Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation?
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916160978550784 |
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| author | Koch, Luise Ghawi, Raji Pfeffer, Jürgen Steinert, Janina Isabel |
| author_facet | Koch, Luise Ghawi, Raji Pfeffer, Jürgen Steinert, Janina Isabel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07523 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation? Koch, Luise Ghawi, Raji Pfeffer, Jürgen Steinert, Janina Isabel Social and Information Networks 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. |
| title | Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation? |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2403.07523 |