  We report the first, to the best of our knowledge, hand-in-hand collaboration
between human rights activists and machine learners, leveraging crowd-sourcing
to study online abuse against women on Twitter. On a technical front, we
carefully curate an unbiased yet low-variance dataset of labeled tweets,
analyze it to account for the variability of abuse perception, and establish
baselines, preparing it for release to community research efforts. On a social
impact front, this study provides the technical backbone for a media campaign
aimed at raising public and deciders' awareness and elevating the standards
expected from social media companies.
