  Toxic language, such as hate speech, can deter users from participating in
online communities and enjoying popular platforms. Previous approaches to
detecting toxic language and norm violations have been primarily concerned with
conversations from online forums and social media, such as Reddit and Twitter.
These approaches are less effective when applied to conversations on
live-streaming platforms, such as Twitch and YouTube Live, as each comment is
only visible for a limited time and lacks a thread structure that establishes
its relationship with other comments. In this work, we share the first NLP
study dedicated to detecting norm violations in conversations on live-streaming
platforms. We define norm violation categories in live-stream chats and
annotate 4,583 moderated comments from Twitch. We articulate several facets of
live-stream data that differ from other forums, and demonstrate that existing
models perform poorly in this setting. By conducting a user study, we identify
the informational context humans use in live-stream moderation, and train
models leveraging context to identify norm violations. Our results show that
appropriate contextual information can boost moderation performance by 35\%.
