Policycorpus XL: An Italian Corpus for the Detection of Hate Speech Against Politics
Authors: Fabio Celli, Mirko Lai, Armend Duzha, Cristina Bosco, Viviana Patti (Gruppo Maggioli, Università di Torino, 2021)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
English. 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.

Introduction
In recent years, computer mediated communication on social media and microblogging websites has become more and more aggressive <cit.>. It is well known that people uses social media like Twitter for a variety of purposes like keeping in touch with friends, raising the visibility of their interests, gathering useful information, seeking help and release stress <cit.>, but the spread of fake news <cit.> <cit.> has exacerbated a cultural clash between social classes that emerged after the debate about Brexit <cit.>. Despite the behavior online is different from the behavior offline <cit.>, we observe more and more hate speech in social media, to the point where it has become a serious problem for free speech and social cohesion.Hate speech is defined as any expression that is abusive, insulting, intimidating, harassing, and/or incites, supports and facilitates violence, hatred, or discrimination. It is directed against people (individuals or groups) on the basis of their race, ethnic origin, religion, gender, age, physical condition, disability, sexual orientation, political conviction, and so forth <cit.>.In response to the growing hate messages, the Natural language Processing (NLP) community focused on the classification of hate speech <cit.> and the analysis of online debates <cit.>. In particular, many worked on systems to detect offensive language against specific vulnerable groups (e.g., immigrants, LGBTQ communities etc.) <cit.> <cit.>, as well as aggressive language against women <cit.>. An under-researched - yet important - area of investigation is anti-policy hate: the hate speech against politicians, policy making and laws at any level (national, regional and local). While anti-policy hate speech has been addressed in Arabic <cit.> and German <cit.>, most European languages have been under-researched.The bottleneck in this field of research is the availability of data to train good hate speech detection models. In recent years, scientific research contributed to the automatic detection of hate speech from text with datasets annotated with hate labels, aggressiveness, offensiveness, and other related dimensions <cit.>. Scholars have presented systems for the detection of hate speech in social media focused on specific targets, such as immigrants <cit.>, and language domains, such as racism <cit.>, misogyny <cit.> or cyberbullying <cit.>. Each type of hate speech has its own vocabulary and its own dynamics, thus the selection of a specific domain is crucial to obtain clean data and to restrict the scope of experiments and learning tasks.In this paper we present a new corpus, called Policycorpus XL, for hate speech detection from Twitter in Italian. This corpus is an extension of the Policycorpus <cit.>. We selected Twitter as the source of data and Italian as the target language because Italy has, at least since the elections in 2018, a large audience that pays attention to hyper-partisan sources on Twitter that are prone to produce and retweet messages of hate against policy making <cit.>.The paper is structured as follows: after a literature review (Section <ref>), we describe how we collected and annotated the data (Section <ref>), we evaluate some baselines (Section <ref>), and we pave the way for future work (Section <ref>).

Conclusion
We presented a large corpus of Twitter data in Italian, manually annotated with hate speech labels. The corpus is an extension of a previous one, the first corpus annotated with hate speech in the political domain in Italian. Given the rising amount of hate messages online, not just against minorities but more and more against policies and policymakers, it is urgent to understand the phenomenon and train classifiers that could prevent people to disseminate hate in the public debate. This is very important to keep democracies alive and grant a free speech that is respectful of other people's freedom. We plan to distribute the corpus in the next edition of EVALITA for a specific HaSpeeDe-tw task.