An unsupervised approach to COVID-19 fake tweet detection - Jarana, Bulungisa (Department of Statistical Sciences, 2024)
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)