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The possibility of raising awareness about misbehaviour online, such as hate speech, especially in young generations could help society to reduce their impact, and thus, their consequences. The Computer Science Department of the University of Turin has designed various technologies that support educational projects and activities in this perspective. We implemented an annotation platform for Italian tweets employed in a laboratory called #DeactivHate, specifically designed for secondary school students. The laboratory aims at countering hateful phenomena online and making students aware of technologies that they use on a daily basis. We describe our teaching experience in high schools and the usefulness of the technologies and activities tested.

In this paper we present a large corpus of Twitter data in Italian, manually annotated with hate speech in the political domain. Given the rising amount of hate messages in the public debate, we believe that this is a valuable resource for the NLP community. Here we describe the collection of data and test some baseline with classification algorithms. In questo articolo presentiamo un corpus di dati Twitter di dominio politico in Italiano, annotati manualmente con etichette di odio. Dato il dilagare di messaggi di odio nel diabattito pubblico, crediamo che questa risorsa sia preziosa per la comunità di ricerca nell’elaborazione automatica del linguaggio. Qui viene descritta la raccolta dei dati e vengono applicati algoritmi di classificazione per effettuare la valutazione di base.

Inside the NLP community there is a considerable amount of language resources created, annotated and released every day with the aim of studying specific linguistic phenomena. Despite a variety of attempts in order to organize such resources has been carried on, a lack of systematic methods and of possible interoperability between resources are still present. Furthermore, when storing linguistic information, still nowadays, the most common practice is the concept of "gold standard", which is in contrast with recent trends in NLP that aim at stressing the importance of different subjectivities and points of view when training machine learning and deep learning methods. In this paper we present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG) for the collection of linguistic annotated data. O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community. The ontology has also been designed to account for a perspectivist approach, since it provides a model for encoding both gold standard and single-annotator labels in the KG. The paper is structured as follows. In Section 1 the motivations of our work are outlined. Section 2 describes the O-Dang! Ontology, that provides a common semantic model for the integration of datasets in the KG. The Ontology Population stage with information about corpora, users, and annotations is presented in Section 3. Finally, in Section 4 an analysis of offensiveness across corpora is provided as a first case study for the resource.

In this paper we discuss the role of natural language generation (NLG) in modern dialogue systems (DSs). In particular, we will study the role that a linguistically sound NLG architecture can have in a DS. Using real examples from a new corpus of dialogue in customer-care domain, we will study how the non-linguistic contextual data can be exploited by using NLG.

The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.

Healthcare-associated Legionnaires’ disease (HCA LD) causes significant morbidity and mortality, with varying guidance on prevention. We describe the evaluation of a case of possible HCA LD and note the pitfalls of relying solely on an epidemiologic definition for association of a case with a facility. Our detailed investigation led to the identification of a new Legionella pneumophila serogroup 1 sequence type, confirmed a healthcare association and helped build the framework for our ongoing preventive efforts. Our experience highlights the role of routine environmental cultures in the assessment of risk for a given facility. As clinicians increasingly rely on urinary antigen testing for the detection of L. pneumophila, our investigation emphasises the importance of clinical cultures in an epidemiologic investigation.