Researching Stereotypes towards LGBTQIA+ Community Members with Multilingual Natural Language Processing

Start date
July 1, 2024
End date
June 30, 2026
Sponsor
European Union (Marie Skłodowska-Curie Action)

About RAINBOW

The RAINBOW project addresses the pressing issue of stereotypes and biases directed at LGBTQIA+ individuals online. In an era where digital discourse shapes societal attitudes, it is imperative to address the rising tide of stereotypes targeting queer people. These stereotypes can escalate into hate speech and violence, perpetuating discrimination. RAINBOW's objective is to preemptively counteract these stereotypes, fostering a more inclusive and tolerant online environment.


While much research exists on hate speech detection, there is a significant scarcity of initiatives that focus on early intervention to prevent stereotypes from evolving into hate speech and violence. The project seeks to fill this gap by pioneering advanced Multilingual Natural Language Processing (NLP) techniques specifically tailored to recognize and counteract emerging stereotypes and biased narratives (in English, Spanish and Italian). This proactive approach, combined with collaboration across linguistics, NLP, sociology, and queer studies, offers a comprehensive understanding of the issue and the development of tools to address it effectively.


Furthermore, the findings of this project will play a pivotal role in the development of an Educational Toolkit specifically designed to raise awareness about stereotypes and biases directed at LGBTQIA+ individuals. This tool will be tailored for the age group that spends the most time online (16-24 years old), ensuring that the next generation is equipped with the knowledge and understanding regarding AI technologies and to foster inclusivity and combat stereotypes in the digital realm.


Essentially, the RAINBOW project aims at establishing a safer online environment for LGBTQIA+ individuals while advancing the frontiers of Multilingual NLP technology to combat online discrimination.