twitter. Using a mixed-methods approach, we study the users who share tweets
with warning labels on Twitter and their political leaning, the engagement that
these tweets receive, and how users interact with tweets that have warning
labels. Among other things, we find that 72% of the tweets with warning labels
are shared by Republicans, while only 11% are shared by Democrats. By analyzing
content engagement, we find that tweets with warning labels had more engagement
compared to tweets without warning labels. Also, we qualitatively analyze how
users interact with content that has warning labels finding that the most
popular interactions are related to further debunking false claims, mocking the
author or content of the disputed tweet, and further reinforcing or resharing
false claims. Finally, we describe concrete examples of inconsistencies, such
as warning labels that are incorrectly added or warning labels that are not
added on tweets despite sharing questionable and potentially harmful
information.
