Studying Political Discourse at COP Using Text Mining
| dc.contributor.author | Meletakos, Christina | |
| dc.contributor.co-supervisor | Eyita-Okon, Ekeminiabasi | |
| dc.contributor.supervisor | Alence, Rod | |
| dc.date.accessioned | 2024-08-08T08:50:16Z | |
| dc.date.available | 2024-08-08T08:50:16Z | |
| dc.date.issued | 2023-07 | |
| dc.department | Department of International Relations | |
| dc.department | Department of e-Sciences | |
| dc.description | A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Arts in the field of e-Science in the School of Social Sciences, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | Climate change has become one of the most pressing issues of our time and it is increasingly important for nations to come together and address the crisis. Every year since 1995, countries from around the world congregate at COP (Conference of the Parties) in the attempt to find consensus on how to tackle the problem. This dissertation studies the political speeches given by country representatives at the conference. 552 transcripts were used to perform multiple analyses. A sentiment study showed that the majority of speeches were overwhelmingly positive, and that the language used by delegates showed that they wanted to come across as being trustworthy and knowledgeable. Wordscores illustrated that prior to 2016, speeches were more alike. At the onset of US President Donald Trump’s announcement that he was pulling out of the Paris Agreement, most countries turned away from the US’ positioning. While a narrative of marketization was prevalent, it was the nationalist discourse used by the president that deterred countries. Lastly a regression model was run which showed that GDP, population, and region played an important part in how a country positioned itself on the world stage. | |
| dc.description.sponsorship | The National e-Science Postgraduate Teaching and Training Platform. DST–CSIR. | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Humanities | |
| dc.identifier | https://orcid.org/0000-0001-9460-4048 | |
| dc.identifier.citation | Meletakos, Christina. (2023). Studying Political Discourse at COP Using Text Mining. [Master's dissertation, University of the Witwatersrand, Johannesburg]. WIReDSpace. https://hdl.handle.net/10539/40036 | |
| dc.identifier.uri | https://hdl.handle.net/10539/40036 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 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 | Text mining | |
| dc.subject | Conference of the Parties (COP) | |
| dc.subject | Environment | |
| dc.subject | Wordscores | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-13: Climate action | |
| dc.title | Studying Political Discourse at COP Using Text Mining | |
| dc.type | Dissertation |