A computational study of media bias in South African online political news reporting over the period 2021 - 2023
| dc.contributor.author | Ngwenya, Nonhlanhla Nomusa | |
| dc.contributor.supervisor | Alence, Rod | |
| dc.date.accessioned | 2025-04-24T09:37:15Z | |
| dc.date.issued | 2024 | |
| dc.description | A research report Submitted in fulfillment of the requirements for a Master of Arts in the field of e-Science, In the Faculty of Humanities , School of Social Sciences, University of the Witwatersrand, Johannesburg, 2024 | |
| dc.description.abstract | The study examined the presence of tonality bias in South African political news reporting over the period 2021 until mid-2023. The study employed the methods of the Lexicoder Sentiment Dictionary, a lexical-based method, and Latent Semantic Scaling, a semi-supervised machine learning method. Sentiment was utilised as a proxy for tonality. Online commercial media publishers were contrasted against the state-owned news publisher to ascertain how online news reporting contributed to shaping the national agenda, and the framing of political actors and their respective political parties. The Lexicoder Sentiment Dictionary and the Latent Semantic Scaling evidenced that commercial media publishers exhibited positive tonality bias for the Democratic Alliance during the 2021 Municipal Elections. South African media publishers were found to exhibit consistent negative tonality bias when reporting on protest action. The state-owned media publisher was found to drive a pro ruling party sentiment whereas commercial media publishers’ sentiment was anti- populist and agenda-setting. The congruency in political news reporting gave grounds to the call for diversity in publishing | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Humanities | |
| dc.identifier.citation | Ngwenya, Nonhlanhla Nomusa. (2024). A computational study of media bias in South African online political news reporting over the period 2021 - 2023 [Masters dissertation, University of the Witwatersrand, Johannesburg]. WIReDSpace. https://hdl.handle.net/10539/44853 | |
| dc.identifier.uri | https://hdl.handle.net/10539/44853 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | © 2024 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Social Sciences | |
| dc.subject | UCTD | |
| dc.subject | Bias | |
| dc.subject | sentiment analysis | |
| dc.subject.primarysdg | SDG-9: Industry, innovation and infrastructure | |
| dc.title | A computational study of media bias in South African online political news reporting over the period 2021 - 2023 | |
| dc.type | Dissertation |