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 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.

Alessandra Teresa Cignarella, Cristina Bosco, Paolo Rosso. Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019). 2019.

The paper describes a research about the socio-political debate on the reform of the education sector in Italy. It includes the development of an Italian dataset for sentiment analysis from two different comparable sources: Twitter and the online institutional platform implemented for supporting the debate. We describe the collection methodology, which is based on theoretical hypotheses about the communicative behavior of actors in the debate, the annotation scheme and the results of its application to the collected dataset. Finally, a comparative analysis of data is presented.

This article describes a Twitter corpus of social media contents in the Subjective Well-Being domain. A multilayered manual annotation for exploring attitudes on fertility and parenthood has been applied. The corpus was further analysed by using sentiment and emotion lexicons in order to highlight relationships between the use of affective language and specific sub-topics in the domain. This analysis is useful to identify features for the development of an automatic tool for sentiment-related classification tasks in this domain. The gold standard is available to the community.