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Interpretable machine learning in natural language processing for misinformation data
Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613687783620608 |
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| access_status_str | Open Access |
| author2 | Marivate, Vukosi |
| author_browse | Marivate, Vukosi |
| author_facet | Marivate, Vukosi |
| collection | Thesis |
| dc_rights_str_mv | © 2021 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 (MIT (Big Data Science))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/92768 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:07.008Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| 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:2263/92768 Interpretable machine learning in natural language processing for misinformation data Marivate, Vukosi Nkalashe, Yolanda UCTD Disinformation Interpretability Prototypes Example-based Interpretable machine learning Natural language processing (NLP) Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2022. The interpretability of models has been one of the focal research topics in the machine learning community due to a rise in the use of black box models and complex state-of-the-art models [6]. Most of these models are debugged through trial and error, based on end-to-end learning [7, 48]. This creates some uneasiness and distrust among the end-user consumers of the models, which has resulted in limited use of black box models in disciplines where explainability is required [33]. However, alternative models, ”white-box models,” come with a trade-off of accuracy and predictive power [7]. This research focuses on interpretability in natural language processing for misinformation data. First, we explore example-based techniques through prototype selection to determine if we can observe any key behavioural insights from a misinformation dataset. We use four prototype selection techniques: Clustering, Set Cover, MMD-critic, and Influential examples. We analyse the quality of each technique’s prototype set and use two prototype sets that have the optimal quality to further process for word analysis, linguistic characteristics, and together with the LIME technique for interpretability. Secondly, we compare if there are any critical insights in the South African disinformation context. bs2026 Computer Science MIT (Big Data Science) Unrestricted SDG-09: Industry, innovation and infrastructure SDG-16: Peace, justice and strong institutions 2023-10-09T08:02:21Z 2023-10-09T08:02:21Z 2023-04 2022-11 Mini Dissertation * A2023 http://hdl.handle.net/2263/92768 en © 2021 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 Disinformation Interpretability Prototypes Example-based Interpretable machine learning Natural language processing (NLP) Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 Interpretable machine learning in natural language processing for misinformation data |
| title | Interpretable machine learning in natural language processing for misinformation data |
| title_full | Interpretable machine learning in natural language processing for misinformation data |
| title_fullStr | Interpretable machine learning in natural language processing for misinformation data |
| title_full_unstemmed | Interpretable machine learning in natural language processing for misinformation data |
| title_short | Interpretable machine learning in natural language processing for misinformation data |
| title_sort | interpretable machine learning in natural language processing for misinformation data |
| topic | UCTD Disinformation Interpretability Prototypes Example-based Interpretable machine learning Natural language processing (NLP) Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 |
| url | http://hdl.handle.net/2263/92768 |