Building on the existing literature on gendered political violence, this article seeks to deepen the analysis of specific violence experienced by women in politics on the Internet from a racial perspective. This study analyses online hate on Twitter against three black and brown candidates (hereafter referred to as "black") and three white candidates running for federal deputies for left-wing parties during the Brazilian 2022 electoral campaign. The comparison between the acts of violence is made from an intersectional perspective, placing the relationality between gender and race at the centre of this article. Based on a deductive mixed methods methodology, tweets tagging the six female candidates were extracted first, and then these comments' toxicity was measured using Google's API tool. A sample of comments was then randomly selected to qualitatively verify the robustness of the categorisation made by the machine learning tool. This analysis revealed that the tool was not able to capture the full spectrum of toxicity of the comments, confirming previous considerations from the literature that these technologies lack greater linguistic diversity beyond English. Furthermore, the results show that black women receive more hate in terms of both quantity and quality (as they combine misogyny and racism), suggesting that political gender-based violence is an even more robust barrier to the election and persistence of these women. Such findings point to the need for policies to address gendered political violence that also address racism.

You can read the full article in Portuguese on the journal´s website.

This publication is part of the research project "Understanding, Detecting, and Mitigating Online Misogyny against Politically Active Women" and is funded by the Bavarian Research Institute for Digital Transformation (bidt).

To learn more about it, you can visit the project´s general homepage and the the projects presentation on Prof. Janina Steinerts homepage.