  Content moderators play a key role in keeping the conversation on social
media healthy. While the high volume of content they need to judge represents a
bottleneck to the moderation pipeline, no studies have explored how models
could support them to make faster decisions. There is, by now, a vast body of
research into detecting hate speech, sometimes explicitly motivated by a desire
to help improve content moderation, but published research using real content
moderators is scarce. In this work we investigate the effect of explanations on
the speed of real-world moderators. Our experiments show that while generic
explanations do not affect their speed and are often ignored, structured
explanations lower moderators' decision making time by 7.4%.
