A Large Language Model LLM Approach to COVID-19 Fake News Detection

Abstract

Text classification is a widely studied problem in information retrieval and data mining, with applications ranging from healthcare diagnosis to the detection of COVID-19-related fake news. Social media platforms generate vast volumes of user-generated text, and the rapid spread of misinformation during the pandemic has made automated fake news detection an important task. In this study, we apply pretrained transformer-based transfer learning (TL) models and large language models (LLMs), namely BERT-base, ALBERT-base, XLM-RoBERTa-base, DistilBERT, GPT-2, XLNet, and T5, to the COVID-19 English Tweets dataset, which contains 6,420 tweets labeled as real or fake. To further improve performance, we apply six late-fusion optimization techniques: weighted fusion, particle swarm optimization (PSO) fusion, the Nelder-Mead algorithm, Powell's method, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, and trust-region constrained optimization. Models are evaluated using accuracy, precision, recall, and F1-score, and confusion-matrix heatmaps are used to visualize the predictions of each model. GPT-2 achieved the best performance, with an accuracy of 0.9631, precision of 0.9612, recall of 0.9610, and F1-score of 0.9600, while Nelder-Mead fusion achieved the best result among the fusion methods with an accuracy of 0.9610. These results highlight the value of LLMs for COVID-19 fake news detection. We conclude by outlining future directions, including multiclass classification, multilingual datasets, and real-time social media streams.