  Technology-facilitated gender-based violence has become a global threat to
women's political representation and democracy. Understanding how online hate
affects its targets is thus paramount. We analyse 10 million tweets directed at
female candidates in the Brazilian election in 2022 and examine their reactions
to online misogyny. Using a self-trained machine learning classifier to detect
Portuguese misogynistic tweets and a quantitative analysis of the candidates'
tweeting behaviour, we investigate how the number of misogynistic attacks
received alters the online activity of the female candidates. We find that
young and left-wing candidates and candidates with higher visibility online
received significantly more attacks. Furthermore, we find that an increase in
misogynistic attacks in the previous week is associated with a decrease in
female candidates' tweets in the following week. This potentially threatens
their equal participation in public opinion building and silences women's
voices in political discourse.
