Women's Professions and Targeted Misogyny Online
Authors: Alessio Cascione, Aldo Cerulli, Marta Marchiori Manerba, Lucia C. Passaro (Università di Pisa, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
With the increasing popularity of social media platforms, the dissemination of misogynistic content has become more prevalent and challenging to address. In this paper, we investigate the phenomenon of online misogyny on Twitter through the lens of hurtfulness, qualifying its different manifestation in English tweets considering the profession of the targets of misogynistic attacks. By leveraging manual annotation and a \textsc{BERTweet} model trained for fine-grained misogyny identification, we find that specific types of misogynistic speech are more intensely directed towards particular professions. For example, derailing discourse predominantly targets authors and cultural figures, while dominance-oriented speech and sexual harassment are mainly directed at politicians and athletes. Additionally, we use the HurtLex lexicon and ItEM to assign hurtfulness scores to tweets based on different hate speech categories. Our analysis reveals that these scores align with the profession-based distribution of misogynistic speech, highlighting the targeted nature of such attacks.

Introduction
%and Background Misogyny is a radical manifestation of sexism directed toward the female gender, which becomes subject of hatred. Its effects are widespread and systematic, bearing severe both social and individual consequences, such verbal and physical violence, rape and femicide. Indeed, misogyny, prejudice, and contempt towards women continue to persist in various forms in our society. While overt acts of discrimination and sexism have received attention, it is crucial to acknowledge that misogyny often manifests in subtle and nuanced ways AUTHOR. Moreover, with the increasing popularity of social media platforms, the dissemination of misogynistic content has become more prevalent and challenging to address AUTHOR. From a socio-historical perspective, women have faced numerous barriers that limited their access to certain professions, hindered their career progression, and subjected them to belittlement and offense related to their work AUTHOR. These gendered biases not only perpetuate inequality but also serve as breeding grounds for misogyny. In this paper, we focus on automated misogyny detection, specifically investigating whether different professional roles trigger varying degrees of hurtfulness across social media posts. By examining the correlation between the profession of offended women and the prevalence of misogynistic attitudes, we aim to shed light on the extent to which misogyny is perpetuated within specific professional domains. ~\citet{fontanella2024we} highlight how research focusing on automatic detection of misogyny tends to show weak connections with other conceptual areas addressing different aspects of the phenomenon. %, such as politics and visual media. The finding suggests that current research has not yet adequately addressed the fine-grained manifestations of online misogynistic attacks. Our contribution conducts novel analyses to uncover and measure misogynistic attitudes within different professional fields. Specifically, we examine how different types of misogyny are distributed across various women's professions and how the language used in misogynistic posts varies across them. To explore this relationship, we expand the % annotated English %language misogyny identification dataset introduced by~\citet{fersini2018overview}, known as AMI, by incorporating the professions of the women targeted. By adding professional categories to AMI, we enable novel analyses on how misogynistic attacks against women differ based on their profession. Our research is driven by the following research questions: \item [RQ1] How does misogyny distribute across professions? We analyze women's profession according to the type of misogyny directed towards them. \item [RQ2] How does the language used in misogynistic tweets vary across different professions? We investigate how specific hurtful expressions are directed at specific professions more frequently than others. To address our RQs, we proceed following the workflow depicted in Figure fig:workflow_drawio. We begin by utilizing a subset of the AMI dataset, which contains ground-truth annotations for misogyny. This subset is manually labeled with the professions of the victims of misogynistic attacks, as detailed in Section~PRF_dataset. %, which includes ground-truth professional categories. We then employ a misogyny classifier to automatically annotate with various types of misogyny a novel collection, the Profession (PRF) dataset, which comprises 760 tweets labeled with professions. The final step involves combining the manually annotated AMI subset with the automatically annotated PRF dataset, resulting in the AMI-PRF dataset. This enriched dataset provides a resource that enables a thorough investigation of the phenomenon. \centering \includegraphics[width=0.9\linewidth]{img/clic_graph_horizontal.pdf . The PRF dataset, featuring professions by-design, is extracted and automatically annotated with misogyny types using a \textsc{BERTweet} model trained on the AMI dataset. The manually annotated AMI subset and the automatically annotated PRF dataset are then combined to form the AMI-PRF dataset. Labels distributions of each dataset are displayed in the workflow. The remainder of this paper is organized as follows. Section sec:relatedwork discusses previous works that closely related to ours, while Section sec:dataset details the enrichment of the AMI dataset with professional categories. Section sec:exp reports the experiments conducted to answer our RQs, whereas Section sec:concl outlines conclusions, limitations, and future directions of the work.

Conclusion
In this paper, we investigated the phenomenon of misogyny on Twitter through the lens of hurtfulness, qualifying its different manifestation considering the profession of the targets of the misogynistic attacks. Specifically, we examined how different types of misogyny are distributed across various professions, unveiling how derailing discourse is mostly used to attack authors, while dominance and sexual harassment speech targets especially politicians. Additionally, we studied through a hurtfulness score measure how the language used in misogynistic tweets varies across different professions: politicians tend to be targeted with hate speech revolving around sexuality (female/male genitalia, homosexuality) and crime, while artists seem to be insulted mainly through general derogatory terms. On the other hand, less heterogeneous results were obtained for athletes and authors, except for peaks in hurtful topics regarding crimes and professions. We acknowledge two potential limitations of our contribution: the incomplete coverage of our dataset's vocabulary by the Hurtlex-based ItEM lexicon, and our decision to focus on just four professions, which, as motivated, was guided by the representation of those professions in the AMI dataset. We therefore plan to extend the approach adopting a richer vocabulary w.r.t. datasets as well as expanding the set of professions. Indeed, as further future investigations, it could be assessed how hurtfulness dimensions change using different lexicons or automatic approaches. We also intend to investigate the distribution of misogynistic language both textual and multi-modal, as well as the broader expression of emotions in posts associated with different professions. Research partially funded by PNRR-PE00000013 “FAIR - Future Artificial Intelligence Research” - Spoke 1 “Human-centered AI” under NextGeneration EU, ERC-2018-ADG G.A. 834756 XAI: Science and technology for the eXplanation of AI decision making under Horizon 2020, and PRIN 2022 PIANO (Personalized Interventions Against Online Toxicity) project, CUP B53D23013290006. \newpage \onecolumn \appendix