  Social media communication has become a significant part of daily activity in
modern societies. For this reason, ensuring safety in social media platforms is
a necessity. Use of dangerous language such as physical threats in online
environments is a somewhat rare, yet remains highly important. Although several
works have been performed on the related issue of detecting offensive and
hateful language, dangerous speech has not previously been treated in any
significant way. Motivated by these observations, we report our efforts to
build a labeled dataset for dangerous speech. We also exploit our dataset to
develop highly effective models to detect dangerous content. Our best model
performs at 59.60% macro F1, significantly outperforming a competitive
baseline.
