Sentiment analysis showed an increase in relative negative sentiment associated with female leaders ( p = 0.0152) and an increase in positive sentiment associated with male leaders over time ( p = 0.0216).Ĭonclusion: Natural language processing is a powerful method for gaining insights into and quantifying trends in gender biases and sentiment in news media. Male words (e.g., he, him, man) were mentioned 1.84 million more times than female words from 1998 to 2019. Women continue to be associated with domestic terms while men continue to be associated with influence terms, for both regular gender words and female and male political leaders’ names. Results: Bias in leadership words for men and women measured from Daily Nation word embeddings corresponded to temporal trends in men and women’s participation in political leadership (i.e., parliamentary seats) using GloVe (correlation 0.8936, p = 0.0067, r 2 = 0.799) and word2vec (correlation 0.844, p = 0.0169, r 2 = 0.712) algorithms.
We measured gender bias in these embeddings and used sentiment analysis to predict quantitative sentiment scores for sentences surrounding female leader names compared to male leader names. Methods: We trained GloVe and word2vec word embeddings on text from 1998 to 2019 from Kenya’s Daily Nation newspaper.
Natural language processing (NLP) methods, such as word embedding and sentiment analysis, can efficiently quantify media biases at a scope previously unavailable in the social sciences. Investigating gender bias in the media is a useful tool for understanding socio-cultural barriers to implementing legislation for gender equality. 6School of Graduate Studies, Research and Extension, United States International University - Africa, Nairobi, Kenyaīackground: Despite a 2010 Kenyan constitutional amendment limiting members of elected public bodies to 5Department of Biomedical Data Science, Stanford University, Stanford, CA, United States.4Research Computing Center, Stanford University, Stanford, CA, United States.3School of Public Health, University of Nevada, Reno, NV, United States.2Department of Neuroscience, University of Texas – Dallas, Dallas, TX, United States.1Department of Pediatrics, Global Center for Gender Equality, School of Medicine, Stanford University, Stanford, CA, United States.
Weber 3, Valerie Meausoone 4, James Zou 5, Amos Njuguna 6 and Gary L.
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