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Spatial-temporal topic modelling of COVID-19 tweets in South Africa
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2023.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2024
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| _version_ | 1867613452256673792 |
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| access_status_str | Open Access |
| author2 | Mazarura, Jocelyn |
| author_browse | Mazarura, Jocelyn |
| author_facet | Mazarura, Jocelyn |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2023. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/94530 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:22.373Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/94530 Spatial-temporal topic modelling of COVID-19 tweets in South Africa Mazarura, Jocelyn u18073159@tuks.co.za Fabris-Rotelli, Inger Nicolette Jafta, Papama Hlumela Gandhi UCTD Short-text topic modelling COVID-19 Covariate-dependent weighting matrix Spatial-temporal K-medoids Sustainable Development Goals (SDGs) SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-09 SDG-16: Peace, justice and strong institutions Natural and agricultural sciences theses SDG-16 Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2023. In the era of social media, the analysis of Twitter data has become increasingly important for understanding the dynamics of online discourse. This research introduces a novel approach for tracking the spatial and temporal evolution of topics in Twitter data. Leveraging the spatial and temporal labels provided by Twitter for tweets, we propose the Clustered Biterm Topic Model. This model combines the Biterm Topic Model with K-medoid clustering to uncover the intricate topic development patterns over space and time. To enhance the accuracy and applicability of our model, we introduce an innovative element: a covariate-dependent matrix. This matrix incorporates essential covariate information and geographic proximity into the dissimilarity matrix used by K-Medoids clustering. By considering the inherent semantic relationships between topics and the contextual information provided by covariates and geographic proximity, our model captures the complex interplay of topics as they emerge and evolve across different regions and timeframes on Twitter. The proposed Clustered Biterm Topic Model offers a robust and versatile tool for researchers, policymakers, and businesses to gain deeper insights into the dynamic landscape of online conversations, which are inherently shaped by space and time. STATOMET TUKS Cricket Statistics MSc (Advanced Data Analytics) Restricted Faculty of Natural and Agricultural Sciences 2024-02-13T09:41:21Z 2024-02-13T09:41:21Z 2024-04 2023-12-07 Mini Dissertation * A2024 http://hdl.handle.net/2263/94530 10.25403/UPresearchdata.25208939 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Short-text topic modelling COVID-19 Covariate-dependent weighting matrix Spatial-temporal K-medoids Sustainable Development Goals (SDGs) SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-09 SDG-16: Peace, justice and strong institutions Natural and agricultural sciences theses SDG-16 Spatial-temporal topic modelling of COVID-19 tweets in South Africa |
| title | Spatial-temporal topic modelling of COVID-19 tweets in South Africa |
| title_full | Spatial-temporal topic modelling of COVID-19 tweets in South Africa |
| title_fullStr | Spatial-temporal topic modelling of COVID-19 tweets in South Africa |
| title_full_unstemmed | Spatial-temporal topic modelling of COVID-19 tweets in South Africa |
| title_short | Spatial-temporal topic modelling of COVID-19 tweets in South Africa |
| title_sort | spatial temporal topic modelling of covid 19 tweets in south africa |
| topic | UCTD Short-text topic modelling COVID-19 Covariate-dependent weighting matrix Spatial-temporal K-medoids Sustainable Development Goals (SDGs) SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-09 SDG-16: Peace, justice and strong institutions Natural and agricultural sciences theses SDG-16 |
| url | http://hdl.handle.net/2263/94530 |