Mixed-methods analysis reveals gender disparities in online abuse of candidates, highlighting systemic misogyny.
This study investigates gender differences in online toxicity towards a sample of candidates in the 2024 European Parliament elections. Using data from five EU countries and drawing on a dataset of over 3.2 million posts mentioning 88 candidates, we applied a mixed-methods approach to assess the volume and nature of online abuse. Toxicity levels were measured using a state-of-the-art toxicity detection algorithm. After developing a comprehensive coding scheme, qualitative content analysis was performed on a subset of posts with the highest probability of being perceived as toxic to examine gendered patterns of abuse.While a purely quantitative analysis did not reveal significant differences between the number of toxic replies received by female and male candidates that the gender alone could explain, qualitative insights uncovered systematic semiotic violence against women in politics. Female politicians were more often targeted with personal and degrading attacks, including sexist slurs, sexually explicit content, and attempts to silence or delegitimise them. In contrast, male candidates were likelier to receive insults tied to their political conduct and professional criticism. Moreover, replies to women are significantly more likely to include multiple elements of toxicity, indicating a higher intensity of online harassment.These results underscore the need for platform and user accountability, nuanced moderation policies, and legal protections that reflect the gendered dimensions of online political violence. Our research highlights the persistence of digital misogyny as a barrier to gender parity in democratic participation and stresses the urgency of cross-sectoral strategies to combat this issue during electoral processes.
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Sala et al. (2025) studied this question.
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