An Italian Twitter Corpus of Hate Speech against Immigrants

An Italian Twitter Corpus of Hate Speech against Immigrants Manuela Sanguinetti⋆, Fabio Poletto⋆, Cristina Bosco⋆, Viviana Patti⋆, Marco Stranisci⋄ ⋆Dipartimento di Informatica, University of Turin, Italy ⋄ACMOS, Turin, Italy fabio.poletto@edu.unito.it, {msanguin, bosco, patti}@di.unito.it,marco.stranisci@acmos.net Abstract The paper describes a recently-created Twitter corpus of about 6,000 tweets, annotated for hate speech against immigrants, and developed to be a reference dataset for an automatic system of hate speech monitoring. The annotation scheme was therefore specifically designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion, and to offer a broader tagset capable of better representing all those factors, which may increase, or rather mitigate, the impact of the message. This resulted in a scheme that includes, besides hate speech, the following categories: aggressiveness, offensiveness, irony, stereotype, and (on an experimental basis) intensity. The paper hereby presented namely focuses on how this annotation scheme was designed and applied to the corpus. In particular, also comparing the annotation produced by CrowdFlower contributors and by expert annotators, we make some remarks about the value of the novel resource as gold standard, which stems from a preliminary qualitative analysis of the annotated data and on future corpus development. Keywords: hate speech, social media, immigrants, Italian 1. Introduction The global spread of the so-called “web 2.0” and of social network sites allows users to find, create and share knowl- edge more easily than ever before, with scarcely any skill and cost required. This enormously increased the amount of user-generated content, within a process that some call ”democratization” of the web (Silva et al., 2016). Yet, this freedom also allows for the publication of content which is abusive and harmful both towards the principles of democ- racy and the rights of some groups of people - namely hate speech (henceforth, HS). HS can be defined as any expression “that is abusive, insulting, intimidating, harass- ing, and/or incites to violence, hatred, or discrimination. It is directed against people on the basis of their race, ethnic origin, religion, gender, age, physical condition, disability, sexual orientation, political conviction, and so forth” (Er- javec and Kovaˇciˇc, 2012). Although definitions and approaches to HS are varied and depend on the juridical tradition of the country, many agree that what is identified as such can not fall under the protec- tion granted by the right to freedom of expression, and must be prohibited. Online platforms like Twitter or Youtube dis- courage hateful content, but its removal mainly relies on users reports and lacks a systematic control. In this regard, a promising direction of research is the training of auto- mated classifiers based on manually annotated corpora. Our Contribution The work hereby presented deals with the creation of a Twitter corpus aimed at obtaining a large and richly-annotated dataset for the development of an au- tomatic system of HS identification1. Moreover, given the multiplicity of factors that can contribute to the definition of the HS notion, the annotation scheme was specifically de- signed to account for this …

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