Modeling Biases in News and Political Debates

Biases and stereotypes in society can be reflected in different sectors of our everyday life including work environment, education and politics. News and political speeches are only two examples of textual content in which stereotypes are present. In this project, we focus on both gender and racial bias. Using approaches from NLP, first we explore how adjectives are used by female and male politicians (and particular when they refer to male and female gender) . Next, we model the evolution of biased language over the decades using a collection of debates from the UK House of Commons. Finally, we use sentiment analysis to analyze how different races are described in news. Our results indicate that adjectives are used in a different way from male and female politicians and that bias exists across several decades. Finally, we found that articles that discuss ethnic outgroups have a negative tone and emotion.