ReCLAIM Project: Exploring Italian Slurs Reappropriation with Large Language Models
Authors: Marco Cuccarini, Lia Draetta, Chiara Ferrando, Liam James, Viviana Patti (Università di Napoli Federico II, Università di Perugia, Università di Torino, Università di Bologna, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Recently, social networks have become the primary means of communication for many people, leading computational linguistics researchers to focus on the language used on these platforms. As online interactions grow, recognizing and preventing offensive messages targeting various groups has become urgent. However, finding a balance between detecting hate speech and preserving free expression while promoting inclusive language is challenging. Previous studies have highlighted the risks of automated analysis misinterpreting context, which can lead to the censorship of marginalized groups. Our study is the first to explore the reappropriative use of slurs in Italian by leveraging Large Language Models (LLMs) with a zero-shot approach. We revised annotations of an existing Italian homotransphobic dataset, developed new guidelines, and designed various prompts to address the LLMs task. Our findings illustrate the difficulty of this challenge and provide preliminary results on using LLMs for such a language specific task. \\ %\LaTeX{ \noindent \textbf{Warning}: This paper contains examples of explicitly offensive content. \noindent \textbf{Our positionality:} This paper is situated in Italy in 2024 and is authored by researchers specializing in Natural Language Processing (NLP). Beyond our academic work, we are sensitive to anti-hate speech issues. Our backgrounds fields are theoretical linguistics, computer science and NLP.

Introduction
In recent years, social networks have become the primary means of communication for most people. With the daily growth of online interactions, it has become urgent to recognize and prevent the spread of offensive messages against different target groups based on gender, sex, sexual orientation, race, religion, language, or political orientation. Moreover, categorizing hate speech with clear-cut boundaries is overly simplistic, as it includes various forms of abusive language that imply disrespect and hostility. A recent challenge is finding a balance between detecting hate speech and preserving the free spread of ideas and opinions on the web, while promoting inclusive and fair language. Thiago et al. (2021) AUTHOR highlighted how automated analysis can misinterpret context, risking the censorship of marginalized groups languages, such as those of the LGBT+ community. Another study by Pamungkas and colleagues (2020) AUTHOR emphasized the importance of considering context in Natural Language Processing (NLP) tasks to avoid misinterpretations of word meanings, noting that the same swear word can be used both abusively and non-abusively. An example of this phenomenon is the semantic reappropriation, a practice in which terms historically used as slurs against a specific target group lose their offensive intent in certain contexts, by expressing a sense of belonging and solidarity within the group members AUTHOR. Although community visibility and the use of specific slang have been approached for years, to our knowledge only some hate speech studies specifically addressed slurs, and few focused on slurs semantic reappropriation AUTHOR. Nowadays, recognizing this kind of semantic shift through NLP tools is crucial to avoid the risk of removing not abusive speech in online contents, which could paradoxically harm marginalized users AUTHOR. Our study is the first with the aim of investigating reappropriative use of slurs in Italian, highlighting the need to take a step ahead from the existing abusive language detection models. Having in mind the capability of LLMs in classification task, we leveraged a LLM with a zero-shot approach in order to recognize the presence of reappropriative uses in our dataset. This study makes the following contributions: \item We partially revised the original annotation previously conducted on the HODI dataset (Homotransphobic Dataset in Italian) AUTHOR, by developing new annotation guidelines. \item We used a LLM specifically fine-tuned on Italian language by leveraging prompt engineering. \item From a linguistic point of view, we showed why certain features of the Italian language make this task particularly challenging. This paper is structured as follows: in the Section sec:Related work we review the most significant related work on hate speech detection and zero-shot approaches leveraging LLMs. In the Section sec:Methodology we describe our methodology for the dataset creation and the implementation of zero-shot tasks. In Sections tab:result and sec:Limitations-future works we respectively report results, analysis and main limitations of this work. Finally, in the last Section sec:Conclusion we draw conclusions of the current research.

Conclusion
This paper presents the first attempt to specifically address the detection of slur reappropriation in the Italian language. One of the reasons that motivated us to undertake this task is the need to ensure a safe linguistic environment on social networks without risking the censorship of individual freedom of expression. Since there was no existing dataset to explore homophobic slurs in the Italian language, we filtered a pre-existing homotransphobic dataset to build a subset containing only tweets with slurs occurrences, used both abusively and non-abusively. We then designed precise new guidelines and annotated the filtered subset, focusing on the presence of slur semantic reappropriation. With the newly annotated dataset, we approached a classification task using LLMs with zero-shot techniques. Leveraging the Qwen model AUTHOR, we proposed four different prompts. As suggested by previous literature, more specific prompts and those better suited to the dataset yielded better performance. In this work, we proposed an important and under-explored task through a two-fold contribution. On one hand, we highlighted the lack of data in the Italian language dealing with this phenomenon and the necessity of building an up-to-date corpus that comprehensively includes multiple sources and semantic contexts. On the other hand, we demonstrated a possible approach by leveraging new state-of-the-art LLMs. Finally, it is important to have in mind that compared to English, Italian has a different history and cultural background, resulting in a much slower linguistic evolution. This makes establishing precise characteristics of this topic a challenging task due to the lack of solid foundational knowledge. In conclusion, we believe that bringing attention to the issue will lead to anti-discrimination activities, the creation of safer spaces in online communication, and the inclusion and acceptance of LGBT+ communities. \clearpage \appendix