Year: 2025
Type: article
Abstract: Does race bias manifest in South African news, and how can computational methods like word embeddings reveal it? After apartheid's end in 1994, South Africa implemented policies to address racial and economic divides and transform institutions and structures, including the news media. This study introduces a computational approach to quantify race bias in South African news using neural embeddings. We trained word2vec word embeddings on COVID-19 vaccination news articles from 76 South African news sources. These large-scale embeddings are unbiased by design but can detect and reveal hidden biases in language. We found consistent race bias in the coverage of socioeconomic phenomena, while health results were weaker, mixed and likely corpus-dependent. COVID-19 may have also amplified associations between "Black" and unhealthy terms in news coverage. Our methodology complements traditional qualitative techniques and allows for a more objective and representative way of investigating racism in South African news. Findings are validated through multiple methods, including human ratings, and have implications for South African news and this research field. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-025-00594-2.
Source: EPJ Data Science
Institution: University of Johannesburg
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: gold
Estimated APC paid: $1,890
Awards: Developmental Change and Individual Differences in Sensory Discrimination and Working Memory: The Impact and Interplay of Chronological Age and Human Intelligence, PONs - People-Opinion Networks: A study of polarization in word embeddings and social networks in Switzerland and Southern Africa., G8208409376
OpenAlex ID: W4416733737