Uncovering Gender Bias in Media Coverage of Politicians with Machine Learning
This addresses the problem of gender bias in political media representation for researchers and policymakers, though it is incremental as it applies existing AI methods to new data within a specific domain.
The paper tackled gender bias in media coverage of politicians by analyzing newspaper articles about Irish ministers over 15 years using NLP and machine learning, finding evidence of bias in portrayal, policy associations, and performance evaluations of female politicians.
This paper presents research uncovering systematic gender bias in the representation of political leaders in the media, using artificial intelligence. Newspaper coverage of Irish ministers over a fifteen year period was gathered and analysed with natural language processing techniques and machine learning. Findings demonstrate evidence of gender bias in the portrayal of female politicians, the kind of policies they were associated with and how they were evaluated in terms of their performance as political leaders. This paper also sets out a methodology whereby media content may be analysed on a large scale utilising techniques from artificial intelligence within a theoretical framework founded in gender theory and feminist linguistics.