Hate Speech in social media is a complex phenomenon, whose detection has recently gained significant traction in the Natural Language Processing community, as attested by several recent review works. Annotated corpora and benchmarks are key resources, considering the vast number of supervised approaches that have been proposed. Lexica play an important role as well for the development of hate speech detection systems. In this review, we systematically analyze the resources made available by the community at large, including their development methodology, topical focus, language coverage, and other factors. The results of our analysis highlight a heterogeneous, growing landscape, marked by several issues and venues for improvement.

The Hate Speech Detection (HaSpeeDe) task is a shared task on Italian social media (Facebook and Twitter) for the detection of hateful content, and it has been proposed for the first time at EVALITA 2018. Providing two datasets from two different online social platforms differently featured from the linguistic and communicative point of view, we organized the task in three tasks where systems must be trained and tested on the same resource or using one in training and the other in testing: HaSpeeDe-FB, HaSpeeDe-TW and Cross-HaSpeeDe (further sub-divided into Cross-HaSpeeDe_FB and Cross-HaSpeeDe_TW sub-tasks). Overall, 9 teams participated in the task, and the best system achieved a macro F1-score of 0.8288 for HaSpeeDe-FB, 0.7993 for HaSpeeDe-TW, 0.6541 for Cross-HaSpeeDe_FB and 0.6985 for Cross-HaSpeeDe_TW. In this report, we describe the datasets released and the evaluation measures, and we discuss results.

The paper describes the development of a corpus from social media built with the aim of representing and analysing hate speech against some minority groups in Italy. The issues related to data collection and annotation are introduced, focusing on the challenges we addressed in designing a multifaceted set of labels where the main features of verbal hate expressions may be modelled. Moreover, an analysis of the disagreement among the annotators is presented in order to carry out a preliminary evaluation of the data set and the scheme.

This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this article is twofold: (1) to provide a condensed, though comprehensive, overview of such treebanks—based on available literature—along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The overarching goal of this article is to provide a common framework for researchers interested in developing similar resources in UD, thus promoting cross-linguistic consistency, which is a principle that has always been central to the spirit of UD.