Exploring YouTube Comments Reacting to Femicide News in Italian
Authors: Chiara Ferrando, Marco Madeddu, Beatrice Antola, Sveva Silvia Pasini, Giulia Telari, Mirko Lai, Viviana Patti (Università di Torino, Università di Padova, Università di Pavia, Università del Piemonte Orientale, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In recent years, the Gender Based Violence (GBV) has become an important issue in modern society and a central topic in different research areas due to its alarming spread. Several Natural Language Processing (NLP) studies, concerning Hate Speech directed against women, have focused on misogynistic behaviours, slurs or incel communities. The main contribution of our work is the creation of the first dataset on social media comments to GBV, in particular to a femicide event. Our dataset, named GBV-Maltesi, contains 2,934 YouTube comments annotated following a new schema that we developed in order to study GBV and misogyny with an intersectional approach. During the experimental phase, we trained models on different corpora for binary misogyny detection and found that datasets that mostly include explicit expressions of misogyny are an easier challenge, compared to more implicit forms of misogyny contained in GBV-Maltesi. \noindent \textbf{Warning}: This paper contains examples of offensive content.

Introduction
Nowadays, the term Gender Based Violence (GBV) is used to identify all forms of abuse based on gender hatred and sexist discrimination AUTHOR. Scholars in social science have defined as ``rape culture'' the society that normalizes sexist behaviours: from more common occurrences like victim blaming, slut shaming and gender pay gap %at the base, through catcalling, stalking, stealthing, to the apex of violence with femicide AUTHOR. While general violent crimes decreased over time, GBV did not, alarming various bodies in modern society}. A report from the EU commission} states that 31%, 5% and 43% of European women suffered respectively from physical, sexual and psychological violence. Regarding the Internet sphere, a survey found that 73% of women journalists experienced online violence (threats, belittling, shaming,...) AUTHOR. These statistics become even more alarming when we consider studies that show the correlation between misogynistic online posts and GBV AUTHOR. \\ Like other countries, Italy is affected by GBV, with the national observatory managed by the ``Non Una di Meno'' association reporting 117 femicides in 2022, 120 in 2023 and more than 40 until June 2024}. Several studies about Hate Speech (HS) directed towards women often focus on developing taxonomies AUTHOR rather than investigating low resource subjects in computational linguistics like GBV. These works often gather corpora by keyword search of gender slurs AUTHOR, retrieving comments left on misogynistic spaces like incel blogs AUTHOR or considering messages directed towards %controversial popular women figures highly debated on social media AUTHOR. As GBV is a broad topic, we want to clarify that we focus on GBV in Western societies, particularly in Italy. The main goal of this project is to show what is the current perception of femicides expressed through comments on social media, focusing on the specific case of Carol Maltesi. We chose this femicide because the victim was a sex worker, meaning that she presented an intersectional trait, and it was a popular case in the media, enabling us to select enough material for the study. Further, we want to highlight how the socio-demographic characteristics of the victims determine the way they are described and how this influences the perception of the news. For instance, victim's features such as age, job, origin, skin color, nationality, religion have different weight and determine the lesser or greater spread of the news AUTHOR. To overcome the cited issues in current literature, in this research we considered the phenomenon by focusing on users' reactions in social media to news about femicides. We collected YouTube comments in response to videos talking about a specific case. In order to overcome the constraints of traditional sentiment analysis schemas, we annotated the data following a new semantic grid that can be used as a standard for comments regarding GBV. In the experimental phase of this work, we created models based on different Italian misogyny datasets (including ours). The goal of such experiments is to analyze the different features of these corpora and what forms of misogyny are harder to detect. We performed both a quantitative and qualitative analysis of the results. In the next sections, we describe: related work on hate speech and misogyny detection(Section sec:Related), the annotation scheme and both a quantitative and qualitative analysis of the dataset (Section sec:Dataset), and the results obtained in our experiments (Section sec:Experiments). Lastly, we present some conclusions and delineate possible future developments (Section sec:conclusions).

Conclusion
In this paper, we presented GBV-Maltesi which is the first dataset regarding social reactions to GBV, in particular to a femicide case. The topic was chosen to shed light on the importance of having misogyny corpora that include forms of sexism that are more implicit and complicated to detect compared to the existing ones that focus on slurs and offensive terms. We also focused on the intersectionality aspects to better explore online hate. GBV-Maltesi is composed of 2,934 comments all annotated by 5 annotators and it is available at . In order to overcome limitations of generic semantic schema, the corpus has been annotated following a new schema specifically created for cases of GBV. In the experimental phase of our work, we created different misogyny binary classifiers and tested them in a cross-dataset way. We found that datasets gathered on keyword collection are easier benchmarks as the model showed bias towards slurs and not identifying more implicit cases of misogyny. This research on online discourse about GBV is not meant to be exhaustive, as several questions are still open. As future works, we intend to focus on how different framing of news can cause different online reactions, analyzing the differences between video transcripts of femicide news and the comments collected, in terms of words used, implicit references, attributions of guilt and descriptions of the people involved in the story. We also intend to gather more annotated corpora regarding femicides to explore how other characteristics of the victim (e.g., origin or skin color) and time of the murder differently influence the online reactions. In this regard, we intend to explore the question by investigating whether and how the discourse on misogyny changes depending on whether it is addressed to living or dead women (i.e., Giulia Cecchettin femicide and abusive discourse against her sister, Elena Cecchettin). Lastly, we would like to extend our research by following an intersectional approach, considering all the dimensions and characteristics that make up the identity of both victim and perpetrator. To conclude, we strongly advocate the importance of write the news correctly, as this has deep consequences on the readers' perception and the way they talk about it.