Hate Speech Detection in an Italian Incel Forum Using Bilingual Data for Pre-Training and Fine-Tuning
Authors: Paolo Gajo, Silvia Bernardini, Adriano Ferraresi, Alberto Barrón-Cedeño (Università di Bologna, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this study, we aim to enhance hate speech detection in Italian incel posts. We pre-train monolingual (Italian) and multilingual Transformer models on corpora built from two incel forums, one in Italian and one in English, using masked language modeling. Then, we fine-tune the models on combinations of English and Italian corpora, annotated for hate speech. Experiments on a hate speech corpus derived from the Italian incel forum show that the best results are achieved by training multilingual models on bilingual data rather than training monolingual models on Italian-only data. This emphasizes the importance of using training and testing data from a similar linguistic domain, even when the languages differ.

Introduction
While there is no scarcity of English-language models and training resources for the detection of hate speech (HS), especially with the recent rise in popularity of this research topic, much work can still be carried out on this problem in other languages. For less-resourced languages, such as Italian, one of the main difficulties of combating this phenomenon is the lack of annotated data. The problem is even more severe when considering the detection of hate speech in niche contexts, such as in forums frequented by incels, short for “involuntary celibates,” a community known for its hateful language and use of specific misogynous and racist lexicon. In particular, it seems no work has yet been done on the detection of hate speech in Italian incel forums. In this paper, we present a simple approach to improve the performance of hate speech detection models in Italian forums frequented by incels. Our contribution is two-fold: (i) Masked language modeling. We adapt monolingual Italian models to the linguistic domain of Italian incel forums by training them on the masked language modeling (MLM) task. As training material, we use an unlabelled corpus compiled from an Italian incel forum. We also adopt an existing multilingual model, already domain-adapted to the incel domain in both English and Italian. We release these novel models, which can be used for further research on the topic. (ii) Hate speech detection. We fine-tune the vanilla and domain-adapted models on the downstream task of detecting hate speech in Italian incel posts. Monolingual models are trained on Italian-only combinations of corpora binary-annotated for hate speech, while Italian–English combinations are used for the multilingual models. Testing the performance of the models on a labelled hate speech corpus, obtained by annotating posts from the Italian incel forum, shows that the best results are obtained by first training the base multilingual model on bilingual data taken from both the Italian and English incel forums, using the MLM task, and then fine-tuning it on combinations of Italian and English corpora, annotated for hate speech. In the approached scenarios, pre-training and fine-tuning on bilingual in-domain incel annotated data may therefore be more effective than training on general target-language labelled corpora, despite part of the training data not being in the language of the downstream task. In addition, the results show that this strategy can be used to improve model performance when in-domain target-language data is scarce, by using in-domain data from other languages. The rest of the paper is organized as follows: Section 2 presents related work on hate speech detection in Italian and English, as well as multilingual approaches to the problem. Section 3 describes the corpora used in this study. Section 4 presents the employed models. Section 5 describes the experiments conducted and discusses the results. Section 6 closes the contribution with conclusions and future work.

Conclusion
In this paper, we have presented an approach to improve the performance of hate speech detection models in Italian incel posts. Our experiments show that domain-adapting transformer models to the contents of incel forums boosts their performance when predicting the hatefulness of incel forum posts, both when using Italian-only and multilingual models. The increase in performance obtained through MLM pre-training is particularly high when using bilingual training data with mBERTbase, which might indicate that the model is learning about incel hate speech by learning language-agnostic incel concepts. We have also shown that for the base Italian models (AlBERTo and UmBERTo) fine-tuning on combinations of different Italian corpora can lead to a boost in performance. However, this performance boost is nullified after MLM pre-training, which appears to be a more effective strategy for improving the performance of the models. When looking at racism vs. misogyny identification in posts extracted from Il forum dei brutti, the former appears to be much easier to detect. This seems due to the fact that racist language is much more explicit than misogynous language in the scrutinized forum, but further research is needed to ascertain such a supposition. In future work, we plan to experiment with different resources for MLM pre-training, using corpora in different languages, since it seems multilingual models such as mBERTbase are capable of learning about hate speech in a language-agnostic way from multiple languages. In addition, with more computational resources, larger corpora and more training epochs could be used to further improve the performance of the models. Lastly, further experiments can be carried out as regards the performance of the scrutinized models on the individual sub-tasks of misogyny and racism identification, respectively.