Harassment detection: a benchmark on the #HackHarassment dataset
This work addresses online harassment detection for researchers and practitioners, but it is incremental as it primarily benchmarks existing methods on a new dataset.
The paper tackles the problem of online harassment detection by introducing the first models built on the new #HackHarassment dataset v1.0, which is superior in size and quality to previous datasets, enabling future improvements in machine learning models.
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.