Hurtlex: A Multilingual Lexicon of Words to Hurt
Authors: Elisa Bassignana, Valerio Basile, Viviana Patti (Università di Torino, 2018)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
We describe the creation of HurtLex, a multilingual lexicon of hate words. The starting point is the Italian hate lexicon developed by the linguist Tullio De Mauro, organized in 17 categories. It has been expanded through the link to available synset-based computational lexical resources such as MultiWordNet and BabelNet, and evolved in a multi-lingual perspective by semiautomatic translation and expert annotation. A twofold evaluation of HurtLex as a resource for hate speech detection in social media is provided: a qualitative evaluation against an Italian annotated Twitter corpus of hate against immigrants, and an extrinsic evaluation in the context of the AMI@Ibereval2018 shared task, where the resource was exploited for extracting domain-speciﬁc lexicon-based features for the supervised classiﬁcation of misogyny in English and Spanish tweets.

Introduction
Communication between people is rapidly changing, in particular due to the exponential growth of the use of social media. As a privileged place for expressing opinions and feelings, social media are also used to convey expressions of hostility and hate speech, mirroring social and political tensions. Social media enable a wide and viral dissemination of hate messages. The extreme expressions of verbal violence and their proliferation in the network are progressively being conﬁgured as unavoidable emergencies. Therefore, the development of new linguistic resources and computational techniques for the analysis of large amounts of data becomes increasingly important, with particular emphasis on the identiﬁcation of hate in language (Schmidt and Wiegand, 2017; Waseem and Hovy, 2016; Davidson et al., 2017). The main objective of this work is the development of a lexicon of hate words that can be used as a resource to analyze and identify hate speech in social media texts in a multilingual perspective. The starting point is the lexicon ‘Le parole per ferire’ developed by the Italian linguist Tullio De Mauro for the “Joe Cox” Committee on intolerance, xenophobia, racism and hate phenomena of the Italian Chamber of Deputies. The lexicon consists of more than 1,000 Italian hate words organized along different semantic categories of hate (De Mauro, 2016). In this work, we present a computational version of the lexicon. The hate categories and lemmas have been represented in a machine-readable format and a semi-automatic extension and enrichment with additional information has been provided using lexical databases and ontologies. In particular we augmented the original Italian lexicon with translations in multiple languages. HurtLex, the hate lexicon obtained with the method described in Section 3, has been tested with a corpus-based evaluation, through the analysis of a hate corpus of about 6,000 Italian tweets (Section 4.1), and through an extrinsic evaluation in the context of the shared task on Automatic Misogyny Identiﬁcation at IberEval 2018, focusing on the identiﬁcation of hate against women in Twitter in English and Spanish (Section 4.2). The resource is available for download at http://hatespeech.di.unito.it/ resources.html

Conclusion
Our main contribution is a machine-readable version of the hate words lexicon by De Mauro, enriched with lexical features from available computational resources. We make HurtLex available for download as a tool for hate speech detection. A ﬁrst evaluation of the lexicon against corpora featuring different targets of hate (immigrants and women) has been presented. The multilingual evaluation of HurtLex showed also promising results. Although we are aware that hate speech-related phenomena tend to follow regional and cultural patterns, our semi-automatically produced resource was able to partially ﬁll the gap towards hate speech detection in less represented languages. To this end, we aim at investigating the potential and pitfalls of semi-automating mappings further. In particular, two possible extensions of our method involve using distributional semantic models to automatically expand the lexicon with synonyms and lemmas semantically related to the original ones, and exploiting De Mauro’s derivational rules.