How do we counter dangerous speech in Italy?
Authors: Vittoria Tonini, Simona Frenda, Marco Antonio Stranisci, Viviana Patti (Università di Torino, Aequa-tech, Heriot-Watt University, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The phenomenon of online dangerous speech is a growing challenge and various organisations try to prevent its spread answering promptly to hateful messages online. In this context, we propose a new dataset of activists’ and users’ comments on Facebook reacting to specific news headlines: AmnestyCounterHS. Taking into account the literature on counterspeech, we defined a newschema of annotation and applied it to our dataset, in order to examine the most used counter-narrative strategies in Italy. This research aims to support the future development of automatic counterspeech generation. This paper presents also a comparative analysis of our dataset with other two datasets in Italian (Counter-TWIT and multilingual CONAN) containing dangerous speech and counter narratives. Through this analysis, we will understand how the environment (artificial vs. ecological) and the topics of discussions online influence the nature of counter narratives. Our findings highlight the predominance of negative sentiment and emotions, the varying presence of stereotypes, and the strategic differences in counter narratives across datasets.

Introduction
Recently, the attention about dangerous speech (DS) online has increased in different sectors, ranging from initiatives for monitoring the DS’ spread, particularly in Italy (e.g., by VOX1, or by researchers like Capozzi et al. [1]), to prevent the escalation of DS online using methods of detection and removal of dangerous contents (e.g., following the policies of social platforms). Moreover, specific actions of countering DS online, like the Amnesty Task Force on Hate Speech2, which reassembles specialized activists who actively intervene by writing counterspeech, were promoted3 in response to potential or effective dangerous speech or news on various topics. In this context, the new techniques of Natural Language Understanding (NLU) and Natural Language Generation (NLG) can play a very important role. On DS detection, the literature is vast [4, 5] and covers various nuances of DS [6, 7], different types of manifestation (i.e., explicit and implicit, [8]), and co-occurrences with other psychological and linguistic phenomena, like stereotypes [9] and sarcasm [10]. Regarding works on countering DS, some studies focused on imitating the operators of Non-Governmental Organizations (NGOs) in their intervention in online discussions, or selecting the most suitable responses from a database [11], or creating generative models able to reply automatically to hateful content using counter narratives (CN), avoiding hallucinations [12]. The development of NLU and NLG models are mainly based on data-driven approaches, which imply the creation of a specific dataset to detect DS or generate adequate CN. According to the survey by Bonaldi et al. [2], in literature, the available datasets in languages other than English are very few. Among them, currently, only two datasets contain Italian texts: CONAN [13] and Counter-TWIT [14]. The creation environment of CONAN is artificial (i.e., activists have been asked to write CN to specific hateful comments), and the one of Counter-TWIT is entirely ecological (i.e., collection of tweets written by users). In this scenario, in our work, we propose a new dataset, AmnestyCounterHS, which, differently from the existing ones, reflects the real action of activists online. Indeed, our dataset, compiled from Facebook, includes interactions guided by the Amnesty Task Force on Hate Speech (HS), representing an ecological and spontaneous context. Here, the intervention of counterspeech is guided by Amnesty International activists who decided to intervene under certain posts potentially dangerous spread by online newspapers or users (e.g., verbal attacks to women, immigrants, and so on). Moreover, inspired by existing strategy taxonomies [15, 13, 14], we mapped a more complete taxonomy inclusive of both existing and new strategies found in our dataset. This new resource allows us to analyze the used strategies of CN in the Italian language across different types of messages and contexts (CONAN, Counter-TWIT, AmnestyCounterHS). By comparing these datasets, we propose to examine: 1) which strategy of CN is the most used in the different contexts and discussions online; 2) what the differences are in terms of sentiments, emotions, and the presence of stereotypes, between potentially dangerous messages posted online and the counterspeech produced by activists/users in all the datasets. The importance of understanding how these strategies of CN are used relies on the need to raise social awareness about real events, the necessity to be correctly informed about facts (avoiding fake news), as well as to be conscious of the consequences of dangerous speech in the target groups [16].

Conclusion
In this paper, we examine the strategy of CN used in various contexts, looking at their characteristics and typology across different datasets in Italian: CONAN, Counter-TWIT, and AmnestyCounterHS. Thanks to this comparative analysis, we noticed that different environments and topics affect the type of strategy used by activists or users who want to counter DS [18]. One of the main points that we want to underline is the importance of the conversational context [19, 20, 21, 22]. In our dataset, AmnestyCounterHS, the annotators showed difficulties in understanding the position of the author of the message, without the entire conversational thread. For instance, let us consider this comment written under some news about COVID-19: "Infatti. Ampiamente dimostrato"26. Without the full conversation, it is challenging to determine whether this comment is supporting or contradicting an argument about COVID-19. Similarly, let us take a look at the comment: "Grande argomentazione, scuola di Demostene? #posailfiasco"27 written under this newstitle: "Un milione di profughi sono ostaggio di Erdogan"28. We can clearly see that the comment is ironic, but we cannot understand its stance on integration. For this reason, future developments in automatic counterspeech generation should focus on incorporating comprehensive conversational threads to enhance accuracy and relevance. This approach will be fundamental to create effective AI-driven counter-narrative systems.