Citizen preferences for online hate speech regulation

The shift of public discourse to online platforms has intensified the debate over content moderation by platforms and the regulation of online speech. Designing rules that are met with wide acceptance requires learning about public preferences. We present a visual vignette study using a sample ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2,622</mml:mn></mml:math> ) of German and US citizens that were exposed to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>20,976</mml:mn></mml:math> synthetic social media vignettes mimicking actual cases of hateful speech. We find people's evaluations to be primarily shaped by message type and severity, and less by contextual factors. While focused measures like deleting hateful content are popular, more extreme sanctions like job loss find little support even in cases of extreme hate. Further evidence suggests in-group favoritism among political partisans. Experimental evidence shows that exposure to hateful speech reduces tolerance of unpopular opinions.