AI-DRIVEN CYBER ABUSE AGAINST WOMEN POLITICIANS AND ITS IMPACT ON DEMOCRATIC PARTICIPATION

Abstract

The growing impact of generative AI on political communication has transformed the landscape in ways that have never been seen before, creating unprecedented opportunities for technology-enabled gender-based violence. The focus of this research is on the current hybrid fusion of artificial intelligence, digital misogyny, and democratic participation in particular on the topic of artificial intelligence-supported cyber abuse against female politicians. The study uses a large-scale social media dataset analysis complemented by targeted psychological impact assessments to examine two main parameters: the typology and amount of machine generated abuse targeted at political candidates, and the resulting effects of such exposure on the levels of democratic participation and self-censorship. The empirical results show that there is a significant gender difference in the content of digital hostility. Gender-based attacks against politicians are typically the same as against men, but women candidates face disproportionate exposure to sexualized, identity-based violence, such as intimate imagery without consent and sexual deepfakes. Moreover, the study highlights the extent of the regional differences in the psychological and professional effects of such abuse, notably that for women, these effects are heightened in conservative sociopolitical regions, where they risk of experiencing offline violence is higher and they are more likely to be subjected to intra-party gatekeeping. The data shows finally that a specific level of chilling results in a withdrawal of women from the public online domain in response to concerted, targeted online hostility. This phenomenon is a structural barrier to equal representation and establishes that automated gendered disinformation is a coordinated strategy of democratic exclusion.