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Over the past few years, there is a heated debate and serious public concernsregarding online content moderation, censorship, and the principle of freespeech on the Web. To ease these concerns, social media platforms like Twitterand Facebook refined their content moderation systems to support softmoderation interventions. Soft moderation interventions refer to warning labelsattached to potentially questionable or harmful content to inform other usersabout the content and its nature while the content remains accessible, hencealleviating concerns related to censorship and free speech. In this work, weperform one of the first empirical studies on soft moderation interventions onTwitter. Using a mixed-methods approach, we study the users who share tweetswith warning labels on Twitter and their political leaning, the engagement thatthese tweets receive, and how users interact with tweets that have warninglabels. Among other things, we find that 72% of the tweets with warning labelsare shared by Republicans, while only 11% are shared by Democrats. By analyzingcontent engagement, we find that tweets with warning labels had more engagementcompared to tweets without warning labels. Also, we qualitatively analyze howusers interact with content that has warning labels finding that the mostpopular interactions are related to further debunking false claims, mocking theauthor or content of the disputed tweet, and further reinforcing or resharingfalse claims. Finally, we describe concrete examples of inconsistencies, suchas warning labels that are incorrectly added or warning labels that are notadded on tweets despite sharing questionable and potentially harmfulinformation.

"I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter