CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2819–2829 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 2819 CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech Yi-Ling Chung1,2, Elizaveta Kuzmenko2, Serra Sinem Tekiro˘glu1, and Marco Guerini1 1Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy ychung@fbk.eu, tekiroglu@fbk.eu, guerini@fbk.eu 2University of Trento, Italy elizaveta.kuzmenko@studenti.unitn.it Abstract Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem. Tackling hate speech in the standard way of content deletion or user sus- pension may be charged with censorship and overblocking. One alternate strategy, that has received little attention so far by the research community, is to actually oppose hate con- tent with counter-narratives (i.e. informed tex- tual responses). In this paper, we describe the creation of the first large-scale, multilingual, expert-based dataset of hate speech/counter- narrative pairs. This dataset has been built with the effort of more than 100 operators from three different NGOs that applied their train- ing and expertise to the task. Together with the collected data we also provide additional an- notations about expert demographics, hate and response type, and data augmentation through translation and paraphrasing. Finally, we pro- vide initial experiments to assess the quality of our data. 1 Introduction Together with the rapid growth of social media platforms, the amount of user-generated content is steadily increasing. At the same time, abusive and offensive language can spread quickly and is difficult to monitor. Defining hate speech is chal- lenging for the broadness and the nuances in cul- tures and languages. For instance, according to UNESCO hate speech refers to “expressions that advocate incitement to harm based upon the tar- gets being identified with a certain social or de- mographic group” (Gagliardone et al., 2015). Victims of hate speech are usually targeted be- cause of various aspects such as gender, race, reli- gion, sexual orientation, physical appearance. For example, Sentence 1 shows explicit hostility to- wards a specific group with no reasons explained1. (1) I hate Muslims. They should not exist. Online hate speech can deepen prejudice and stereotypes (Citron and Norton, 2011) and by- standers may receive false messages and con- sider them correct. Although Social Media Platforms (SMP) and governmental organizations have elicited unprecedented attention to take ad- equate actions against hate speech by implement- ing laws and policies (Gagliardone et al., 2015), they do not seem to achieve the desired effect, since hate content is continuously evolving and adapting, making its identification a tough prob- lem (Davidson et al., 2017). The standard approach used on SMPs to pre- vent hate spreading is the suspension of user ac- counts or deletion of hate comments, while try- ing to weigh the right to freedom of speech. An- other strategy, which has received little attention so far, is to use counter-narratives. A …

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