Inters8: A Corpus to Study Misogyny and Intersectionality on Twitter
Authors: Ivan Spada, Mirko Lai, Viviana Patti (Università di Torino, Aequa-tech, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This paper presents our research on the detection of online misogyny on social media and its intersection with other hate categories. Focusing on the phenomenon of misogyny, we carried out a corpus-based data analysis around victims of online hate campaigns. Targets were selected to study how misogyny and sexism intersect with other categories of social hatred and discrimination such as xenophobia, racism, and Islamophobia. This study includes an event-driven analysis of hate on Twitter concerning specific targets, the process of developing the Inters8 corpus, and its manual annotation according to a novel multi-level scheme designed to assess the presence of intersectional hatred.

Introduction
The term intersectionality refers to the coexistence of multiple forms of social categorizations such as ethnicity, gender, sexual orientation, social class, disability, etc., which can lead to discrimination and generate obstacles in the daily lives of those affected [1]. In the specific case of misogyny, discrimination takes different shapes depending on the other co-existing forms of discrimination such as racism, classism, ableism, or homophobia [1]. Therefore, the phenomenon should not be studied in isolation. The coexistence of different forms of discrimination suggests the study of social interactions and the intersectionality between multiple categories of hate. It is interesting to explore how language may vary when interactions involve people who are at the intersection of multiple discriminated social categories that are henceforth referred to as dimensionalities. In particular, [1] explained how intersectionality between multiple dimensionalities may generate a new discriminated category framed and treated differently. The objective of this paper is to analyze how misogynistic hatred intersects with other dimensionalities and how this appears in a micro-blogging social platform such as Twitter. The first contribution of this paper is an analysis of gender discrimination, inequality comparisons, presence of stereotypes, also oriented to shed light on how users interact with, support, and attack targets of misogynous hatred on social media. As a second contribution, we created Inters8, an Italian corpus containing a subset of TWITA [2] filtered by following a target and event selection process, for the purpose of studying intersectionality. Thereafter, a portion of the corpus related to Silvia Romano’s liberation [3] was annotated (called Inters8_SRomano) so as to explore characteristics of intersectional hatred in a specific pilot study. Outline. After a brief technical contextualization (Section 2), we describe the target-event driven analysis, the creation of the Inters8 corpus and the annotation task (Section 3). Then, the novel annotation scheme applied to the data is described (Section 4) and a discussion of the outcome of the annotation process is presented (Section 5). Conclusions and future work end the paper.

Conclusion
Developing the Inters8 corpus, built considering an intersectional target-event pair, allowed us to explore a case study and analyze Twitter interactions related to Silvia Romano on social media. The manual annotation applied highlights how multiple dimensionalities coexist and intertwine in cases of intersectional hate. Despite the evidence of the phenomenon and its dynamics, results presented here are related to the specific case study taken into account, and to the target and the event selected. In fact, at the current stage of development, the Inters8 corpus includes content related to the specific intersectional Silvia Romano’s liberation target-event pair. We plan to expand the corpus with additional targets, events, and social categories. It would then be interesting to compare multiple targets in the same intersection and study how local culture might influence the phenomenon over several countries. As the corpus is built around the Italian context, the data are exclusively in Italian. The integration of multiple languages would allow for greater generalization and the study of the geographic distribution of the phenomenon around known people and events. Finally, there are many interactions on social networks, and the experimental study for automatic detection of intersectional hate may be a challenge of particular interest.