Papers by Arianna Muti
Untangling Hate Speech Definitions: A Semantic Componential Analysis Across Cultures and Domains (2025.findings-naacl)
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| Challenge: | a new framework for analyzing hate speech definitions is proposed to address cultural differences in interpretations . a dataset of 493 definitions from more than 100 cultures is used to analyze hate speech . |
| Approach: | They propose a framework for a cross-cultural and cross-domain analysis of hate speech definitions . they use open-source LLMs to analyze the impact of different definitions on hate speech detection . |
| Outcome: | The proposed framework enables cross-cultural and cross-domain analysis of hate speech definitions . it reveals that many domains borrow definitions from one another without taking into account target culture . |
A Corpus for Sentence-Level Subjectivity Detection on English News Articles (2024.lrec-main)
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Francesco Antici, Federico Ruggeri, Andrea Galassi, Katerina Korre, Arianna Muti, Alessandra Bardi, Alice Fedotova, Alberto Barrón-Cedeño
| Challenge: | Existing approaches to spotting subjectivity require language-specific tools. |
| Approach: | They develop annotation guidelines for sentence-level subjectivity detection that are not limited to language-specific cues. |
| Outcome: | The proposed framework enables subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. |
Misogyny and Aggressiveness Tend to Come Together and Together We Address Them (2022.lrec-1)
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| Challenge: | Using a binary task to identify whether a tweet is misogynous and aggressive, we compare two approaches to address these problems: one multi-class model that discriminates between all the classes at once; and a cascaded approach where the binary classification is carried out separately. |
| Approach: | They propose a multi-class model that discriminates between all the classes at once and a cascaded approach where the binary classification is carried out separately and then joined together. |
| Outcome: | The proposed models outperform the top submissions to Evalita on the 2020 shared task on automatic misogyny and aggressiveness identification in Italian tweets. |
The Challenges of Creating a Parallel Multilingual Hate Speech Corpus: An Exploration (2024.lrec-main)
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| Challenge: | Hate speech is one of the most demanding topics in Natural Language Processing, as its multifaceted nature is accompanied by a handful of challenges, such as multilinguality and cross-linguality. |
| Approach: | They propose a pipeline that could be used to create a parallel multilingual hate speech dataset using machine translation. |
| Outcome: | The proposed pipeline will be able to create a parallel multilingual hate speech dataset using machine translation. |
Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts (2024.emnlp-main)
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| Challenge: | a new study aims to understand the implicit reasoning used to convey misogynistic comments in Italian and English. |
| Approach: | They propose misogyny detection as an Argumentative Reasoning task and use argumentation theory to build large language models to understand the implicit reasoning used to convey misogany in Italian and English. |
| Outcome: | The proposed task is an argumentative reasoning task in Italian and English. |
A Checkpoint on Multilingual Misogyny Identification (2022.acl-srw)
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| Challenge: | a study on hate speech against minorities in Italian tweets found that 1 women are the most targeted group. |
| Approach: | They propose to train monolingual transformers and multilingual transformer models with monolingual data in English, Italian, and Spanish to detect misogyny in tweets. |
| Outcome: | The proposed model achieves state-of-the-art on English, Italian, and Spanish. |
The “r” in “woman” stands for rights. Auditing LLMs in Uncovering Social Dynamics in Implicit Misogyny (2025.findings-emnlp)
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| Challenge: | a recent study examined misogynistic expressions in English and Italian . a taxonomy of social dynamics is used to identify misogorical expressions . |
| Approach: | They examine misogynistic expressions in English and Italian using a taxonomy of social dynamics . they find that LLMs struggle to follow instructions and reason in all settings . |
| Outcome: | The results show that misogynistic expressions are more often implicit than openly hostile . the authors show that LLMs struggle to follow instructions and reason in all settings . |
PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets (2024.lrec-main)
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Arianna Muti, Federico Ruggeri, Cagri Toraman, Alberto Barrón-Cedeño, Samuel Algherini, Lorenzo Musetti, Silvia Ronchi, Gianmarco Saretto, Caterina Zapparoli
| Challenge: | Disambiguating the meaning of pejorative words might help misogyny detection . state-of-the-art models struggle to correctly classify misogoyne when sentences contain such terms. |
| Approach: | They present a corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level. |
| Outcome: | The proposed model improves on 1,200 manually annotated Italian tweets and on two benchmarks. |