The depiction of Orania in the media (2013-2022): A quantitative analysis using Natural Language Processing (NLP)


  • Burgert Senekal University of the Free State, South Africa


Media studies, media fairness, journalism, Orania, machine learning, political bias classification, sentiment analysis, South African media


The current article investigates the depiction of the town of Orania in the media. Being an exclusive Afrikaner town, this town is highly controversial and is often seen as a remnant of apartheid, leading residents of this town to form the perception that the media treats them unfairly. Using Natural Language Processing (NLP) techniques, namely a lexicon-based sentiment analysis classification and a machine-learning political bias classification, it is shown that the vast majority of news reports and opinion pieces on this town exhibit minimal political bias, and publications on this town are evenly distributed between left and right political bias. In addition, while the majority of news reports and opinion pieces published on this town are neutral, more publications are positive than negative. However, differences in the depiction of this town based on the language of publications are also discussed, with English publications more negative and Afrikaans publications more positive, and the majority of publications on this town are in Afrikaans. Overall, the study finds that while some individual publications present Orania in a very negative light, in general, the media reports on this town in a balanced way.


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