  The damaging effects of hate speech on social media are evident during the
last few years, and several organizations, researchers and social media
platforms tried to harness them in various ways. Despite these efforts, social
media users are still affected by hate speech. The problem is even more
apparent to social groups that promote public discourse, such as journalists.
In this work, we focus on countering hate speech that is targeted to
journalistic social media accounts. To accomplish this, a group of journalists
assembled a definition of hate speech, taking into account the journalistic
point of view and the types of hate speech that are usually targeted against
journalists. We then compile a large pool of tweets referring to
journalism-related accounts in multiple languages. In order to annotate the
pool of unlabeled tweets according to the definition, we follow a concise
annotation strategy that involves active learning annotation stages. The
outcome of this paper is a novel, publicly available collection of Twitter
datasets in five different languages. Additionally, we experiment with
state-of-the-art deep learning architectures for hate speech detection and use
our annotated datasets to train and evaluate them. Finally, we propose an
ensemble detection model that outperforms all individual models.
