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An unsupervised approach to COVID-19 fake tweet detection
Context: With the ongoing COVID-19 pandemic, social media platforms have become a crucial source of information. However, not all information shared on these platforms is accurate. The dissemination of fake news, intentional or unintentional, can lead to panic among readers and further exacerbate th...
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| Format: | Thesis |
| Language: | Eng |
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Department of Statistical Sciences
2024
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| _version_ | 1867613181468213248 |
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| access_status_str | Open Access |
| author | Jarana, Bulungisa |
| author2 | Ngwenya, Mzabalazo |
| author_browse | Jarana, Bulungisa Ngwenya, Mzabalazo |
| author_facet | Ngwenya, Mzabalazo Jarana, Bulungisa |
| author_sort | Jarana, Bulungisa |
| collection | Thesis |
| description | Context: With the ongoing COVID-19 pandemic, social media platforms have become a crucial source of information. However, not all information shared on these platforms is accurate. The dissemination of fake news, intentional or unintentional, can lead to panic among readers and further exacerbate the effects of the pandemic. Objectives: This research project aims to explore the potential of unsupervised machine learning algorithms in differentiating between genuine and fake COVID-19 news shared on Twitter. The methodology includes a literature review, experimental analysis, and the utilization of a Twitter dataset. Methods: The study used both Mini-Batch K-means and K-means algorithms of clustering techniques to provide us with ‘grouping' of Twitter data in the two of clusters. Word embedding techniques such as TF-IDF, Word2Vec, and BERT were employed because machine learning models cannot process unprocessed text data directly, and word embedding resolves this issue. Results: The results on the test data show that K-means algorithm was the best performing algorithm (76% accuracy was achieved) in determining fake tweets about Covid-19. K-means algorithm using Bert word embedding is the best performing model followed by Mini-Batch K-means using TF-IDF word embedding (69% accuracy was achieved). Conclusions: The study demonstrates that clustering Twitter COVID-19 news as genuine or fake using K-means and Mini-Batch K-means algorithms is feasible Keywords: Clustering, Machine Learning, unsupervised learning, K-Means, MiniBatch K-Means, TF-IDF, Word2Vec, Bert, Confusion Matrix, Truncated SVD (Singular Value Decomposition), t-distributed stochastic neighbourhood embedding (t-SNE) |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40266 |
| institution | University of Cape Town (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:32:03.909Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40266 An unsupervised approach to COVID-19 fake tweet detection Jarana, Bulungisa Ngwenya, Mzabalazo Statistical Sciences Context: With the ongoing COVID-19 pandemic, social media platforms have become a crucial source of information. However, not all information shared on these platforms is accurate. The dissemination of fake news, intentional or unintentional, can lead to panic among readers and further exacerbate the effects of the pandemic. Objectives: This research project aims to explore the potential of unsupervised machine learning algorithms in differentiating between genuine and fake COVID-19 news shared on Twitter. The methodology includes a literature review, experimental analysis, and the utilization of a Twitter dataset. Methods: The study used both Mini-Batch K-means and K-means algorithms of clustering techniques to provide us with ‘grouping' of Twitter data in the two of clusters. Word embedding techniques such as TF-IDF, Word2Vec, and BERT were employed because machine learning models cannot process unprocessed text data directly, and word embedding resolves this issue. Results: The results on the test data show that K-means algorithm was the best performing algorithm (76% accuracy was achieved) in determining fake tweets about Covid-19. K-means algorithm using Bert word embedding is the best performing model followed by Mini-Batch K-means using TF-IDF word embedding (69% accuracy was achieved). Conclusions: The study demonstrates that clustering Twitter COVID-19 news as genuine or fake using K-means and Mini-Batch K-means algorithms is feasible Keywords: Clustering, Machine Learning, unsupervised learning, K-Means, MiniBatch K-Means, TF-IDF, Word2Vec, Bert, Confusion Matrix, Truncated SVD (Singular Value Decomposition), t-distributed stochastic neighbourhood embedding (t-SNE) 2024-07-04T13:37:19Z 2024-07-04T13:37:19Z 2024 2024-07-03T13:39:11Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40266 Eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Jarana, Bulungisa An unsupervised approach to COVID-19 fake tweet detection |
| thesis_degree_str | Master's |
| title | An unsupervised approach to COVID-19 fake tweet detection |
| title_full | An unsupervised approach to COVID-19 fake tweet detection |
| title_fullStr | An unsupervised approach to COVID-19 fake tweet detection |
| title_full_unstemmed | An unsupervised approach to COVID-19 fake tweet detection |
| title_short | An unsupervised approach to COVID-19 fake tweet detection |
| title_sort | unsupervised approach to covid 19 fake tweet detection |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/40266 |
| work_keys_str_mv | AT jaranabulungisa anunsupervisedapproachtocovid19faketweetdetection AT jaranabulungisa unsupervisedapproachtocovid19faketweetdetection |