  Online harassment has been a problem to a greater or lesser extent since the
early days of the internet. Previous work has applied anti-spam techniques like
machine-learning based text classification (Reynolds, 2011) to detecting
harassing messages. However, existing public datasets are limited in size, with
labels of varying quality. The #HackHarassment initiative (an alliance of 1
tech companies and NGOs devoted to fighting bullying on the internet) has begun
to address this issue by creating a new dataset superior to its predecssors in
terms of both size and quality. As we (#HackHarassment) complete further rounds
of labelling, later iterations of this dataset will increase the available
samples by at least an order of magnitude, enabling corresponding improvements
in the quality of machine learning models for harassment detection. In this
paper, we introduce the first models built on the #HackHarassment dataset v1.0
(a new open dataset, which we are delighted to share with any interested
researcherss) as a benchmark for future research.
