Male and female politicians on Twitter: A machine learning approach
How does the language of male and female politicians differ when they communicate directly with the public on social media? Do citizens address them differently? We apply Lasso logistic regression models to identify the linguistic features that most differentiate the language used by or addressed to...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/187901 |
| Acceso en línea: | https://hdl.handle.net/2117/187901 https://dx.doi.org/10.1111/1475-6765.12392 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Sex differences Social networks Politicians Gender differences Social media Aprenentatge automàtic Diferències entre sexes Xarxes socials Polítics Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | How does the language of male and female politicians differ when they communicate directly with the public on social media? Do citizens address them differently? We apply Lasso logistic regression models to identify the linguistic features that most differentiate the language used by or addressed to male and female Spanish politicians. Male politicians use more words related to politics, sports, ideology, and infrastructure, while female politicians talk about gender and social affairs. The choice of emojis varies greatly across genders. In a novel analysis of tweets written by citizens, we find evidence of gender-specific insults, and note that mentions of physical appearance and infantilizing words are disproportionately found in text addressed to female politicians. The results suggest that politicians conform to gender stereotypes online and reveal ways in which citizens treat politicians differently depending on their gender. |
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