2021/01/18 by Savvas Zannettou, Zannettou, Savvas · 3 citations
Computer Science · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Misinformation and Its Impacts #Social Media and Politics #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2101.07183
openalex publication_date 2021/01/18 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Over the past few years, there is a heated debate and serious public concerns\nregarding online content moderation, censorship, and the principle of free\nspeech on the Web. To ease these concerns, social media platforms like Twitter\nand Facebook refined their content moderation systems to support soft\nmoderation interventions. Soft moderation interventions refer to warning labels\nattached to potentially questionable or harmful content to inform other users\nabout the content and its nature while the content remains accessible, hence\nalleviating concerns related to censorship and free speech. In this work, we\nperform one of the first empirical studies on soft moderation interventions on\nTwitter. Using a mixed-methods approach, we study the users who share tweets\nwith warning labels on Twitter and their political leaning, the engagement that\nthese tweets receive, and how users interact with tweets that have warning\nlabels. Among other things, we find that 72% of the tweets with warning labels\nare shared by Republicans, while only 11% are shared by Democrats. By analyzing\ncontent engagement, we find that tweets with warning labels had more engagement\ncompared to tweets without warning labels. Also, we qualitatively analyze how\nusers interact with content that has warning labels finding that the most\npopular interactions are related to further debunking false claims, mocking the\nauthor or content of the disputed tweet, and further reinforcing or resharing\nfalse claims. Finally, we describe concrete examples of inconsistencies, such\nas warning labels that are incorrectly added or warning labels that are not\nadded on tweets despite sharing questionable and potentially harmful\ninformation.\n