  Hate speech detection research has predominantly focused on purely
content-based methods, without exploiting any additional context. We briefly
critique pros and cons of this task formulation. We then investigate profiling
users by their past utterances as an informative prior to better predict
whether new utterances constitute hate speech. To evaluate this, we augment
three Twitter hate speech datasets with additional timeline data, then embed
this additional context into a strong baseline model. Promising results suggest
merit for further investigation, though analysis is complicated by differences
in annotation schemes and processes, as well as Twitter API limitations and
data sharing policies.
