Whose voice matters? Word embeddings reveal identity bias in news quotes - Nnaemeka Ohamadike; Kevin Durrheim; Mpho Primus (EPJ Data Science)
Abstract This paper investigates identity bias (gender and race) in the South African news selection and representation of COVID-19 vaccination quotes. Social bias studies have qualitatively examined race and gender bias in South African news, given South Africa’s apartheid history; yet, studies that examine and quantify these biases at the speaker level using news quotes from a representative South African news corpus remain limited. To address this gap, we examined race and gender bias in news selection and framing of quotes. We used word embedding trained on 22,627 vaccination quotes from 76 South African news sources between 2020 and 2023. These large-scale processing embeddings are unbiased by design but can learn and uncover biases hidden in language. Our findings reveal gender and race bias in the news selection and framing of quotes – journalists privilege White voices as more authoritative and connected to global and technical vaccination discourse but confine black voices to primarily localised contexts. They also quote male speakers more frequently in the news than females. In an era where human biases are becoming increasingly implicit, we argue that embeddings offer a robust tool to unearth, monitor, and evaluate these biases at the micro or speaker level in the news.