Original Research

Not just a language with white faces: Analysing #taalmonument on Instagram using machine learning

Eduan Kotzé, Burgert Senekal
The Journal for Transdisciplinary Research in Southern Africa | Vol 16, No 1 | a871 | DOI: https://doi.org/10.4102/td.v16i1.871 | © 2020 Eduan Kotzé, Burgert Senekal | This work is licensed under CC Attribution 4.0
Submitted: 29 April 2020 | Published: 15 December 2020

About the author(s)

Eduan Kotzé, Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa
Burgert Senekal, Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa


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Abstract

rom the late 19th century, and especially during apartheid (1948–1994), Afrikaans became inextricably tied with white people, white domination and apartheid. This association has persisted after 1994, and calls to preserve Afrikaans are often derided with claims that the preference for Afrikaans is also a preference for racial segregation. In such anti-Afrikaans views, Afrikaans is seen as synonymous with white people and apartheid despite the fact that Afrikaans was never exclusively spoken by white people. This prejudice towards Afrikaans is also shown towards the Afrikaanse Taalmonument, which was unveiled in 1975 to commemorate this language. Using machine learning and a large dataset of social media posts from Instagram, we show that not only white people visit this monument to Afrikaans, but also take pictures here and post about it on one of the largest social media platforms. As such, we show that the interest in this monument – just like the language itself – is not exclusively tied to one race. We also make suggestions for further research, such as using machine learning for image recognition using social media datasets that could illuminate how other South African monuments are seen in the contemporary world.

Keywords

Afrikaans; Taalmonument; machine learning; convolutional neural networks; Instagram; apartheid.

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